Skip to content

Instantly share code, notes, and snippets.

@ischurov

ischurov/Python ICEF 2018 Lesson 4.ipynb

Created March 9, 2018 10:35
Show Gist options
  • Save ischurov/c97897dda98ddd6204cde2f05822ee55 to your computer and use it in GitHub Desktop.
Save ischurov/c97897dda98ddd6204cde2f05822ee55 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Python ICEF 2018 Lesson 4.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# https://goo.gl/bdbY43\n",
"tables = pd.read_html(\n",
" \"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(tables)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tables)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"table = tables[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>STATION_ID</td>\n",
" <td>STATION_NM</td>\n",
" <td>DATE_OBS</td>\n",
" <td>TMPMAX</td>\n",
" <td>Q</td>\n",
" <td>TMPMIN</td>\n",
" <td>Q</td>\n",
" <td>TMPMN</td>\n",
" <td>Q</td>\n",
" <td>PRECIP</td>\n",
" <td>Q</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-01</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-02</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-03</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-04</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-05</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-06</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-07</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-08</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-09</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-10</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-11</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-12</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-13</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-14</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-15</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-16</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-17</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-18</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-19</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-20</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-21</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-22</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-23</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-24</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-25</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-26</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-27</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-28</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>1948-01-29</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21460</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-02</td>\n",
" <td>13.6</td>\n",
" <td>0</td>\n",
" <td>7.2</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21461</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-03</td>\n",
" <td>14.7</td>\n",
" <td>0</td>\n",
" <td>10.9</td>\n",
" <td>0</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" <td>10.2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21462</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-04</td>\n",
" <td>14.3</td>\n",
" <td>0</td>\n",
" <td>10.3</td>\n",
" <td>0</td>\n",
" <td>12.6</td>\n",
" <td>0</td>\n",
" <td>4.9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21463</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-05</td>\n",
" <td>15.9</td>\n",
" <td>0</td>\n",
" <td>11.6</td>\n",
" <td>0</td>\n",
" <td>13.7</td>\n",
" <td>0</td>\n",
" <td>3.8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21464</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-06</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>11.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21465</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-07</td>\n",
" <td>13.9</td>\n",
" <td>0</td>\n",
" <td>7.9</td>\n",
" <td>0</td>\n",
" <td>10.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21466</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-08</td>\n",
" <td>14.4</td>\n",
" <td>0</td>\n",
" <td>7.1</td>\n",
" <td>0</td>\n",
" <td>10.8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21467</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-09</td>\n",
" <td>12.3</td>\n",
" <td>0</td>\n",
" <td>9.9</td>\n",
" <td>0</td>\n",
" <td>10.9</td>\n",
" <td>0</td>\n",
" <td>2.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21468</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-10</td>\n",
" <td>11.5</td>\n",
" <td>0</td>\n",
" <td>8.5</td>\n",
" <td>0</td>\n",
" <td>9.8</td>\n",
" <td>0</td>\n",
" <td>.6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21469</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-11</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21470</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-12</td>\n",
" <td>8.7</td>\n",
" <td>0</td>\n",
" <td>.4</td>\n",
" <td>0</td>\n",
" <td>5.3</td>\n",
" <td>0</td>\n",
" <td>2.2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21471</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-13</td>\n",
" <td>9.8</td>\n",
" <td>0</td>\n",
" <td>5.1</td>\n",
" <td>0</td>\n",
" <td>7.6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21472</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-14</td>\n",
" <td>7.9</td>\n",
" <td>0</td>\n",
" <td>4.7</td>\n",
" <td>0</td>\n",
" <td>5.8</td>\n",
" <td>0</td>\n",
" <td>1.2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21473</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>0</td>\n",
" <td>-1.4</td>\n",
" <td>0</td>\n",
" <td>1.3</td>\n",
" <td>0</td>\n",
" <td>2.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21474</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-16</td>\n",
" <td>.8</td>\n",
" <td>0</td>\n",
" <td>-1.7</td>\n",
" <td>0</td>\n",
" <td>-.4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21475</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>0</td>\n",
" <td>-4.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21476</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>2.2</td>\n",
" <td>0</td>\n",
" <td>.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21477</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-19</td>\n",
" <td>7.1</td>\n",
" <td>0</td>\n",
" <td>3.8</td>\n",
" <td>0</td>\n",
" <td>5.5</td>\n",
" <td>0</td>\n",
" <td>.6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21478</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-20</td>\n",
" <td>4.4</td>\n",
" <td>0</td>\n",
" <td>2.3</td>\n",
" <td>0</td>\n",
" <td>3.5</td>\n",
" <td>0</td>\n",
" <td>.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21479</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-21</td>\n",
" <td>4.1</td>\n",
" <td>0</td>\n",
" <td>.5</td>\n",
" <td>0</td>\n",
" <td>1.9</td>\n",
" <td>0</td>\n",
" <td>3.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21480</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-22</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>3.9</td>\n",
" <td>0</td>\n",
" <td>8.8</td>\n",
" <td>0</td>\n",
" <td>.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21481</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-23</td>\n",
" <td>12.6</td>\n",
" <td>0</td>\n",
" <td>9.1</td>\n",
" <td>0</td>\n",
" <td>10.8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21482</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-24</td>\n",
" <td>13.2</td>\n",
" <td>0</td>\n",
" <td>9.9</td>\n",
" <td>0</td>\n",
" <td>11.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21483</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-25</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>11.4</td>\n",
" <td>0</td>\n",
" <td>12.6</td>\n",
" <td>0</td>\n",
" <td>4.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21484</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-26</td>\n",
" <td>12.2</td>\n",
" <td>0</td>\n",
" <td>5.8</td>\n",
" <td>0</td>\n",
" <td>8.5</td>\n",
" <td>0</td>\n",
" <td>6.9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21485</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>0</td>\n",
" <td>2.7</td>\n",
" <td>0</td>\n",
" <td>5.6</td>\n",
" <td>0</td>\n",
" <td>.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21486</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-28</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>4.1</td>\n",
" <td>0</td>\n",
" <td>8.2</td>\n",
" <td>0</td>\n",
" <td>.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21487</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>0</td>\n",
" <td>1.4</td>\n",
" <td>0</td>\n",
" <td>2.4</td>\n",
" <td>0</td>\n",
" <td>.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21488</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>0</td>\n",
" <td>-1.9</td>\n",
" <td>0</td>\n",
" <td>-.7</td>\n",
" <td>0</td>\n",
" <td>3.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21489</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>0</td>\n",
" <td>-7.7</td>\n",
" <td>0</td>\n",
" <td>-3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21490 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 \\\n",
"0 STATION_ID STATION_NM DATE_OBS TMPMAX Q TMPMIN Q TMPMN Q \n",
"1 27612 МОСКВА ВДНХ 1948-01-01 NaN 9 NaN 9 NaN 9 \n",
"2 27612 МОСКВА ВДНХ 1948-01-02 NaN 9 NaN 9 NaN 9 \n",
"3 27612 МОСКВА ВДНХ 1948-01-03 NaN 9 NaN 9 NaN 9 \n",
"4 27612 МОСКВА ВДНХ 1948-01-04 NaN 9 NaN 9 NaN 9 \n",
"5 27612 МОСКВА ВДНХ 1948-01-05 NaN 9 NaN 9 NaN 9 \n",
"6 27612 МОСКВА ВДНХ 1948-01-06 NaN 9 NaN 9 NaN 9 \n",
"7 27612 МОСКВА ВДНХ 1948-01-07 NaN 9 NaN 9 NaN 9 \n",
"8 27612 МОСКВА ВДНХ 1948-01-08 NaN 9 NaN 9 NaN 9 \n",
"9 27612 МОСКВА ВДНХ 1948-01-09 NaN 9 NaN 9 NaN 9 \n",
"10 27612 МОСКВА ВДНХ 1948-01-10 NaN 9 NaN 9 NaN 9 \n",
"11 27612 МОСКВА ВДНХ 1948-01-11 NaN 9 NaN 9 NaN 9 \n",
"12 27612 МОСКВА ВДНХ 1948-01-12 NaN 9 NaN 9 NaN 9 \n",
"13 27612 МОСКВА ВДНХ 1948-01-13 NaN 9 NaN 9 NaN 9 \n",
"14 27612 МОСКВА ВДНХ 1948-01-14 NaN 9 NaN 9 NaN 9 \n",
"15 27612 МОСКВА ВДНХ 1948-01-15 NaN 9 NaN 9 NaN 9 \n",
"16 27612 МОСКВА ВДНХ 1948-01-16 NaN 9 NaN 9 NaN 9 \n",
"17 27612 МОСКВА ВДНХ 1948-01-17 NaN 9 NaN 9 NaN 9 \n",
"18 27612 МОСКВА ВДНХ 1948-01-18 NaN 9 NaN 9 NaN 9 \n",
"19 27612 МОСКВА ВДНХ 1948-01-19 NaN 9 NaN 9 NaN 9 \n",
"20 27612 МОСКВА ВДНХ 1948-01-20 NaN 9 NaN 9 NaN 9 \n",
"21 27612 МОСКВА ВДНХ 1948-01-21 NaN 9 NaN 9 NaN 9 \n",
"22 27612 МОСКВА ВДНХ 1948-01-22 NaN 9 NaN 9 NaN 9 \n",
"23 27612 МОСКВА ВДНХ 1948-01-23 NaN 9 NaN 9 NaN 9 \n",
"24 27612 МОСКВА ВДНХ 1948-01-24 NaN 9 NaN 9 NaN 9 \n",
"25 27612 МОСКВА ВДНХ 1948-01-25 NaN 9 NaN 9 NaN 9 \n",
"26 27612 МОСКВА ВДНХ 1948-01-26 NaN 9 NaN 9 NaN 9 \n",
"27 27612 МОСКВА ВДНХ 1948-01-27 NaN 9 NaN 9 NaN 9 \n",
"28 27612 МОСКВА ВДНХ 1948-01-28 NaN 9 NaN 9 NaN 9 \n",
"29 27612 МОСКВА ВДНХ 1948-01-29 NaN 9 NaN 9 NaN 9 \n",
"... ... ... ... ... .. ... .. ... .. \n",
"21460 27612 МОСКВА ВДНХ 2006-10-02 13.6 0 7.2 0 10 0 \n",
"21461 27612 МОСКВА ВДНХ 2006-10-03 14.7 0 10.9 0 12.5 0 \n",
"21462 27612 МОСКВА ВДНХ 2006-10-04 14.3 0 10.3 0 12.6 0 \n",
"21463 27612 МОСКВА ВДНХ 2006-10-05 15.9 0 11.6 0 13.7 0 \n",
"21464 27612 МОСКВА ВДНХ 2006-10-06 14 0 9 0 11.1 0 \n",
"21465 27612 МОСКВА ВДНХ 2006-10-07 13.9 0 7.9 0 10.3 0 \n",
"21466 27612 МОСКВА ВДНХ 2006-10-08 14.4 0 7.1 0 10.8 0 \n",
"21467 27612 МОСКВА ВДНХ 2006-10-09 12.3 0 9.9 0 10.9 0 \n",
"21468 27612 МОСКВА ВДНХ 2006-10-10 11.5 0 8.5 0 9.8 0 \n",
"21469 27612 МОСКВА ВДНХ 2006-10-11 9 0 6 0 7 0 \n",
"21470 27612 МОСКВА ВДНХ 2006-10-12 8.7 0 .4 0 5.3 0 \n",
"21471 27612 МОСКВА ВДНХ 2006-10-13 9.8 0 5.1 0 7.6 0 \n",
"21472 27612 МОСКВА ВДНХ 2006-10-14 7.9 0 4.7 0 5.8 0 \n",
"21473 27612 МОСКВА ВДНХ 2006-10-15 4.8 0 -1.4 0 1.3 0 \n",
"21474 27612 МОСКВА ВДНХ 2006-10-16 .8 0 -1.7 0 -.4 0 \n",
"21475 27612 МОСКВА ВДНХ 2006-10-17 2.9 0 -4.1 0 0 0 \n",
"21476 27612 МОСКВА ВДНХ 2006-10-18 5.4 0 -1 0 2.2 0 \n",
"21477 27612 МОСКВА ВДНХ 2006-10-19 7.1 0 3.8 0 5.5 0 \n",
"21478 27612 МОСКВА ВДНХ 2006-10-20 4.4 0 2.3 0 3.5 0 \n",
"21479 27612 МОСКВА ВДНХ 2006-10-21 4.1 0 .5 0 1.9 0 \n",
"21480 27612 МОСКВА ВДНХ 2006-10-22 12 0 3.9 0 8.8 0 \n",
"21481 27612 МОСКВА ВДНХ 2006-10-23 12.6 0 9.1 0 10.8 0 \n",
"21482 27612 МОСКВА ВДНХ 2006-10-24 13.2 0 9.9 0 11.3 0 \n",
"21483 27612 МОСКВА ВДНХ 2006-10-25 14 0 11.4 0 12.6 0 \n",
"21484 27612 МОСКВА ВДНХ 2006-10-26 12.2 0 5.8 0 8.5 0 \n",
"21485 27612 МОСКВА ВДНХ 2006-10-27 8.7 0 2.7 0 5.6 0 \n",
"21486 27612 МОСКВА ВДНХ 2006-10-28 11 0 4.1 0 8.2 0 \n",
"21487 27612 МОСКВА ВДНХ 2006-10-29 4.6 0 1.4 0 2.4 0 \n",
"21488 27612 МОСКВА ВДНХ 2006-10-30 1.8 0 -1.9 0 -.7 0 \n",
"21489 27612 МОСКВА ВДНХ 2006-10-31 1.3 0 -7.7 0 -3 0 \n",
"\n",
" 9 10 11 \n",
"0 PRECIP Q D \n",
"1 NaN 9 9 \n",
"2 NaN 9 9 \n",
"3 NaN 9 9 \n",
"4 NaN 9 9 \n",
"5 NaN 9 9 \n",
"6 NaN 9 9 \n",
"7 NaN 9 9 \n",
"8 NaN 9 9 \n",
"9 NaN 9 9 \n",
"10 NaN 9 9 \n",
"11 NaN 9 9 \n",
"12 NaN 9 9 \n",
"13 NaN 9 9 \n",
"14 NaN 9 9 \n",
"15 NaN 9 9 \n",
"16 NaN 9 9 \n",
"17 NaN 9 9 \n",
"18 NaN 9 9 \n",
"19 NaN 9 9 \n",
"20 NaN 9 9 \n",
"21 NaN 9 9 \n",
"22 NaN 9 9 \n",
"23 NaN 9 9 \n",
"24 NaN 9 9 \n",
"25 NaN 9 9 \n",
"26 NaN 9 9 \n",
"27 NaN 9 9 \n",
"28 NaN 9 9 \n",
"29 NaN 9 9 \n",
"... ... .. .. \n",
"21460 0 0 2 \n",
"21461 10.2 0 0 \n",
"21462 4.9 0 0 \n",
"21463 3.8 0 0 \n",
"21464 0 0 2 \n",
"21465 0 0 2 \n",
"21466 3 0 0 \n",
"21467 2.5 0 0 \n",
"21468 .6 0 0 \n",
"21469 0 0 2 \n",
"21470 2.2 0 0 \n",
"21471 0 0 2 \n",
"21472 1.2 0 0 \n",
"21473 2.1 0 0 \n",
"21474 0 0 2 \n",
"21475 0 0 2 \n",
"21476 .1 0 0 \n",
"21477 .6 0 0 \n",
"21478 .7 0 0 \n",
"21479 3.7 0 0 \n",
"21480 .7 0 0 \n",
"21481 0 0 2 \n",
"21482 0 0 2 \n",
"21483 4.7 0 0 \n",
"21484 6.9 0 0 \n",
"21485 .3 0 0 \n",
"21486 .3 0 0 \n",
"21487 .5 0 0 \n",
"21488 3.5 0 0 \n",
"21489 0 0 2 \n",
"\n",
"[21490 rows x 12 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# https://goo.gl/bdbY43\n",
"tables = pd.read_html(\n",
" \"http://math-info.hse.ru/f/2014-15/\"\n",
" \"nes-stat/climate.html\",\n",
" header=0\n",
")\n",
"# header=0 говорит, что первая строка в таблице должна превратиться\n",
"# в названия колонок"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"table = tables[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"STATION_ID int64\n",
"STATION_NM object\n",
"DATE_OBS object\n",
"TMPMAX float64\n",
"Q int64\n",
"TMPMIN float64\n",
"Q.1 int64\n",
"TMPMN float64\n",
"Q.2 int64\n",
"PRECIP float64\n",
"Q.3 int64\n",
"D int64\n",
"dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"table.dropna(inplace=True)\n",
"# выкинуть все строки с NaN'ами"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"table = table.loc[366:]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATION_ID</th>\n",
" <th>STATION_NM</th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>Q</th>\n",
" <th>TMPMIN</th>\n",
" <th>Q.1</th>\n",
" <th>TMPMN</th>\n",
" <th>Q.2</th>\n",
" <th>PRECIP</th>\n",
" <th>Q.3</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21484</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>0</td>\n",
" <td>2.7</td>\n",
" <td>0</td>\n",
" <td>5.6</td>\n",
" <td>0</td>\n",
" <td>0.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21485</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-28</td>\n",
" <td>11.0</td>\n",
" <td>0</td>\n",
" <td>4.1</td>\n",
" <td>0</td>\n",
" <td>8.2</td>\n",
" <td>0</td>\n",
" <td>0.3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21486</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>0</td>\n",
" <td>1.4</td>\n",
" <td>0</td>\n",
" <td>2.4</td>\n",
" <td>0</td>\n",
" <td>0.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21487</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>0</td>\n",
" <td>-1.9</td>\n",
" <td>0</td>\n",
" <td>-0.7</td>\n",
" <td>0</td>\n",
" <td>3.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21488</th>\n",
" <td>27612</td>\n",
" <td>МОСКВА ВДНХ</td>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>0</td>\n",
" <td>-7.7</td>\n",
" <td>0</td>\n",
" <td>-3.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATION_ID STATION_NM DATE_OBS TMPMAX Q TMPMIN Q.1 TMPMN \\\n",
"21484 27612 МОСКВА ВДНХ 2006-10-27 8.7 0 2.7 0 5.6 \n",
"21485 27612 МОСКВА ВДНХ 2006-10-28 11.0 0 4.1 0 8.2 \n",
"21486 27612 МОСКВА ВДНХ 2006-10-29 4.6 0 1.4 0 2.4 \n",
"21487 27612 МОСКВА ВДНХ 2006-10-30 1.8 0 -1.9 0 -0.7 \n",
"21488 27612 МОСКВА ВДНХ 2006-10-31 1.3 0 -7.7 0 -3.0 \n",
"\n",
" Q.2 PRECIP Q.3 D \n",
"21484 0 0.3 0 0 \n",
"21485 0 0.3 0 0 \n",
"21486 0 0.5 0 0 \n",
"21487 0 3.5 0 0 \n",
"21488 0 0.0 0 2 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.tail()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"table.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"27612 21121\n",
"Name: STATION_ID, dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table['STATION_ID'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"МОСКВА ВДНХ 20953\n",
"МОСКВА ВДН?? 27\n",
"М??СКВА ВДНХ 21\n",
"??ОСКВА ВДНХ 20\n",
"МОСКВА ВД??Х 18\n",
"МОСК??А ВДНХ 18\n",
"МОС??ВА ВДНХ 15\n",
"МОСКВА ??ДНХ 15\n",
"МОСКВА В??НХ 13\n",
"МОСКВ?? ВДНХ 11\n",
"МО??КВА ВДНХ 10\n",
"Name: STATION_NM, dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.STATION_NM.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"table = table[['DATE_OBS', 'TMPMAX', \n",
" 'TMPMIN', 'TMPMN', 'PRECIP']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1949-01-01</td>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1949-01-02</td>\n",
" <td>-0.5</td>\n",
" <td>-6.7</td>\n",
" <td>-1.2</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"0 1949-01-01 -2.1 -6.7 -4.2 0.0\n",
"1 1949-01-02 -0.5 -6.7 -1.2 4.2\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.loc[[0, 1, 2]]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1949-01-01</td>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1949-01-02</td>\n",
" <td>-0.5</td>\n",
" <td>-6.7</td>\n",
" <td>-1.2</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949-01-04</td>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1949-01-06</td>\n",
" <td>-0.8</td>\n",
" <td>-3.2</td>\n",
" <td>-2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1949-01-07</td>\n",
" <td>-0.6</td>\n",
" <td>-5.1</td>\n",
" <td>-3.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1949-01-10</td>\n",
" <td>-1.1</td>\n",
" <td>-4.2</td>\n",
" <td>-2.5</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1949-01-11</td>\n",
" <td>-3.0</td>\n",
" <td>-5.3</td>\n",
" <td>-4.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1949-01-12</td>\n",
" <td>-3.3</td>\n",
" <td>-8.4</td>\n",
" <td>-5.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1949-01-13</td>\n",
" <td>-4.8</td>\n",
" <td>-10.2</td>\n",
" <td>-6.8</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1949-01-14</td>\n",
" <td>-1.0</td>\n",
" <td>-5.7</td>\n",
" <td>-2.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1949-01-15</td>\n",
" <td>-1.9</td>\n",
" <td>-3.2</td>\n",
" <td>-2.9</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1949-01-16</td>\n",
" <td>-0.5</td>\n",
" <td>-4.9</td>\n",
" <td>-2.5</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1949-01-17</td>\n",
" <td>-0.7</td>\n",
" <td>-4.0</td>\n",
" <td>-3.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1949-01-18</td>\n",
" <td>-3.8</td>\n",
" <td>-10.3</td>\n",
" <td>-7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1949-01-19</td>\n",
" <td>-6.9</td>\n",
" <td>-10.4</td>\n",
" <td>-8.8</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1949-01-20</td>\n",
" <td>-2.4</td>\n",
" <td>-13.5</td>\n",
" <td>-8.6</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1949-01-23</td>\n",
" <td>-11.4</td>\n",
" <td>-15.5</td>\n",
" <td>-13.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1949-01-24</td>\n",
" <td>-10.5</td>\n",
" <td>-14.4</td>\n",
" <td>-12.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1949-01-26</td>\n",
" <td>-0.7</td>\n",
" <td>-6.8</td>\n",
" <td>-3.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1949-01-30</td>\n",
" <td>-0.5</td>\n",
" <td>-8.1</td>\n",
" <td>-6.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21091</th>\n",
" <td>2006-10-02</td>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21092</th>\n",
" <td>2006-10-03</td>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21093</th>\n",
" <td>2006-10-04</td>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21094</th>\n",
" <td>2006-10-05</td>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21095</th>\n",
" <td>2006-10-06</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21096</th>\n",
" <td>2006-10-07</td>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21097</th>\n",
" <td>2006-10-08</td>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21098</th>\n",
" <td>2006-10-09</td>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21099</th>\n",
" <td>2006-10-10</td>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21100</th>\n",
" <td>2006-10-11</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21101</th>\n",
" <td>2006-10-12</td>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21102</th>\n",
" <td>2006-10-13</td>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21103</th>\n",
" <td>2006-10-14</td>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21104</th>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21105</th>\n",
" <td>2006-10-16</td>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21106</th>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21107</th>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21108</th>\n",
" <td>2006-10-19</td>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21109</th>\n",
" <td>2006-10-20</td>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21110</th>\n",
" <td>2006-10-21</td>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21111</th>\n",
" <td>2006-10-22</td>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21112</th>\n",
" <td>2006-10-23</td>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21113</th>\n",
" <td>2006-10-24</td>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21114</th>\n",
" <td>2006-10-25</td>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21115</th>\n",
" <td>2006-10-26</td>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21116</th>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21117</th>\n",
" <td>2006-10-28</td>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21118</th>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21119</th>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21120</th>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21121 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"0 1949-01-01 -2.1 -6.7 -4.2 0.0\n",
"1 1949-01-02 -0.5 -6.7 -1.2 4.2\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0\n",
"3 1949-01-04 3.3 0.9 2.3 0.0\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8\n",
"5 1949-01-06 -0.8 -3.2 -2.3 0.0\n",
"6 1949-01-07 -0.6 -5.1 -3.4 0.0\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0\n",
"9 1949-01-10 -1.1 -4.2 -2.5 1.6\n",
"10 1949-01-11 -3.0 -5.3 -4.3 0.0\n",
"11 1949-01-12 -3.3 -8.4 -5.8 0.0\n",
"12 1949-01-13 -4.8 -10.2 -6.8 0.2\n",
"13 1949-01-14 -1.0 -5.7 -2.5 0.0\n",
"14 1949-01-15 -1.9 -3.2 -2.9 2.2\n",
"15 1949-01-16 -0.5 -4.9 -2.5 0.9\n",
"16 1949-01-17 -0.7 -4.0 -3.2 0.1\n",
"17 1949-01-18 -3.8 -10.3 -7.5 0.0\n",
"18 1949-01-19 -6.9 -10.4 -8.8 0.2\n",
"19 1949-01-20 -2.4 -13.5 -8.6 2.5\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0\n",
"22 1949-01-23 -11.4 -15.5 -13.4 0.0\n",
"23 1949-01-24 -10.5 -14.4 -12.4 0.0\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4\n",
"25 1949-01-26 -0.7 -6.8 -3.1 0.0\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0\n",
"29 1949-01-30 -0.5 -8.1 -6.5 0.0\n",
"... ... ... ... ... ...\n",
"21091 2006-10-02 13.6 7.2 10.0 0.0\n",
"21092 2006-10-03 14.7 10.9 12.5 10.2\n",
"21093 2006-10-04 14.3 10.3 12.6 4.9\n",
"21094 2006-10-05 15.9 11.6 13.7 3.8\n",
"21095 2006-10-06 14.0 9.0 11.1 0.0\n",
"21096 2006-10-07 13.9 7.9 10.3 0.0\n",
"21097 2006-10-08 14.4 7.1 10.8 3.0\n",
"21098 2006-10-09 12.3 9.9 10.9 2.5\n",
"21099 2006-10-10 11.5 8.5 9.8 0.6\n",
"21100 2006-10-11 9.0 6.0 7.0 0.0\n",
"21101 2006-10-12 8.7 0.4 5.3 2.2\n",
"21102 2006-10-13 9.8 5.1 7.6 0.0\n",
"21103 2006-10-14 7.9 4.7 5.8 1.2\n",
"21104 2006-10-15 4.8 -1.4 1.3 2.1\n",
"21105 2006-10-16 0.8 -1.7 -0.4 0.0\n",
"21106 2006-10-17 2.9 -4.1 0.0 0.0\n",
"21107 2006-10-18 5.4 -1.0 2.2 0.1\n",
"21108 2006-10-19 7.1 3.8 5.5 0.6\n",
"21109 2006-10-20 4.4 2.3 3.5 0.7\n",
"21110 2006-10-21 4.1 0.5 1.9 3.7\n",
"21111 2006-10-22 12.0 3.9 8.8 0.7\n",
"21112 2006-10-23 12.6 9.1 10.8 0.0\n",
"21113 2006-10-24 13.2 9.9 11.3 0.0\n",
"21114 2006-10-25 14.0 11.4 12.6 4.7\n",
"21115 2006-10-26 12.2 5.8 8.5 6.9\n",
"21116 2006-10-27 8.7 2.7 5.6 0.3\n",
"21117 2006-10-28 11.0 4.1 8.2 0.3\n",
"21118 2006-10-29 4.6 1.4 2.4 0.5\n",
"21119 2006-10-30 1.8 -1.9 -0.7 3.5\n",
"21120 2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[21121 rows x 5 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x125ba8be0>"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x13787a9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table.plot()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x137b975c0>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmYJFWV9/+9EZmRW+1LV3VX9b7S0NBAswkoIJsMAjo4wKiDOg4vjjP4Orig4M+VGZx3xnEcRhQVwVFpGHBBFhFQkB2apmma3veurural9xjyfv7I+JGRmRFLlWVVZVVdT7P009n5XorK/PEN77n3HMY5xwEQRDE7Eea7gUQBEEQUwMFfIIgiDkCBXyCIIg5AgV8giCIOQIFfIIgiDkCBXyCIIg5AgV8giCIOQIFfIIgiDkCBXyCIIg5gm+6F+CkqamJL1myZLqXQRAEMaN44403+jjnzcXuV1EBf8mSJdi0adN0L4MgCGJGwRg7VMr9yNIhCIKYI1DAJwiCmCNQwCcIgpgjVJSHTxDE3EbTNHR0dCCVSk33UiqSYDCI9vZ2+P3+cT2eAj5BEBVDR0cHqqursWTJEjDGpns5FQXnHP39/ejo6MDSpUvH9Rxk6RAEUTGkUik0NjZSsPeAMYbGxsYJnf1QwCcIoqKgYJ+fib43FPCJWY1uZPDA64ehG5npXgpBTDsU8IlZzQObjuCLD7+Nn71c0r4UYo7T39+P9evXY/369WhtbUVbW5v9M2MMH/nIR+z76rqO5uZmXH755QCAe++9F83NzVi/fj3Wrl2LH/3oR/b1jDE8/fTT9mN/85vfgDGGhx56CABw3nnnYcOGDfbtmzZtwnnnnVf2348CPjGrGUpoAIDuEar6IIrT2NiILVu2YMuWLbjxxhvx2c9+1v45Eolg27ZtSCaTAICnnnoKbW1trsdfc8012LJlC5599ll8+ctfRnd3NwBg3bp12Lhxo32/+++/HyeddJLrsT09PXjiiScm9fejgE/Mavyy6XmqZOkQZeCyyy7DY489BsAM2tddd53n/ebNm4fly5fj0CHzzPLcc8/Fa6+9Bk3TEIvFsHfvXqxfv971mM9//vO4/fbbJ3X9VJZJzGpiaQMAkNIo4M80vv67d7C9c6Ssz7l2QQ2++v7jx/34a6+9Ft/4xjdw+eWXY+vWrfjEJz6B559/ftT99u/fj/3792PFihXYvn07GGO48MIL8eSTT2J4eBhXXHEFDhw44HrMWWedhV//+tf405/+hOrq6nGvsRCk8IlZzWBcBQD0xdLTvBJiNnDiiSfi4MGDuP/++3HZZZeNuv2BBx7A+vXrcd111+GHP/whGhoa7NuuvfZabNy4ERs3bsx7ZnDbbbfhW9/61qStnxQ+MasZTJgBv4c8/BnHRJT4ZHLFFVfgc5/7HJ599ln09/e7brvmmmtw5513ej7u9NNPx9tvv41wOIxVq1Z53ueCCy7AbbfdhldeeaXs6wYo4BOzHBHwu0dmr8LXjQx8Mp2sTxWf+MQnUFdXh3Xr1uHZZ58d02PvuOMOBIPBgve57bbbcOONN2LZsmUTWKU39CkhKoqRlIb/77fbEE/rZXm+gbhZpdMbS8PI8LI853SxuzsKzt2/wy9fPYwVtz6BniidwUwV7e3tuOmmm8b12Pe97304//zzC97nsssuQ3Nz0Vkm44LlfoCmkw0bNnAagDK3+eaj2/GTFw7gW1edgI+cuXjcz8M5x00bt+B3b3Xa171+64Vorg6UY5medA4lcbAvjnetaCr7c/9pZw8+fu/r+O4163HVydlSwPP/7Vkc6IvjsZvOwfELasv+ulPNjh07cNxxx033Mioar/eIMfYG53xDnofYkMInKorDAwkAQE1ofN0ABZsPD9rBfp4V5EdS2sQWV4Tz/+1Z/PWPX52U537j0CAA4GB/3HW9yE1oRuUIN6JyoYBPVBTHhs0Alpmg/XL/a0fsy8LKiabKYxPlI62bpZ8TXbsX/Va1UUNEcV0fV82y04Q6ub8bMTugpC1RUXRZAT82QQ9/T08M71reiBPb67C8OYLPP7QV0UlU+M5ePQnNQFWgvF8tUV7qbJ7ltGMT1n6D2QDnnBqo5WGiFjwpfKJiyGS4XS8/UcUaTWmoDyu45X1rsK7d9LZHkpOngvf3Za2WciWcnQxYAT+lZgN7X0y1Lye02RHwg8Eg+vv7JxzYZiOiH36xKp9CkMInKgbn5qj4OBTr7u4oPnnfJvzik2cgmtJRHTQ/3tVBMx9QLoU/lFDxh3e68VenLbSvc+4IjaV1tJTllbL0x833JukI7EcGE/blxCQcZKaD9vZ2dHR0oLe3d7qXUpGIiVfjZcIBnzG2EMDPALQA4ADu5pz/J2OsAcADAJYAOAjgrzjngxN9PWL2InxqYLTCT2kG9vfGsXZBDQAzeN/+2A5cckIrzl89DwDw7Sd24vBAAs/t7kXMEfBrrP/L5eHf/OBbeGZnD05ZXI8V86oAAFuODNm3T4bC7xkZHfAHXe/X7FD4fr9/3NOciOKUw9LRAdzMOV8L4EwAn2aMrQVwC4BnOOcrATxj/UwQeXEGsLgjgKV1A2ff8Udc9r3nMWRtpNp0aBAbXz+Cj//0dQwnNXDO8aYVdIeTGpKaYSv7iOIDY+Wr0tnTE7NfR/DGoUFIlu080fxDLtGUhqj1nEnH+zJQ4ABJEF5MOOBzzrs455uty1EAOwC0AbgSwH3W3e4DcNVEX4uY3QwkHAHMCnA9Iyk8s6PHVv/C9hlxBNt4WkdvLG0HwF3HogBgJ04liaEq4CubwhcquzeazTds7xrBuSubrfWUV22L1wHcAX8wMfsUPjG5lNXDZ4wtAXAygFcBtHDOu6ybjgFltzWJWYZQ+M3VAcRVA5xzXHHni24bw+pvP+II3qqegVPg7jxm+unC0gGAmqC/bApfBN1ea3frs7t6YWQ4zlvdjOd295bd0nEFfM2p8DUoPgkBn0QBnyiJslXpMMaqADwM4P9yzl09TbmZcvdMuzPGbmCMbWKMbaJEzdxGtEFoqwshoeoYiKs4NpJyWSdCxTsVvmpkEHdE/N3dpuUiLB3zsq8sVTpGhtv2SW80Dc45vvfMHixrjuDSE1oBlN/SEdU4jI328BvCCiKKjywdoiTKEvAZY36Ywf4XnPNfWVd3M8bmW7fPB9Dj9VjO+d2c8w2c8w2T1T+CmBkMJlTUhvyoCfkRTxuuUkeReBUevjPgp7WMrXCXNUVGPca87C9LlU7XcBJiX1VvLI3eWBo7j0Xx4TMWo8Y6wJRf4ZtnEosawkg5FX5CRX1EQTggu3IeBJGPCQd8Zu6Q+AmAHZzz7zhuegTA9dbl6wH8dqKvRcwcUuOoCx+Iq2iIKIgoMhKqjgO92YB/0sI6AE5Lx6nwDTvIrmypsq93KvyaUHk8/L1WwhYwFX6/pb4X1AYRVmQwNvGA/9AbHa52zr2xNGSJYX5t0O3hx1U0RPwIK/KsKcskJpdyKPyzAXwUwAWMsS3Wv8sA3AHgIsbYHgAXWj8Tc4A/7+7Fmq/83u7/UiqDCRV1YT/Cig/xtIF9fdnguqa1Goos2YlKpz2T1rMK/8T2Ovt6p4dfHfQjmp64wt98eAgSAzYsrncF/MaqABhjiCg+e8rWeOiJpvC5/30LN/78Dfu6vqiKxoiCqoDP7eEnVNSHFYQVH3n4RElMOGnLOX8BQL590O+d6PMTM48/7TLduy1HhnDq4vqSHzcQV9FaE0QkkFX486oDGElpOKGtFvURv53YHUlpYAzg3B3wNzhezxnwa0N+DMXLEPAPDWJNaw2WNEXw0t4+e0OU6HETCcgTUvjZoevZRG1vLI3m6gCCftlD4SuIpXX7wEMQhaDWCkTZUa0mYopvbB+vwbjlSSs+xFXTw1+/sA4v3/JevP/EBagPK1lLJ6mhuSpgv55IWi6fl7V0qhwBv6UmiGhan1BC1chw+yDWXB1AbyxtJ1SbqkTA9yE2gQRqn1WR43zv+mJpNFUFEPLLtsI3MhxDSbN9BCVtiVKhgE+UHRHwA2OYwmRkuK1kI4oMVc9gX28MS5sjqI8okCSGurDfTtoOJzU0OQK+qH2PKNkgH/DJ9uUFdWb/kWPDyXH/Xru7o4ildTPgVwWgGRz7emOQJWYnbKsCvgkp/F5rn4Ffzp4094yY70tIyQb8PT1RcA4sbAibHj5ZOkQJUMAnyo5qjF3hd4+koBkcC+vDiFgbpjgHljdlFbtL4ad0NFl97tOWwmcMCPolXLNhIYJ+92vPrw0BADqHxj4ZKq0b6I+l7ZzEKYvq7UEqO7tG0GAdkAAz4McmkBwW1ozfOlhqRgbd0RQW1IVMhW8F9pf3mbNUz1zWQAGfKBlqnkaUHaHwRRAshSPW4JP2+hBCSlaZL23Ollk2RBT0xcza95GkZtsoQuGbLRQYvn31ifj21Se6nn9+ranwu8ah8L/16A78zyuHcOayBjRVBbCwIWQ/z65jUSxsCNv3ba4O4M3DQ/meqijOBnKAOR+Ac6CtLojOoRTSegaZDMfL+/qxsCGE9vowwgGydIjSIIVPlB0xCMTIZIrcM0vHoBlAFzaEcfLCbKWNs65+SWMEQwkNncMp6Bluq2xVN5DUdIQdB4pcWmqCYCzbb78UfvnqYfREU3hwkzlM5ZX9AzhlUR0YY/Zrx1UDjVXZoSTza0M4NpwqOgQlrRu48s4X8OPn97uuFwpflJAeHTLfl7a6sP37pXQDW44M4bTFDQCAsF+GZnD7QEsQ+aCAT5QdEXg0vfSe5h2DSTBmeu0+WUJLjRlQnROels8zg/9my1qZV22q9rRQ+AWGjig+CU1VAXSVaOn0jKTw5V+/jSv+60Wk9QwuPK4Ff3/ecvzTxasAwDUbtzGSvTy/NgjVyLg6f3px5x/34q2OYdz17D7X9ULhi93FR60D4YK6oH3m85XfvIOeaBqLG833I2z93skx2DrRlIbP/+9bo84oiNkNWTpE2bEDvofCF4MtcicaHRlMoKU6aCdan/jMu9EfS7vut7zZ9PNfPzgAIGvTiCqdkD+/wgfMzVGdJVo6YmPXMWsD1D9esMLe/AXANdFKrMu5pjcODeJ7z+zBnX99MpY5bgfM9+DhNzoAAHVh9+zePkfZaSbD0TkkAn4IbXVmHuLhzR3WdeZrCeWf0HTUwnsWsGZk7LwAAHz/2X343zc6sHxeFW58z/Ii7wYxWyCFT5SdtCEUvjvgH+yL46Sv/wFX/feLox6zvzeG9vqQ/XNDRMHKlmrXfdrrw1BkCa8fFAo/AIlZvXTSBiKBwgG/Nqy4mq4VQtTDA2ZQPqGt1nW780B0/ppsS5AFVlD+n1cOYnvXCB6xBqk72d8XR+dwCmFFRsdg0jXdqd9S3JwD0bSOo0NJNFUpCPplXLBmHu744Dr7vm3W+yUCfr4unS/v68fKW5/A5sPZjXBvHMq+h0729sRo2tQshgI+UXaEwtdzfOzfbDmKkZSOtzqGXdd3j6Tw5pEhnLOyqeDzyhLD0qYIdnSZvfkaIgoUn2RX6YSVwiesYb+MZInJTWfDtlMW1UMukIA+YUH2YCAU/ot7zSqa//rjXvzoz/vxpiPYPr/bbBJ43emLkNYzrlGFQwnN7gE0ktTQMZi0lT1jDKc4NpaJ68XvnS9x+7VH3gEAPL61Cy/t7cPXHnkHrx0wz5I0xyzeTQcHcOF3nsMvXzuc93clZjYU8Imyo+qm0lQNt8IXidnccs0n3u4C58DlJ84v+tyLG7MVMY2RABRZsiyd4go/HJBHqeA/7erBMzu6R93XqfA3LPHeLfzTj52Guz58iqsaqSGi2DX0iizByHDc/vgOfGbjFls5v7C3D4sawnjX8kYAwE33v4mRlAbNyCCW1rHI+h2Hkxr298ZclpDz92+1Di4RYel4ePj9sTR2dZvzAX78wgFc/9PXcO9LB+3b046zMJEgfnFvn+fvS8x8yMMnyo4IIrrhVvii9FK1SgtFoNzWOYKWmgBWzHNbOF6015sBT5YYakI+BPyy3VqhqMJ3bFwS/OfTe5DSDLz3OPe4BqHwv37F8fjwGYs8n+/8NfNGXccYwxUnteGNQwP48mXHIZbW0TGYxHee2o3d3TEc6Ivjud29+NCGhXY558v7+/Hsrl77ALCoIYxtR0fQNZxC53AKyx2lqc7NZOJyyA74oxW+CPYCzfqbfPmyNfjnx3d6Nrkr16AYovKggE+UnZRmefh5FD5gHhREoBqxWgSUwsIG08bgnIMxBkWWkNYNRFOaK5HqRdjRgqA3msbjb3eheySFWErHfS8dxKUntKKlxlTNIuB/5MzFBe0cL/79r05y/dwxmMB3ntqNS777Z/u6d69swsL6rFofjKv2WcXqlho8/vYxW2kvz0n6rmurdY03FNVJXgp/jzUbYHVLtSv4r241ZwOntezfSLScLjXPQcw8yNIhyo5oLaA5FL5uZHBsJIVqKzg5leVISkNNyLu6JBeh8EV6IOCTkEgbGEnpRQ8aYUVGSsvAyHA8vLkDX33kHXQNpxBN6/jqI+/g568cAgB0DiWxvWsE1UHfmIN9vjVfeFwLmqoUfPDkNpy2pB5nr2hCSJGx85uXwi8zdI+kMJw0g/gJbTWQJYanLavJ2R8IAH776bPx/BfOd/1eAJDwSNru6o6iJuizbSLBinlVkCWGlJ59jDjgvHVkCLf95m3azDULIYVPlBUjw23bxKnwu4ZTMDIcK1qq8ObhIVegGUnqdnVLMYTCFyg+Cd3WgJCGquIBHzCnRjl77QvePmomk991xx8BwFU1NFF+fP0Gz+uDfhnzqoPoHknbAbepKoCF9SEc7E9AYm7fHhi9g7lQ0nZPdxSrWqpdNfoBn4T5NUEEfZJ9Nga4E9U/f+UwhhIa7vzrU8b4mxKVDCl8oqw4g45ujE4ICnsiN9DUhErTHm05B4aAT8Ixa/dsQxGFH3IExgP9HgG/Y9hVklho5245mVcTsBS+GXDrwn4ssXYYr2uvc/n2XthlmR6WztHBJBY1hvGp85Yj4JOwrq0Wy5qrIEkMQb+MtFPhJ93to1/ZPzCh34uoPEjhE2XFNVzcYemIRKDYQTvK0gmWZumIKVbnWiWcik+y2yU4d+V6EXFYHwf7Rgf8/riKPY6JVqXuyp0oLdVB7OuN2Qq/LqSg1rK4zltVfOxnwCdBYt47beOqgaqAD2evaMKub70PRwYSdlI9kKPwhxLmUPQLVs/DkqYIfvDcvpLKXYmZAyl8oqyInaGAW+EL5S98dhHwMxmOWFq3A1wpvPmVi/CjvzEtEqf6bSzR0umPp9ET9W4p8NT2bIlmdIrGBrZYCn8oaQ51qQ767F7/Z1mVO4UQk7Z+9vJBexeyIKkarmZ0CxvCWGHlBIJ+2XXgHU6qOHVRPX7w0VNx/AIzqXvYqqwiZgcU8Imy4gz4mivgm4FFBGWhLKMpHZyj5KQtANRHzJ2ngLumv1jSVlg62ztHXNf/8u/OwMYbzgQAvHogGzBPy1N/X27m1QQxktJxbDiJmqAfksTwTxevwg8/eirOWNpQ0nME/DJGUjo+9IOX7et0IwPVyCDs91booqRVMJTQ7APvIqtk9HB/aQH/pX19rjnDRGVC52pEWRGllwtqg64qHVG502A1GhNJWxEkaoLj+ygqsjPgFz5oCEtnn5WwFaWK71reZCvdNyyF/OznzrP77U82q6wWEg9u6rB/h7DiwyXHt5b8HF5N0ETyPKR46zrT0nF7+KK3j0gUl6Lw07qBv/7Rq2irC+HFWy4oec3E1EMKnygrnUNJ1If9qAn5PRW+SKymrUAjEpVjUfhOhMKvC/vhKzJhS1gbh6yE7T0fPw0H/uUyAKa9URM0Ryu21gSxpClStK6/XFx43Dz84wUrAMC2W8qB8PRDeTz4oF+y6/A55xhOaKi1An5tyI/qoA/bjg57PtaJ2L18dChJKr/CoYBPlI1vProdv3j1MNrqQ/DL0qiAr8iS3f5AWDpZhT++gB+wAn6xhC2QLV88ZKnWpirF1QRNtDxe0hQe/eBJhDGGmy9ejec+fx6+d93JE3quRsf7IA6y4TxdRJ1VOtG0DtXI2LaYuWN4AX6zpdOzAZwT50hHMYmLqEwo4BNl4ycvHABg1tX7ZOZqnpZQdYQDsu29CytB7O4cS9LWiRhUntv10Qth6RzuT6A25B9V7ij66y91DF2ZShY3RuxRjOPFudvWDvh5ykudVTodA9YAGsfu329ceQLa6kJ44u2ugq/pbFcxWGQOADG9UMAnyobwf9+zqhl+q6mZIKEaCPtlW5GLZOFI0lSHpdbh5/Kp85bj/119Im7/wLqi9xWWjp7h9nhEJ/OsktEljdMT8CfCC188Hx86tR1JzbAPplkPP7/CF7kU4dU7N7bJEsOZyxrxyv7+ghO8nAo/t5afqCwo4BNlwycx/MW6+fjK5Wvh91T4vlEKfzBhKsLxKvx51UF8aMPCUf1mvHDWkzd7nBGIUsjpUvgTob0+jJMXmVVF4j1N2go/j4fvk20Pv2PQDPiLGtx21lnLGzGY0EY1YXPiPKtwdhklKg8K+ETZiKV1tNWHoPgkTw8/rDgtHfO2vlgaQb80JQlSWWL2GUZT1eiALxT+TAz4QLZKaTBuBl2x9yHfJLCAX3Ip/OqAb9SBV9Tj7/doRSFwKnzRD4ioTKgskygLupFBSssgYqlJnyS5yjITaTPgyxKDX8427eqNptFcHRg18nCyqAr4kNZVT4V/2br5iKb0ks4WKpE6K+E6JBR+CZaOUPhHBhJY2BAe9XcQ1VPxApvQhML3SYwUfoVDCp8oC6KPi6jCUXzMrfC17Bb9oC+7w7Mvpnqq7clCDFnxeXTBbK8P4+aLV49qTjZTqI9YCj8hFH7hpG3QZyp8zjkODyRGNaYDsrN7C+06Fq+zoC5EAb/CoYBPlAWhAEWA8EmSu7WCpfABc4ensHR6o2nbO58KvnTZcfjImYvwoQ0Lp+w1pwqxx+Ffn9yJ4aTm8PDzWToyODcT6B2DyVH+PZD9e8YK9MgX1tH82qCr4yZReVDAJ8qCCPhiGIfp4Tt22qq6HXjMDT9C4ac97ZXJIuiX8a2r1tm7W2cTDREFq1uqcag/ga/8ZltRS0fkMzoGzYZqCz0CviwxhBUZsXT+QC42Xi2oC1HAr3Ao4BNlIZaj8P0y80jaWpaOVQ6oGxkMJKbW0pnN+GQJT3723bjpghV45K1O7DoWhSwxV/sJJyKBLqZieQV8wPybxgpaOjoCPgkNEcXOHxCVCQV8oiwIledW+Nlt+84h40G/ueFnIK6Cc+8SSWL8nLPSbKn8TucwQn45b0JcBPzdIuDX5wn4QV/BObdxVUck4ENdyI+4auDp7d34zlO7KfhXIFSlQ5SFmG3pmEHEJzN7iLlqmGMFhcKvDvjRPZKyWxSTwi8vovHZvt54wYOpOBvb0WV2D8034au6qMI38zNi492/PLED+3rj6I+lS9oQR0wdZVH4jLF7GGM9jLFtjusaGGNPMcb2WP9PTa9ZYlrITdoqsgTVUvhi1qrw8N+9qhnvdI7gcWvLvleykBg/86oDtj9faGqX2N28rXMYLTUBW/HnUhX0FU7apg1EFJ9dwtlvtVcQg2nKxcG+OJbc8hhe3U/9esZLuSydewFcmnPdLQCe4ZyvBPCM9TMxS4mr7qSts5eOnTy0AspfrDNLI7//7D6saa3GcfNnXwJ1OmGM2QfRfJuugGzDuo7BZMEePsU8/LiqI6TIdsAXpZmFHjMeHt1qNnF7ZmdPWZ93LlGWgM85/zOA3AGYVwK4z7p8H4CryvFaRGUyOmkrwchwZDLcrrkXCnJRYxifPGcpGAP+9pylU7bpai4R8Jtf7ZMX1eW9j7NDaaFuo1UBv8vDNzIc97xwwC77FPmZ3JkGXkPVJ4IYPzkTex1VCpPp4bdwzkWbvWMAWibxtYhpJp7WXa0L/FZliJbJ2DX3QX9WX9x2+VrcfPHqvCWDxMRY3BjBtqMj+PvzVuS9j7NhXV2B4THVQR+ijj73j27txDce3Y6+WBpfuHQN4mkdDZGwPW9YIBL55UJUExUZe0AUYEreOs45B+DZbo8xdgNjbBNjbFNvb+9ULIeYBOJpAxElWxHil83/NYPbbRQCOfYCBfvJ45+vWofHbzo3b6klAFf/ooYC4yGFpWN+jYGeETPZntIyeHFvH/b3xtFSExg106Dcls5eS+GrRv7OnURhJjPgdzPG5gOA9b+n8cY5v5tzvoFzvqG5uXkSl0NMJiNJzaXwRO132tGuN+ijAD9V1Ib9WGs1PsuHT5bsGQH1hSydoA8Zns3FZPM1Mn70/H40VSn43MWrUZ1j6RTqvyPgnOO/ntmDXcdGd+PsjabxzUe3Q9Uz5j+rCMC5g5sYG5MZ8B8BcL11+XoAv53E1yKmmf64ag8oB4CwpR4TqmE36HJaOkRlIBKthSyd3PYKCTW7g7dnJI0182tQF1bs5niAWWqbUI2CffQBs0fPvz+1Gx/9yaujbvvD9mP4yQsHsK1z2HXw0Cjgj5tylWXeD+BlAKsZYx2Msb8FcAeAixhjewBcaP1MzFL642nXeD3RNTOhGqOStkTlIFR5MUsHyDZQE1aNYXD0xdL2MBnGmP18rbVm/X+8SOK2z9qL4XVc6BxK2v/HXAGfLJ3xUpakLef8ujw3vbccz09UPgMxFWtasxZC2NqAlVB128OngF95CN+9rkDAF7X8oipn2Cq7jKsG+uPu1hjVQR+GEhpaa4LYdnQE8bQxKpnrpNcK+FWB0Z+No4NJ+3/ncHdS+OOHzrGJCcM5R19cdSl8MTjbVPhk6VQqwtIRrZW9EDukha3SFzODdNdwEkaGu3bzVgfM52mpMecDF0vc9sVU12s46RxKWf8nXZaOTgp/3NA3kJgwsbQOVc+4PHyxASue1ilpW8GUYunYZ2uODqeAOTQFcLfGEKWerVbA/9TP30DPSP4dt71R87aq4OiAf9SydI4OJRFzlHiSwh8/FPCJCTNgbaVvjGS/+MIGcCt8CviVxlgsHdEiQ7ROODxgBmS3pWMp/Foz4O/pieHHLxzI+9xC4Yv9GwLdyOCYdaA4OpRyKXyVAv64oYBPlMy+3phnSZz40jZ4KXw1q/Bzv9R4VmMXAAAgAElEQVTE9HPK4jqcuawBSoG/TTYBb/4tResEofSbq7N/dztpayl8AAUH3AgPX1RyCQ72J2BkOEJ+GUcHE2TplAn6BhIlMRBXccl//BmPbzvmeRsANHko/KRqIKUbUHzSjB0dOJv5wMnt2HjDWQXv4zxbE501nbgsHUvht9ZmA36h3I04aIgaf8DMCX36F5sRVmRcdXIbRlK67ecrjrbbxNihgE+UxEBchZ7hnn5sv/WlddXh24k+sw4/SOp+xhJ2lNi+fXQYALCsyexn45cZakPZhO8JbbVYO7/GVdcv6va96LU+O86+O4MJDbu6o/i/F67E+auzvf0Bc78AlWWOH+qHT5SEKMnzGoQh+to7A77oqyNsAPLvZy5BvwTGzKC8rzeGpioFy5oj2N8XR2Mk4Gp+d/Wp7bj61HZXAHeq91z6LTsw5bB0ui1R0V4fxlLrwPJO5wj8MkMk4COFPwFIdhE2X/3tNlz+X8973ia+wLG0jrc7hvG/m47Yt3UNJ9FUpSCQU4UTCfhsD58C/syFMYawX0ZCNbDt6DBOaKtFyFL9TdXeyd6w4sPNF60CUDjgi6lYzgOECPgtNQEsbAiDMbNSJxLwwScx6BkK+OOFAj5hc9/Lh7Dt6GiPFsiW5MVSOm751VZ8/qGteHaX2R6pcyjl2U89rMhIpM0qHarBn9mEA+YQlP19caxqqUbI+nsWmlb2j+9didqQH6k8lo6qZxB3tFgWiDPGedVBBP0y2urMz1ZE8cEvS1B1snTGC30LCQDuRleiK6ITYenEVB31Vgnffzy1G4Cp8Oc7knSCiOIzyzJ1UvgznYgiY19vDKqewZLGiO3rFxtPGVbkvB7+UNJK9lcpSOsZu++OyBOJDV3C1qkK+OCXSeFPBAr4BADgrY4h+3JKG/2FEl/aWEq3T9Hf6hhGbzSNruGUZ8APB+SspUObrmY0IcWH7VaFzpKmsH0ALzaAPuSX81o6okXDAkvBi/t1j6RRF/bbr3HyInM6qpbJwE9VOhOCAj4BAHi7Y9i+HE1ro25POjz84aRmj9B7YlsXoikd8+vyWDrWxqsAWTozmohDqS9rqrJHJxZT+EG/bO/DyGXQCvhCLCRUA/e8cAD/88ohV+2+qNTZ3xuHT2ZUpTMB6FtIADDnmgq8BlaLL3s8rWMooeFdyxsxvzaIHz63HwC8Fb7iQzytI61nyNKZ4YhhNSG/jJaagF2b31SVf4eueFw+hT9oJWxF/ielGfjXJ3cCAA5bbRsA4KR2c0xjdcBHCn+CUMAnAGRb0QLeDa/ijrLMkaSG2rAfH9qw0O534jVnVKjCNFXpzHjEbtslTREwxhC0An6hXbSAeZaXzOPhZy2drMI/bUkDAOBrVxxv30+SGH73D+fgsZvOhV+WaKftBKCATwAwy96qcwZdOBGWTl8sDdXIoC6k4LrTF6IxouDG9yzHie21ox5TFfRhJKVZHj591GYyovXCaUtMP118VubVjD6zcxL050/a5ir8pGZA1TM4fWkDrjt9keu+69prsagxDL/Mxq3w+2Npz4KEuQR9CwkAZsBf3VoNIDvowon40qZ188tWG/Jjfm0Ir916IW553xrX5htBa00QQwkNw0mNFP4MR8yTPXNZIwDgkuNb8Z/Xrsfy5tFndk5CBTz8oaQGv8zsDXsJVYdqZAr2XPLJ0riap+3tieHUbz2N+187UvzOsxgK+ARGUhqiKR2rrIDvrfDdX1qxnV4u0B9H9ESPq0bBmalE5SOO52csNS2XkCLjyvVtngd6J4WqdIYSqjUa0TxbSKqmwhfzkL1QxmnpiFbOj27tHPNjZxPUWoGw/fs1IuAXUPiCQjNQBc4GWoutqh5iZnLXh0/FWx1DaCzi2ecSKuDhdw2n0GDNwgWylk6hzp0+aXyWjjhrLWWw+myGAj6BrmFzo4sYI+cZ8DVvhV8IZ+XOokYK+DOZRY3hcf0NQ4rsua8jmtLw0r5+fPiMRfYejZSWgWoUDvh+nzSuskzRhrnYBK7ZDlk6hF0t0VIThCJLng3SkjnDqEsJ+C01pPDnOiG/DNXI4M4/7nFd//SObqh6BpefON9uu5HSils6/nEqfNGGWbTynqtQwCcwnDQDfm3Ij6qgDzGPjVcJ1bArcSQGNJTgyTuHVxfbkUnMTsQGrX/7w26oejZQb+8cQcAn4eSF9Qj4hcIvbumYZZljD/hC4Q9aRQRzFbJ0CIxYX4CaoB9VVpOsXJKqgeMW1ODB/3MWBhOqPdGqVIol94jZiU/O/t0H4qqd1xlOaqgL+yFJzD4olBLwffLELB0AONyfwDqPMmInupHBSEovSdjMJEjhExhOagj5ZSg+CVUBn6elk1ANhP0ygn7ZszNmPj513nLcdMGKci6XmEEccwzMEbYKAAwlNNSFzGDqlxkkZnr4aaNYlQ6DlsmMuZ6+L5bGPOsss3M4WeTewBcffhunfPMpGJnZVbdPAZ/AcFKzPfnGKsX1xRQkVN2uphgLX7x0Df7p4tUTXiMxM/n4u5bihLYaAOZ0q3VfexIPvH7Y9ZljjCFolW+WovA5x6hAfGQggV9t7sj7uN5o2rYku4aKB/yHredK6/l7+c9EKOBPAX2xNL7w0Ft5N6BMBqqewS0Pb8X+3ph93Xef3o0n3u4adV/nl29BbQidw+4xhkaGI5rWS0rUEoST1togvnftyQCAPd1RRFM6vv677RhOaqhxfJ6Cftm2Egsmba3b9JyA/5d3vYR/evAtV55AkMlw9MdVrGqphiJLdlVaKeQOV5/pzLqA/+r+fuw85j3EY7r419/vxIObOvDY1tHBdrLY0TWCja8fwSfufR0AMBhX8d2n9+BTv9iMTQcHXPd1Bfy6EHqjaaR1w06ODSc1cA7UhWeXn0lMDaJ2f+exKAAg4JMwYnn4gpBftru0Fk7amjmB3N22PQXKLgcTKowMR0tNEK21wbEFfI8DyExm1gX8a+5+BZd+13tM33QhVIczb7m3Jzqp/qDofnmwPwHdyGBbZ7b98ebDg677jqR0W22JRlZPvtONFbc+gVf399s9T2ZbAouYGmqC5uCSHV0i4MsYcogMAAj4JYwkLYVfpEoHALQ8gTiaGl2BIwalN1UFML82iK4SPHwBWTrEmBGnn6INwesHB3Dhd/6Mn79yaNJe89BA3L788OYObHX0u+8ecXv0I0kNNSGz6kYMo3jynWMAgLue22fPHS1ldy1B5MIYQ0NEsc+8ZYkhoRqugB/0yXa5ZGEP3/wO5Vo6Aq+Cg76o+fltrg5gQV0InUOFFb4zyJPCr2CcmftKqrUVSl78/8DrZgOnLUeG8j5mohwZSKA+7McZSxvwrUd3YPOhQSxpDGNpU8QeEi3ItXTE4wHgpX396I+ZX5h6snSIcdIYCUB8PUUuyykggn4JI5Y6L8XDd3r1zjNlr4DfGzM/701VCubXBtE9kip4dt3jEEROD39PdxTffXq3Z55gpjCrAr7Tv9vTHZ3GlbgRakT0lP/9NlM97++L533MRDnUn8CSpgiuWL8A0bSOzYcHsWJeFeZVB1wfaN3IIOZIyIp2CDut029Vz9in4mTpEOOl0TEopd/a7epU+CFFtveDlOLhOxW+s8bey9JxKvz5dSHoGe5ZiSZwlpI61f4vXj2M7z69B//04BYkVQP/5382uYoiZgKzKuA7Vf3u7sr5Q4jhzIm0DlXP2AemHZ0jk6YWDvUnsKghbKvywYSGhoiCeTVBdEezH+gRSxGJL1/QL6OpSnElxbZa827J0iHGy9oFNaOuq8mxdMRnsVB7ZEU2S4OdgdhZV++t8NMI+s09JgssQdM5lMRwUrO/m076HAcQp6UjmsA9urULdz23D0++041bf70t71orkVkV8IcS2YB/ZDBR4J5TiwiecdVAwupJc8qiOqhGBnt6JudMpCeaQmttEHWOL1V9WEFLdQDdIynb/jrQZx4YnY3OxMYqoabe6hiCT2KoGuPuWoIQXL5uwajr6nLKMoXNUkjhRwJmwI+nswH/6KAz4JsxIJPheOOQWZzQG02jqSoAxpj92X54cwdO+vofcM+LB0a9hrMzrLOUui+WxtKmCBoiCr73jNkbaKiCrONSmPSAzxi7lDG2izG2lzF2y2S+1ojjzfc6tZsuhHKJp3Vb3Z9ozek81D/+A9Pu7iie29076vq0bkAzOKoDPlcpZV1YQUtNECktY6/p5X39AIDTlzba9xOVOmsX1MInMfTFzL7l1B6BGC9i85UTp0XoHHIvVLwXQnTE0zo457j6rpfw7d/vtG+PpnT8dstRfPqXm/GXd72E7Z0j6Iul7V5OQtj8/JXDALKlok6c/fudCr8vrqK9PoTTrTGMgDvmFIJzXhHe/6QGfMaYDOC/AbwPwFoA1zHG1k7W6w27An7ltEGNWutKqLqtTETveeew5rHyF997Htff89qo7oEJ6zUiAZ/LhmmI+DGvxvzgv7yvDwDwyv4BrGmtdn35ROK2uSqAtvqQ/ViCGC+MMWz/xiW4/QMn2NctrM92UA05JqL55fzCIuII+H0xFZsODaJjMIlVLVUI+CQc6IvjMxu34AkrT9YTTdkKHxhtS3p1aEi5Ar5D4UfTaK4K2IOCAKBrOFl0Q+WuY1Hc8fudWHXbE9Me9Cdb4Z8OYC/nfD/nXAWwEcCVk/ViIuA3RpSKCvii+iCWNhC3LJ2W2iAaIoqt8D/7wBY8s6N7TM8rmki9lVPtI84iIorPVVlTF1Zw1vJGtNWF8JmNW5BQdbx5eNAeHC1YUJsN8kIR0aYrYqKEFR8aI2bgrQn6IDmmpTlHYBaydITCj6V1HHW0SFjdWoPqoB9PbXd/h3qjaQwlNNSHs20cnHg5Ac6BLaJKh3OO/ngajVUKVrVU2bdnOHCgQPFF51ASl3z3z/jhc/sBAMfGsOlrMpjsgN8GwDlEssO6blIQflp7Q3jMls4NP9uEk77+B8/b7nnhAHZ5nPqVAufcPhAl0ro9cacq4MPChjAOD8TBOcdvthzFi3v7x/TcYpD01T94Gfc6vEhxUIkEfAgpsp0Eqw8rmFcdxGfeuxJpPYOOwSTiqmGreIFQ+PURBeeubEZDRMGN71k2jt+eINyIfkyLG92zcINOS6egh59V+J2OgO+XGGqCPkTTOhSfhI+fvQSAuQN3OGeTl+Ck9lrv2Q8elk5cNZDSMmiqCmB1S7Xr/oWGqoiSZkHH0PTmFqc9acsYu4Extokxtqm3d7QfPRaGkxp8EkNLdcDetVcqf9je7Vm7PxhX8Y1Ht+OTP3t9XGtKaRlbicfVbMAPKzIWN4RxeCCBlJYB57ATuqXAOYeWyZ4evrI/2y4hbls65pdLnMYKlVNr/b/PGkzdmFNuKTz8hrCCT5+/Apu/chEuWNNS8toIIh+iPPPK9e4krph6BRSu0rGTtqphJ2tPaKvBpy9YgbB127tXNuOr7z8e1UEfjg4lkdQM1xnqv3xwHT55zlI0VgXss28nSc2wN0kKS+dBa+9MU1UAS5rMg1WrmNlcIODnHgyKbfqabCY74B8FsNDxc7t1nQ3n/G7O+QbO+Ybm5uYJvZjosV0T8iOa0rC/N4av/nYbfvfW+AcX7+gydwc6Pcax4PxAxdMGYlYwrgr4sLgxjM6hlH02Es8z+9P7eXWktAw+dd5yLKgNIuMwI8UHUKghYeuIQeKiOmKvFfCbcoaTLGuqQnXAhzXzRyfaCGIiHL+gFs/c/B787TlLXdeHHJ1YCyVtAz4ZfpnZlk5VwIff/cM5WN5chW1Hze/qB08xTYTm6gD2WuXZzhLQ605fhNsuX4vqoHcr8JRm2N+RtJbB4f4EvvHodgDmAcsvS3jhi+fjhx89FYC7YigX8V383xvPAuCuKJoOJjvgvw5gJWNsKWNMAXAtgEcm68XM3aWK/Yd8eHMH7nv5EP7l8R0lP0dun+3tVsBf1lTldfeiiE0hPokhruq2io8EfJhXE4SR4ThifQgSY5i32WNtDjlufg0WNYbtfjeAI+ArZsAXp7PiQyzUzj5r00hTxB3wa8N+bP3axXjPqokdgAnCi+XNVaO89ECJHj5gfnfiVsBfUBe0n0t46xetNc9G51UH7LJnL0unJujP6+FXBX2QJYaUns27AUCbZXe214ftQod4gTNzcVt9WEFzdQCdQ0moegY3P/iWvZt9KpnUwmrOuc4Y+wcATwKQAdzDOX9nMl6rcyiJF/f24e/PWwFJYoipuu2f9UTTMDLcPk0rhGpkEHCcXopdplXB8b1VHdZ+gNWt1eiPqa6EqrBSxH0KfXByEf1wWqoDaIgorhyDOFOocij86qAPPmtburB49oqAXz06IUslmMRUEvSV5uED5ncnltZxdDBpB2AA+OXfnYmkatjtF+ZVB22r0yvgC2HIOXd93pOagZBfRtAnIa1loFuW7D9esAIrHf69OIMuJNRijpzdgroQjg4l8dqBATy8uQPdIyn8/JNnFPxdy82ke/ic88c556s458s557dP1us8urUTGQ5cc9pC1AR94Bx2Fl/PcPQX2ErtJPf0bLfVomG8vexF18rVrdWIW0lbiZlJqmzAtxS+w9LpHkkVXLPoh9NSE0R9WMFgwmkdWXkCy9M8Y1mDS62LD/++HrO6oDFC82aJ6cU58L5YwK8K+DCU0LC3J4ZVjgDcVBXAwoZsqadzjnKdZ8D3Q89wV5IWAJJaBkG/jIBfRlrP2Bsnc6vZRAI614pNaYZduZO1V2W014XQOZS0hWdui+epYNqTtuWiazhlV77UWMOznTXupfbAzk3A9FhtCMbbNe/IQALVAR8W1IaspK2BSMAHxpjdJ1yc2jkD/hn//AxO/dbTeZ9XtHxtthT+kNXzG3CrCgD4+NlLcedfn2I/NuiXEfRLSGoGaoK+ol8wgphszlyW3fhXqHkaYAbPzYcHoRqZgrNp2x3VZ/kUPjB6z05KNRV+wCdZmxjN774/Z10Bn2R1/nQ//hevHsZl//m81UbFKqBQfGisUjCQUMFhfk9z989MBbPmm+4svRJ/yMMDCbt3RqkBP6EaGEqo+PbvdyKtGxiwGj2NN+B3DCbR3hDG8nkRZDhw70sHbW9dKHzRBmIsHn40pUGWGMKKjPqwggzP7vpLqDpkiRWsdhDzRHMTtgQxHSg+ybYaC228AkwrRbRRObGtLu/9nOrf08O3rsv18ZOaYZczp/WMHZgVn3tdjJnfv1xXoHskhaRmIGad0YcVGZLEUB9WMJzU7MleFPAnwIgr4Jv/cw670uRYiUMPYmkdP/zzftz17D7c++JBu6QynXPa9+lfbsbT24tvlDoymEB7fQhXrW/DFSeZpWh+64NTG/JDlpht6YylSieeNlBlnSmI5NGAlbiNpw2EFbmgDy++XLkJW4KYLv5483n45SfPKJo/EmeutSE/FjaE8t5vpWODVE0BhT+So/CFhx/wyUhrmbwKHzCVe67Ct6vurIAvvP6GiALOs+JT5AamklkT8IcS2YAvhnkAwLKmiDnHcqRUhZ9tFfy6YxRgWs/Yah8AHtvahU/+bFPR5zs6mER7fQiMMVx+4nwAwJEBM8CLo37W0hmt8PNt6oim9GxS1gr4g9b6Ymm9aKMz4SMe79HjhCCmg4aIgnetaCp6PxFAT1tSX/Dg0FyVFTNeBRs1IuDn7L9Jqobl4ZuWjqqbgdknjQ6X4YCMuGrgsw9swb9aPX3s3lmq7vouNuQUaZDCnwBOS8eZrKmPKGitDdpbmjcdHMAXHnoL//6HXXhw05FRzxNP67bVIgaU+GWGLUeGcNrtT6NjMOHZUtULI8MRVw3bPjnD0aBM0FRl2jGA2Soht9dG15D3mUksrWU/SFaZpTggJdSsqsjHO51muenlJ47uYkgQlYz4Lp+/Zl7B+xU7UxC5vlyFn9IMhBSpqKUDWAo/reOV/f3YdNDszhm1myUalsI3k7sNOUUa2jQo/FnT73bYMRS51ZHxrwv7zcHF1g637z+7D3/c2WPf/qFT210fjHjasJOffVZZ5/zaEA4PJGBkOLpH0q6KgkIIxS6y+bVhP3wSw6mL6+37OAdDiMf45ezpZ9dwylUK5lynKBUVz9ETTWPb0WE8/vYxnFQgmQWYm1N+tfkoTlmU3wMliEpEBODzVhcO+ABw90dPHTXhTSCEoXOACuC2dBKqXtDSER5+Xyxtf89jLkvHsHN2YgOkXT04DQp/VgV8ofAZY6gP+zGY0FAfNseaicHdixxlW4DZ+GhZc9brS6i6e9o4gNbaoF3xk9KMkoePiyZMzl2E279xqev0MrckMqEaCCvZP0u+gctRx5Sq1pogFJ+EQ/1xPLLF3FXcWSRJ/W9Xn4R//sA6qrcnZhz//lcn4Y1Dg64a/HxcfHxr3ttqQ34oPsnexAiYGy+zAV/CYKKIhx/wYXd3FJrB7Ule0Zx26KIBoVD4YrdtbjnoVDArLJ2UZiCtZ1yJGTHooCZoKvzu4TQyGW6r7ns+tgEARvWTj6UNGDlH3gWO4SAJtfSAL5KwYcW9i9AZ8BfkfGgTqu76IOTrvRFLaXbzNEliWNIYxoG+BNLW2m++aFXBtUkSc3UoJIiZQnt9GFeun3gPRsYYWmoCrjOAtG72tQoqpoef0gyolvWST+ELi2YooUE3MtmAr5q7dIW9KhwIcWAYsTZ9TSWzIuCLpmfOXtdnrzD98pAiY35NEKqRwUBCRTxtYHlzBBesaUHQL9l+oAjCCVV3zcs0nzdruyQ1Y9Tt+ci1dLxoz+lUGU8broogZ6I4937CGwSAJY0RHOyPo2ckhb88pR3Xnr6opDUSxFympTpo71oHshssQ34ZiixBMzg0K6/mtT8gorhNkjePDNn9s3KrdIJ+GRFHLBA5vqlkVlg6IuA7a22/cOkanLW8Eacurrc9umPDKdcRN6L4EFd1ZDLctWkpnPNHdCrh1BgUftbSyf82jwr4OQrf2SPHiZn9z/6+S5sieGZnD4wMt7tdEgRRmJaaIHYcG7F/TjoCvl+WoBkOS8cjaRsOuMXch37wsn05lhZVOtn71EcUxNWsTTuc1KZ0dOisUPhiE4Yz4PtlyW7pKwJg13AKCatGHTD/WIm04WoznEgbdjLlpx8/Df/2oZNcG5gSql5ywBc7ZyMFFP7CnJxCIm323RZ4tWzOZLj5QXL091nSFLHXJewsgiAKM68mgB6Hwhc77UOKDL/PDPjijD5fHX4+DvTFkdIyrlxDY5U7Z1fqiMRyMSsCvpfCd9JqefDHhpOmwlfcCt9ZHhVTdWjWH/i8Vc24+tR2l8JPapkxB/xQgYCfm3gqReEnNNEcLfu8JyzIVuXMJ4VPECXRUhNELK3j3hcPYNvRYew6ZjYUXGrt31H1jF0q7fOo5V8+LzLqOoEYon6co814e873fapr8WdFwK8J+vCeVc2u+nsnTZEA/DLD0aGUWQVjnUKFFRkJ1bA9OgBWd7wMfBKzK1icCj+p6tAzpf2Rkprw8POrgNzEaUozbB9xfm0Qg/HRCkBszXZaOs4h0QtI4RNESYgS7q/9bjuuv+c1bDkyCL/MsLq1GorP8vCNDPwy86xoK7SPRTRQc86VaM/ZGVyqeCwXsyLgn7GsEfd94vS8VoYkMbTXh3FkIGEmUSzFLfpqO4+y4hTO5+jnEXCMX0tqBpzxvtAmrIRHlY4XF69tsYc2JFXDVvittUFPSyeWNq9zWjqMMTtRTR4+QZTGJce34qvvX4ubL1qF/riKHz1/AGtaa+xBK8LD97JzANPmefQfz8FPP3aa5+1tdSGX89Be77ZwM1NcpTMrkralsKghjEMDcVede1iR0RtNu9qU6hnrD+zYRu3sj59QDZfCT+lGXgXvVYfvxd1/swEJVcevNh9FSs/YVTrza4N48/AQVN1UGJybB6/s1Cz38/7k+tOw81jU7iVEEERhQoqMj5+9FCnNwA+e24e4auCENtMe9csS9AxHWs8f8AHY93/iM+fi+8/uc03YO36Bu3XJwvpchV+u36Q0ZoXCL4XFjWEc6ksgrmaz5l4evqZz6EaOwvflKHzHUTlRoKxKdNELl1DvLmZ6vn10GHc8YfbkEDt6h5Iq7vj9Tlz9g5cAeFs6gGkPrV9IO2cJYqwE/TLu+MsTcf1Zi/G35ywBkE3SJhxDVQpx3Pwa3PHBdbjlfWtse/ndOVPjchX+VFs6c0rhR+3BIJbCD5jbop39a7RMBnomA9ml8LOXUzl1+Im0AeSZfpjQdCiyZE+aKoQkMSg+CY9t7bKvEzv0hhMaXj8wgC1HhpDWDXRau2/z5SwIghg77z9pAd5/UtaTV+yAr0Mp0rJZEAn4cON7ltui7bzVuQHfrfCn2tKZMwrf2VLB9vCVPB6+wV09uZ3zNhOq4WprmtDy97BPqsaoOt1CBHP617daOYnBhIZ9vXFkOHCoP4Hdx6II+KRRbSIIgigfIgbE0wb8YxwS9NEzF6Mu7B+l6IN+Gd+48nj88wfWAaCk7aSxpClbPpX18H1I6xk7SRr0S6alk5O09TvKsZKq29IpNLE+oRol2TmCXK9fKPz9vTE7ebuvJ4bdPTGsmFdV0oxegiDGhwjyZkPDsYXKb151At78ykWet/3NWUuwZr7ZENEghT85LHUEfNGSQPw/bG3ciig+aB5JW+cfJbe1QrKAh59UjaIJWye5JZpCwT/j6O65rzeGPd1R1zQfgiDKjwjy8XRpHn4uhRoTytZtpbZaLxdzJuA7/2BC4YsWC0OWeg4psm3pOBW+08JJ5rRWiHsMLREk1NFtGgoR9LkDfktNECvnVeEpa7JWwCdhy5Ehq2VynsQBQRBlYTwefqmIs3OydCaRZZbKF0pa1McPWbtZI4rPsnQyruk2zrODZE57ZK8pVdnbxqjwPe57tjUBqL0+hJMW1uHV/eYUrqWN+Xf4EQQxcWyFrxolFV6MBUkofLJ0Jo8L15q9dUTVjWixIPzxcEC26vDdSdslTRFs+/ol+PAZi0Yp/FgxD6LyTBEAABDESURBVH8sAd8jMXT1qe1oqwvhrg+figW1QbvSqKWWNlcRxGQiYkAirRcdrD5Wsgq/rE9blDlTlgkAX7hkNc5d2YSTrFp1UUEjmq+FFRmqbpZl5h7RqwI+hPzyKIWfO/HeSULVsVApvc2B82zgM+9dCcDc1PHiLRcAcAf5+RTwCWJSsZO22vg8/EKIp6Ok7STikyWcuzJbFysUvvDww4oPeoZDM7hno6SwIltJ2+xhOZYqXJYZ8o/dw7/8xPn4rMcAE9H3Q2LuAc0EQZQf4eFz7t0LfyJIlLSdekQvmj6rX37YTtpmXElbQcAvg3MgqToCfrqAh6+5h5QUI+TYH+CFUPXN1YGye4oEQbhxqvryK3xK2k45bXUhSAzY0xMFYCp8zbDq8KXRb41Q/Wk969sXUvhjTtpaTdrybdYSrRZaSxyiThDE+HH69mPdeFUMofDJ0plCgn4ZCxvC6IuJKh0z0KY0wzNJI47yYqOWxGAnUXMxMhyqnkF4DJaOaNKWL9Er+vq3UMAniEnHrfAnJ2lLls4Us7w5W88uAm1CNTwVvvijp62JVHVhJa/CL2WebS7ibCBf7X5zVQCyxChhSxBTgOJQ9eX28G1LhxT+1LK82dFywdqIldIMTw9f+OYpy9KpC/nzeviltkZ2Eiyi8H2yhO9esx4fP3tpyc9JEMT4mEwPf7qStnOqLNOLpU2mwl/WHHEpfK8/sO3hWwq/JuT3HFACwJ5GPzaF794f4IWzmx9BEJOHU+F7CcCJMF1J2zkf8C89oRVdw0l88txlePxtszWxaenk9/BthR/2o2MwOep+5nOM3dIRO4DHclZAEMTk4PTty27p2Enbsj5tUSb0WzDGPsQYe4cxlmGMbci57UuMsb2MsV2MsUsmtszJoyGi4OaLV6M25Hepeq+yRzvgW+q9NuS3xw3mkrV0xlCH73c3dSMIYvpwBnml3FU61tPNtKTtNgAfBPBn55WMsbUArgVwPIBLAXyfMVbxUcxVhuXp4YuyTCtpG/IjpWU8J8+LSViRcSj8sTRcIwhicnAKwLqwUtbnnpFJW875Ds75Lo+brgSwkXOe5pwfALAXwOkTea2pwPkH9uo1Lw4CKasss9b6EMQ9EreJcSRtmyLm8zVVlffDRRDE2HHGg4ZIeedE23X4M0zh56MNwBHHzx3WdRVNsay8KNVMWUlbMY0+6lGamdSEh1+6Wj9reSMev+lcrJhHve4JYrpxnuXXT5LCr7gqHcbY0wBaPW66lXP+24kugDF2A4AbAGDRokUTfboJ4czEl5S0tQL+iEcDtcQ4qnQYY1ibM+WeIIjpwTnApCFS5oA/TTttiwZ8zvmF43jeowAWOn5ut67zev67AdwNABs2bJjinLUbpWjS1m3pNFlDxEW3TSeJ9NgtHYIgKpNyK3xplu20fQTAtYyxAGNsKYCVAF6bpNcqGy5Lx0Phi4OASNqKjpUDcXXUfW2FP4aZtgRBVCblVviAaevMqKQtY+wDjLEOAGcBeIwx9iQAcM7fAfAggO0Afg/g05zz/JNCKgSXpVNg45Xw8JsthT+Y8Aj4mg5FlqirJUHMAsZizZaKzNjMGoDCOf81gF/nue12ALdP5PmnGqVIsyRRi5vSDEgMqA+bHn5/bHTAT6pG3q6XBEHMLAoNJB8vkkQjDqcV18YrL0vHbq1gNlfzyRJqQ35vha8aZOcQBJEXibFZU5Y5Iylm6WSrdDJ2WVVDREG/h4efHGMvfIIg5hbyNAR82tLpQCmm8B1VOs6AP+iZtNVpxyxBzHAuWtuCtrrS51KPBUliU27pUERyUHIvHc1AddD07+vDCjoGE6PuGyeFTxAznh/9zYbidxonskSWzrTitHS8miX5rZ22GZ49A2iI+D3LMpOqMSmZfYIgZgcSm3qFTwHfgVPhL2uKjLrdeUCQ7IAfwGBCBc/5w5mWDgV8giC8kaXZ00tnRuIsxVzVMrqfjVfrhYaIH5rBR02+SqoGQmOYZ0sQxNxiOurwKeA78Bfpf+13zLkV3e4aItndtl98aCte3NsHAEhoBvW1JwgiL5LERjkDk/6aU/pqFY5Q7VUBb2UuScyuzhFqX7RN7Ymm8cCmI/jwj18FYNbhU9KWIIh8TEdrBfIcHDDG8IOPnIoT22vz3sdnZdZF4BdNlQ73J6znMH05Vc8gTJYOQRB5oDr8CuDSE7w6QWfxyxLSesZub9poWTqHBsyAH/BJ45pnSxDE3GI66vDJ0hkjwsqxFb5l6RzujwMwxxSOZ9oVQRBzi+lQ+BTwx4hI7IrAXxXwQZElW+GHHAGfFD5BEPmQJKrSqXhEn3xh6TDGUB/x2x6+qfDJ0iEIojAydcusfETLBeeQ8/pwtoFawCchaVs6lCIhCMIbsnRmALkePgA0VmWn4Tg9/AgpfIIg8kBJ2xmA4qHwxeYrAAj6JUraEgRRFFL4MwCh8H2OXbcL67PtUxkYkprw8MnSIQjCG4m6ZVY+ItBLDoW/xNFoTTMyiKepSocgiMLI1C2z8vHbCj8b8JfmBPwkWToEQRSB+uHPAOw6fKfCb8wGfNXgSGpWwKeZtgRB5EGSGIypjfcU8MeKOANb2pwN8k2OKh3NyCCtmyMQ/R5TswiCIABAZkCGFH5ls683BgA4sa3Ovo4xhsaIGfQ1I4O0lkHAo70yQRCEgCydGUBPNA0AozpqbrrtQlx9ajs0PYO0TgGfIIjC0IjDGUR7vXuSPWOmhaNlONK6gYCP/HuCIPIzHQqfCsXHyGM3nYPeaBqMsVG3KTKzPPwMgn46lhIEkR+JBqBUPscvyD8cxS9LpqWjZUjhEwRREJkxStrOZPw+CZphWTqk8AmCKMB0jDikqFRG/BKDamSQoiodgiCKIDGGDPXDn7mIuvu4qpOlQxBEQWTJnH/9+21deHZXz5S8JgX8MuK3VH0spZPCJwiiIMLSufHnm/Gxn76OaEqb9NekqFRGhMKPpnXy8AmCKIiUk7S976WDk/+aE3kwY+z/McZ2Msa2MsZ+zRirc9z2JcbYXsbYLsbYJRNfauWjWI3VTIVPlg5BEPkRCr+lxpyncdqShkl/zYnK0KcAnMA5PxHAbgBfAgDG2FoA1wI4HsClAL7PGJv1EVAo/KRmkKVDEERBJGsASlI18LF3LcEZyxon/zUn8mDO+R8457r14ysA2q3LVwLYyDlPc84PANgL4PSJvNZMwNksjQI+QRCFkCXT0klpGQSnqLNuOaPSJwA8YV1uA3DEcVuHdd2sxu8I8lP1ByQIYmYiSwyawaEamSlrpV50py1j7GkArR433co5/611n1sB6AB+MdYFMMZuAHADACxatGisD68ohIcPkMInCKIwEjP37QBASJmaeFE04HPOLyx0O2PsYwAuB/Bezu1tY0cBLHTcrd26zuv57wZwNwBs2LBhiscBlBfnnNsAKXyCIArgHJcxIywdxtilAL4A4ArOecJx0yMArmWMBRhjSwGsBPDaRF5rJuC0dEjhEwRRCNnRgHGqAv5Em6fdCSAA4Cmre+QrnPMbOefvMMYeBLAdptXzac65McHXqnj8ZOkQBFEikmNMasV4+IXgnK8ocNvtAG6fyPPPNBRXlQ5ZOgRB5Mep8Kcq4JMMLSOuskzaaUsQRAFcCl+hgD/joDp8giBKpTbkty/PiKQt4UbxOT18snQIgshPY0SxL0/VhDwK+GWkuTpoXyaFTxBEIRocAZ88/BlIbcgPkYchD58giEI0VjkCPnn4M5NPvWc5AGqtQBBEYerDU6/waYh5mfn8JatxyfGtBYedEwRB1IWdHj4p/BkJYwwnLawrfkeCIOY0sjT1GzUp4BMEQUwzzLEJazKhgE8QBDFHoIBPEAQxR6CkLUEQxDRx/9+diY7BRPE7lgkK+ARBENPEWcsbAUz+LFsBWToEQRBzBAr4BEEQcwQK+ARBEHMECvgEQRBzBAr4BEEQcwQK+ARBEHMECvgEQRBzBAr4BEEQcwTGOZ/uNdgwxnoBHBrnw5sA9JVxOZMFrbN8zIQ1ArTOcjMT1jnVa1zMOW8udqeKCvgTgTG2iXO+YbrXUQxaZ/mYCWsEaJ3lZiass1LXSJYOQRDEHIECPkEQxBxhNgX8u6d7ASVC6ywfM2GNAK2z3MyEdVbkGmeNh08QBEEUZjYpfIIgCKIAsyLgM8YuZYztYoztZYzdMt3rccIYO8gYe5sxtoUxtsm6roEx9hRjbI/1f/0Ur+kexlgPY2yb4zrPNTGT71nv7VbG2CnTvM6vMcaOWu/nFsbYZY7bvmStcxdj7JIpXOdCxtifGGPbGWPvMMY+Y11fMe9pgTVW1PvJGAsyxl5jjL1lrfPr1vVLGWOvWut5gDGmWNcHrJ/3WrcvmeZ13ssYO+B4P9db10/b98gF53xG/wMgA9gHYBkABcBbANZO97oc6zsIoCnnun8FcIt1+RYA357iNb0bwCkAthVbE4DLADwBgAE4E8Cr07zOrwH4nMd911p/+wCApdZnQp6idc4HcIp1uRrAbms9FfOeFlhjRb2f1ntSZV32A3jVeo8eBHCtdf0PAHzKuvz3AH5gXb4WwANT9DfPt857AVztcf9p+x45/80GhX86gL2c8/2ccxXARgBXTvOainElgPusy/cBuGoqX5xz/mcAAyWu6UoAP+MmrwCoY4zNn8Z15uNKABs552nO+QEAe2F+NiYdznkX53yzdTkKYAeANlTQe1pgjfmYlvfTek9i1o9+6x8HcAGAh6zrc99L8R4/BOC9jDE2jevMx7R9j5zMhoDfBuCI4+cOFP4gTzUcwB8YY28wxm6wrmvhnHdZl48BaJmepbnIt6ZKfH//wTotvsdhh1XEOi1L4WSYiq8i39OcNQIV9n4yxmTG2BYAPQCegnl2McQ51z3WYq/Tun0YUzQzMHednHPxft5uvZ//wRgL5K7TYlo+n7Mh4Fc653DOTwHwPgCfZoy923kjN8/3KqpUqhLX5OAuAMsBrAfQBeDfp3c5WRhjVQAeBvB/Oecjztsq5T31WGPFvZ+cc4Nzvh5AO8yzijXTvCRPctfJGDsBwJdgrvc0AA0AvjiNSxzFbAj4RwEsdPzcbl1XEXDOj1r/9wD4NcwPcLc4nbP+75m+FdrkW1NFvb+c827ri5YB8CNkbYZpXSdjzA8zkP6Cc/4r6+qKek+91lip76e1tiEAfwJwFkwLxOexFnud1u21APqnaZ2XWtYZ55ynAfwUFfR+ArMj4L8OYKWVxVdgJm4emeY1AQAYYxHGWLW4DOBiANtgru96627XA/jt9KzQRb41PQLgb6wqgzMBDDtsiiknx/f8AMz3EzDXea1VtbEUwEoAr03RmhiAnwDYwTn/juOminlP862x0t5PxlgzY6zOuhwCcBHMfMOfAFxt3S33vRTv8dUA/midTU3HOnc6DvAMZp7B+X5O//doOjLF5f4HMwO+G6bXd+t0r8exrmUwKx3eAvCOWBtMj/EZAHsAPA2gYYrXdT/M03cNppf4t/nWBLOq4L+t9/ZtABumeZ3/Y61jK8wv0XzH/W+11rkLwPumcJ3n/P/t2zEKwkAQBdDf6TlyAA/ioTyNXe7gFRRJmcOkyICpxC7BeQ+22EkzDOQHlmzW45p3klet65Fm+qXHQ80zySXJs/qZktyqPmT94MxJxiSnqp9rP9fzYec+HzXPKck9nz95dnuPtstNW4Am/uFIB4AfCHyAJgQ+QBMCH6AJgQ/QhMAHaELgAzQh8AGaWACBEMzF+8DQDwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x137b22160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table.loc[:365].plot(y='TMPMN')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
" series.name = label\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1368b2668>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x137e04390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table.loc[:365].plot(y='TMPMN')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1361185f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table.loc[:365].assign(\n",
" diff=lambda x: x['TMPMAX'] - x['TMPMIN']).plot(\n",
" y='diff');"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" <th>diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1949-01-01</td>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" <td>4.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1949-01-02</td>\n",
" <td>-0.5</td>\n",
" <td>-6.7</td>\n",
" <td>-1.2</td>\n",
" <td>4.2</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949-01-04</td>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1949-01-06</td>\n",
" <td>-0.8</td>\n",
" <td>-3.2</td>\n",
" <td>-2.3</td>\n",
" <td>0.0</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1949-01-07</td>\n",
" <td>-0.6</td>\n",
" <td>-5.1</td>\n",
" <td>-3.4</td>\n",
" <td>0.0</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1949-01-10</td>\n",
" <td>-1.1</td>\n",
" <td>-4.2</td>\n",
" <td>-2.5</td>\n",
" <td>1.6</td>\n",
" <td>3.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1949-01-11</td>\n",
" <td>-3.0</td>\n",
" <td>-5.3</td>\n",
" <td>-4.3</td>\n",
" <td>0.0</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1949-01-12</td>\n",
" <td>-3.3</td>\n",
" <td>-8.4</td>\n",
" <td>-5.8</td>\n",
" <td>0.0</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1949-01-13</td>\n",
" <td>-4.8</td>\n",
" <td>-10.2</td>\n",
" <td>-6.8</td>\n",
" <td>0.2</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1949-01-14</td>\n",
" <td>-1.0</td>\n",
" <td>-5.7</td>\n",
" <td>-2.5</td>\n",
" <td>0.0</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1949-01-15</td>\n",
" <td>-1.9</td>\n",
" <td>-3.2</td>\n",
" <td>-2.9</td>\n",
" <td>2.2</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1949-01-16</td>\n",
" <td>-0.5</td>\n",
" <td>-4.9</td>\n",
" <td>-2.5</td>\n",
" <td>0.9</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1949-01-17</td>\n",
" <td>-0.7</td>\n",
" <td>-4.0</td>\n",
" <td>-3.2</td>\n",
" <td>0.1</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1949-01-18</td>\n",
" <td>-3.8</td>\n",
" <td>-10.3</td>\n",
" <td>-7.5</td>\n",
" <td>0.0</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1949-01-19</td>\n",
" <td>-6.9</td>\n",
" <td>-10.4</td>\n",
" <td>-8.8</td>\n",
" <td>0.2</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1949-01-20</td>\n",
" <td>-2.4</td>\n",
" <td>-13.5</td>\n",
" <td>-8.6</td>\n",
" <td>2.5</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" <td>11.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1949-01-23</td>\n",
" <td>-11.4</td>\n",
" <td>-15.5</td>\n",
" <td>-13.4</td>\n",
" <td>0.0</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1949-01-24</td>\n",
" <td>-10.5</td>\n",
" <td>-14.4</td>\n",
" <td>-12.4</td>\n",
" <td>0.0</td>\n",
" <td>3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" <td>13.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1949-01-26</td>\n",
" <td>-0.7</td>\n",
" <td>-6.8</td>\n",
" <td>-3.1</td>\n",
" <td>0.0</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1949-01-30</td>\n",
" <td>-0.5</td>\n",
" <td>-8.1</td>\n",
" <td>-6.5</td>\n",
" <td>0.0</td>\n",
" <td>7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>1949-12-03</td>\n",
" <td>2.0</td>\n",
" <td>-3.6</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>1949-12-04</td>\n",
" <td>2.0</td>\n",
" <td>-2.6</td>\n",
" <td>-0.7</td>\n",
" <td>5.3</td>\n",
" <td>4.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338</th>\n",
" <td>1949-12-05</td>\n",
" <td>4.2</td>\n",
" <td>-2.2</td>\n",
" <td>1.6</td>\n",
" <td>1.5</td>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <td>1949-12-06</td>\n",
" <td>3.2</td>\n",
" <td>0.3</td>\n",
" <td>1.4</td>\n",
" <td>0.0</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>1949-12-07</td>\n",
" <td>1.9</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>12.7</td>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>1949-12-08</td>\n",
" <td>0.9</td>\n",
" <td>-2.5</td>\n",
" <td>-0.2</td>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>342</th>\n",
" <td>1949-12-09</td>\n",
" <td>2.2</td>\n",
" <td>-1.6</td>\n",
" <td>1.4</td>\n",
" <td>0.8</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>343</th>\n",
" <td>1949-12-10</td>\n",
" <td>2.5</td>\n",
" <td>-2.3</td>\n",
" <td>0.3</td>\n",
" <td>0.1</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>344</th>\n",
" <td>1949-12-11</td>\n",
" <td>-1.7</td>\n",
" <td>-6.9</td>\n",
" <td>-4.0</td>\n",
" <td>0.0</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>345</th>\n",
" <td>1949-12-12</td>\n",
" <td>-2.8</td>\n",
" <td>-8.6</td>\n",
" <td>-4.4</td>\n",
" <td>0.0</td>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>346</th>\n",
" <td>1949-12-13</td>\n",
" <td>-1.5</td>\n",
" <td>-3.2</td>\n",
" <td>-2.2</td>\n",
" <td>0.1</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>347</th>\n",
" <td>1949-12-14</td>\n",
" <td>-1.8</td>\n",
" <td>-5.4</td>\n",
" <td>-4.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>348</th>\n",
" <td>1949-12-15</td>\n",
" <td>-2.2</td>\n",
" <td>-6.2</td>\n",
" <td>-3.6</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>1949-12-16</td>\n",
" <td>-2.0</td>\n",
" <td>-8.5</td>\n",
" <td>-4.9</td>\n",
" <td>0.3</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>1949-12-17</td>\n",
" <td>-0.2</td>\n",
" <td>-2.8</td>\n",
" <td>-1.4</td>\n",
" <td>0.0</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>1949-12-18</td>\n",
" <td>-2.3</td>\n",
" <td>-9.7</td>\n",
" <td>-5.5</td>\n",
" <td>0.3</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>1949-12-19</td>\n",
" <td>-2.0</td>\n",
" <td>-5.9</td>\n",
" <td>-4.8</td>\n",
" <td>6.5</td>\n",
" <td>3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>1949-12-20</td>\n",
" <td>-3.0</td>\n",
" <td>-4.8</td>\n",
" <td>-3.7</td>\n",
" <td>8.8</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>354</th>\n",
" <td>1949-12-21</td>\n",
" <td>1.8</td>\n",
" <td>-3.3</td>\n",
" <td>0.3</td>\n",
" <td>0.0</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>355</th>\n",
" <td>1949-12-22</td>\n",
" <td>1.7</td>\n",
" <td>-1.9</td>\n",
" <td>-0.1</td>\n",
" <td>0.5</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>356</th>\n",
" <td>1949-12-23</td>\n",
" <td>1.6</td>\n",
" <td>-1.3</td>\n",
" <td>0.5</td>\n",
" <td>5.2</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>357</th>\n",
" <td>1949-12-24</td>\n",
" <td>-0.3</td>\n",
" <td>-2.4</td>\n",
" <td>-1.2</td>\n",
" <td>0.9</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>1949-12-25</td>\n",
" <td>-0.6</td>\n",
" <td>-5.1</td>\n",
" <td>-3.8</td>\n",
" <td>0.1</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359</th>\n",
" <td>1949-12-26</td>\n",
" <td>-2.2</td>\n",
" <td>-7.5</td>\n",
" <td>-5.4</td>\n",
" <td>0.6</td>\n",
" <td>5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>1949-12-27</td>\n",
" <td>0.1</td>\n",
" <td>-2.8</td>\n",
" <td>-0.8</td>\n",
" <td>4.2</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>1949-12-28</td>\n",
" <td>-0.1</td>\n",
" <td>-9.8</td>\n",
" <td>-3.9</td>\n",
" <td>3.1</td>\n",
" <td>9.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>1949-12-29</td>\n",
" <td>-9.3</td>\n",
" <td>-27.0</td>\n",
" <td>-21.4</td>\n",
" <td>0.9</td>\n",
" <td>17.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363</th>\n",
" <td>1949-12-30</td>\n",
" <td>-20.9</td>\n",
" <td>-27.5</td>\n",
" <td>-23.9</td>\n",
" <td>0.2</td>\n",
" <td>6.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td>1949-12-31</td>\n",
" <td>-15.6</td>\n",
" <td>-21.7</td>\n",
" <td>-18.6</td>\n",
" <td>0.2</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>365</th>\n",
" <td>1950-01-01</td>\n",
" <td>-16.4</td>\n",
" <td>-22.7</td>\n",
" <td>-19.0</td>\n",
" <td>0.1</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>366 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP diff\n",
"0 1949-01-01 -2.1 -6.7 -4.2 0.0 4.6\n",
"1 1949-01-02 -0.5 -6.7 -1.2 4.2 6.2\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0 3.2\n",
"3 1949-01-04 3.3 0.9 2.3 0.0 2.4\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8 4.9\n",
"5 1949-01-06 -0.8 -3.2 -2.3 0.0 2.4\n",
"6 1949-01-07 -0.6 -5.1 -3.4 0.0 4.5\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7 3.5\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0 2.4\n",
"9 1949-01-10 -1.1 -4.2 -2.5 1.6 3.1\n",
"10 1949-01-11 -3.0 -5.3 -4.3 0.0 2.3\n",
"11 1949-01-12 -3.3 -8.4 -5.8 0.0 5.1\n",
"12 1949-01-13 -4.8 -10.2 -6.8 0.2 5.4\n",
"13 1949-01-14 -1.0 -5.7 -2.5 0.0 4.7\n",
"14 1949-01-15 -1.9 -3.2 -2.9 2.2 1.3\n",
"15 1949-01-16 -0.5 -4.9 -2.5 0.9 4.4\n",
"16 1949-01-17 -0.7 -4.0 -3.2 0.1 3.3\n",
"17 1949-01-18 -3.8 -10.3 -7.5 0.0 6.5\n",
"18 1949-01-19 -6.9 -10.4 -8.8 0.2 3.5\n",
"19 1949-01-20 -2.4 -13.5 -8.6 2.5 11.1\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6 4.2\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0 11.9\n",
"22 1949-01-23 -11.4 -15.5 -13.4 0.0 4.1\n",
"23 1949-01-24 -10.5 -14.4 -12.4 0.0 3.9\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4 13.2\n",
"25 1949-01-26 -0.7 -6.8 -3.1 0.0 6.1\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0 10.2\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0 3.8\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0 4.0\n",
"29 1949-01-30 -0.5 -8.1 -6.5 0.0 7.6\n",
".. ... ... ... ... ... ...\n",
"336 1949-12-03 2.0 -3.6 -0.4 0.0 5.6\n",
"337 1949-12-04 2.0 -2.6 -0.7 5.3 4.6\n",
"338 1949-12-05 4.2 -2.2 1.6 1.5 6.4\n",
"339 1949-12-06 3.2 0.3 1.4 0.0 2.9\n",
"340 1949-12-07 1.9 0.0 1.0 12.7 1.9\n",
"341 1949-12-08 0.9 -2.5 -0.2 6.3 3.4\n",
"342 1949-12-09 2.2 -1.6 1.4 0.8 3.8\n",
"343 1949-12-10 2.5 -2.3 0.3 0.1 4.8\n",
"344 1949-12-11 -1.7 -6.9 -4.0 0.0 5.2\n",
"345 1949-12-12 -2.8 -8.6 -4.4 0.0 5.8\n",
"346 1949-12-13 -1.5 -3.2 -2.2 0.1 1.7\n",
"347 1949-12-14 -1.8 -5.4 -4.6 0.0 3.6\n",
"348 1949-12-15 -2.2 -6.2 -3.6 0.0 4.0\n",
"349 1949-12-16 -2.0 -8.5 -4.9 0.3 6.5\n",
"350 1949-12-17 -0.2 -2.8 -1.4 0.0 2.6\n",
"351 1949-12-18 -2.3 -9.7 -5.5 0.3 7.4\n",
"352 1949-12-19 -2.0 -5.9 -4.8 6.5 3.9\n",
"353 1949-12-20 -3.0 -4.8 -3.7 8.8 1.8\n",
"354 1949-12-21 1.8 -3.3 0.3 0.0 5.1\n",
"355 1949-12-22 1.7 -1.9 -0.1 0.5 3.6\n",
"356 1949-12-23 1.6 -1.3 0.5 5.2 2.9\n",
"357 1949-12-24 -0.3 -2.4 -1.2 0.9 2.1\n",
"358 1949-12-25 -0.6 -5.1 -3.8 0.1 4.5\n",
"359 1949-12-26 -2.2 -7.5 -5.4 0.6 5.3\n",
"360 1949-12-27 0.1 -2.8 -0.8 4.2 2.9\n",
"361 1949-12-28 -0.1 -9.8 -3.9 3.1 9.7\n",
"362 1949-12-29 -9.3 -27.0 -21.4 0.9 17.7\n",
"363 1949-12-30 -20.9 -27.5 -23.9 0.2 6.6\n",
"364 1949-12-31 -15.6 -21.7 -18.6 0.2 6.1\n",
"365 1950-01-01 -16.4 -22.7 -19.0 0.1 6.3\n",
"\n",
"[366 rows x 6 columns]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.loc[:365].assign(\n",
" diff=lambda qqq: qqq['TMPMAX'] - qqq['TMPMIN'])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"# table['diff'] = table['TMPMAX'] - table['TMPMIN']"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949-01-04</td>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1949-02-04</td>\n",
" <td>2.2</td>\n",
" <td>-11.0</td>\n",
" <td>-2.0</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1949-02-16</td>\n",
" <td>1.0</td>\n",
" <td>-4.8</td>\n",
" <td>-2.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1949-02-17</td>\n",
" <td>1.5</td>\n",
" <td>-3.2</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1949-02-18</td>\n",
" <td>4.4</td>\n",
" <td>0.6</td>\n",
" <td>2.6</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1949-02-19</td>\n",
" <td>2.6</td>\n",
" <td>-5.1</td>\n",
" <td>-1.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1949-02-21</td>\n",
" <td>1.4</td>\n",
" <td>-9.8</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1949-02-22</td>\n",
" <td>3.8</td>\n",
" <td>-0.3</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1949-02-23</td>\n",
" <td>4.5</td>\n",
" <td>1.1</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>1949-02-24</td>\n",
" <td>2.6</td>\n",
" <td>-0.8</td>\n",
" <td>1.0</td>\n",
" <td>5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1949-02-25</td>\n",
" <td>1.4</td>\n",
" <td>-1.5</td>\n",
" <td>-0.5</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1949-02-26</td>\n",
" <td>0.8</td>\n",
" <td>-5.0</td>\n",
" <td>-2.9</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>1949-02-28</td>\n",
" <td>2.6</td>\n",
" <td>-1.8</td>\n",
" <td>0.2</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>1949-03-03</td>\n",
" <td>1.7</td>\n",
" <td>-7.9</td>\n",
" <td>-5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>1949-03-05</td>\n",
" <td>3.1</td>\n",
" <td>-4.0</td>\n",
" <td>-1.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>1949-03-08</td>\n",
" <td>0.7</td>\n",
" <td>-13.2</td>\n",
" <td>-6.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>1949-03-10</td>\n",
" <td>1.2</td>\n",
" <td>-10.8</td>\n",
" <td>-5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>1949-03-11</td>\n",
" <td>0.5</td>\n",
" <td>-12.1</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>1949-03-12</td>\n",
" <td>0.4</td>\n",
" <td>-15.2</td>\n",
" <td>-8.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>1949-03-13</td>\n",
" <td>0.2</td>\n",
" <td>-13.9</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21091</th>\n",
" <td>2006-10-02</td>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21092</th>\n",
" <td>2006-10-03</td>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21093</th>\n",
" <td>2006-10-04</td>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21094</th>\n",
" <td>2006-10-05</td>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21095</th>\n",
" <td>2006-10-06</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21096</th>\n",
" <td>2006-10-07</td>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21097</th>\n",
" <td>2006-10-08</td>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21098</th>\n",
" <td>2006-10-09</td>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21099</th>\n",
" <td>2006-10-10</td>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21100</th>\n",
" <td>2006-10-11</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21101</th>\n",
" <td>2006-10-12</td>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21102</th>\n",
" <td>2006-10-13</td>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21103</th>\n",
" <td>2006-10-14</td>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21104</th>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21105</th>\n",
" <td>2006-10-16</td>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21106</th>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21107</th>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21108</th>\n",
" <td>2006-10-19</td>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21109</th>\n",
" <td>2006-10-20</td>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21110</th>\n",
" <td>2006-10-21</td>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21111</th>\n",
" <td>2006-10-22</td>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21112</th>\n",
" <td>2006-10-23</td>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21113</th>\n",
" <td>2006-10-24</td>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21114</th>\n",
" <td>2006-10-25</td>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21115</th>\n",
" <td>2006-10-26</td>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21116</th>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21117</th>\n",
" <td>2006-10-28</td>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21118</th>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21119</th>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21120</th>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15975 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0\n",
"3 1949-01-04 3.3 0.9 2.3 0.0\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0\n",
"34 1949-02-04 2.2 -11.0 -2.0 4.8\n",
"46 1949-02-16 1.0 -4.8 -2.2 0.0\n",
"47 1949-02-17 1.5 -3.2 -0.6 0.0\n",
"48 1949-02-18 4.4 0.6 2.6 0.2\n",
"49 1949-02-19 2.6 -5.1 -1.6 0.0\n",
"51 1949-02-21 1.4 -9.8 -2.6 0.0\n",
"52 1949-02-22 3.8 -0.3 1.5 0.0\n",
"53 1949-02-23 4.5 1.1 2.0 0.0\n",
"54 1949-02-24 2.6 -0.8 1.0 5.3\n",
"55 1949-02-25 1.4 -1.5 -0.5 1.5\n",
"56 1949-02-26 0.8 -5.0 -2.9 1.0\n",
"58 1949-02-28 2.6 -1.8 0.2 0.7\n",
"61 1949-03-03 1.7 -7.9 -5.2 6.0\n",
"63 1949-03-05 3.1 -4.0 -1.4 0.0\n",
"66 1949-03-08 0.7 -13.2 -6.7 0.0\n",
"68 1949-03-10 1.2 -10.8 -5.1 0.0\n",
"69 1949-03-11 0.5 -12.1 -6.4 0.0\n",
"70 1949-03-12 0.4 -15.2 -8.3 0.0\n",
"71 1949-03-13 0.2 -13.9 -6.4 0.0\n",
"... ... ... ... ... ...\n",
"21091 2006-10-02 13.6 7.2 10.0 0.0\n",
"21092 2006-10-03 14.7 10.9 12.5 10.2\n",
"21093 2006-10-04 14.3 10.3 12.6 4.9\n",
"21094 2006-10-05 15.9 11.6 13.7 3.8\n",
"21095 2006-10-06 14.0 9.0 11.1 0.0\n",
"21096 2006-10-07 13.9 7.9 10.3 0.0\n",
"21097 2006-10-08 14.4 7.1 10.8 3.0\n",
"21098 2006-10-09 12.3 9.9 10.9 2.5\n",
"21099 2006-10-10 11.5 8.5 9.8 0.6\n",
"21100 2006-10-11 9.0 6.0 7.0 0.0\n",
"21101 2006-10-12 8.7 0.4 5.3 2.2\n",
"21102 2006-10-13 9.8 5.1 7.6 0.0\n",
"21103 2006-10-14 7.9 4.7 5.8 1.2\n",
"21104 2006-10-15 4.8 -1.4 1.3 2.1\n",
"21105 2006-10-16 0.8 -1.7 -0.4 0.0\n",
"21106 2006-10-17 2.9 -4.1 0.0 0.0\n",
"21107 2006-10-18 5.4 -1.0 2.2 0.1\n",
"21108 2006-10-19 7.1 3.8 5.5 0.6\n",
"21109 2006-10-20 4.4 2.3 3.5 0.7\n",
"21110 2006-10-21 4.1 0.5 1.9 3.7\n",
"21111 2006-10-22 12.0 3.9 8.8 0.7\n",
"21112 2006-10-23 12.6 9.1 10.8 0.0\n",
"21113 2006-10-24 13.2 9.9 11.3 0.0\n",
"21114 2006-10-25 14.0 11.4 12.6 4.7\n",
"21115 2006-10-26 12.2 5.8 8.5 6.9\n",
"21116 2006-10-27 8.7 2.7 5.6 0.3\n",
"21117 2006-10-28 11.0 4.1 8.2 0.3\n",
"21118 2006-10-29 4.6 1.4 2.4 0.5\n",
"21119 2006-10-30 1.8 -1.9 -0.7 3.5\n",
"21120 2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[15975 rows x 5 columns]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table[table['TMPMAX'] > 0]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 True\n",
"3 True\n",
"4 True\n",
"5 False\n",
"6 False\n",
"7 True\n",
"8 True\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"15 False\n",
"16 False\n",
"17 False\n",
"18 False\n",
"19 False\n",
"20 True\n",
"21 True\n",
"22 False\n",
"23 False\n",
"24 True\n",
"25 False\n",
"26 True\n",
"27 True\n",
"28 True\n",
"29 False\n",
" ... \n",
"21091 True\n",
"21092 True\n",
"21093 True\n",
"21094 True\n",
"21095 True\n",
"21096 True\n",
"21097 True\n",
"21098 True\n",
"21099 True\n",
"21100 True\n",
"21101 True\n",
"21102 True\n",
"21103 True\n",
"21104 True\n",
"21105 True\n",
"21106 True\n",
"21107 True\n",
"21108 True\n",
"21109 True\n",
"21110 True\n",
"21111 True\n",
"21112 True\n",
"21113 True\n",
"21114 True\n",
"21115 True\n",
"21116 True\n",
"21117 True\n",
"21118 True\n",
"21119 True\n",
"21120 True\n",
"Name: TMPMAX, Length: 21121, dtype: bool"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table['TMPMAX'] > 0"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949-01-04</td>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1949-02-04</td>\n",
" <td>2.2</td>\n",
" <td>-11.0</td>\n",
" <td>-2.0</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1949-02-16</td>\n",
" <td>1.0</td>\n",
" <td>-4.8</td>\n",
" <td>-2.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1949-02-17</td>\n",
" <td>1.5</td>\n",
" <td>-3.2</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1949-02-18</td>\n",
" <td>4.4</td>\n",
" <td>0.6</td>\n",
" <td>2.6</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1949-02-19</td>\n",
" <td>2.6</td>\n",
" <td>-5.1</td>\n",
" <td>-1.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1949-02-21</td>\n",
" <td>1.4</td>\n",
" <td>-9.8</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1949-02-22</td>\n",
" <td>3.8</td>\n",
" <td>-0.3</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1949-02-23</td>\n",
" <td>4.5</td>\n",
" <td>1.1</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>1949-02-24</td>\n",
" <td>2.6</td>\n",
" <td>-0.8</td>\n",
" <td>1.0</td>\n",
" <td>5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1949-02-25</td>\n",
" <td>1.4</td>\n",
" <td>-1.5</td>\n",
" <td>-0.5</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1949-02-26</td>\n",
" <td>0.8</td>\n",
" <td>-5.0</td>\n",
" <td>-2.9</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>1949-02-28</td>\n",
" <td>2.6</td>\n",
" <td>-1.8</td>\n",
" <td>0.2</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>1949-03-03</td>\n",
" <td>1.7</td>\n",
" <td>-7.9</td>\n",
" <td>-5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>1949-03-05</td>\n",
" <td>3.1</td>\n",
" <td>-4.0</td>\n",
" <td>-1.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>1949-03-08</td>\n",
" <td>0.7</td>\n",
" <td>-13.2</td>\n",
" <td>-6.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>1949-03-10</td>\n",
" <td>1.2</td>\n",
" <td>-10.8</td>\n",
" <td>-5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>1949-03-11</td>\n",
" <td>0.5</td>\n",
" <td>-12.1</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>1949-03-12</td>\n",
" <td>0.4</td>\n",
" <td>-15.2</td>\n",
" <td>-8.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>1949-03-13</td>\n",
" <td>0.2</td>\n",
" <td>-13.9</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21091</th>\n",
" <td>2006-10-02</td>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21092</th>\n",
" <td>2006-10-03</td>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21093</th>\n",
" <td>2006-10-04</td>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21094</th>\n",
" <td>2006-10-05</td>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21095</th>\n",
" <td>2006-10-06</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21096</th>\n",
" <td>2006-10-07</td>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21097</th>\n",
" <td>2006-10-08</td>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21098</th>\n",
" <td>2006-10-09</td>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21099</th>\n",
" <td>2006-10-10</td>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21100</th>\n",
" <td>2006-10-11</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21101</th>\n",
" <td>2006-10-12</td>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21102</th>\n",
" <td>2006-10-13</td>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21103</th>\n",
" <td>2006-10-14</td>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21104</th>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21105</th>\n",
" <td>2006-10-16</td>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21106</th>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21107</th>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21108</th>\n",
" <td>2006-10-19</td>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21109</th>\n",
" <td>2006-10-20</td>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21110</th>\n",
" <td>2006-10-21</td>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21111</th>\n",
" <td>2006-10-22</td>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21112</th>\n",
" <td>2006-10-23</td>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21113</th>\n",
" <td>2006-10-24</td>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21114</th>\n",
" <td>2006-10-25</td>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21115</th>\n",
" <td>2006-10-26</td>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21116</th>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21117</th>\n",
" <td>2006-10-28</td>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21118</th>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21119</th>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21120</th>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15975 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0\n",
"3 1949-01-04 3.3 0.9 2.3 0.0\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0\n",
"34 1949-02-04 2.2 -11.0 -2.0 4.8\n",
"46 1949-02-16 1.0 -4.8 -2.2 0.0\n",
"47 1949-02-17 1.5 -3.2 -0.6 0.0\n",
"48 1949-02-18 4.4 0.6 2.6 0.2\n",
"49 1949-02-19 2.6 -5.1 -1.6 0.0\n",
"51 1949-02-21 1.4 -9.8 -2.6 0.0\n",
"52 1949-02-22 3.8 -0.3 1.5 0.0\n",
"53 1949-02-23 4.5 1.1 2.0 0.0\n",
"54 1949-02-24 2.6 -0.8 1.0 5.3\n",
"55 1949-02-25 1.4 -1.5 -0.5 1.5\n",
"56 1949-02-26 0.8 -5.0 -2.9 1.0\n",
"58 1949-02-28 2.6 -1.8 0.2 0.7\n",
"61 1949-03-03 1.7 -7.9 -5.2 6.0\n",
"63 1949-03-05 3.1 -4.0 -1.4 0.0\n",
"66 1949-03-08 0.7 -13.2 -6.7 0.0\n",
"68 1949-03-10 1.2 -10.8 -5.1 0.0\n",
"69 1949-03-11 0.5 -12.1 -6.4 0.0\n",
"70 1949-03-12 0.4 -15.2 -8.3 0.0\n",
"71 1949-03-13 0.2 -13.9 -6.4 0.0\n",
"... ... ... ... ... ...\n",
"21091 2006-10-02 13.6 7.2 10.0 0.0\n",
"21092 2006-10-03 14.7 10.9 12.5 10.2\n",
"21093 2006-10-04 14.3 10.3 12.6 4.9\n",
"21094 2006-10-05 15.9 11.6 13.7 3.8\n",
"21095 2006-10-06 14.0 9.0 11.1 0.0\n",
"21096 2006-10-07 13.9 7.9 10.3 0.0\n",
"21097 2006-10-08 14.4 7.1 10.8 3.0\n",
"21098 2006-10-09 12.3 9.9 10.9 2.5\n",
"21099 2006-10-10 11.5 8.5 9.8 0.6\n",
"21100 2006-10-11 9.0 6.0 7.0 0.0\n",
"21101 2006-10-12 8.7 0.4 5.3 2.2\n",
"21102 2006-10-13 9.8 5.1 7.6 0.0\n",
"21103 2006-10-14 7.9 4.7 5.8 1.2\n",
"21104 2006-10-15 4.8 -1.4 1.3 2.1\n",
"21105 2006-10-16 0.8 -1.7 -0.4 0.0\n",
"21106 2006-10-17 2.9 -4.1 0.0 0.0\n",
"21107 2006-10-18 5.4 -1.0 2.2 0.1\n",
"21108 2006-10-19 7.1 3.8 5.5 0.6\n",
"21109 2006-10-20 4.4 2.3 3.5 0.7\n",
"21110 2006-10-21 4.1 0.5 1.9 3.7\n",
"21111 2006-10-22 12.0 3.9 8.8 0.7\n",
"21112 2006-10-23 12.6 9.1 10.8 0.0\n",
"21113 2006-10-24 13.2 9.9 11.3 0.0\n",
"21114 2006-10-25 14.0 11.4 12.6 4.7\n",
"21115 2006-10-26 12.2 5.8 8.5 6.9\n",
"21116 2006-10-27 8.7 2.7 5.6 0.3\n",
"21117 2006-10-28 11.0 4.1 8.2 0.3\n",
"21118 2006-10-29 4.6 1.4 2.4 0.5\n",
"21119 2006-10-30 1.8 -1.9 -0.7 3.5\n",
"21120 2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[15975 rows x 5 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.query('TMPMAX > 0')"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949-01-04</td>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1949-02-04</td>\n",
" <td>2.2</td>\n",
" <td>-11.0</td>\n",
" <td>-2.0</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1949-02-16</td>\n",
" <td>1.0</td>\n",
" <td>-4.8</td>\n",
" <td>-2.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1949-02-17</td>\n",
" <td>1.5</td>\n",
" <td>-3.2</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1949-02-18</td>\n",
" <td>4.4</td>\n",
" <td>0.6</td>\n",
" <td>2.6</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1949-02-19</td>\n",
" <td>2.6</td>\n",
" <td>-5.1</td>\n",
" <td>-1.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1949-02-21</td>\n",
" <td>1.4</td>\n",
" <td>-9.8</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1949-02-22</td>\n",
" <td>3.8</td>\n",
" <td>-0.3</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1949-02-23</td>\n",
" <td>4.5</td>\n",
" <td>1.1</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>1949-02-24</td>\n",
" <td>2.6</td>\n",
" <td>-0.8</td>\n",
" <td>1.0</td>\n",
" <td>5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1949-02-25</td>\n",
" <td>1.4</td>\n",
" <td>-1.5</td>\n",
" <td>-0.5</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1949-02-26</td>\n",
" <td>0.8</td>\n",
" <td>-5.0</td>\n",
" <td>-2.9</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>1949-02-28</td>\n",
" <td>2.6</td>\n",
" <td>-1.8</td>\n",
" <td>0.2</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>1949-03-03</td>\n",
" <td>1.7</td>\n",
" <td>-7.9</td>\n",
" <td>-5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>1949-03-05</td>\n",
" <td>3.1</td>\n",
" <td>-4.0</td>\n",
" <td>-1.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>1949-03-08</td>\n",
" <td>0.7</td>\n",
" <td>-13.2</td>\n",
" <td>-6.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>1949-03-10</td>\n",
" <td>1.2</td>\n",
" <td>-10.8</td>\n",
" <td>-5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>1949-03-11</td>\n",
" <td>0.5</td>\n",
" <td>-12.1</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>1949-03-12</td>\n",
" <td>0.4</td>\n",
" <td>-15.2</td>\n",
" <td>-8.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>1949-03-13</td>\n",
" <td>0.2</td>\n",
" <td>-13.9</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21091</th>\n",
" <td>2006-10-02</td>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21092</th>\n",
" <td>2006-10-03</td>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21093</th>\n",
" <td>2006-10-04</td>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21094</th>\n",
" <td>2006-10-05</td>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21095</th>\n",
" <td>2006-10-06</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21096</th>\n",
" <td>2006-10-07</td>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21097</th>\n",
" <td>2006-10-08</td>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21098</th>\n",
" <td>2006-10-09</td>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21099</th>\n",
" <td>2006-10-10</td>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21100</th>\n",
" <td>2006-10-11</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21101</th>\n",
" <td>2006-10-12</td>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21102</th>\n",
" <td>2006-10-13</td>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21103</th>\n",
" <td>2006-10-14</td>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21104</th>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21105</th>\n",
" <td>2006-10-16</td>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21106</th>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21107</th>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21108</th>\n",
" <td>2006-10-19</td>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21109</th>\n",
" <td>2006-10-20</td>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21110</th>\n",
" <td>2006-10-21</td>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21111</th>\n",
" <td>2006-10-22</td>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21112</th>\n",
" <td>2006-10-23</td>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21113</th>\n",
" <td>2006-10-24</td>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21114</th>\n",
" <td>2006-10-25</td>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21115</th>\n",
" <td>2006-10-26</td>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21116</th>\n",
" <td>2006-10-27</td>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21117</th>\n",
" <td>2006-10-28</td>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21118</th>\n",
" <td>2006-10-29</td>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21119</th>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21120</th>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15975 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0\n",
"3 1949-01-04 3.3 0.9 2.3 0.0\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0\n",
"34 1949-02-04 2.2 -11.0 -2.0 4.8\n",
"46 1949-02-16 1.0 -4.8 -2.2 0.0\n",
"47 1949-02-17 1.5 -3.2 -0.6 0.0\n",
"48 1949-02-18 4.4 0.6 2.6 0.2\n",
"49 1949-02-19 2.6 -5.1 -1.6 0.0\n",
"51 1949-02-21 1.4 -9.8 -2.6 0.0\n",
"52 1949-02-22 3.8 -0.3 1.5 0.0\n",
"53 1949-02-23 4.5 1.1 2.0 0.0\n",
"54 1949-02-24 2.6 -0.8 1.0 5.3\n",
"55 1949-02-25 1.4 -1.5 -0.5 1.5\n",
"56 1949-02-26 0.8 -5.0 -2.9 1.0\n",
"58 1949-02-28 2.6 -1.8 0.2 0.7\n",
"61 1949-03-03 1.7 -7.9 -5.2 6.0\n",
"63 1949-03-05 3.1 -4.0 -1.4 0.0\n",
"66 1949-03-08 0.7 -13.2 -6.7 0.0\n",
"68 1949-03-10 1.2 -10.8 -5.1 0.0\n",
"69 1949-03-11 0.5 -12.1 -6.4 0.0\n",
"70 1949-03-12 0.4 -15.2 -8.3 0.0\n",
"71 1949-03-13 0.2 -13.9 -6.4 0.0\n",
"... ... ... ... ... ...\n",
"21091 2006-10-02 13.6 7.2 10.0 0.0\n",
"21092 2006-10-03 14.7 10.9 12.5 10.2\n",
"21093 2006-10-04 14.3 10.3 12.6 4.9\n",
"21094 2006-10-05 15.9 11.6 13.7 3.8\n",
"21095 2006-10-06 14.0 9.0 11.1 0.0\n",
"21096 2006-10-07 13.9 7.9 10.3 0.0\n",
"21097 2006-10-08 14.4 7.1 10.8 3.0\n",
"21098 2006-10-09 12.3 9.9 10.9 2.5\n",
"21099 2006-10-10 11.5 8.5 9.8 0.6\n",
"21100 2006-10-11 9.0 6.0 7.0 0.0\n",
"21101 2006-10-12 8.7 0.4 5.3 2.2\n",
"21102 2006-10-13 9.8 5.1 7.6 0.0\n",
"21103 2006-10-14 7.9 4.7 5.8 1.2\n",
"21104 2006-10-15 4.8 -1.4 1.3 2.1\n",
"21105 2006-10-16 0.8 -1.7 -0.4 0.0\n",
"21106 2006-10-17 2.9 -4.1 0.0 0.0\n",
"21107 2006-10-18 5.4 -1.0 2.2 0.1\n",
"21108 2006-10-19 7.1 3.8 5.5 0.6\n",
"21109 2006-10-20 4.4 2.3 3.5 0.7\n",
"21110 2006-10-21 4.1 0.5 1.9 3.7\n",
"21111 2006-10-22 12.0 3.9 8.8 0.7\n",
"21112 2006-10-23 12.6 9.1 10.8 0.0\n",
"21113 2006-10-24 13.2 9.9 11.3 0.0\n",
"21114 2006-10-25 14.0 11.4 12.6 4.7\n",
"21115 2006-10-26 12.2 5.8 8.5 6.9\n",
"21116 2006-10-27 8.7 2.7 5.6 0.3\n",
"21117 2006-10-28 11.0 4.1 8.2 0.3\n",
"21118 2006-10-29 4.6 1.4 2.4 0.5\n",
"21119 2006-10-30 1.8 -1.9 -0.7 3.5\n",
"21120 2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[15975 rows x 5 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table[lambda x: x.TMPMAX > 0]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 -2.1\n",
"1 -0.5\n",
"2 1.1\n",
"3 3.3\n",
"4 4.0\n",
"5 -0.8\n",
"6 -0.6\n",
"7 0.5\n",
"8 0.8\n",
"9 -1.1\n",
"10 -3.0\n",
"11 -3.3\n",
"12 -4.8\n",
"13 -1.0\n",
"14 -1.9\n",
"15 -0.5\n",
"16 -0.7\n",
"17 -3.8\n",
"18 -6.9\n",
"19 -2.4\n",
"20 1.6\n",
"21 0.2\n",
"22 -11.4\n",
"23 -10.5\n",
"24 1.7\n",
"25 -0.7\n",
"26 1.1\n",
"27 1.2\n",
"28 1.4\n",
"29 -0.5\n",
" ... \n",
"21091 13.6\n",
"21092 14.7\n",
"21093 14.3\n",
"21094 15.9\n",
"21095 14.0\n",
"21096 13.9\n",
"21097 14.4\n",
"21098 12.3\n",
"21099 11.5\n",
"21100 9.0\n",
"21101 8.7\n",
"21102 9.8\n",
"21103 7.9\n",
"21104 4.8\n",
"21105 0.8\n",
"21106 2.9\n",
"21107 5.4\n",
"21108 7.1\n",
"21109 4.4\n",
"21110 4.1\n",
"21111 12.0\n",
"21112 12.6\n",
"21113 13.2\n",
"21114 14.0\n",
"21115 12.2\n",
"21116 8.7\n",
"21117 11.0\n",
"21118 4.6\n",
"21119 1.8\n",
"21120 1.3\n",
"Name: TMPMAX, Length: 21121, dtype: float64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table['TMPMAX']"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 -2.1\n",
"1 -0.5\n",
"2 1.1\n",
"3 3.3\n",
"4 4.0\n",
"5 -0.8\n",
"6 -0.6\n",
"7 0.5\n",
"8 0.8\n",
"9 -1.1\n",
"10 -3.0\n",
"11 -3.3\n",
"12 -4.8\n",
"13 -1.0\n",
"14 -1.9\n",
"15 -0.5\n",
"16 -0.7\n",
"17 -3.8\n",
"18 -6.9\n",
"19 -2.4\n",
"20 1.6\n",
"21 0.2\n",
"22 -11.4\n",
"23 -10.5\n",
"24 1.7\n",
"25 -0.7\n",
"26 1.1\n",
"27 1.2\n",
"28 1.4\n",
"29 -0.5\n",
" ... \n",
"21091 13.6\n",
"21092 14.7\n",
"21093 14.3\n",
"21094 15.9\n",
"21095 14.0\n",
"21096 13.9\n",
"21097 14.4\n",
"21098 12.3\n",
"21099 11.5\n",
"21100 9.0\n",
"21101 8.7\n",
"21102 9.8\n",
"21103 7.9\n",
"21104 4.8\n",
"21105 0.8\n",
"21106 2.9\n",
"21107 5.4\n",
"21108 7.1\n",
"21109 4.4\n",
"21110 4.1\n",
"21111 12.0\n",
"21112 12.6\n",
"21113 13.2\n",
"21114 14.0\n",
"21115 12.2\n",
"21116 8.7\n",
"21117 11.0\n",
"21118 4.6\n",
"21119 1.8\n",
"21120 1.3\n",
"Name: TMPMAX, Length: 21121, dtype: float64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.TMPMAX"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DATE_OBS</th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949-01-03</td>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949-01-05</td>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1949-01-08</td>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1949-01-09</td>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1949-01-21</td>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1949-01-22</td>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1949-01-25</td>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1949-01-27</td>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1949-01-28</td>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1949-01-29</td>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1949-02-04</td>\n",
" <td>2.2</td>\n",
" <td>-11.0</td>\n",
" <td>-2.0</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1949-02-16</td>\n",
" <td>1.0</td>\n",
" <td>-4.8</td>\n",
" <td>-2.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1949-02-17</td>\n",
" <td>1.5</td>\n",
" <td>-3.2</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1949-02-19</td>\n",
" <td>2.6</td>\n",
" <td>-5.1</td>\n",
" <td>-1.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1949-02-21</td>\n",
" <td>1.4</td>\n",
" <td>-9.8</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1949-02-22</td>\n",
" <td>3.8</td>\n",
" <td>-0.3</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>1949-02-24</td>\n",
" <td>2.6</td>\n",
" <td>-0.8</td>\n",
" <td>1.0</td>\n",
" <td>5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1949-02-25</td>\n",
" <td>1.4</td>\n",
" <td>-1.5</td>\n",
" <td>-0.5</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1949-02-26</td>\n",
" <td>0.8</td>\n",
" <td>-5.0</td>\n",
" <td>-2.9</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>1949-02-28</td>\n",
" <td>2.6</td>\n",
" <td>-1.8</td>\n",
" <td>0.2</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>1949-03-03</td>\n",
" <td>1.7</td>\n",
" <td>-7.9</td>\n",
" <td>-5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>1949-03-05</td>\n",
" <td>3.1</td>\n",
" <td>-4.0</td>\n",
" <td>-1.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>1949-03-08</td>\n",
" <td>0.7</td>\n",
" <td>-13.2</td>\n",
" <td>-6.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>1949-03-10</td>\n",
" <td>1.2</td>\n",
" <td>-10.8</td>\n",
" <td>-5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>1949-03-11</td>\n",
" <td>0.5</td>\n",
" <td>-12.1</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>1949-03-12</td>\n",
" <td>0.4</td>\n",
" <td>-15.2</td>\n",
" <td>-8.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>1949-03-13</td>\n",
" <td>0.2</td>\n",
" <td>-13.9</td>\n",
" <td>-6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>1949-03-15</td>\n",
" <td>2.7</td>\n",
" <td>-4.7</td>\n",
" <td>-1.4</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>1949-03-16</td>\n",
" <td>1.8</td>\n",
" <td>-0.7</td>\n",
" <td>0.6</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>1949-03-17</td>\n",
" <td>2.2</td>\n",
" <td>-2.4</td>\n",
" <td>-0.2</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20888</th>\n",
" <td>2006-03-13</td>\n",
" <td>2.8</td>\n",
" <td>-1.1</td>\n",
" <td>1.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20889</th>\n",
" <td>2006-03-14</td>\n",
" <td>2.3</td>\n",
" <td>-0.8</td>\n",
" <td>0.3</td>\n",
" <td>0.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20890</th>\n",
" <td>2006-03-15</td>\n",
" <td>1.4</td>\n",
" <td>-5.1</td>\n",
" <td>-1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20891</th>\n",
" <td>2006-03-16</td>\n",
" <td>4.4</td>\n",
" <td>-6.1</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20892</th>\n",
" <td>2006-03-17</td>\n",
" <td>7.9</td>\n",
" <td>-8.4</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20893</th>\n",
" <td>2006-03-18</td>\n",
" <td>8.1</td>\n",
" <td>-6.5</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20894</th>\n",
" <td>2006-03-19</td>\n",
" <td>4.4</td>\n",
" <td>-2.7</td>\n",
" <td>0.3</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20895</th>\n",
" <td>2006-03-20</td>\n",
" <td>0.5</td>\n",
" <td>-4.2</td>\n",
" <td>-1.8</td>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20898</th>\n",
" <td>2006-03-23</td>\n",
" <td>0.3</td>\n",
" <td>-13.1</td>\n",
" <td>-5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20900</th>\n",
" <td>2006-03-25</td>\n",
" <td>1.9</td>\n",
" <td>-9.5</td>\n",
" <td>-3.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20901</th>\n",
" <td>2006-03-26</td>\n",
" <td>3.3</td>\n",
" <td>-7.3</td>\n",
" <td>-1.9</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20902</th>\n",
" <td>2006-03-27</td>\n",
" <td>1.5</td>\n",
" <td>-6.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20903</th>\n",
" <td>2006-03-28</td>\n",
" <td>5.7</td>\n",
" <td>-10.9</td>\n",
" <td>-1.9</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20912</th>\n",
" <td>2006-04-06</td>\n",
" <td>9.7</td>\n",
" <td>-1.0</td>\n",
" <td>5.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20914</th>\n",
" <td>2006-04-08</td>\n",
" <td>6.1</td>\n",
" <td>-2.1</td>\n",
" <td>2.2</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20919</th>\n",
" <td>2006-04-13</td>\n",
" <td>10.3</td>\n",
" <td>-2.5</td>\n",
" <td>4.5</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20923</th>\n",
" <td>2006-04-17</td>\n",
" <td>13.0</td>\n",
" <td>-0.6</td>\n",
" <td>6.9</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20930</th>\n",
" <td>2006-04-24</td>\n",
" <td>9.6</td>\n",
" <td>-1.7</td>\n",
" <td>4.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20931</th>\n",
" <td>2006-04-25</td>\n",
" <td>12.0</td>\n",
" <td>-2.0</td>\n",
" <td>5.9</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20932</th>\n",
" <td>2006-04-26</td>\n",
" <td>14.4</td>\n",
" <td>-1.0</td>\n",
" <td>7.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20934</th>\n",
" <td>2006-04-28</td>\n",
" <td>11.3</td>\n",
" <td>-1.9</td>\n",
" <td>6.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20935</th>\n",
" <td>2006-04-29</td>\n",
" <td>11.6</td>\n",
" <td>-0.7</td>\n",
" <td>6.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20936</th>\n",
" <td>2006-04-30</td>\n",
" <td>12.0</td>\n",
" <td>-1.7</td>\n",
" <td>6.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20937</th>\n",
" <td>2006-05-01</td>\n",
" <td>16.7</td>\n",
" <td>-1.6</td>\n",
" <td>8.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21104</th>\n",
" <td>2006-10-15</td>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21105</th>\n",
" <td>2006-10-16</td>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21106</th>\n",
" <td>2006-10-17</td>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21107</th>\n",
" <td>2006-10-18</td>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21119</th>\n",
" <td>2006-10-30</td>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21120</th>\n",
" <td>2006-10-31</td>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3595 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" DATE_OBS TMPMAX TMPMIN TMPMN PRECIP\n",
"2 1949-01-03 1.1 -2.1 -0.7 0.0\n",
"4 1949-01-05 4.0 -0.9 1.1 0.8\n",
"7 1949-01-08 0.5 -3.0 -0.6 1.7\n",
"8 1949-01-09 0.8 -1.6 -0.6 0.0\n",
"20 1949-01-21 1.6 -2.6 0.0 3.6\n",
"21 1949-01-22 0.2 -11.7 -4.2 2.0\n",
"24 1949-01-25 1.7 -11.5 -1.8 4.4\n",
"26 1949-01-27 1.1 -9.1 -3.7 0.0\n",
"27 1949-01-28 1.2 -2.6 -1.2 0.0\n",
"28 1949-01-29 1.4 -2.6 -0.1 0.0\n",
"34 1949-02-04 2.2 -11.0 -2.0 4.8\n",
"46 1949-02-16 1.0 -4.8 -2.2 0.0\n",
"47 1949-02-17 1.5 -3.2 -0.6 0.0\n",
"49 1949-02-19 2.6 -5.1 -1.6 0.0\n",
"51 1949-02-21 1.4 -9.8 -2.6 0.0\n",
"52 1949-02-22 3.8 -0.3 1.5 0.0\n",
"54 1949-02-24 2.6 -0.8 1.0 5.3\n",
"55 1949-02-25 1.4 -1.5 -0.5 1.5\n",
"56 1949-02-26 0.8 -5.0 -2.9 1.0\n",
"58 1949-02-28 2.6 -1.8 0.2 0.7\n",
"61 1949-03-03 1.7 -7.9 -5.2 6.0\n",
"63 1949-03-05 3.1 -4.0 -1.4 0.0\n",
"66 1949-03-08 0.7 -13.2 -6.7 0.0\n",
"68 1949-03-10 1.2 -10.8 -5.1 0.0\n",
"69 1949-03-11 0.5 -12.1 -6.4 0.0\n",
"70 1949-03-12 0.4 -15.2 -8.3 0.0\n",
"71 1949-03-13 0.2 -13.9 -6.4 0.0\n",
"73 1949-03-15 2.7 -4.7 -1.4 3.5\n",
"74 1949-03-16 1.8 -0.7 0.6 14.0\n",
"75 1949-03-17 2.2 -2.4 -0.2 15.6\n",
"... ... ... ... ... ...\n",
"20888 2006-03-13 2.8 -1.1 1.1 0.0\n",
"20889 2006-03-14 2.3 -0.8 0.3 0.4\n",
"20890 2006-03-15 1.4 -5.1 -1.5 0.0\n",
"20891 2006-03-16 4.4 -6.1 -0.5 0.0\n",
"20892 2006-03-17 7.9 -8.4 -0.4 0.0\n",
"20893 2006-03-18 8.1 -6.5 0.5 0.0\n",
"20894 2006-03-19 4.4 -2.7 0.3 1.4\n",
"20895 2006-03-20 0.5 -4.2 -1.8 6.4\n",
"20898 2006-03-23 0.3 -13.1 -5.1 0.0\n",
"20900 2006-03-25 1.9 -9.5 -3.6 0.0\n",
"20901 2006-03-26 3.3 -7.3 -1.9 0.0\n",
"20902 2006-03-27 1.5 -6.6 -2.6 0.0\n",
"20903 2006-03-28 5.7 -10.9 -1.9 0.0\n",
"20912 2006-04-06 9.7 -1.0 5.1 0.0\n",
"20914 2006-04-08 6.1 -2.1 2.2 0.7\n",
"20919 2006-04-13 10.3 -2.5 4.5 0.3\n",
"20923 2006-04-17 13.0 -0.6 6.9 0.0\n",
"20930 2006-04-24 9.6 -1.7 4.8 0.0\n",
"20931 2006-04-25 12.0 -2.0 5.9 0.0\n",
"20932 2006-04-26 14.4 -1.0 7.8 0.0\n",
"20934 2006-04-28 11.3 -1.9 6.3 0.0\n",
"20935 2006-04-29 11.6 -0.7 6.4 0.0\n",
"20936 2006-04-30 12.0 -1.7 6.6 0.0\n",
"20937 2006-05-01 16.7 -1.6 8.4 0.0\n",
"21104 2006-10-15 4.8 -1.4 1.3 2.1\n",
"21105 2006-10-16 0.8 -1.7 -0.4 0.0\n",
"21106 2006-10-17 2.9 -4.1 0.0 0.0\n",
"21107 2006-10-18 5.4 -1.0 2.2 0.1\n",
"21119 2006-10-30 1.8 -1.9 -0.7 3.5\n",
"21120 2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[3595 rows x 5 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.query('TMPMAX > 0 and TMPMIN < 0')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1180c0908>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118021630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table['PRECIP'].hist(log=True)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 -inf\n",
"1 1.435085\n",
"2 -inf\n",
"3 -inf\n",
"4 -0.223144\n",
"5 -inf\n",
"6 -inf\n",
"7 0.530628\n",
"8 -inf\n",
"9 0.470004\n",
"10 -inf\n",
"11 -inf\n",
"12 -1.609438\n",
"13 -inf\n",
"14 0.788457\n",
"15 -0.105361\n",
"16 -2.302585\n",
"17 -inf\n",
"18 -1.609438\n",
"19 0.916291\n",
"20 1.280934\n",
"21 0.693147\n",
"22 -inf\n",
"23 -inf\n",
"24 1.481605\n",
"25 -inf\n",
"26 -inf\n",
"27 -inf\n",
"28 -inf\n",
"29 -inf\n",
" ... \n",
"21091 -inf\n",
"21092 2.322388\n",
"21093 1.589235\n",
"21094 1.335001\n",
"21095 -inf\n",
"21096 -inf\n",
"21097 1.098612\n",
"21098 0.916291\n",
"21099 -0.510826\n",
"21100 -inf\n",
"21101 0.788457\n",
"21102 -inf\n",
"21103 0.182322\n",
"21104 0.741937\n",
"21105 -inf\n",
"21106 -inf\n",
"21107 -2.302585\n",
"21108 -0.510826\n",
"21109 -0.356675\n",
"21110 1.308333\n",
"21111 -0.356675\n",
"21112 -inf\n",
"21113 -inf\n",
"21114 1.547563\n",
"21115 1.931521\n",
"21116 -1.203973\n",
"21117 -1.203973\n",
"21118 -0.693147\n",
"21119 1.252763\n",
"21120 -inf\n",
"Name: PRECIP, Length: 21121, dtype: float64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table['PRECIP'].apply(lambda x: np.log(np.abs(x)))"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119ed0320>"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119eee160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(table.\n",
" loc[:365*5, 'TMPMN'].\n",
" plot())\n",
"(table.\n",
" loc[:365*5, 'TMPMN'].\n",
" rolling(window=100, center=True).\n",
" mean().\n",
" plot(lw=3, color='purple'))"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11a42cc88>"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a4458d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(table.\n",
" loc[:, 'TMPMN'].\n",
" rolling(window=10*365, center=True).\n",
" mean().\n",
" plot(lw=1, color='purple'))"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"table.sort_values('TMPMAX', ascending=False).to_csv(\"sorted_table.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/user/prj/oldhse-2010-11/repo/2017-18/icef-python'"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('O')"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table['DATE_OBS'].dtype"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"table.index = pd.to_datetime(table['DATE_OBS'])"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"table.drop('DATE_OBS', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [],
"source": [
"table = table.copy()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE_OBS</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949-01-01</th>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-02</th>\n",
" <td>-0.5</td>\n",
" <td>-6.7</td>\n",
" <td>-1.2</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-03</th>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-04</th>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-05</th>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-06</th>\n",
" <td>-0.8</td>\n",
" <td>-3.2</td>\n",
" <td>-2.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-07</th>\n",
" <td>-0.6</td>\n",
" <td>-5.1</td>\n",
" <td>-3.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-08</th>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-09</th>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-10</th>\n",
" <td>-1.1</td>\n",
" <td>-4.2</td>\n",
" <td>-2.5</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-11</th>\n",
" <td>-3.0</td>\n",
" <td>-5.3</td>\n",
" <td>-4.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-12</th>\n",
" <td>-3.3</td>\n",
" <td>-8.4</td>\n",
" <td>-5.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-13</th>\n",
" <td>-4.8</td>\n",
" <td>-10.2</td>\n",
" <td>-6.8</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-14</th>\n",
" <td>-1.0</td>\n",
" <td>-5.7</td>\n",
" <td>-2.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-15</th>\n",
" <td>-1.9</td>\n",
" <td>-3.2</td>\n",
" <td>-2.9</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-16</th>\n",
" <td>-0.5</td>\n",
" <td>-4.9</td>\n",
" <td>-2.5</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-17</th>\n",
" <td>-0.7</td>\n",
" <td>-4.0</td>\n",
" <td>-3.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-18</th>\n",
" <td>-3.8</td>\n",
" <td>-10.3</td>\n",
" <td>-7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-19</th>\n",
" <td>-6.9</td>\n",
" <td>-10.4</td>\n",
" <td>-8.8</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-20</th>\n",
" <td>-2.4</td>\n",
" <td>-13.5</td>\n",
" <td>-8.6</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-21</th>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-22</th>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-23</th>\n",
" <td>-11.4</td>\n",
" <td>-15.5</td>\n",
" <td>-13.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-24</th>\n",
" <td>-10.5</td>\n",
" <td>-14.4</td>\n",
" <td>-12.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-25</th>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-26</th>\n",
" <td>-0.7</td>\n",
" <td>-6.8</td>\n",
" <td>-3.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-27</th>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-28</th>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-29</th>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-30</th>\n",
" <td>-0.5</td>\n",
" <td>-8.1</td>\n",
" <td>-6.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-02</th>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-03</th>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-04</th>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-05</th>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-06</th>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-07</th>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-08</th>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-09</th>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-10</th>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-11</th>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-12</th>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-13</th>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-14</th>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-15</th>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-16</th>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-17</th>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-18</th>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-19</th>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-20</th>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-21</th>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-22</th>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-23</th>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-24</th>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-25</th>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-26</th>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-27</th>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-28</th>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-29</th>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-30</th>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-31</th>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21121 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" TMPMAX TMPMIN TMPMN PRECIP\n",
"DATE_OBS \n",
"1949-01-01 -2.1 -6.7 -4.2 0.0\n",
"1949-01-02 -0.5 -6.7 -1.2 4.2\n",
"1949-01-03 1.1 -2.1 -0.7 0.0\n",
"1949-01-04 3.3 0.9 2.3 0.0\n",
"1949-01-05 4.0 -0.9 1.1 0.8\n",
"1949-01-06 -0.8 -3.2 -2.3 0.0\n",
"1949-01-07 -0.6 -5.1 -3.4 0.0\n",
"1949-01-08 0.5 -3.0 -0.6 1.7\n",
"1949-01-09 0.8 -1.6 -0.6 0.0\n",
"1949-01-10 -1.1 -4.2 -2.5 1.6\n",
"1949-01-11 -3.0 -5.3 -4.3 0.0\n",
"1949-01-12 -3.3 -8.4 -5.8 0.0\n",
"1949-01-13 -4.8 -10.2 -6.8 0.2\n",
"1949-01-14 -1.0 -5.7 -2.5 0.0\n",
"1949-01-15 -1.9 -3.2 -2.9 2.2\n",
"1949-01-16 -0.5 -4.9 -2.5 0.9\n",
"1949-01-17 -0.7 -4.0 -3.2 0.1\n",
"1949-01-18 -3.8 -10.3 -7.5 0.0\n",
"1949-01-19 -6.9 -10.4 -8.8 0.2\n",
"1949-01-20 -2.4 -13.5 -8.6 2.5\n",
"1949-01-21 1.6 -2.6 0.0 3.6\n",
"1949-01-22 0.2 -11.7 -4.2 2.0\n",
"1949-01-23 -11.4 -15.5 -13.4 0.0\n",
"1949-01-24 -10.5 -14.4 -12.4 0.0\n",
"1949-01-25 1.7 -11.5 -1.8 4.4\n",
"1949-01-26 -0.7 -6.8 -3.1 0.0\n",
"1949-01-27 1.1 -9.1 -3.7 0.0\n",
"1949-01-28 1.2 -2.6 -1.2 0.0\n",
"1949-01-29 1.4 -2.6 -0.1 0.0\n",
"1949-01-30 -0.5 -8.1 -6.5 0.0\n",
"... ... ... ... ...\n",
"2006-10-02 13.6 7.2 10.0 0.0\n",
"2006-10-03 14.7 10.9 12.5 10.2\n",
"2006-10-04 14.3 10.3 12.6 4.9\n",
"2006-10-05 15.9 11.6 13.7 3.8\n",
"2006-10-06 14.0 9.0 11.1 0.0\n",
"2006-10-07 13.9 7.9 10.3 0.0\n",
"2006-10-08 14.4 7.1 10.8 3.0\n",
"2006-10-09 12.3 9.9 10.9 2.5\n",
"2006-10-10 11.5 8.5 9.8 0.6\n",
"2006-10-11 9.0 6.0 7.0 0.0\n",
"2006-10-12 8.7 0.4 5.3 2.2\n",
"2006-10-13 9.8 5.1 7.6 0.0\n",
"2006-10-14 7.9 4.7 5.8 1.2\n",
"2006-10-15 4.8 -1.4 1.3 2.1\n",
"2006-10-16 0.8 -1.7 -0.4 0.0\n",
"2006-10-17 2.9 -4.1 0.0 0.0\n",
"2006-10-18 5.4 -1.0 2.2 0.1\n",
"2006-10-19 7.1 3.8 5.5 0.6\n",
"2006-10-20 4.4 2.3 3.5 0.7\n",
"2006-10-21 4.1 0.5 1.9 3.7\n",
"2006-10-22 12.0 3.9 8.8 0.7\n",
"2006-10-23 12.6 9.1 10.8 0.0\n",
"2006-10-24 13.2 9.9 11.3 0.0\n",
"2006-10-25 14.0 11.4 12.6 4.7\n",
"2006-10-26 12.2 5.8 8.5 6.9\n",
"2006-10-27 8.7 2.7 5.6 0.3\n",
"2006-10-28 11.0 4.1 8.2 0.3\n",
"2006-10-29 4.6 1.4 2.4 0.5\n",
"2006-10-30 1.8 -1.9 -0.7 3.5\n",
"2006-10-31 1.3 -7.7 -3.0 0.0\n",
"\n",
"[21121 rows x 4 columns]"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11a7130f0>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1180517b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(table.\n",
" loc[:, 'TMPMN'].\n",
" rolling(window=3650, center=True).\n",
" mean().\n",
" plot(lw=1, color='purple'))"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE_OBS</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1984-02-01</th>\n",
" <td>-8.9</td>\n",
" <td>-16.8</td>\n",
" <td>-12.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TMPMAX TMPMIN TMPMN PRECIP\n",
"DATE_OBS \n",
"1984-02-01 -8.9 -16.8 -12.6 0.0"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table[\"1984-02-01\":\"1984-02-01\"]"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" ...\n",
" 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],\n",
" dtype='int64', name='DATE_OBS', length=21121)"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.index.month"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
"table['month'] = table.index.month\n",
"table['year'] = table.index.year"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" <th>month</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949</th>\n",
" <td>10.073151</td>\n",
" <td>1.778904</td>\n",
" <td>5.725753</td>\n",
" <td>1.740548</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1950</th>\n",
" <td>8.135342</td>\n",
" <td>0.593151</td>\n",
" <td>4.200000</td>\n",
" <td>1.755616</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1951</th>\n",
" <td>8.798904</td>\n",
" <td>0.516438</td>\n",
" <td>4.517260</td>\n",
" <td>1.346027</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1952</th>\n",
" <td>8.850820</td>\n",
" <td>0.925137</td>\n",
" <td>4.715027</td>\n",
" <td>2.209290</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1953</th>\n",
" <td>8.496164</td>\n",
" <td>0.885753</td>\n",
" <td>4.518356</td>\n",
" <td>1.818082</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954</th>\n",
" <td>8.854795</td>\n",
" <td>0.560000</td>\n",
" <td>4.612055</td>\n",
" <td>1.356712</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1955</th>\n",
" <td>8.356986</td>\n",
" <td>0.146301</td>\n",
" <td>4.173425</td>\n",
" <td>1.620548</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956</th>\n",
" <td>7.101913</td>\n",
" <td>-0.720492</td>\n",
" <td>3.043169</td>\n",
" <td>1.731967</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1957</th>\n",
" <td>9.830137</td>\n",
" <td>2.232329</td>\n",
" <td>5.858082</td>\n",
" <td>1.585753</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>8.490959</td>\n",
" <td>0.586575</td>\n",
" <td>4.428493</td>\n",
" <td>1.846027</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>8.769041</td>\n",
" <td>0.951233</td>\n",
" <td>4.694795</td>\n",
" <td>1.633425</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>8.712568</td>\n",
" <td>1.158470</td>\n",
" <td>4.920765</td>\n",
" <td>1.821311</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>9.632055</td>\n",
" <td>2.157808</td>\n",
" <td>5.889041</td>\n",
" <td>1.572877</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>9.045753</td>\n",
" <td>1.414795</td>\n",
" <td>5.176164</td>\n",
" <td>1.972603</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963</th>\n",
" <td>8.268493</td>\n",
" <td>-0.540548</td>\n",
" <td>3.894521</td>\n",
" <td>1.381644</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964</th>\n",
" <td>9.375410</td>\n",
" <td>0.807377</td>\n",
" <td>5.048087</td>\n",
" <td>1.084153</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1965</th>\n",
" <td>8.005205</td>\n",
" <td>0.185205</td>\n",
" <td>4.023014</td>\n",
" <td>1.949315</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1966</th>\n",
" <td>9.130137</td>\n",
" <td>1.611507</td>\n",
" <td>5.265205</td>\n",
" <td>2.147945</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1967</th>\n",
" <td>9.557808</td>\n",
" <td>1.358630</td>\n",
" <td>5.358630</td>\n",
" <td>1.586301</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1968</th>\n",
" <td>8.660656</td>\n",
" <td>0.266393</td>\n",
" <td>4.296721</td>\n",
" <td>1.902732</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>7.507671</td>\n",
" <td>-0.856712</td>\n",
" <td>3.172055</td>\n",
" <td>1.641096</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>8.758904</td>\n",
" <td>0.974795</td>\n",
" <td>4.829041</td>\n",
" <td>2.247123</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1971</th>\n",
" <td>8.701644</td>\n",
" <td>1.245479</td>\n",
" <td>4.901644</td>\n",
" <td>1.856164</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td>10.203552</td>\n",
" <td>1.739891</td>\n",
" <td>5.933060</td>\n",
" <td>1.326776</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>8.998082</td>\n",
" <td>1.491781</td>\n",
" <td>5.221096</td>\n",
" <td>2.363562</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1974</th>\n",
" <td>9.872877</td>\n",
" <td>2.505479</td>\n",
" <td>6.072055</td>\n",
" <td>1.824658</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>10.611781</td>\n",
" <td>2.588219</td>\n",
" <td>6.624384</td>\n",
" <td>1.716986</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1976</th>\n",
" <td>7.194536</td>\n",
" <td>-0.311202</td>\n",
" <td>3.281967</td>\n",
" <td>2.265847</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977</th>\n",
" <td>8.983836</td>\n",
" <td>1.522740</td>\n",
" <td>5.086301</td>\n",
" <td>2.212603</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1978</th>\n",
" <td>7.738082</td>\n",
" <td>0.230411</td>\n",
" <td>3.876986</td>\n",
" <td>1.630137</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1979</th>\n",
" <td>8.890685</td>\n",
" <td>1.332877</td>\n",
" <td>5.126301</td>\n",
" <td>1.801918</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>7.885792</td>\n",
" <td>0.442350</td>\n",
" <td>4.067486</td>\n",
" <td>2.075410</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>10.295890</td>\n",
" <td>2.848493</td>\n",
" <td>6.503562</td>\n",
" <td>2.110959</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>9.103562</td>\n",
" <td>1.749863</td>\n",
" <td>5.360822</td>\n",
" <td>1.919452</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>10.067945</td>\n",
" <td>2.759726</td>\n",
" <td>6.343014</td>\n",
" <td>1.919178</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>8.988525</td>\n",
" <td>1.470219</td>\n",
" <td>5.115574</td>\n",
" <td>1.750546</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985</th>\n",
" <td>8.098630</td>\n",
" <td>0.369863</td>\n",
" <td>4.124658</td>\n",
" <td>2.096438</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986</th>\n",
" <td>8.962192</td>\n",
" <td>1.181644</td>\n",
" <td>4.976438</td>\n",
" <td>2.132055</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1987</th>\n",
" <td>7.162192</td>\n",
" <td>-0.600000</td>\n",
" <td>3.236712</td>\n",
" <td>1.560000</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1988</th>\n",
" <td>9.475956</td>\n",
" <td>2.044262</td>\n",
" <td>5.639617</td>\n",
" <td>1.759290</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1989</th>\n",
" <td>11.005479</td>\n",
" <td>3.557260</td>\n",
" <td>7.161096</td>\n",
" <td>2.200000</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td>9.696986</td>\n",
" <td>2.973973</td>\n",
" <td>6.291233</td>\n",
" <td>2.257260</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991</th>\n",
" <td>10.114876</td>\n",
" <td>2.944353</td>\n",
" <td>6.392011</td>\n",
" <td>2.372452</td>\n",
" <td>6.556474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992</th>\n",
" <td>9.720765</td>\n",
" <td>2.217486</td>\n",
" <td>5.937705</td>\n",
" <td>1.516667</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993</th>\n",
" <td>8.409863</td>\n",
" <td>1.113699</td>\n",
" <td>4.732055</td>\n",
" <td>2.279178</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994</th>\n",
" <td>8.556438</td>\n",
" <td>1.005753</td>\n",
" <td>4.721644</td>\n",
" <td>1.904658</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995</th>\n",
" <td>10.540548</td>\n",
" <td>2.754795</td>\n",
" <td>6.581644</td>\n",
" <td>1.579452</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996</th>\n",
" <td>9.462842</td>\n",
" <td>1.596995</td>\n",
" <td>5.443716</td>\n",
" <td>1.533060</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997</th>\n",
" <td>8.769863</td>\n",
" <td>1.466575</td>\n",
" <td>5.042466</td>\n",
" <td>1.861370</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998</th>\n",
" <td>8.955616</td>\n",
" <td>1.512329</td>\n",
" <td>5.138082</td>\n",
" <td>2.416712</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999</th>\n",
" <td>10.733699</td>\n",
" <td>2.652055</td>\n",
" <td>6.635342</td>\n",
" <td>1.550959</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>10.243169</td>\n",
" <td>3.142350</td>\n",
" <td>6.628689</td>\n",
" <td>2.132240</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
" <td>9.923014</td>\n",
" <td>2.206301</td>\n",
" <td>5.955342</td>\n",
" <td>2.090137</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
" <td>10.310685</td>\n",
" <td>2.093699</td>\n",
" <td>6.234247</td>\n",
" <td>1.480822</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
" <td>9.643562</td>\n",
" <td>2.056712</td>\n",
" <td>5.753425</td>\n",
" <td>1.921096</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
" <td>9.652732</td>\n",
" <td>2.398634</td>\n",
" <td>5.893716</td>\n",
" <td>2.363934</td>\n",
" <td>6.513661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
" <td>10.305479</td>\n",
" <td>2.487945</td>\n",
" <td>6.240000</td>\n",
" <td>1.865753</td>\n",
" <td>6.526027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
" <td>10.859539</td>\n",
" <td>2.452961</td>\n",
" <td>6.601645</td>\n",
" <td>1.779934</td>\n",
" <td>5.526316</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TMPMAX TMPMIN TMPMN PRECIP month\n",
"year \n",
"1949 10.073151 1.778904 5.725753 1.740548 6.526027\n",
"1950 8.135342 0.593151 4.200000 1.755616 6.526027\n",
"1951 8.798904 0.516438 4.517260 1.346027 6.526027\n",
"1952 8.850820 0.925137 4.715027 2.209290 6.513661\n",
"1953 8.496164 0.885753 4.518356 1.818082 6.526027\n",
"1954 8.854795 0.560000 4.612055 1.356712 6.526027\n",
"1955 8.356986 0.146301 4.173425 1.620548 6.526027\n",
"1956 7.101913 -0.720492 3.043169 1.731967 6.513661\n",
"1957 9.830137 2.232329 5.858082 1.585753 6.526027\n",
"1958 8.490959 0.586575 4.428493 1.846027 6.526027\n",
"1959 8.769041 0.951233 4.694795 1.633425 6.526027\n",
"1960 8.712568 1.158470 4.920765 1.821311 6.513661\n",
"1961 9.632055 2.157808 5.889041 1.572877 6.526027\n",
"1962 9.045753 1.414795 5.176164 1.972603 6.526027\n",
"1963 8.268493 -0.540548 3.894521 1.381644 6.526027\n",
"1964 9.375410 0.807377 5.048087 1.084153 6.513661\n",
"1965 8.005205 0.185205 4.023014 1.949315 6.526027\n",
"1966 9.130137 1.611507 5.265205 2.147945 6.526027\n",
"1967 9.557808 1.358630 5.358630 1.586301 6.526027\n",
"1968 8.660656 0.266393 4.296721 1.902732 6.513661\n",
"1969 7.507671 -0.856712 3.172055 1.641096 6.526027\n",
"1970 8.758904 0.974795 4.829041 2.247123 6.526027\n",
"1971 8.701644 1.245479 4.901644 1.856164 6.526027\n",
"1972 10.203552 1.739891 5.933060 1.326776 6.513661\n",
"1973 8.998082 1.491781 5.221096 2.363562 6.526027\n",
"1974 9.872877 2.505479 6.072055 1.824658 6.526027\n",
"1975 10.611781 2.588219 6.624384 1.716986 6.526027\n",
"1976 7.194536 -0.311202 3.281967 2.265847 6.513661\n",
"1977 8.983836 1.522740 5.086301 2.212603 6.526027\n",
"1978 7.738082 0.230411 3.876986 1.630137 6.526027\n",
"1979 8.890685 1.332877 5.126301 1.801918 6.526027\n",
"1980 7.885792 0.442350 4.067486 2.075410 6.513661\n",
"1981 10.295890 2.848493 6.503562 2.110959 6.526027\n",
"1982 9.103562 1.749863 5.360822 1.919452 6.526027\n",
"1983 10.067945 2.759726 6.343014 1.919178 6.526027\n",
"1984 8.988525 1.470219 5.115574 1.750546 6.513661\n",
"1985 8.098630 0.369863 4.124658 2.096438 6.526027\n",
"1986 8.962192 1.181644 4.976438 2.132055 6.526027\n",
"1987 7.162192 -0.600000 3.236712 1.560000 6.526027\n",
"1988 9.475956 2.044262 5.639617 1.759290 6.513661\n",
"1989 11.005479 3.557260 7.161096 2.200000 6.526027\n",
"1990 9.696986 2.973973 6.291233 2.257260 6.526027\n",
"1991 10.114876 2.944353 6.392011 2.372452 6.556474\n",
"1992 9.720765 2.217486 5.937705 1.516667 6.513661\n",
"1993 8.409863 1.113699 4.732055 2.279178 6.526027\n",
"1994 8.556438 1.005753 4.721644 1.904658 6.526027\n",
"1995 10.540548 2.754795 6.581644 1.579452 6.526027\n",
"1996 9.462842 1.596995 5.443716 1.533060 6.513661\n",
"1997 8.769863 1.466575 5.042466 1.861370 6.526027\n",
"1998 8.955616 1.512329 5.138082 2.416712 6.526027\n",
"1999 10.733699 2.652055 6.635342 1.550959 6.526027\n",
"2000 10.243169 3.142350 6.628689 2.132240 6.513661\n",
"2001 9.923014 2.206301 5.955342 2.090137 6.526027\n",
"2002 10.310685 2.093699 6.234247 1.480822 6.526027\n",
"2003 9.643562 2.056712 5.753425 1.921096 6.526027\n",
"2004 9.652732 2.398634 5.893716 2.363934 6.513661\n",
"2005 10.305479 2.487945 6.240000 1.865753 6.526027\n",
"2006 10.859539 2.452961 6.601645 1.779934 5.526316"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.groupby('year').mean()"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>after1984</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE_OBS</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949-01-01</th>\n",
" <td>-2.1</td>\n",
" <td>-6.7</td>\n",
" <td>-4.2</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-02</th>\n",
" <td>-0.5</td>\n",
" <td>-6.7</td>\n",
" <td>-1.2</td>\n",
" <td>4.2</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-03</th>\n",
" <td>1.1</td>\n",
" <td>-2.1</td>\n",
" <td>-0.7</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-04</th>\n",
" <td>3.3</td>\n",
" <td>0.9</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-05</th>\n",
" <td>4.0</td>\n",
" <td>-0.9</td>\n",
" <td>1.1</td>\n",
" <td>0.8</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-06</th>\n",
" <td>-0.8</td>\n",
" <td>-3.2</td>\n",
" <td>-2.3</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-07</th>\n",
" <td>-0.6</td>\n",
" <td>-5.1</td>\n",
" <td>-3.4</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-08</th>\n",
" <td>0.5</td>\n",
" <td>-3.0</td>\n",
" <td>-0.6</td>\n",
" <td>1.7</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-09</th>\n",
" <td>0.8</td>\n",
" <td>-1.6</td>\n",
" <td>-0.6</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-10</th>\n",
" <td>-1.1</td>\n",
" <td>-4.2</td>\n",
" <td>-2.5</td>\n",
" <td>1.6</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-11</th>\n",
" <td>-3.0</td>\n",
" <td>-5.3</td>\n",
" <td>-4.3</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-12</th>\n",
" <td>-3.3</td>\n",
" <td>-8.4</td>\n",
" <td>-5.8</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-13</th>\n",
" <td>-4.8</td>\n",
" <td>-10.2</td>\n",
" <td>-6.8</td>\n",
" <td>0.2</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-14</th>\n",
" <td>-1.0</td>\n",
" <td>-5.7</td>\n",
" <td>-2.5</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-15</th>\n",
" <td>-1.9</td>\n",
" <td>-3.2</td>\n",
" <td>-2.9</td>\n",
" <td>2.2</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-16</th>\n",
" <td>-0.5</td>\n",
" <td>-4.9</td>\n",
" <td>-2.5</td>\n",
" <td>0.9</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-17</th>\n",
" <td>-0.7</td>\n",
" <td>-4.0</td>\n",
" <td>-3.2</td>\n",
" <td>0.1</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-18</th>\n",
" <td>-3.8</td>\n",
" <td>-10.3</td>\n",
" <td>-7.5</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-19</th>\n",
" <td>-6.9</td>\n",
" <td>-10.4</td>\n",
" <td>-8.8</td>\n",
" <td>0.2</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-20</th>\n",
" <td>-2.4</td>\n",
" <td>-13.5</td>\n",
" <td>-8.6</td>\n",
" <td>2.5</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-21</th>\n",
" <td>1.6</td>\n",
" <td>-2.6</td>\n",
" <td>0.0</td>\n",
" <td>3.6</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-22</th>\n",
" <td>0.2</td>\n",
" <td>-11.7</td>\n",
" <td>-4.2</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-23</th>\n",
" <td>-11.4</td>\n",
" <td>-15.5</td>\n",
" <td>-13.4</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-24</th>\n",
" <td>-10.5</td>\n",
" <td>-14.4</td>\n",
" <td>-12.4</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-25</th>\n",
" <td>1.7</td>\n",
" <td>-11.5</td>\n",
" <td>-1.8</td>\n",
" <td>4.4</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-26</th>\n",
" <td>-0.7</td>\n",
" <td>-6.8</td>\n",
" <td>-3.1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-27</th>\n",
" <td>1.1</td>\n",
" <td>-9.1</td>\n",
" <td>-3.7</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-28</th>\n",
" <td>1.2</td>\n",
" <td>-2.6</td>\n",
" <td>-1.2</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-29</th>\n",
" <td>1.4</td>\n",
" <td>-2.6</td>\n",
" <td>-0.1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1949-01-30</th>\n",
" <td>-0.5</td>\n",
" <td>-8.1</td>\n",
" <td>-6.5</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-02</th>\n",
" <td>13.6</td>\n",
" <td>7.2</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-03</th>\n",
" <td>14.7</td>\n",
" <td>10.9</td>\n",
" <td>12.5</td>\n",
" <td>10.2</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-04</th>\n",
" <td>14.3</td>\n",
" <td>10.3</td>\n",
" <td>12.6</td>\n",
" <td>4.9</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-05</th>\n",
" <td>15.9</td>\n",
" <td>11.6</td>\n",
" <td>13.7</td>\n",
" <td>3.8</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-06</th>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.1</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-07</th>\n",
" <td>13.9</td>\n",
" <td>7.9</td>\n",
" <td>10.3</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-08</th>\n",
" <td>14.4</td>\n",
" <td>7.1</td>\n",
" <td>10.8</td>\n",
" <td>3.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-09</th>\n",
" <td>12.3</td>\n",
" <td>9.9</td>\n",
" <td>10.9</td>\n",
" <td>2.5</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-10</th>\n",
" <td>11.5</td>\n",
" <td>8.5</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-11</th>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-12</th>\n",
" <td>8.7</td>\n",
" <td>0.4</td>\n",
" <td>5.3</td>\n",
" <td>2.2</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-13</th>\n",
" <td>9.8</td>\n",
" <td>5.1</td>\n",
" <td>7.6</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-14</th>\n",
" <td>7.9</td>\n",
" <td>4.7</td>\n",
" <td>5.8</td>\n",
" <td>1.2</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-15</th>\n",
" <td>4.8</td>\n",
" <td>-1.4</td>\n",
" <td>1.3</td>\n",
" <td>2.1</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-16</th>\n",
" <td>0.8</td>\n",
" <td>-1.7</td>\n",
" <td>-0.4</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-17</th>\n",
" <td>2.9</td>\n",
" <td>-4.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-18</th>\n",
" <td>5.4</td>\n",
" <td>-1.0</td>\n",
" <td>2.2</td>\n",
" <td>0.1</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-19</th>\n",
" <td>7.1</td>\n",
" <td>3.8</td>\n",
" <td>5.5</td>\n",
" <td>0.6</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-20</th>\n",
" <td>4.4</td>\n",
" <td>2.3</td>\n",
" <td>3.5</td>\n",
" <td>0.7</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-21</th>\n",
" <td>4.1</td>\n",
" <td>0.5</td>\n",
" <td>1.9</td>\n",
" <td>3.7</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-22</th>\n",
" <td>12.0</td>\n",
" <td>3.9</td>\n",
" <td>8.8</td>\n",
" <td>0.7</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-23</th>\n",
" <td>12.6</td>\n",
" <td>9.1</td>\n",
" <td>10.8</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-24</th>\n",
" <td>13.2</td>\n",
" <td>9.9</td>\n",
" <td>11.3</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-25</th>\n",
" <td>14.0</td>\n",
" <td>11.4</td>\n",
" <td>12.6</td>\n",
" <td>4.7</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-26</th>\n",
" <td>12.2</td>\n",
" <td>5.8</td>\n",
" <td>8.5</td>\n",
" <td>6.9</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-27</th>\n",
" <td>8.7</td>\n",
" <td>2.7</td>\n",
" <td>5.6</td>\n",
" <td>0.3</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-28</th>\n",
" <td>11.0</td>\n",
" <td>4.1</td>\n",
" <td>8.2</td>\n",
" <td>0.3</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-29</th>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>2.4</td>\n",
" <td>0.5</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-30</th>\n",
" <td>1.8</td>\n",
" <td>-1.9</td>\n",
" <td>-0.7</td>\n",
" <td>3.5</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-10-31</th>\n",
" <td>1.3</td>\n",
" <td>-7.7</td>\n",
" <td>-3.0</td>\n",
" <td>0.0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21121 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" TMPMAX TMPMIN TMPMN PRECIP month year after1984\n",
"DATE_OBS \n",
"1949-01-01 -2.1 -6.7 -4.2 0.0 1 1949 False\n",
"1949-01-02 -0.5 -6.7 -1.2 4.2 1 1949 False\n",
"1949-01-03 1.1 -2.1 -0.7 0.0 1 1949 False\n",
"1949-01-04 3.3 0.9 2.3 0.0 1 1949 False\n",
"1949-01-05 4.0 -0.9 1.1 0.8 1 1949 False\n",
"1949-01-06 -0.8 -3.2 -2.3 0.0 1 1949 False\n",
"1949-01-07 -0.6 -5.1 -3.4 0.0 1 1949 False\n",
"1949-01-08 0.5 -3.0 -0.6 1.7 1 1949 False\n",
"1949-01-09 0.8 -1.6 -0.6 0.0 1 1949 False\n",
"1949-01-10 -1.1 -4.2 -2.5 1.6 1 1949 False\n",
"1949-01-11 -3.0 -5.3 -4.3 0.0 1 1949 False\n",
"1949-01-12 -3.3 -8.4 -5.8 0.0 1 1949 False\n",
"1949-01-13 -4.8 -10.2 -6.8 0.2 1 1949 False\n",
"1949-01-14 -1.0 -5.7 -2.5 0.0 1 1949 False\n",
"1949-01-15 -1.9 -3.2 -2.9 2.2 1 1949 False\n",
"1949-01-16 -0.5 -4.9 -2.5 0.9 1 1949 False\n",
"1949-01-17 -0.7 -4.0 -3.2 0.1 1 1949 False\n",
"1949-01-18 -3.8 -10.3 -7.5 0.0 1 1949 False\n",
"1949-01-19 -6.9 -10.4 -8.8 0.2 1 1949 False\n",
"1949-01-20 -2.4 -13.5 -8.6 2.5 1 1949 False\n",
"1949-01-21 1.6 -2.6 0.0 3.6 1 1949 False\n",
"1949-01-22 0.2 -11.7 -4.2 2.0 1 1949 False\n",
"1949-01-23 -11.4 -15.5 -13.4 0.0 1 1949 False\n",
"1949-01-24 -10.5 -14.4 -12.4 0.0 1 1949 False\n",
"1949-01-25 1.7 -11.5 -1.8 4.4 1 1949 False\n",
"1949-01-26 -0.7 -6.8 -3.1 0.0 1 1949 False\n",
"1949-01-27 1.1 -9.1 -3.7 0.0 1 1949 False\n",
"1949-01-28 1.2 -2.6 -1.2 0.0 1 1949 False\n",
"1949-01-29 1.4 -2.6 -0.1 0.0 1 1949 False\n",
"1949-01-30 -0.5 -8.1 -6.5 0.0 1 1949 False\n",
"... ... ... ... ... ... ... ...\n",
"2006-10-02 13.6 7.2 10.0 0.0 10 2006 True\n",
"2006-10-03 14.7 10.9 12.5 10.2 10 2006 True\n",
"2006-10-04 14.3 10.3 12.6 4.9 10 2006 True\n",
"2006-10-05 15.9 11.6 13.7 3.8 10 2006 True\n",
"2006-10-06 14.0 9.0 11.1 0.0 10 2006 True\n",
"2006-10-07 13.9 7.9 10.3 0.0 10 2006 True\n",
"2006-10-08 14.4 7.1 10.8 3.0 10 2006 True\n",
"2006-10-09 12.3 9.9 10.9 2.5 10 2006 True\n",
"2006-10-10 11.5 8.5 9.8 0.6 10 2006 True\n",
"2006-10-11 9.0 6.0 7.0 0.0 10 2006 True\n",
"2006-10-12 8.7 0.4 5.3 2.2 10 2006 True\n",
"2006-10-13 9.8 5.1 7.6 0.0 10 2006 True\n",
"2006-10-14 7.9 4.7 5.8 1.2 10 2006 True\n",
"2006-10-15 4.8 -1.4 1.3 2.1 10 2006 True\n",
"2006-10-16 0.8 -1.7 -0.4 0.0 10 2006 True\n",
"2006-10-17 2.9 -4.1 0.0 0.0 10 2006 True\n",
"2006-10-18 5.4 -1.0 2.2 0.1 10 2006 True\n",
"2006-10-19 7.1 3.8 5.5 0.6 10 2006 True\n",
"2006-10-20 4.4 2.3 3.5 0.7 10 2006 True\n",
"2006-10-21 4.1 0.5 1.9 3.7 10 2006 True\n",
"2006-10-22 12.0 3.9 8.8 0.7 10 2006 True\n",
"2006-10-23 12.6 9.1 10.8 0.0 10 2006 True\n",
"2006-10-24 13.2 9.9 11.3 0.0 10 2006 True\n",
"2006-10-25 14.0 11.4 12.6 4.7 10 2006 True\n",
"2006-10-26 12.2 5.8 8.5 6.9 10 2006 True\n",
"2006-10-27 8.7 2.7 5.6 0.3 10 2006 True\n",
"2006-10-28 11.0 4.1 8.2 0.3 10 2006 True\n",
"2006-10-29 4.6 1.4 2.4 0.5 10 2006 True\n",
"2006-10-30 1.8 -1.9 -0.7 3.5 10 2006 True\n",
"2006-10-31 1.3 -7.7 -3.0 0.0 10 2006 True\n",
"\n",
"[21121 rows x 7 columns]"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.assign(after1984 = lambda x: x.index.year > 1984)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMPMAX</th>\n",
" <th>TMPMIN</th>\n",
" <th>TMPMN</th>\n",
" <th>PRECIP</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>after1984</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>8.887261</td>\n",
" <td>1.072188</td>\n",
" <td>4.885117</td>\n",
" <td>1.797924</td>\n",
" <td>6.522930</td>\n",
" <td>1966.501027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>9.563021</td>\n",
" <td>1.979516</td>\n",
" <td>5.691295</td>\n",
" <td>1.939852</td>\n",
" <td>6.486453</td>\n",
" <td>1995.421099</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TMPMAX TMPMIN TMPMN PRECIP month year\n",
"after1984 \n",
"False 8.887261 1.072188 4.885117 1.797924 6.522930 1966.501027\n",
"True 9.563021 1.979516 5.691295 1.939852 6.486453 1995.421099"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(table.assign(after1984 = lambda x: x.index.year > 1984)\n",
" .groupby('after1984').mean())"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>month</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949</th>\n",
" <td>-3.670968</td>\n",
" <td>-7.339286</td>\n",
" <td>-2.780645</td>\n",
" <td>4.280000</td>\n",
" <td>15.225806</td>\n",
" <td>16.973333</td>\n",
" <td>17.425806</td>\n",
" <td>16.074194</td>\n",
" <td>11.506667</td>\n",
" <td>4.800000</td>\n",
" <td>-0.423333</td>\n",
" <td>-4.322581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1950</th>\n",
" <td>-18.022581</td>\n",
" <td>-6.764286</td>\n",
" <td>-2.225806</td>\n",
" <td>9.043333</td>\n",
" <td>11.738710</td>\n",
" <td>15.123333</td>\n",
" <td>16.177419</td>\n",
" <td>14.080645</td>\n",
" <td>11.990000</td>\n",
" <td>4.745161</td>\n",
" <td>-0.433333</td>\n",
" <td>-5.503226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1951</th>\n",
" <td>-12.138710</td>\n",
" <td>-12.264286</td>\n",
" <td>-3.996774</td>\n",
" <td>8.403333</td>\n",
" <td>9.738710</td>\n",
" <td>17.706667</td>\n",
" <td>18.590323</td>\n",
" <td>18.325806</td>\n",
" <td>11.990000</td>\n",
" <td>2.780645</td>\n",
" <td>-4.810000</td>\n",
" <td>-1.251613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1952</th>\n",
" <td>-4.135484</td>\n",
" <td>-7.096552</td>\n",
" <td>-9.106452</td>\n",
" <td>5.166667</td>\n",
" <td>10.358065</td>\n",
" <td>17.340000</td>\n",
" <td>17.870968</td>\n",
" <td>16.848387</td>\n",
" <td>12.136667</td>\n",
" <td>3.925806</td>\n",
" <td>-1.133333</td>\n",
" <td>-5.883871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1953</th>\n",
" <td>-10.403226</td>\n",
" <td>-15.614286</td>\n",
" <td>-2.606452</td>\n",
" <td>7.183333</td>\n",
" <td>11.645161</td>\n",
" <td>19.193333</td>\n",
" <td>19.025806</td>\n",
" <td>17.290323</td>\n",
" <td>10.046667</td>\n",
" <td>5.790323</td>\n",
" <td>-3.156667</td>\n",
" <td>-5.632258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954</th>\n",
" <td>-14.306452</td>\n",
" <td>-14.000000</td>\n",
" <td>-3.425806</td>\n",
" <td>3.030000</td>\n",
" <td>12.883871</td>\n",
" <td>18.963333</td>\n",
" <td>21.025806</td>\n",
" <td>18.354839</td>\n",
" <td>12.286667</td>\n",
" <td>5.738710</td>\n",
" <td>-1.573333</td>\n",
" <td>-4.974194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1955</th>\n",
" <td>-6.312903</td>\n",
" <td>-6.792857</td>\n",
" <td>-4.664516</td>\n",
" <td>1.466667</td>\n",
" <td>10.506452</td>\n",
" <td>15.100000</td>\n",
" <td>17.906452</td>\n",
" <td>17.916129</td>\n",
" <td>13.856667</td>\n",
" <td>7.712903</td>\n",
" <td>-3.023333</td>\n",
" <td>-14.306452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956</th>\n",
" <td>-10.916129</td>\n",
" <td>-18.503448</td>\n",
" <td>-3.600000</td>\n",
" <td>3.940000</td>\n",
" <td>10.664516</td>\n",
" <td>20.570000</td>\n",
" <td>15.190323</td>\n",
" <td>14.625806</td>\n",
" <td>8.423333</td>\n",
" <td>4.670968</td>\n",
" <td>-5.180000</td>\n",
" <td>-4.254839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1957</th>\n",
" <td>-6.038710</td>\n",
" <td>-1.760714</td>\n",
" <td>-6.319355</td>\n",
" <td>6.640000</td>\n",
" <td>14.435484</td>\n",
" <td>15.240000</td>\n",
" <td>18.554839</td>\n",
" <td>17.087097</td>\n",
" <td>12.313333</td>\n",
" <td>5.241935</td>\n",
" <td>-0.893333</td>\n",
" <td>-4.622581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>-6.932258</td>\n",
" <td>-7.710714</td>\n",
" <td>-6.080645</td>\n",
" <td>4.063333</td>\n",
" <td>13.290323</td>\n",
" <td>14.890000</td>\n",
" <td>18.367742</td>\n",
" <td>15.696774</td>\n",
" <td>9.080000</td>\n",
" <td>6.038710</td>\n",
" <td>-0.786667</td>\n",
" <td>-7.641935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>-4.335484</td>\n",
" <td>-5.492857</td>\n",
" <td>-1.425806</td>\n",
" <td>6.630000</td>\n",
" <td>11.490323</td>\n",
" <td>16.886667</td>\n",
" <td>20.538710</td>\n",
" <td>16.932258</td>\n",
" <td>8.253333</td>\n",
" <td>2.229032</td>\n",
" <td>-5.110000</td>\n",
" <td>-10.990323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>-9.351613</td>\n",
" <td>-7.651724</td>\n",
" <td>-5.480645</td>\n",
" <td>5.046667</td>\n",
" <td>11.625806</td>\n",
" <td>18.486667</td>\n",
" <td>21.009677</td>\n",
" <td>16.190323</td>\n",
" <td>9.800000</td>\n",
" <td>2.338710</td>\n",
" <td>-3.620000</td>\n",
" <td>0.167742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>-6.174194</td>\n",
" <td>-2.335714</td>\n",
" <td>0.245161</td>\n",
" <td>4.310000</td>\n",
" <td>12.058065</td>\n",
" <td>19.173333</td>\n",
" <td>19.374194</td>\n",
" <td>16.825806</td>\n",
" <td>9.686667</td>\n",
" <td>6.590323</td>\n",
" <td>-1.550000</td>\n",
" <td>-8.070968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>-4.248387</td>\n",
" <td>-6.057143</td>\n",
" <td>-5.012903</td>\n",
" <td>7.590000</td>\n",
" <td>13.225806</td>\n",
" <td>13.506667</td>\n",
" <td>16.361290</td>\n",
" <td>14.832258</td>\n",
" <td>10.790000</td>\n",
" <td>6.451613</td>\n",
" <td>1.343333</td>\n",
" <td>-7.351613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963</th>\n",
" <td>-15.929032</td>\n",
" <td>-10.057143</td>\n",
" <td>-9.429032</td>\n",
" <td>3.886667</td>\n",
" <td>17.022581</td>\n",
" <td>13.540000</td>\n",
" <td>19.132258</td>\n",
" <td>17.796774</td>\n",
" <td>13.216667</td>\n",
" <td>5.651613</td>\n",
" <td>-0.216667</td>\n",
" <td>-8.751613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964</th>\n",
" <td>-8.074194</td>\n",
" <td>-9.975862</td>\n",
" <td>-6.190323</td>\n",
" <td>4.206667</td>\n",
" <td>11.529032</td>\n",
" <td>18.983333</td>\n",
" <td>19.990323</td>\n",
" <td>16.006452</td>\n",
" <td>11.806667</td>\n",
" <td>6.893548</td>\n",
" <td>-2.306667</td>\n",
" <td>-2.858065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1965</th>\n",
" <td>-9.529032</td>\n",
" <td>-10.071429</td>\n",
" <td>-3.303226</td>\n",
" <td>2.523333</td>\n",
" <td>9.738710</td>\n",
" <td>16.016667</td>\n",
" <td>16.661290</td>\n",
" <td>15.735484</td>\n",
" <td>12.923333</td>\n",
" <td>3.806452</td>\n",
" <td>-5.786667</td>\n",
" <td>-1.493548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1966</th>\n",
" <td>-9.664516</td>\n",
" <td>-8.857143</td>\n",
" <td>0.009677</td>\n",
" <td>8.716667</td>\n",
" <td>15.422581</td>\n",
" <td>16.540000</td>\n",
" <td>19.212903</td>\n",
" <td>16.645161</td>\n",
" <td>9.543333</td>\n",
" <td>6.087097</td>\n",
" <td>-0.900000</td>\n",
" <td>-10.525806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1967</th>\n",
" <td>-13.922581</td>\n",
" <td>-10.435714</td>\n",
" <td>0.338710</td>\n",
" <td>6.566667</td>\n",
" <td>16.945161</td>\n",
" <td>16.453333</td>\n",
" <td>17.706452</td>\n",
" <td>18.361290</td>\n",
" <td>11.353333</td>\n",
" <td>9.003226</td>\n",
" <td>0.420000</td>\n",
" <td>-9.583871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1968</th>\n",
" <td>-15.548387</td>\n",
" <td>-8.265517</td>\n",
" <td>-1.000000</td>\n",
" <td>6.020000</td>\n",
" <td>12.383871</td>\n",
" <td>18.330000</td>\n",
" <td>15.725806</td>\n",
" <td>17.712903</td>\n",
" <td>10.790000</td>\n",
" <td>2.954839</td>\n",
" <td>-2.533333</td>\n",
" <td>-5.322581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>-16.170968</td>\n",
" <td>-13.435714</td>\n",
" <td>-6.912903</td>\n",
" <td>5.973333</td>\n",
" <td>11.125806</td>\n",
" <td>14.736667</td>\n",
" <td>17.803226</td>\n",
" <td>16.445161</td>\n",
" <td>10.220000</td>\n",
" <td>4.690323</td>\n",
" <td>1.786667</td>\n",
" <td>-9.158065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>-10.374194</td>\n",
" <td>-8.325000</td>\n",
" <td>-2.851613</td>\n",
" <td>5.833333</td>\n",
" <td>12.600000</td>\n",
" <td>15.820000</td>\n",
" <td>19.316129</td>\n",
" <td>16.258065</td>\n",
" <td>11.090000</td>\n",
" <td>5.393548</td>\n",
" <td>-1.823333</td>\n",
" <td>-5.887097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1971</th>\n",
" <td>-3.538710</td>\n",
" <td>-9.417857</td>\n",
" <td>-4.161290</td>\n",
" <td>3.826667</td>\n",
" <td>12.774194</td>\n",
" <td>16.380000</td>\n",
" <td>17.467742</td>\n",
" <td>16.729032</td>\n",
" <td>10.780000</td>\n",
" <td>3.222581</td>\n",
" <td>-0.630000</td>\n",
" <td>-5.651613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td>-14.874194</td>\n",
" <td>-7.403448</td>\n",
" <td>-2.541935</td>\n",
" <td>5.880000</td>\n",
" <td>12.574194</td>\n",
" <td>18.983333</td>\n",
" <td>22.412903</td>\n",
" <td>20.632258</td>\n",
" <td>10.983333</td>\n",
" <td>5.167742</td>\n",
" <td>-0.186667</td>\n",
" <td>-0.906452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>-10.248387</td>\n",
" <td>-3.385714</td>\n",
" <td>-1.016129</td>\n",
" <td>7.880000</td>\n",
" <td>13.254839</td>\n",
" <td>18.240000</td>\n",
" <td>17.977419</td>\n",
" <td>15.909677</td>\n",
" <td>7.633333</td>\n",
" <td>3.845161</td>\n",
" <td>-2.060000</td>\n",
" <td>-5.861290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1974</th>\n",
" <td>-10.219355</td>\n",
" <td>-1.460714</td>\n",
" <td>-0.619355</td>\n",
" <td>3.403333</td>\n",
" <td>9.641935</td>\n",
" <td>16.446667</td>\n",
" <td>18.180645</td>\n",
" <td>15.874194</td>\n",
" <td>13.103333</td>\n",
" <td>8.680645</td>\n",
" <td>1.776667</td>\n",
" <td>-2.335484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>-3.670968</td>\n",
" <td>-6.385714</td>\n",
" <td>1.238710</td>\n",
" <td>10.066667</td>\n",
" <td>15.638710</td>\n",
" <td>17.776667</td>\n",
" <td>18.500000</td>\n",
" <td>15.019355</td>\n",
" <td>13.663333</td>\n",
" <td>4.093548</td>\n",
" <td>-3.296667</td>\n",
" <td>-4.032258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1976</th>\n",
" <td>-12.241935</td>\n",
" <td>-11.455172</td>\n",
" <td>-2.558065</td>\n",
" <td>5.723333</td>\n",
" <td>10.925806</td>\n",
" <td>13.713333</td>\n",
" <td>16.103226</td>\n",
" <td>14.500000</td>\n",
" <td>9.653333</td>\n",
" <td>-0.948387</td>\n",
" <td>-0.803333</td>\n",
" <td>-3.690323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977</th>\n",
" <td>-11.254839</td>\n",
" <td>-6.332143</td>\n",
" <td>-0.851613</td>\n",
" <td>7.030000</td>\n",
" <td>14.229032</td>\n",
" <td>16.836667</td>\n",
" <td>18.764516</td>\n",
" <td>15.825806</td>\n",
" <td>9.506667</td>\n",
" <td>3.100000</td>\n",
" <td>1.726667</td>\n",
" <td>-8.174194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1978</th>\n",
" <td>-7.270968</td>\n",
" <td>-9.500000</td>\n",
" <td>0.390323</td>\n",
" <td>4.613333</td>\n",
" <td>10.493548</td>\n",
" <td>14.313333</td>\n",
" <td>16.345161</td>\n",
" <td>15.774194</td>\n",
" <td>9.723333</td>\n",
" <td>3.341935</td>\n",
" <td>2.003333</td>\n",
" <td>-14.509677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1979</th>\n",
" <td>-9.954839</td>\n",
" <td>-8.828571</td>\n",
" <td>-0.883871</td>\n",
" <td>3.310000</td>\n",
" <td>17.106452</td>\n",
" <td>17.153333</td>\n",
" <td>16.674194</td>\n",
" <td>16.938710</td>\n",
" <td>11.730000</td>\n",
" <td>3.838710</td>\n",
" <td>-0.913333</td>\n",
" <td>-5.658065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>-11.312903</td>\n",
" <td>-7.151724</td>\n",
" <td>-6.370968</td>\n",
" <td>5.846667</td>\n",
" <td>8.370968</td>\n",
" <td>17.870000</td>\n",
" <td>17.200000</td>\n",
" <td>14.709677</td>\n",
" <td>10.536667</td>\n",
" <td>5.190323</td>\n",
" <td>-2.040000</td>\n",
" <td>-4.248387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>-5.393548</td>\n",
" <td>-4.907143</td>\n",
" <td>-3.135484</td>\n",
" <td>3.340000</td>\n",
" <td>14.019355</td>\n",
" <td>19.813333</td>\n",
" <td>21.512903</td>\n",
" <td>17.396774</td>\n",
" <td>10.816667</td>\n",
" <td>7.806452</td>\n",
" <td>-0.583333</td>\n",
" <td>-3.509677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>-10.158065</td>\n",
" <td>-8.792857</td>\n",
" <td>-0.648387</td>\n",
" <td>5.336667</td>\n",
" <td>11.977419</td>\n",
" <td>13.856667</td>\n",
" <td>18.400000</td>\n",
" <td>16.645161</td>\n",
" <td>11.753333</td>\n",
" <td>4.064516</td>\n",
" <td>2.033333</td>\n",
" <td>-1.135484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>-3.967742</td>\n",
" <td>-6.878571</td>\n",
" <td>-1.403226</td>\n",
" <td>9.290000</td>\n",
" <td>15.612903</td>\n",
" <td>14.550000</td>\n",
" <td>17.938710</td>\n",
" <td>16.032258</td>\n",
" <td>12.413333</td>\n",
" <td>6.200000</td>\n",
" <td>-1.466667</td>\n",
" <td>-3.180645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>-4.409677</td>\n",
" <td>-10.268966</td>\n",
" <td>-2.354839</td>\n",
" <td>7.466667</td>\n",
" <td>15.970968</td>\n",
" <td>15.590000</td>\n",
" <td>17.567742</td>\n",
" <td>15.125806</td>\n",
" <td>12.410000</td>\n",
" <td>6.800000</td>\n",
" <td>-3.543333</td>\n",
" <td>-9.590323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985</th>\n",
" <td>-10.012903</td>\n",
" <td>-14.014286</td>\n",
" <td>-3.067742</td>\n",
" <td>5.346667</td>\n",
" <td>12.974194</td>\n",
" <td>14.663333</td>\n",
" <td>16.438710</td>\n",
" <td>19.419355</td>\n",
" <td>10.123333</td>\n",
" <td>6.070968</td>\n",
" <td>-3.333333</td>\n",
" <td>-6.535484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986</th>\n",
" <td>-6.725806</td>\n",
" <td>-13.539286</td>\n",
" <td>0.158065</td>\n",
" <td>6.680000</td>\n",
" <td>13.648387</td>\n",
" <td>18.633333</td>\n",
" <td>17.796774</td>\n",
" <td>16.516129</td>\n",
" <td>8.580000</td>\n",
" <td>4.174194</td>\n",
" <td>-0.093333</td>\n",
" <td>-7.454839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1987</th>\n",
" <td>-17.506452</td>\n",
" <td>-6.057143</td>\n",
" <td>-5.312903</td>\n",
" <td>2.810000</td>\n",
" <td>12.832258</td>\n",
" <td>17.736667</td>\n",
" <td>16.764516</td>\n",
" <td>15.087097</td>\n",
" <td>9.046667</td>\n",
" <td>3.590323</td>\n",
" <td>-3.646667</td>\n",
" <td>-6.983871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1988</th>\n",
" <td>-7.241935</td>\n",
" <td>-6.113793</td>\n",
" <td>-0.993548</td>\n",
" <td>5.346667</td>\n",
" <td>13.848387</td>\n",
" <td>19.530000</td>\n",
" <td>21.587097</td>\n",
" <td>16.474194</td>\n",
" <td>11.280000</td>\n",
" <td>4.870968</td>\n",
" <td>-4.440000</td>\n",
" <td>-6.935484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1989</th>\n",
" <td>-2.112903</td>\n",
" <td>-0.503571</td>\n",
" <td>1.990323</td>\n",
" <td>7.656667</td>\n",
" <td>13.396774</td>\n",
" <td>20.053333</td>\n",
" <td>19.161290</td>\n",
" <td>16.248387</td>\n",
" <td>12.176667</td>\n",
" <td>5.329032</td>\n",
" <td>-2.633333</td>\n",
" <td>-5.293548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td>-5.667742</td>\n",
" <td>0.382143</td>\n",
" <td>1.980645</td>\n",
" <td>8.120000</td>\n",
" <td>10.803226</td>\n",
" <td>14.506667</td>\n",
" <td>17.535484</td>\n",
" <td>15.954839</td>\n",
" <td>9.320000</td>\n",
" <td>5.358065</td>\n",
" <td>0.250000</td>\n",
" <td>-3.393548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991</th>\n",
" <td>-4.875862</td>\n",
" <td>-6.678571</td>\n",
" <td>-1.219355</td>\n",
" <td>7.030000</td>\n",
" <td>13.387097</td>\n",
" <td>18.783333</td>\n",
" <td>18.080645</td>\n",
" <td>16.090323</td>\n",
" <td>10.973333</td>\n",
" <td>6.538710</td>\n",
" <td>0.970000</td>\n",
" <td>-3.974194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992</th>\n",
" <td>-5.270968</td>\n",
" <td>-4.313793</td>\n",
" <td>1.654839</td>\n",
" <td>5.140000</td>\n",
" <td>11.932258</td>\n",
" <td>16.693333</td>\n",
" <td>18.622581</td>\n",
" <td>18.041935</td>\n",
" <td>13.146667</td>\n",
" <td>2.154839</td>\n",
" <td>-2.533333</td>\n",
" <td>-4.396774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993</th>\n",
" <td>-4.416129</td>\n",
" <td>-4.882143</td>\n",
" <td>-1.932258</td>\n",
" <td>5.730000</td>\n",
" <td>14.490323</td>\n",
" <td>14.023333</td>\n",
" <td>17.506452</td>\n",
" <td>15.425806</td>\n",
" <td>6.870000</td>\n",
" <td>4.645161</td>\n",
" <td>-8.003333</td>\n",
" <td>-3.612903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994</th>\n",
" <td>-3.422581</td>\n",
" <td>-11.300000</td>\n",
" <td>-2.929032</td>\n",
" <td>7.226667</td>\n",
" <td>9.848387</td>\n",
" <td>14.546667</td>\n",
" <td>17.561290</td>\n",
" <td>15.870968</td>\n",
" <td>13.676667</td>\n",
" <td>4.958065</td>\n",
" <td>-2.526667</td>\n",
" <td>-7.948387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995</th>\n",
" <td>-5.874194</td>\n",
" <td>-0.846429</td>\n",
" <td>0.587097</td>\n",
" <td>9.116667</td>\n",
" <td>14.470968</td>\n",
" <td>19.683333</td>\n",
" <td>17.548387</td>\n",
" <td>16.793548</td>\n",
" <td>12.823333</td>\n",
" <td>6.683871</td>\n",
" <td>-2.816667</td>\n",
" <td>-9.506452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996</th>\n",
" <td>-10.019355</td>\n",
" <td>-9.689655</td>\n",
" <td>-3.045161</td>\n",
" <td>6.403333</td>\n",
" <td>15.703226</td>\n",
" <td>16.526667</td>\n",
" <td>18.900000</td>\n",
" <td>17.274194</td>\n",
" <td>9.880000</td>\n",
" <td>6.051613</td>\n",
" <td>3.863333</td>\n",
" <td>-7.019355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997</th>\n",
" <td>-7.732258</td>\n",
" <td>-4.682143</td>\n",
" <td>-0.932258</td>\n",
" <td>4.650000</td>\n",
" <td>11.093548</td>\n",
" <td>17.830000</td>\n",
" <td>18.729032</td>\n",
" <td>17.122581</td>\n",
" <td>8.516667</td>\n",
" <td>3.677419</td>\n",
" <td>-0.843333</td>\n",
" <td>-7.538710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998</th>\n",
" <td>-4.725806</td>\n",
" <td>-7.642857</td>\n",
" <td>-1.348387</td>\n",
" <td>3.900000</td>\n",
" <td>13.712903</td>\n",
" <td>19.976667</td>\n",
" <td>18.864516</td>\n",
" <td>15.487097</td>\n",
" <td>10.700000</td>\n",
" <td>5.658065</td>\n",
" <td>-8.030000</td>\n",
" <td>-5.938710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999</th>\n",
" <td>-4.577419</td>\n",
" <td>-6.253571</td>\n",
" <td>-0.835484</td>\n",
" <td>9.683333</td>\n",
" <td>8.674194</td>\n",
" <td>21.423333</td>\n",
" <td>21.719355</td>\n",
" <td>16.406452</td>\n",
" <td>11.770000</td>\n",
" <td>7.374194</td>\n",
" <td>-4.933333</td>\n",
" <td>-1.706452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>-6.112903</td>\n",
" <td>-2.703448</td>\n",
" <td>-0.700000</td>\n",
" <td>11.110000</td>\n",
" <td>10.825806</td>\n",
" <td>16.236667</td>\n",
" <td>19.332258</td>\n",
" <td>16.754839</td>\n",
" <td>10.043333</td>\n",
" <td>7.180645</td>\n",
" <td>-0.050000</td>\n",
" <td>-2.625806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
" <td>-4.312903</td>\n",
" <td>-7.192857</td>\n",
" <td>-2.125806</td>\n",
" <td>10.966667</td>\n",
" <td>11.251613</td>\n",
" <td>16.266667</td>\n",
" <td>22.967742</td>\n",
" <td>16.964516</td>\n",
" <td>12.193333</td>\n",
" <td>4.806452</td>\n",
" <td>-0.526667</td>\n",
" <td>-10.580645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
" <td>-4.754839</td>\n",
" <td>-0.425000</td>\n",
" <td>2.229032</td>\n",
" <td>7.183333</td>\n",
" <td>12.732258</td>\n",
" <td>17.310000</td>\n",
" <td>22.638710</td>\n",
" <td>17.041935</td>\n",
" <td>12.030000</td>\n",
" <td>2.545161</td>\n",
" <td>-1.476667</td>\n",
" <td>-12.561290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
" <td>-7.351613</td>\n",
" <td>-8.653571</td>\n",
" <td>-2.729032</td>\n",
" <td>4.673333</td>\n",
" <td>15.535484</td>\n",
" <td>12.820000</td>\n",
" <td>20.641935</td>\n",
" <td>16.948387</td>\n",
" <td>11.333333</td>\n",
" <td>5.567742</td>\n",
" <td>1.146667</td>\n",
" <td>-2.061290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
" <td>-6.458065</td>\n",
" <td>-7.006897</td>\n",
" <td>1.345161</td>\n",
" <td>4.556667</td>\n",
" <td>11.412903</td>\n",
" <td>15.283333</td>\n",
" <td>19.022581</td>\n",
" <td>18.383871</td>\n",
" <td>12.133333</td>\n",
" <td>5.935484</td>\n",
" <td>-1.560000</td>\n",
" <td>-2.935484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
" <td>-3.000000</td>\n",
" <td>-8.903571</td>\n",
" <td>-6.029032</td>\n",
" <td>7.116667</td>\n",
" <td>14.832258</td>\n",
" <td>16.506667</td>\n",
" <td>19.309677</td>\n",
" <td>17.638710</td>\n",
" <td>13.123333</td>\n",
" <td>6.022581</td>\n",
" <td>1.370000</td>\n",
" <td>-4.148387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
" <td>-10.787097</td>\n",
" <td>-13.307143</td>\n",
" <td>-3.719355</td>\n",
" <td>6.020000</td>\n",
" <td>12.432258</td>\n",
" <td>18.203333</td>\n",
" <td>17.977419</td>\n",
" <td>17.535484</td>\n",
" <td>13.303333</td>\n",
" <td>7.003226</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"month 1 2 3 4 5 6 \\\n",
"year \n",
"1949 -3.670968 -7.339286 -2.780645 4.280000 15.225806 16.973333 \n",
"1950 -18.022581 -6.764286 -2.225806 9.043333 11.738710 15.123333 \n",
"1951 -12.138710 -12.264286 -3.996774 8.403333 9.738710 17.706667 \n",
"1952 -4.135484 -7.096552 -9.106452 5.166667 10.358065 17.340000 \n",
"1953 -10.403226 -15.614286 -2.606452 7.183333 11.645161 19.193333 \n",
"1954 -14.306452 -14.000000 -3.425806 3.030000 12.883871 18.963333 \n",
"1955 -6.312903 -6.792857 -4.664516 1.466667 10.506452 15.100000 \n",
"1956 -10.916129 -18.503448 -3.600000 3.940000 10.664516 20.570000 \n",
"1957 -6.038710 -1.760714 -6.319355 6.640000 14.435484 15.240000 \n",
"1958 -6.932258 -7.710714 -6.080645 4.063333 13.290323 14.890000 \n",
"1959 -4.335484 -5.492857 -1.425806 6.630000 11.490323 16.886667 \n",
"1960 -9.351613 -7.651724 -5.480645 5.046667 11.625806 18.486667 \n",
"1961 -6.174194 -2.335714 0.245161 4.310000 12.058065 19.173333 \n",
"1962 -4.248387 -6.057143 -5.012903 7.590000 13.225806 13.506667 \n",
"1963 -15.929032 -10.057143 -9.429032 3.886667 17.022581 13.540000 \n",
"1964 -8.074194 -9.975862 -6.190323 4.206667 11.529032 18.983333 \n",
"1965 -9.529032 -10.071429 -3.303226 2.523333 9.738710 16.016667 \n",
"1966 -9.664516 -8.857143 0.009677 8.716667 15.422581 16.540000 \n",
"1967 -13.922581 -10.435714 0.338710 6.566667 16.945161 16.453333 \n",
"1968 -15.548387 -8.265517 -1.000000 6.020000 12.383871 18.330000 \n",
"1969 -16.170968 -13.435714 -6.912903 5.973333 11.125806 14.736667 \n",
"1970 -10.374194 -8.325000 -2.851613 5.833333 12.600000 15.820000 \n",
"1971 -3.538710 -9.417857 -4.161290 3.826667 12.774194 16.380000 \n",
"1972 -14.874194 -7.403448 -2.541935 5.880000 12.574194 18.983333 \n",
"1973 -10.248387 -3.385714 -1.016129 7.880000 13.254839 18.240000 \n",
"1974 -10.219355 -1.460714 -0.619355 3.403333 9.641935 16.446667 \n",
"1975 -3.670968 -6.385714 1.238710 10.066667 15.638710 17.776667 \n",
"1976 -12.241935 -11.455172 -2.558065 5.723333 10.925806 13.713333 \n",
"1977 -11.254839 -6.332143 -0.851613 7.030000 14.229032 16.836667 \n",
"1978 -7.270968 -9.500000 0.390323 4.613333 10.493548 14.313333 \n",
"1979 -9.954839 -8.828571 -0.883871 3.310000 17.106452 17.153333 \n",
"1980 -11.312903 -7.151724 -6.370968 5.846667 8.370968 17.870000 \n",
"1981 -5.393548 -4.907143 -3.135484 3.340000 14.019355 19.813333 \n",
"1982 -10.158065 -8.792857 -0.648387 5.336667 11.977419 13.856667 \n",
"1983 -3.967742 -6.878571 -1.403226 9.290000 15.612903 14.550000 \n",
"1984 -4.409677 -10.268966 -2.354839 7.466667 15.970968 15.590000 \n",
"1985 -10.012903 -14.014286 -3.067742 5.346667 12.974194 14.663333 \n",
"1986 -6.725806 -13.539286 0.158065 6.680000 13.648387 18.633333 \n",
"1987 -17.506452 -6.057143 -5.312903 2.810000 12.832258 17.736667 \n",
"1988 -7.241935 -6.113793 -0.993548 5.346667 13.848387 19.530000 \n",
"1989 -2.112903 -0.503571 1.990323 7.656667 13.396774 20.053333 \n",
"1990 -5.667742 0.382143 1.980645 8.120000 10.803226 14.506667 \n",
"1991 -4.875862 -6.678571 -1.219355 7.030000 13.387097 18.783333 \n",
"1992 -5.270968 -4.313793 1.654839 5.140000 11.932258 16.693333 \n",
"1993 -4.416129 -4.882143 -1.932258 5.730000 14.490323 14.023333 \n",
"1994 -3.422581 -11.300000 -2.929032 7.226667 9.848387 14.546667 \n",
"1995 -5.874194 -0.846429 0.587097 9.116667 14.470968 19.683333 \n",
"1996 -10.019355 -9.689655 -3.045161 6.403333 15.703226 16.526667 \n",
"1997 -7.732258 -4.682143 -0.932258 4.650000 11.093548 17.830000 \n",
"1998 -4.725806 -7.642857 -1.348387 3.900000 13.712903 19.976667 \n",
"1999 -4.577419 -6.253571 -0.835484 9.683333 8.674194 21.423333 \n",
"2000 -6.112903 -2.703448 -0.700000 11.110000 10.825806 16.236667 \n",
"2001 -4.312903 -7.192857 -2.125806 10.966667 11.251613 16.266667 \n",
"2002 -4.754839 -0.425000 2.229032 7.183333 12.732258 17.310000 \n",
"2003 -7.351613 -8.653571 -2.729032 4.673333 15.535484 12.820000 \n",
"2004 -6.458065 -7.006897 1.345161 4.556667 11.412903 15.283333 \n",
"2005 -3.000000 -8.903571 -6.029032 7.116667 14.832258 16.506667 \n",
"2006 -10.787097 -13.307143 -3.719355 6.020000 12.432258 18.203333 \n",
"\n",
"month 7 8 9 10 11 12 \n",
"year \n",
"1949 17.425806 16.074194 11.506667 4.800000 -0.423333 -4.322581 \n",
"1950 16.177419 14.080645 11.990000 4.745161 -0.433333 -5.503226 \n",
"1951 18.590323 18.325806 11.990000 2.780645 -4.810000 -1.251613 \n",
"1952 17.870968 16.848387 12.136667 3.925806 -1.133333 -5.883871 \n",
"1953 19.025806 17.290323 10.046667 5.790323 -3.156667 -5.632258 \n",
"1954 21.025806 18.354839 12.286667 5.738710 -1.573333 -4.974194 \n",
"1955 17.906452 17.916129 13.856667 7.712903 -3.023333 -14.306452 \n",
"1956 15.190323 14.625806 8.423333 4.670968 -5.180000 -4.254839 \n",
"1957 18.554839 17.087097 12.313333 5.241935 -0.893333 -4.622581 \n",
"1958 18.367742 15.696774 9.080000 6.038710 -0.786667 -7.641935 \n",
"1959 20.538710 16.932258 8.253333 2.229032 -5.110000 -10.990323 \n",
"1960 21.009677 16.190323 9.800000 2.338710 -3.620000 0.167742 \n",
"1961 19.374194 16.825806 9.686667 6.590323 -1.550000 -8.070968 \n",
"1962 16.361290 14.832258 10.790000 6.451613 1.343333 -7.351613 \n",
"1963 19.132258 17.796774 13.216667 5.651613 -0.216667 -8.751613 \n",
"1964 19.990323 16.006452 11.806667 6.893548 -2.306667 -2.858065 \n",
"1965 16.661290 15.735484 12.923333 3.806452 -5.786667 -1.493548 \n",
"1966 19.212903 16.645161 9.543333 6.087097 -0.900000 -10.525806 \n",
"1967 17.706452 18.361290 11.353333 9.003226 0.420000 -9.583871 \n",
"1968 15.725806 17.712903 10.790000 2.954839 -2.533333 -5.322581 \n",
"1969 17.803226 16.445161 10.220000 4.690323 1.786667 -9.158065 \n",
"1970 19.316129 16.258065 11.090000 5.393548 -1.823333 -5.887097 \n",
"1971 17.467742 16.729032 10.780000 3.222581 -0.630000 -5.651613 \n",
"1972 22.412903 20.632258 10.983333 5.167742 -0.186667 -0.906452 \n",
"1973 17.977419 15.909677 7.633333 3.845161 -2.060000 -5.861290 \n",
"1974 18.180645 15.874194 13.103333 8.680645 1.776667 -2.335484 \n",
"1975 18.500000 15.019355 13.663333 4.093548 -3.296667 -4.032258 \n",
"1976 16.103226 14.500000 9.653333 -0.948387 -0.803333 -3.690323 \n",
"1977 18.764516 15.825806 9.506667 3.100000 1.726667 -8.174194 \n",
"1978 16.345161 15.774194 9.723333 3.341935 2.003333 -14.509677 \n",
"1979 16.674194 16.938710 11.730000 3.838710 -0.913333 -5.658065 \n",
"1980 17.200000 14.709677 10.536667 5.190323 -2.040000 -4.248387 \n",
"1981 21.512903 17.396774 10.816667 7.806452 -0.583333 -3.509677 \n",
"1982 18.400000 16.645161 11.753333 4.064516 2.033333 -1.135484 \n",
"1983 17.938710 16.032258 12.413333 6.200000 -1.466667 -3.180645 \n",
"1984 17.567742 15.125806 12.410000 6.800000 -3.543333 -9.590323 \n",
"1985 16.438710 19.419355 10.123333 6.070968 -3.333333 -6.535484 \n",
"1986 17.796774 16.516129 8.580000 4.174194 -0.093333 -7.454839 \n",
"1987 16.764516 15.087097 9.046667 3.590323 -3.646667 -6.983871 \n",
"1988 21.587097 16.474194 11.280000 4.870968 -4.440000 -6.935484 \n",
"1989 19.161290 16.248387 12.176667 5.329032 -2.633333 -5.293548 \n",
"1990 17.535484 15.954839 9.320000 5.358065 0.250000 -3.393548 \n",
"1991 18.080645 16.090323 10.973333 6.538710 0.970000 -3.974194 \n",
"1992 18.622581 18.041935 13.146667 2.154839 -2.533333 -4.396774 \n",
"1993 17.506452 15.425806 6.870000 4.645161 -8.003333 -3.612903 \n",
"1994 17.561290 15.870968 13.676667 4.958065 -2.526667 -7.948387 \n",
"1995 17.548387 16.793548 12.823333 6.683871 -2.816667 -9.506452 \n",
"1996 18.900000 17.274194 9.880000 6.051613 3.863333 -7.019355 \n",
"1997 18.729032 17.122581 8.516667 3.677419 -0.843333 -7.538710 \n",
"1998 18.864516 15.487097 10.700000 5.658065 -8.030000 -5.938710 \n",
"1999 21.719355 16.406452 11.770000 7.374194 -4.933333 -1.706452 \n",
"2000 19.332258 16.754839 10.043333 7.180645 -0.050000 -2.625806 \n",
"2001 22.967742 16.964516 12.193333 4.806452 -0.526667 -10.580645 \n",
"2002 22.638710 17.041935 12.030000 2.545161 -1.476667 -12.561290 \n",
"2003 20.641935 16.948387 11.333333 5.567742 1.146667 -2.061290 \n",
"2004 19.022581 18.383871 12.133333 5.935484 -1.560000 -2.935484 \n",
"2005 19.309677 17.638710 13.123333 6.022581 1.370000 -4.148387 \n",
"2006 17.977419 17.535484 13.303333 7.003226 NaN NaN "
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.groupby(['year', 'month']).mean()['TMPMN'].unstack()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"corr = (table.groupby(['year', 'month']).mean()['TMPMN'].\n",
" unstack().corr())"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>year</th>\n",
" <th>1949</th>\n",
" <th>1950</th>\n",
" <th>1951</th>\n",
" <th>1952</th>\n",
" <th>1953</th>\n",
" <th>1954</th>\n",
" <th>1955</th>\n",
" <th>1956</th>\n",
" <th>1957</th>\n",
" <th>1958</th>\n",
" <th>...</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>2000</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1949</th>\n",
" <td>1.000000</td>\n",
" <td>0.907740</td>\n",
" <td>0.947026</td>\n",
" <td>0.974542</td>\n",
" <td>0.968858</td>\n",
" <td>0.971366</td>\n",
" <td>0.952670</td>\n",
" <td>0.956033</td>\n",
" <td>0.968737</td>\n",
" <td>0.987562</td>\n",
" <td>...</td>\n",
" <td>0.968909</td>\n",
" <td>0.972389</td>\n",
" <td>0.936239</td>\n",
" <td>0.945821</td>\n",
" <td>0.949552</td>\n",
" <td>0.918213</td>\n",
" <td>0.975277</td>\n",
" <td>0.971641</td>\n",
" <td>0.987455</td>\n",
" <td>0.974490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1950</th>\n",
" <td>0.907740</td>\n",
" <td>1.000000</td>\n",
" <td>0.948069</td>\n",
" <td>0.888752</td>\n",
" <td>0.936835</td>\n",
" <td>0.954790</td>\n",
" <td>0.879953</td>\n",
" <td>0.906577</td>\n",
" <td>0.926981</td>\n",
" <td>0.923274</td>\n",
" <td>...</td>\n",
" <td>0.933920</td>\n",
" <td>0.882930</td>\n",
" <td>0.901294</td>\n",
" <td>0.958600</td>\n",
" <td>0.906434</td>\n",
" <td>0.878226</td>\n",
" <td>0.929568</td>\n",
" <td>0.933543</td>\n",
" <td>0.900428</td>\n",
" <td>0.947021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1951</th>\n",
" <td>0.947026</td>\n",
" <td>0.948069</td>\n",
" <td>1.000000</td>\n",
" <td>0.940882</td>\n",
" <td>0.980339</td>\n",
" <td>0.975816</td>\n",
" <td>0.884347</td>\n",
" <td>0.967928</td>\n",
" <td>0.933412</td>\n",
" <td>0.938189</td>\n",
" <td>...</td>\n",
" <td>0.942953</td>\n",
" <td>0.937997</td>\n",
" <td>0.966797</td>\n",
" <td>0.968900</td>\n",
" <td>0.924609</td>\n",
" <td>0.864014</td>\n",
" <td>0.952932</td>\n",
" <td>0.971115</td>\n",
" <td>0.946616</td>\n",
" <td>0.986767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1952</th>\n",
" <td>0.974542</td>\n",
" <td>0.888752</td>\n",
" <td>0.940882</td>\n",
" <td>1.000000</td>\n",
" <td>0.944192</td>\n",
" <td>0.943988</td>\n",
" <td>0.950655</td>\n",
" <td>0.922784</td>\n",
" <td>0.977412</td>\n",
" <td>0.977336</td>\n",
" <td>...</td>\n",
" <td>0.953354</td>\n",
" <td>0.943405</td>\n",
" <td>0.944098</td>\n",
" <td>0.951140</td>\n",
" <td>0.953170</td>\n",
" <td>0.903300</td>\n",
" <td>0.945131</td>\n",
" <td>0.944775</td>\n",
" <td>0.986555</td>\n",
" <td>0.951781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1953</th>\n",
" <td>0.968858</td>\n",
" <td>0.936835</td>\n",
" <td>0.980339</td>\n",
" <td>0.944192</td>\n",
" <td>1.000000</td>\n",
" <td>0.985802</td>\n",
" <td>0.916705</td>\n",
" <td>0.990022</td>\n",
" <td>0.926579</td>\n",
" <td>0.962700</td>\n",
" <td>...</td>\n",
" <td>0.958877</td>\n",
" <td>0.956270</td>\n",
" <td>0.964839</td>\n",
" <td>0.966482</td>\n",
" <td>0.947469</td>\n",
" <td>0.884853</td>\n",
" <td>0.962570</td>\n",
" <td>0.977620</td>\n",
" <td>0.963773</td>\n",
" <td>0.991853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954</th>\n",
" <td>0.971366</td>\n",
" <td>0.954790</td>\n",
" <td>0.975816</td>\n",
" <td>0.943988</td>\n",
" <td>0.985802</td>\n",
" <td>1.000000</td>\n",
" <td>0.925735</td>\n",
" <td>0.973728</td>\n",
" <td>0.941913</td>\n",
" <td>0.968681</td>\n",
" <td>...</td>\n",
" <td>0.965355</td>\n",
" <td>0.946228</td>\n",
" <td>0.945389</td>\n",
" <td>0.959077</td>\n",
" <td>0.929715</td>\n",
" <td>0.887171</td>\n",
" <td>0.980068</td>\n",
" <td>0.986436</td>\n",
" <td>0.960221</td>\n",
" <td>0.992831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1955</th>\n",
" <td>0.952670</td>\n",
" <td>0.879953</td>\n",
" <td>0.884347</td>\n",
" <td>0.950655</td>\n",
" <td>0.916705</td>\n",
" <td>0.925735</td>\n",
" <td>1.000000</td>\n",
" <td>0.881310</td>\n",
" <td>0.950061</td>\n",
" <td>0.965662</td>\n",
" <td>...</td>\n",
" <td>0.955837</td>\n",
" <td>0.939129</td>\n",
" <td>0.906725</td>\n",
" <td>0.927315</td>\n",
" <td>0.953645</td>\n",
" <td>0.947226</td>\n",
" <td>0.920892</td>\n",
" <td>0.948935</td>\n",
" <td>0.944265</td>\n",
" <td>0.969116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956</th>\n",
" <td>0.956033</td>\n",
" <td>0.906577</td>\n",
" <td>0.967928</td>\n",
" <td>0.922784</td>\n",
" <td>0.990022</td>\n",
" <td>0.973728</td>\n",
" <td>0.881310</td>\n",
" <td>1.000000</td>\n",
" <td>0.892596</td>\n",
" <td>0.935137</td>\n",
" <td>...</td>\n",
" <td>0.930644</td>\n",
" <td>0.949946</td>\n",
" <td>0.951972</td>\n",
" <td>0.930588</td>\n",
" <td>0.901719</td>\n",
" <td>0.834993</td>\n",
" <td>0.937192</td>\n",
" <td>0.956383</td>\n",
" <td>0.942899</td>\n",
" <td>0.982676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1957</th>\n",
" <td>0.968737</td>\n",
" <td>0.926981</td>\n",
" <td>0.933412</td>\n",
" <td>0.977412</td>\n",
" <td>0.926579</td>\n",
" <td>0.941913</td>\n",
" <td>0.950061</td>\n",
" <td>0.892596</td>\n",
" <td>1.000000</td>\n",
" <td>0.981605</td>\n",
" <td>...</td>\n",
" <td>0.961064</td>\n",
" <td>0.940607</td>\n",
" <td>0.923622</td>\n",
" <td>0.963443</td>\n",
" <td>0.947252</td>\n",
" <td>0.926268</td>\n",
" <td>0.957167</td>\n",
" <td>0.946136</td>\n",
" <td>0.969510</td>\n",
" <td>0.941783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>0.987562</td>\n",
" <td>0.923274</td>\n",
" <td>0.938189</td>\n",
" <td>0.977336</td>\n",
" <td>0.962700</td>\n",
" <td>0.968681</td>\n",
" <td>0.965662</td>\n",
" <td>0.935137</td>\n",
" <td>0.981605</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.975292</td>\n",
" <td>0.955650</td>\n",
" <td>0.934773</td>\n",
" <td>0.966265</td>\n",
" <td>0.964192</td>\n",
" <td>0.927709</td>\n",
" <td>0.977522</td>\n",
" <td>0.966473</td>\n",
" <td>0.986435</td>\n",
" <td>0.974004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>0.955095</td>\n",
" <td>0.880313</td>\n",
" <td>0.913372</td>\n",
" <td>0.943003</td>\n",
" <td>0.934574</td>\n",
" <td>0.919281</td>\n",
" <td>0.951480</td>\n",
" <td>0.898155</td>\n",
" <td>0.947047</td>\n",
" <td>0.957886</td>\n",
" <td>...</td>\n",
" <td>0.979000</td>\n",
" <td>0.966290</td>\n",
" <td>0.948005</td>\n",
" <td>0.959352</td>\n",
" <td>0.979020</td>\n",
" <td>0.980658</td>\n",
" <td>0.919108</td>\n",
" <td>0.951717</td>\n",
" <td>0.936442</td>\n",
" <td>0.948662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>0.959802</td>\n",
" <td>0.924429</td>\n",
" <td>0.978991</td>\n",
" <td>0.956012</td>\n",
" <td>0.960006</td>\n",
" <td>0.970059</td>\n",
" <td>0.885493</td>\n",
" <td>0.949514</td>\n",
" <td>0.955334</td>\n",
" <td>0.953480</td>\n",
" <td>...</td>\n",
" <td>0.954205</td>\n",
" <td>0.950789</td>\n",
" <td>0.963737</td>\n",
" <td>0.961449</td>\n",
" <td>0.917164</td>\n",
" <td>0.877776</td>\n",
" <td>0.960716</td>\n",
" <td>0.962270</td>\n",
" <td>0.951674</td>\n",
" <td>0.967339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>0.959840</td>\n",
" <td>0.915921</td>\n",
" <td>0.916646</td>\n",
" <td>0.942520</td>\n",
" <td>0.941001</td>\n",
" <td>0.948537</td>\n",
" <td>0.967740</td>\n",
" <td>0.913982</td>\n",
" <td>0.954781</td>\n",
" <td>0.969084</td>\n",
" <td>...</td>\n",
" <td>0.992939</td>\n",
" <td>0.968287</td>\n",
" <td>0.945195</td>\n",
" <td>0.960616</td>\n",
" <td>0.954859</td>\n",
" <td>0.969823</td>\n",
" <td>0.927705</td>\n",
" <td>0.963333</td>\n",
" <td>0.930895</td>\n",
" <td>0.957506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>0.974633</td>\n",
" <td>0.919884</td>\n",
" <td>0.923577</td>\n",
" <td>0.973503</td>\n",
" <td>0.950372</td>\n",
" <td>0.945798</td>\n",
" <td>0.963139</td>\n",
" <td>0.915065</td>\n",
" <td>0.975698</td>\n",
" <td>0.990024</td>\n",
" <td>...</td>\n",
" <td>0.955562</td>\n",
" <td>0.931789</td>\n",
" <td>0.915499</td>\n",
" <td>0.959491</td>\n",
" <td>0.973046</td>\n",
" <td>0.923053</td>\n",
" <td>0.960509</td>\n",
" <td>0.943741</td>\n",
" <td>0.986267</td>\n",
" <td>0.973657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963</th>\n",
" <td>0.962042</td>\n",
" <td>0.958062</td>\n",
" <td>0.933456</td>\n",
" <td>0.945666</td>\n",
" <td>0.938787</td>\n",
" <td>0.965837</td>\n",
" <td>0.935118</td>\n",
" <td>0.907263</td>\n",
" <td>0.975002</td>\n",
" <td>0.977198</td>\n",
" <td>...</td>\n",
" <td>0.944920</td>\n",
" <td>0.907437</td>\n",
" <td>0.886716</td>\n",
" <td>0.947895</td>\n",
" <td>0.924909</td>\n",
" <td>0.888317</td>\n",
" <td>0.979236</td>\n",
" <td>0.950308</td>\n",
" <td>0.964327</td>\n",
" <td>0.964328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964</th>\n",
" <td>0.978876</td>\n",
" <td>0.925945</td>\n",
" <td>0.972332</td>\n",
" <td>0.976445</td>\n",
" <td>0.978858</td>\n",
" <td>0.984471</td>\n",
" <td>0.934029</td>\n",
" <td>0.969962</td>\n",
" <td>0.961548</td>\n",
" <td>0.978444</td>\n",
" <td>...</td>\n",
" <td>0.959243</td>\n",
" <td>0.963072</td>\n",
" <td>0.967780</td>\n",
" <td>0.962973</td>\n",
" <td>0.937318</td>\n",
" <td>0.882428</td>\n",
" <td>0.970000</td>\n",
" <td>0.972766</td>\n",
" <td>0.977872</td>\n",
" <td>0.985798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1965</th>\n",
" <td>0.963408</td>\n",
" <td>0.926482</td>\n",
" <td>0.983129</td>\n",
" <td>0.947266</td>\n",
" <td>0.969958</td>\n",
" <td>0.980597</td>\n",
" <td>0.915323</td>\n",
" <td>0.965544</td>\n",
" <td>0.940630</td>\n",
" <td>0.945946</td>\n",
" <td>...</td>\n",
" <td>0.944572</td>\n",
" <td>0.962748</td>\n",
" <td>0.962311</td>\n",
" <td>0.946226</td>\n",
" <td>0.910626</td>\n",
" <td>0.871594</td>\n",
" <td>0.958380</td>\n",
" <td>0.982621</td>\n",
" <td>0.949383</td>\n",
" <td>0.990242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1966</th>\n",
" <td>0.963135</td>\n",
" <td>0.949552</td>\n",
" <td>0.934074</td>\n",
" <td>0.926388</td>\n",
" <td>0.967469</td>\n",
" <td>0.959600</td>\n",
" <td>0.945573</td>\n",
" <td>0.934136</td>\n",
" <td>0.944966</td>\n",
" <td>0.972638</td>\n",
" <td>...</td>\n",
" <td>0.975609</td>\n",
" <td>0.945595</td>\n",
" <td>0.926211</td>\n",
" <td>0.969820</td>\n",
" <td>0.971050</td>\n",
" <td>0.948432</td>\n",
" <td>0.954698</td>\n",
" <td>0.962609</td>\n",
" <td>0.949078</td>\n",
" <td>0.979433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1967</th>\n",
" <td>0.950736</td>\n",
" <td>0.963184</td>\n",
" <td>0.929065</td>\n",
" <td>0.903436</td>\n",
" <td>0.961194</td>\n",
" <td>0.969344</td>\n",
" <td>0.934940</td>\n",
" <td>0.934952</td>\n",
" <td>0.929563</td>\n",
" <td>0.960980</td>\n",
" <td>...</td>\n",
" <td>0.956425</td>\n",
" <td>0.921618</td>\n",
" <td>0.895914</td>\n",
" <td>0.948673</td>\n",
" <td>0.930125</td>\n",
" <td>0.904840</td>\n",
" <td>0.956684</td>\n",
" <td>0.960392</td>\n",
" <td>0.933952</td>\n",
" <td>0.975788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1968</th>\n",
" <td>0.947199</td>\n",
" <td>0.980378</td>\n",
" <td>0.970901</td>\n",
" <td>0.917654</td>\n",
" <td>0.964967</td>\n",
" <td>0.977458</td>\n",
" <td>0.905963</td>\n",
" <td>0.948284</td>\n",
" <td>0.939386</td>\n",
" <td>0.943691</td>\n",
" <td>...</td>\n",
" <td>0.968272</td>\n",
" <td>0.933786</td>\n",
" <td>0.932380</td>\n",
" <td>0.964636</td>\n",
" <td>0.917016</td>\n",
" <td>0.903006</td>\n",
" <td>0.945811</td>\n",
" <td>0.969220</td>\n",
" <td>0.924146</td>\n",
" <td>0.967869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>0.951786</td>\n",
" <td>0.970885</td>\n",
" <td>0.954359</td>\n",
" <td>0.940882</td>\n",
" <td>0.968144</td>\n",
" <td>0.979383</td>\n",
" <td>0.923422</td>\n",
" <td>0.938472</td>\n",
" <td>0.946470</td>\n",
" <td>0.969013</td>\n",
" <td>...</td>\n",
" <td>0.955060</td>\n",
" <td>0.899066</td>\n",
" <td>0.911197</td>\n",
" <td>0.964426</td>\n",
" <td>0.943076</td>\n",
" <td>0.887014</td>\n",
" <td>0.969590</td>\n",
" <td>0.956943</td>\n",
" <td>0.960158</td>\n",
" <td>0.990264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>0.979186</td>\n",
" <td>0.965394</td>\n",
" <td>0.973154</td>\n",
" <td>0.959839</td>\n",
" <td>0.981675</td>\n",
" <td>0.989659</td>\n",
" <td>0.950329</td>\n",
" <td>0.955650</td>\n",
" <td>0.971651</td>\n",
" <td>0.985400</td>\n",
" <td>...</td>\n",
" <td>0.981834</td>\n",
" <td>0.960456</td>\n",
" <td>0.956350</td>\n",
" <td>0.983449</td>\n",
" <td>0.963677</td>\n",
" <td>0.931091</td>\n",
" <td>0.981823</td>\n",
" <td>0.987399</td>\n",
" <td>0.970842</td>\n",
" <td>0.988318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1971</th>\n",
" <td>0.995599</td>\n",
" <td>0.892993</td>\n",
" <td>0.948691</td>\n",
" <td>0.981684</td>\n",
" <td>0.969954</td>\n",
" <td>0.967174</td>\n",
" <td>0.952844</td>\n",
" <td>0.954291</td>\n",
" <td>0.961542</td>\n",
" <td>0.984040</td>\n",
" <td>...</td>\n",
" <td>0.967855</td>\n",
" <td>0.963514</td>\n",
" <td>0.939789</td>\n",
" <td>0.945488</td>\n",
" <td>0.958113</td>\n",
" <td>0.917272</td>\n",
" <td>0.970558</td>\n",
" <td>0.971383</td>\n",
" <td>0.991007</td>\n",
" <td>0.971654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td>0.942199</td>\n",
" <td>0.965451</td>\n",
" <td>0.975815</td>\n",
" <td>0.928152</td>\n",
" <td>0.960821</td>\n",
" <td>0.984002</td>\n",
" <td>0.892585</td>\n",
" <td>0.940473</td>\n",
" <td>0.944671</td>\n",
" <td>0.950185</td>\n",
" <td>...</td>\n",
" <td>0.962810</td>\n",
" <td>0.920609</td>\n",
" <td>0.937582</td>\n",
" <td>0.967628</td>\n",
" <td>0.910495</td>\n",
" <td>0.880718</td>\n",
" <td>0.964934</td>\n",
" <td>0.971409</td>\n",
" <td>0.931399</td>\n",
" <td>0.968629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>0.951816</td>\n",
" <td>0.959813</td>\n",
" <td>0.948496</td>\n",
" <td>0.930494</td>\n",
" <td>0.952550</td>\n",
" <td>0.955305</td>\n",
" <td>0.917613</td>\n",
" <td>0.925382</td>\n",
" <td>0.959430</td>\n",
" <td>0.960632</td>\n",
" <td>...</td>\n",
" <td>0.985677</td>\n",
" <td>0.951167</td>\n",
" <td>0.945190</td>\n",
" <td>0.981195</td>\n",
" <td>0.946093</td>\n",
" <td>0.944966</td>\n",
" <td>0.938086</td>\n",
" <td>0.955545</td>\n",
" <td>0.928352</td>\n",
" <td>0.947163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1974</th>\n",
" <td>0.925560</td>\n",
" <td>0.957277</td>\n",
" <td>0.932388</td>\n",
" <td>0.922347</td>\n",
" <td>0.927725</td>\n",
" <td>0.966911</td>\n",
" <td>0.934065</td>\n",
" <td>0.903032</td>\n",
" <td>0.942655</td>\n",
" <td>0.944745</td>\n",
" <td>...</td>\n",
" <td>0.955544</td>\n",
" <td>0.911455</td>\n",
" <td>0.918603</td>\n",
" <td>0.946567</td>\n",
" <td>0.901861</td>\n",
" <td>0.895149</td>\n",
" <td>0.935502</td>\n",
" <td>0.956870</td>\n",
" <td>0.910894</td>\n",
" <td>0.946068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>0.971038</td>\n",
" <td>0.921795</td>\n",
" <td>0.955234</td>\n",
" <td>0.939151</td>\n",
" <td>0.965156</td>\n",
" <td>0.947704</td>\n",
" <td>0.922919</td>\n",
" <td>0.946821</td>\n",
" <td>0.944425</td>\n",
" <td>0.951913</td>\n",
" <td>...</td>\n",
" <td>0.953755</td>\n",
" <td>0.974801</td>\n",
" <td>0.954119</td>\n",
" <td>0.955493</td>\n",
" <td>0.959841</td>\n",
" <td>0.932645</td>\n",
" <td>0.943056</td>\n",
" <td>0.960383</td>\n",
" <td>0.953644</td>\n",
" <td>0.973148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1976</th>\n",
" <td>0.955682</td>\n",
" <td>0.960495</td>\n",
" <td>0.979765</td>\n",
" <td>0.927655</td>\n",
" <td>0.972251</td>\n",
" <td>0.979597</td>\n",
" <td>0.883145</td>\n",
" <td>0.952800</td>\n",
" <td>0.932698</td>\n",
" <td>0.944414</td>\n",
" <td>...</td>\n",
" <td>0.952139</td>\n",
" <td>0.917293</td>\n",
" <td>0.924918</td>\n",
" <td>0.955063</td>\n",
" <td>0.929977</td>\n",
" <td>0.886810</td>\n",
" <td>0.970375</td>\n",
" <td>0.967450</td>\n",
" <td>0.948907</td>\n",
" <td>0.974272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977</th>\n",
" <td>0.960168</td>\n",
" <td>0.967129</td>\n",
" <td>0.939495</td>\n",
" <td>0.930642</td>\n",
" <td>0.956992</td>\n",
" <td>0.967052</td>\n",
" <td>0.930145</td>\n",
" <td>0.924246</td>\n",
" <td>0.953833</td>\n",
" <td>0.969005</td>\n",
" <td>...</td>\n",
" <td>0.983039</td>\n",
" <td>0.927955</td>\n",
" <td>0.915865</td>\n",
" <td>0.968232</td>\n",
" <td>0.957173</td>\n",
" <td>0.947051</td>\n",
" <td>0.958699</td>\n",
" <td>0.959321</td>\n",
" <td>0.944206</td>\n",
" <td>0.966304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1978</th>\n",
" <td>0.940130</td>\n",
" <td>0.898882</td>\n",
" <td>0.883185</td>\n",
" <td>0.911773</td>\n",
" <td>0.932489</td>\n",
" <td>0.928577</td>\n",
" <td>0.958618</td>\n",
" <td>0.893357</td>\n",
" <td>0.907892</td>\n",
" <td>0.947961</td>\n",
" <td>...</td>\n",
" <td>0.961220</td>\n",
" <td>0.905565</td>\n",
" <td>0.881423</td>\n",
" <td>0.924520</td>\n",
" <td>0.964606</td>\n",
" <td>0.954503</td>\n",
" <td>0.918706</td>\n",
" <td>0.940147</td>\n",
" <td>0.928297</td>\n",
" <td>0.985533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1979</th>\n",
" <td>0.983881</td>\n",
" <td>0.945171</td>\n",
" <td>0.948138</td>\n",
" <td>0.935217</td>\n",
" <td>0.965424</td>\n",
" <td>0.980243</td>\n",
" <td>0.933679</td>\n",
" <td>0.952568</td>\n",
" <td>0.952442</td>\n",
" <td>0.970261</td>\n",
" <td>...</td>\n",
" <td>0.968439</td>\n",
" <td>0.954614</td>\n",
" <td>0.912337</td>\n",
" <td>0.940968</td>\n",
" <td>0.925370</td>\n",
" <td>0.912821</td>\n",
" <td>0.974005</td>\n",
" <td>0.974710</td>\n",
" <td>0.957409</td>\n",
" <td>0.973021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>0.956204</td>\n",
" <td>0.960502</td>\n",
" <td>0.975617</td>\n",
" <td>0.968131</td>\n",
" <td>0.968331</td>\n",
" <td>0.976702</td>\n",
" <td>0.927370</td>\n",
" <td>0.950211</td>\n",
" <td>0.965907</td>\n",
" <td>0.967070</td>\n",
" <td>...</td>\n",
" <td>0.966606</td>\n",
" <td>0.942783</td>\n",
" <td>0.967622</td>\n",
" <td>0.981242</td>\n",
" <td>0.940268</td>\n",
" <td>0.896144</td>\n",
" <td>0.949363</td>\n",
" <td>0.962366</td>\n",
" <td>0.957006</td>\n",
" <td>0.975519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>0.983282</td>\n",
" <td>0.911645</td>\n",
" <td>0.943515</td>\n",
" <td>0.971127</td>\n",
" <td>0.960499</td>\n",
" <td>0.973992</td>\n",
" <td>0.953432</td>\n",
" <td>0.945698</td>\n",
" <td>0.970145</td>\n",
" <td>0.987201</td>\n",
" <td>...</td>\n",
" <td>0.979440</td>\n",
" <td>0.972967</td>\n",
" <td>0.954240</td>\n",
" <td>0.958332</td>\n",
" <td>0.940945</td>\n",
" <td>0.920683</td>\n",
" <td>0.965981</td>\n",
" <td>0.972258</td>\n",
" <td>0.967685</td>\n",
" <td>0.963238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>0.955731</td>\n",
" <td>0.960767</td>\n",
" <td>0.974446</td>\n",
" <td>0.928742</td>\n",
" <td>0.971579</td>\n",
" <td>0.991304</td>\n",
" <td>0.901637</td>\n",
" <td>0.950039</td>\n",
" <td>0.935749</td>\n",
" <td>0.954361</td>\n",
" <td>...</td>\n",
" <td>0.951180</td>\n",
" <td>0.913283</td>\n",
" <td>0.919267</td>\n",
" <td>0.952360</td>\n",
" <td>0.923100</td>\n",
" <td>0.877104</td>\n",
" <td>0.984042</td>\n",
" <td>0.977902</td>\n",
" <td>0.951664</td>\n",
" <td>0.984144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>0.982181</td>\n",
" <td>0.931673</td>\n",
" <td>0.962133</td>\n",
" <td>0.957967</td>\n",
" <td>0.973887</td>\n",
" <td>0.963297</td>\n",
" <td>0.936789</td>\n",
" <td>0.949434</td>\n",
" <td>0.966242</td>\n",
" <td>0.977597</td>\n",
" <td>...</td>\n",
" <td>0.952847</td>\n",
" <td>0.962480</td>\n",
" <td>0.943744</td>\n",
" <td>0.966043</td>\n",
" <td>0.963176</td>\n",
" <td>0.912794</td>\n",
" <td>0.975063</td>\n",
" <td>0.968770</td>\n",
" <td>0.980768</td>\n",
" <td>0.980418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>0.978561</td>\n",
" <td>0.902966</td>\n",
" <td>0.924981</td>\n",
" <td>0.947477</td>\n",
" <td>0.961911</td>\n",
" <td>0.944519</td>\n",
" <td>0.958640</td>\n",
" <td>0.938316</td>\n",
" <td>0.947963</td>\n",
" <td>0.973724</td>\n",
" <td>...</td>\n",
" <td>0.948737</td>\n",
" <td>0.965331</td>\n",
" <td>0.929439</td>\n",
" <td>0.944948</td>\n",
" <td>0.966859</td>\n",
" <td>0.926829</td>\n",
" <td>0.950987</td>\n",
" <td>0.955164</td>\n",
" <td>0.971091</td>\n",
" <td>0.983241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985</th>\n",
" <td>0.974391</td>\n",
" <td>0.933947</td>\n",
" <td>0.969355</td>\n",
" <td>0.944300</td>\n",
" <td>0.987916</td>\n",
" <td>0.982142</td>\n",
" <td>0.937057</td>\n",
" <td>0.970254</td>\n",
" <td>0.941365</td>\n",
" <td>0.971420</td>\n",
" <td>...</td>\n",
" <td>0.956994</td>\n",
" <td>0.949873</td>\n",
" <td>0.937409</td>\n",
" <td>0.959461</td>\n",
" <td>0.942488</td>\n",
" <td>0.887696</td>\n",
" <td>0.975184</td>\n",
" <td>0.984041</td>\n",
" <td>0.969635</td>\n",
" <td>0.989663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986</th>\n",
" <td>0.972765</td>\n",
" <td>0.913396</td>\n",
" <td>0.948100</td>\n",
" <td>0.932304</td>\n",
" <td>0.987437</td>\n",
" <td>0.969526</td>\n",
" <td>0.920320</td>\n",
" <td>0.974819</td>\n",
" <td>0.914487</td>\n",
" <td>0.961544</td>\n",
" <td>...</td>\n",
" <td>0.963736</td>\n",
" <td>0.949966</td>\n",
" <td>0.935266</td>\n",
" <td>0.946522</td>\n",
" <td>0.953029</td>\n",
" <td>0.907487</td>\n",
" <td>0.953994</td>\n",
" <td>0.965175</td>\n",
" <td>0.959348</td>\n",
" <td>0.976244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1987</th>\n",
" <td>0.947505</td>\n",
" <td>0.975007</td>\n",
" <td>0.947033</td>\n",
" <td>0.926103</td>\n",
" <td>0.941759</td>\n",
" <td>0.968701</td>\n",
" <td>0.915245</td>\n",
" <td>0.921837</td>\n",
" <td>0.961595</td>\n",
" <td>0.956640</td>\n",
" <td>...</td>\n",
" <td>0.969691</td>\n",
" <td>0.933887</td>\n",
" <td>0.923518</td>\n",
" <td>0.962458</td>\n",
" <td>0.909104</td>\n",
" <td>0.908165</td>\n",
" <td>0.946739</td>\n",
" <td>0.954901</td>\n",
" <td>0.923629</td>\n",
" <td>0.950520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1988</th>\n",
" <td>0.978995</td>\n",
" <td>0.927510</td>\n",
" <td>0.951705</td>\n",
" <td>0.955468</td>\n",
" <td>0.965563</td>\n",
" <td>0.966757</td>\n",
" <td>0.954420</td>\n",
" <td>0.944144</td>\n",
" <td>0.964335</td>\n",
" <td>0.975559</td>\n",
" <td>...</td>\n",
" <td>0.988065</td>\n",
" <td>0.987897</td>\n",
" <td>0.968640</td>\n",
" <td>0.971440</td>\n",
" <td>0.964360</td>\n",
" <td>0.959164</td>\n",
" <td>0.954974</td>\n",
" <td>0.979503</td>\n",
" <td>0.954457</td>\n",
" <td>0.973551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1989</th>\n",
" <td>0.957979</td>\n",
" <td>0.897514</td>\n",
" <td>0.921133</td>\n",
" <td>0.942381</td>\n",
" <td>0.933759</td>\n",
" <td>0.926902</td>\n",
" <td>0.950348</td>\n",
" <td>0.910731</td>\n",
" <td>0.950756</td>\n",
" <td>0.950741</td>\n",
" <td>...</td>\n",
" <td>0.978168</td>\n",
" <td>0.982235</td>\n",
" <td>0.957439</td>\n",
" <td>0.953824</td>\n",
" <td>0.957073</td>\n",
" <td>0.972427</td>\n",
" <td>0.908777</td>\n",
" <td>0.952094</td>\n",
" <td>0.927080</td>\n",
" <td>0.957811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td>0.938945</td>\n",
" <td>0.953797</td>\n",
" <td>0.942084</td>\n",
" <td>0.928113</td>\n",
" <td>0.939797</td>\n",
" <td>0.947744</td>\n",
" <td>0.937411</td>\n",
" <td>0.898283</td>\n",
" <td>0.959969</td>\n",
" <td>0.956204</td>\n",
" <td>...</td>\n",
" <td>0.986618</td>\n",
" <td>0.941526</td>\n",
" <td>0.943386</td>\n",
" <td>0.984113</td>\n",
" <td>0.959404</td>\n",
" <td>0.965323</td>\n",
" <td>0.935661</td>\n",
" <td>0.964333</td>\n",
" <td>0.920404</td>\n",
" <td>0.942921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991</th>\n",
" <td>0.989693</td>\n",
" <td>0.939203</td>\n",
" <td>0.964650</td>\n",
" <td>0.972098</td>\n",
" <td>0.987454</td>\n",
" <td>0.983304</td>\n",
" <td>0.950812</td>\n",
" <td>0.972075</td>\n",
" <td>0.963386</td>\n",
" <td>0.987808</td>\n",
" <td>...</td>\n",
" <td>0.982533</td>\n",
" <td>0.970177</td>\n",
" <td>0.960488</td>\n",
" <td>0.972670</td>\n",
" <td>0.964607</td>\n",
" <td>0.925710</td>\n",
" <td>0.968715</td>\n",
" <td>0.976590</td>\n",
" <td>0.980586</td>\n",
" <td>0.989602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992</th>\n",
" <td>0.967907</td>\n",
" <td>0.919528</td>\n",
" <td>0.955328</td>\n",
" <td>0.945192</td>\n",
" <td>0.952141</td>\n",
" <td>0.959452</td>\n",
" <td>0.946130</td>\n",
" <td>0.928090</td>\n",
" <td>0.951392</td>\n",
" <td>0.952223</td>\n",
" <td>...</td>\n",
" <td>0.979458</td>\n",
" <td>0.968448</td>\n",
" <td>0.949216</td>\n",
" <td>0.953767</td>\n",
" <td>0.951920</td>\n",
" <td>0.956514</td>\n",
" <td>0.947090</td>\n",
" <td>0.984343</td>\n",
" <td>0.941732</td>\n",
" <td>0.958688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993</th>\n",
" <td>0.954292</td>\n",
" <td>0.876313</td>\n",
" <td>0.928829</td>\n",
" <td>0.922550</td>\n",
" <td>0.933627</td>\n",
" <td>0.922378</td>\n",
" <td>0.906585</td>\n",
" <td>0.914026</td>\n",
" <td>0.946942</td>\n",
" <td>0.948239</td>\n",
" <td>...</td>\n",
" <td>0.943992</td>\n",
" <td>0.977872</td>\n",
" <td>0.943378</td>\n",
" <td>0.946018</td>\n",
" <td>0.922876</td>\n",
" <td>0.909700</td>\n",
" <td>0.934496</td>\n",
" <td>0.948452</td>\n",
" <td>0.929671</td>\n",
" <td>0.954126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994</th>\n",
" <td>0.968197</td>\n",
" <td>0.893866</td>\n",
" <td>0.946615</td>\n",
" <td>0.964304</td>\n",
" <td>0.968824</td>\n",
" <td>0.949672</td>\n",
" <td>0.958369</td>\n",
" <td>0.943043</td>\n",
" <td>0.937691</td>\n",
" <td>0.962355</td>\n",
" <td>...</td>\n",
" <td>0.944903</td>\n",
" <td>0.951117</td>\n",
" <td>0.949436</td>\n",
" <td>0.948911</td>\n",
" <td>0.976582</td>\n",
" <td>0.917401</td>\n",
" <td>0.946036</td>\n",
" <td>0.964879</td>\n",
" <td>0.975920</td>\n",
" <td>0.978448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995</th>\n",
" <td>0.944646</td>\n",
" <td>0.924272</td>\n",
" <td>0.906084</td>\n",
" <td>0.929573</td>\n",
" <td>0.923862</td>\n",
" <td>0.918281</td>\n",
" <td>0.958892</td>\n",
" <td>0.892013</td>\n",
" <td>0.953823</td>\n",
" <td>0.950483</td>\n",
" <td>...</td>\n",
" <td>0.970984</td>\n",
" <td>0.957834</td>\n",
" <td>0.930916</td>\n",
" <td>0.956667</td>\n",
" <td>0.956077</td>\n",
" <td>0.970308</td>\n",
" <td>0.899663</td>\n",
" <td>0.937548</td>\n",
" <td>0.919154</td>\n",
" <td>0.969338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996</th>\n",
" <td>0.971445</td>\n",
" <td>0.951190</td>\n",
" <td>0.939444</td>\n",
" <td>0.943238</td>\n",
" <td>0.968501</td>\n",
" <td>0.977110</td>\n",
" <td>0.932266</td>\n",
" <td>0.942144</td>\n",
" <td>0.951401</td>\n",
" <td>0.981975</td>\n",
" <td>...</td>\n",
" <td>0.966471</td>\n",
" <td>0.918849</td>\n",
" <td>0.904002</td>\n",
" <td>0.956161</td>\n",
" <td>0.947586</td>\n",
" <td>0.905412</td>\n",
" <td>0.977445</td>\n",
" <td>0.958623</td>\n",
" <td>0.968900</td>\n",
" <td>0.985703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997</th>\n",
" <td>0.968909</td>\n",
" <td>0.933920</td>\n",
" <td>0.942953</td>\n",
" <td>0.953354</td>\n",
" <td>0.958877</td>\n",
" <td>0.965355</td>\n",
" <td>0.955837</td>\n",
" <td>0.930644</td>\n",
" <td>0.961064</td>\n",
" <td>0.975292</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.960922</td>\n",
" <td>0.949606</td>\n",
" <td>0.973534</td>\n",
" <td>0.964142</td>\n",
" <td>0.965126</td>\n",
" <td>0.948528</td>\n",
" <td>0.974752</td>\n",
" <td>0.947337</td>\n",
" <td>0.961810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998</th>\n",
" <td>0.972389</td>\n",
" <td>0.882930</td>\n",
" <td>0.937997</td>\n",
" <td>0.943405</td>\n",
" <td>0.956270</td>\n",
" <td>0.946228</td>\n",
" <td>0.939129</td>\n",
" <td>0.949946</td>\n",
" <td>0.940607</td>\n",
" <td>0.955650</td>\n",
" <td>...</td>\n",
" <td>0.960922</td>\n",
" <td>1.000000</td>\n",
" <td>0.968240</td>\n",
" <td>0.942900</td>\n",
" <td>0.935073</td>\n",
" <td>0.924394</td>\n",
" <td>0.929735</td>\n",
" <td>0.964548</td>\n",
" <td>0.941978</td>\n",
" <td>0.976090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999</th>\n",
" <td>0.936239</td>\n",
" <td>0.901294</td>\n",
" <td>0.966797</td>\n",
" <td>0.944098</td>\n",
" <td>0.964839</td>\n",
" <td>0.945389</td>\n",
" <td>0.906725</td>\n",
" <td>0.951972</td>\n",
" <td>0.923622</td>\n",
" <td>0.934773</td>\n",
" <td>...</td>\n",
" <td>0.949606</td>\n",
" <td>0.968240</td>\n",
" <td>1.000000</td>\n",
" <td>0.969211</td>\n",
" <td>0.943708</td>\n",
" <td>0.898854</td>\n",
" <td>0.911372</td>\n",
" <td>0.955283</td>\n",
" <td>0.929218</td>\n",
" <td>0.963987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>0.945821</td>\n",
" <td>0.958600</td>\n",
" <td>0.968900</td>\n",
" <td>0.951140</td>\n",
" <td>0.966482</td>\n",
" <td>0.959077</td>\n",
" <td>0.927315</td>\n",
" <td>0.930588</td>\n",
" <td>0.963443</td>\n",
" <td>0.966265</td>\n",
" <td>...</td>\n",
" <td>0.973534</td>\n",
" <td>0.942900</td>\n",
" <td>0.969211</td>\n",
" <td>1.000000</td>\n",
" <td>0.969893</td>\n",
" <td>0.929979</td>\n",
" <td>0.946657</td>\n",
" <td>0.962733</td>\n",
" <td>0.947933</td>\n",
" <td>0.962612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
" <td>0.949552</td>\n",
" <td>0.906434</td>\n",
" <td>0.924609</td>\n",
" <td>0.953170</td>\n",
" <td>0.947469</td>\n",
" <td>0.929715</td>\n",
" <td>0.953645</td>\n",
" <td>0.901719</td>\n",
" <td>0.947252</td>\n",
" <td>0.964192</td>\n",
" <td>...</td>\n",
" <td>0.964142</td>\n",
" <td>0.935073</td>\n",
" <td>0.943708</td>\n",
" <td>0.969893</td>\n",
" <td>1.000000</td>\n",
" <td>0.963334</td>\n",
" <td>0.933810</td>\n",
" <td>0.947630</td>\n",
" <td>0.956674</td>\n",
" <td>0.961347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
" <td>0.918213</td>\n",
" <td>0.878226</td>\n",
" <td>0.864014</td>\n",
" <td>0.903300</td>\n",
" <td>0.884853</td>\n",
" <td>0.887171</td>\n",
" <td>0.947226</td>\n",
" <td>0.834993</td>\n",
" <td>0.926268</td>\n",
" <td>0.927709</td>\n",
" <td>...</td>\n",
" <td>0.965126</td>\n",
" <td>0.924394</td>\n",
" <td>0.898854</td>\n",
" <td>0.929979</td>\n",
" <td>0.963334</td>\n",
" <td>1.000000</td>\n",
" <td>0.885885</td>\n",
" <td>0.923089</td>\n",
" <td>0.891745</td>\n",
" <td>0.922826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
" <td>0.975277</td>\n",
" <td>0.929568</td>\n",
" <td>0.952932</td>\n",
" <td>0.945131</td>\n",
" <td>0.962570</td>\n",
" <td>0.980068</td>\n",
" <td>0.920892</td>\n",
" <td>0.937192</td>\n",
" <td>0.957167</td>\n",
" <td>0.977522</td>\n",
" <td>...</td>\n",
" <td>0.948528</td>\n",
" <td>0.929735</td>\n",
" <td>0.911372</td>\n",
" <td>0.946657</td>\n",
" <td>0.933810</td>\n",
" <td>0.885885</td>\n",
" <td>1.000000</td>\n",
" <td>0.973246</td>\n",
" <td>0.974325</td>\n",
" <td>0.970291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
" <td>0.971641</td>\n",
" <td>0.933543</td>\n",
" <td>0.971115</td>\n",
" <td>0.944775</td>\n",
" <td>0.977620</td>\n",
" <td>0.986436</td>\n",
" <td>0.948935</td>\n",
" <td>0.956383</td>\n",
" <td>0.946136</td>\n",
" <td>0.966473</td>\n",
" <td>...</td>\n",
" <td>0.974752</td>\n",
" <td>0.964548</td>\n",
" <td>0.955283</td>\n",
" <td>0.962733</td>\n",
" <td>0.947630</td>\n",
" <td>0.923089</td>\n",
" <td>0.973246</td>\n",
" <td>1.000000</td>\n",
" <td>0.954920</td>\n",
" <td>0.981520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
" <td>0.987455</td>\n",
" <td>0.900428</td>\n",
" <td>0.946616</td>\n",
" <td>0.986555</td>\n",
" <td>0.963773</td>\n",
" <td>0.960221</td>\n",
" <td>0.944265</td>\n",
" <td>0.942899</td>\n",
" <td>0.969510</td>\n",
" <td>0.986435</td>\n",
" <td>...</td>\n",
" <td>0.947337</td>\n",
" <td>0.941978</td>\n",
" <td>0.929218</td>\n",
" <td>0.947933</td>\n",
" <td>0.956674</td>\n",
" <td>0.891745</td>\n",
" <td>0.974325</td>\n",
" <td>0.954920</td>\n",
" <td>1.000000</td>\n",
" <td>0.971098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
" <td>0.974490</td>\n",
" <td>0.947021</td>\n",
" <td>0.986767</td>\n",
" <td>0.951781</td>\n",
" <td>0.991853</td>\n",
" <td>0.992831</td>\n",
" <td>0.969116</td>\n",
" <td>0.982676</td>\n",
" <td>0.941783</td>\n",
" <td>0.974004</td>\n",
" <td>...</td>\n",
" <td>0.961810</td>\n",
" <td>0.976090</td>\n",
" <td>0.963987</td>\n",
" <td>0.962612</td>\n",
" <td>0.961347</td>\n",
" <td>0.922826</td>\n",
" <td>0.970291</td>\n",
" <td>0.981520</td>\n",
" <td>0.971098</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>58 rows × 58 columns</p>\n",
"</div>"
],
"text/plain": [
"year 1949 1950 1951 1952 1953 1954 1955 \\\n",
"year \n",
"1949 1.000000 0.907740 0.947026 0.974542 0.968858 0.971366 0.952670 \n",
"1950 0.907740 1.000000 0.948069 0.888752 0.936835 0.954790 0.879953 \n",
"1951 0.947026 0.948069 1.000000 0.940882 0.980339 0.975816 0.884347 \n",
"1952 0.974542 0.888752 0.940882 1.000000 0.944192 0.943988 0.950655 \n",
"1953 0.968858 0.936835 0.980339 0.944192 1.000000 0.985802 0.916705 \n",
"1954 0.971366 0.954790 0.975816 0.943988 0.985802 1.000000 0.925735 \n",
"1955 0.952670 0.879953 0.884347 0.950655 0.916705 0.925735 1.000000 \n",
"1956 0.956033 0.906577 0.967928 0.922784 0.990022 0.973728 0.881310 \n",
"1957 0.968737 0.926981 0.933412 0.977412 0.926579 0.941913 0.950061 \n",
"1958 0.987562 0.923274 0.938189 0.977336 0.962700 0.968681 0.965662 \n",
"1959 0.955095 0.880313 0.913372 0.943003 0.934574 0.919281 0.951480 \n",
"1960 0.959802 0.924429 0.978991 0.956012 0.960006 0.970059 0.885493 \n",
"1961 0.959840 0.915921 0.916646 0.942520 0.941001 0.948537 0.967740 \n",
"1962 0.974633 0.919884 0.923577 0.973503 0.950372 0.945798 0.963139 \n",
"1963 0.962042 0.958062 0.933456 0.945666 0.938787 0.965837 0.935118 \n",
"1964 0.978876 0.925945 0.972332 0.976445 0.978858 0.984471 0.934029 \n",
"1965 0.963408 0.926482 0.983129 0.947266 0.969958 0.980597 0.915323 \n",
"1966 0.963135 0.949552 0.934074 0.926388 0.967469 0.959600 0.945573 \n",
"1967 0.950736 0.963184 0.929065 0.903436 0.961194 0.969344 0.934940 \n",
"1968 0.947199 0.980378 0.970901 0.917654 0.964967 0.977458 0.905963 \n",
"1969 0.951786 0.970885 0.954359 0.940882 0.968144 0.979383 0.923422 \n",
"1970 0.979186 0.965394 0.973154 0.959839 0.981675 0.989659 0.950329 \n",
"1971 0.995599 0.892993 0.948691 0.981684 0.969954 0.967174 0.952844 \n",
"1972 0.942199 0.965451 0.975815 0.928152 0.960821 0.984002 0.892585 \n",
"1973 0.951816 0.959813 0.948496 0.930494 0.952550 0.955305 0.917613 \n",
"1974 0.925560 0.957277 0.932388 0.922347 0.927725 0.966911 0.934065 \n",
"1975 0.971038 0.921795 0.955234 0.939151 0.965156 0.947704 0.922919 \n",
"1976 0.955682 0.960495 0.979765 0.927655 0.972251 0.979597 0.883145 \n",
"1977 0.960168 0.967129 0.939495 0.930642 0.956992 0.967052 0.930145 \n",
"1978 0.940130 0.898882 0.883185 0.911773 0.932489 0.928577 0.958618 \n",
"1979 0.983881 0.945171 0.948138 0.935217 0.965424 0.980243 0.933679 \n",
"1980 0.956204 0.960502 0.975617 0.968131 0.968331 0.976702 0.927370 \n",
"1981 0.983282 0.911645 0.943515 0.971127 0.960499 0.973992 0.953432 \n",
"1982 0.955731 0.960767 0.974446 0.928742 0.971579 0.991304 0.901637 \n",
"1983 0.982181 0.931673 0.962133 0.957967 0.973887 0.963297 0.936789 \n",
"1984 0.978561 0.902966 0.924981 0.947477 0.961911 0.944519 0.958640 \n",
"1985 0.974391 0.933947 0.969355 0.944300 0.987916 0.982142 0.937057 \n",
"1986 0.972765 0.913396 0.948100 0.932304 0.987437 0.969526 0.920320 \n",
"1987 0.947505 0.975007 0.947033 0.926103 0.941759 0.968701 0.915245 \n",
"1988 0.978995 0.927510 0.951705 0.955468 0.965563 0.966757 0.954420 \n",
"1989 0.957979 0.897514 0.921133 0.942381 0.933759 0.926902 0.950348 \n",
"1990 0.938945 0.953797 0.942084 0.928113 0.939797 0.947744 0.937411 \n",
"1991 0.989693 0.939203 0.964650 0.972098 0.987454 0.983304 0.950812 \n",
"1992 0.967907 0.919528 0.955328 0.945192 0.952141 0.959452 0.946130 \n",
"1993 0.954292 0.876313 0.928829 0.922550 0.933627 0.922378 0.906585 \n",
"1994 0.968197 0.893866 0.946615 0.964304 0.968824 0.949672 0.958369 \n",
"1995 0.944646 0.924272 0.906084 0.929573 0.923862 0.918281 0.958892 \n",
"1996 0.971445 0.951190 0.939444 0.943238 0.968501 0.977110 0.932266 \n",
"1997 0.968909 0.933920 0.942953 0.953354 0.958877 0.965355 0.955837 \n",
"1998 0.972389 0.882930 0.937997 0.943405 0.956270 0.946228 0.939129 \n",
"1999 0.936239 0.901294 0.966797 0.944098 0.964839 0.945389 0.906725 \n",
"2000 0.945821 0.958600 0.968900 0.951140 0.966482 0.959077 0.927315 \n",
"2001 0.949552 0.906434 0.924609 0.953170 0.947469 0.929715 0.953645 \n",
"2002 0.918213 0.878226 0.864014 0.903300 0.884853 0.887171 0.947226 \n",
"2003 0.975277 0.929568 0.952932 0.945131 0.962570 0.980068 0.920892 \n",
"2004 0.971641 0.933543 0.971115 0.944775 0.977620 0.986436 0.948935 \n",
"2005 0.987455 0.900428 0.946616 0.986555 0.963773 0.960221 0.944265 \n",
"2006 0.974490 0.947021 0.986767 0.951781 0.991853 0.992831 0.969116 \n",
"\n",
"year 1956 1957 1958 ... 1997 1998 1999 \\\n",
"year ... \n",
"1949 0.956033 0.968737 0.987562 ... 0.968909 0.972389 0.936239 \n",
"1950 0.906577 0.926981 0.923274 ... 0.933920 0.882930 0.901294 \n",
"1951 0.967928 0.933412 0.938189 ... 0.942953 0.937997 0.966797 \n",
"1952 0.922784 0.977412 0.977336 ... 0.953354 0.943405 0.944098 \n",
"1953 0.990022 0.926579 0.962700 ... 0.958877 0.956270 0.964839 \n",
"1954 0.973728 0.941913 0.968681 ... 0.965355 0.946228 0.945389 \n",
"1955 0.881310 0.950061 0.965662 ... 0.955837 0.939129 0.906725 \n",
"1956 1.000000 0.892596 0.935137 ... 0.930644 0.949946 0.951972 \n",
"1957 0.892596 1.000000 0.981605 ... 0.961064 0.940607 0.923622 \n",
"1958 0.935137 0.981605 1.000000 ... 0.975292 0.955650 0.934773 \n",
"1959 0.898155 0.947047 0.957886 ... 0.979000 0.966290 0.948005 \n",
"1960 0.949514 0.955334 0.953480 ... 0.954205 0.950789 0.963737 \n",
"1961 0.913982 0.954781 0.969084 ... 0.992939 0.968287 0.945195 \n",
"1962 0.915065 0.975698 0.990024 ... 0.955562 0.931789 0.915499 \n",
"1963 0.907263 0.975002 0.977198 ... 0.944920 0.907437 0.886716 \n",
"1964 0.969962 0.961548 0.978444 ... 0.959243 0.963072 0.967780 \n",
"1965 0.965544 0.940630 0.945946 ... 0.944572 0.962748 0.962311 \n",
"1966 0.934136 0.944966 0.972638 ... 0.975609 0.945595 0.926211 \n",
"1967 0.934952 0.929563 0.960980 ... 0.956425 0.921618 0.895914 \n",
"1968 0.948284 0.939386 0.943691 ... 0.968272 0.933786 0.932380 \n",
"1969 0.938472 0.946470 0.969013 ... 0.955060 0.899066 0.911197 \n",
"1970 0.955650 0.971651 0.985400 ... 0.981834 0.960456 0.956350 \n",
"1971 0.954291 0.961542 0.984040 ... 0.967855 0.963514 0.939789 \n",
"1972 0.940473 0.944671 0.950185 ... 0.962810 0.920609 0.937582 \n",
"1973 0.925382 0.959430 0.960632 ... 0.985677 0.951167 0.945190 \n",
"1974 0.903032 0.942655 0.944745 ... 0.955544 0.911455 0.918603 \n",
"1975 0.946821 0.944425 0.951913 ... 0.953755 0.974801 0.954119 \n",
"1976 0.952800 0.932698 0.944414 ... 0.952139 0.917293 0.924918 \n",
"1977 0.924246 0.953833 0.969005 ... 0.983039 0.927955 0.915865 \n",
"1978 0.893357 0.907892 0.947961 ... 0.961220 0.905565 0.881423 \n",
"1979 0.952568 0.952442 0.970261 ... 0.968439 0.954614 0.912337 \n",
"1980 0.950211 0.965907 0.967070 ... 0.966606 0.942783 0.967622 \n",
"1981 0.945698 0.970145 0.987201 ... 0.979440 0.972967 0.954240 \n",
"1982 0.950039 0.935749 0.954361 ... 0.951180 0.913283 0.919267 \n",
"1983 0.949434 0.966242 0.977597 ... 0.952847 0.962480 0.943744 \n",
"1984 0.938316 0.947963 0.973724 ... 0.948737 0.965331 0.929439 \n",
"1985 0.970254 0.941365 0.971420 ... 0.956994 0.949873 0.937409 \n",
"1986 0.974819 0.914487 0.961544 ... 0.963736 0.949966 0.935266 \n",
"1987 0.921837 0.961595 0.956640 ... 0.969691 0.933887 0.923518 \n",
"1988 0.944144 0.964335 0.975559 ... 0.988065 0.987897 0.968640 \n",
"1989 0.910731 0.950756 0.950741 ... 0.978168 0.982235 0.957439 \n",
"1990 0.898283 0.959969 0.956204 ... 0.986618 0.941526 0.943386 \n",
"1991 0.972075 0.963386 0.987808 ... 0.982533 0.970177 0.960488 \n",
"1992 0.928090 0.951392 0.952223 ... 0.979458 0.968448 0.949216 \n",
"1993 0.914026 0.946942 0.948239 ... 0.943992 0.977872 0.943378 \n",
"1994 0.943043 0.937691 0.962355 ... 0.944903 0.951117 0.949436 \n",
"1995 0.892013 0.953823 0.950483 ... 0.970984 0.957834 0.930916 \n",
"1996 0.942144 0.951401 0.981975 ... 0.966471 0.918849 0.904002 \n",
"1997 0.930644 0.961064 0.975292 ... 1.000000 0.960922 0.949606 \n",
"1998 0.949946 0.940607 0.955650 ... 0.960922 1.000000 0.968240 \n",
"1999 0.951972 0.923622 0.934773 ... 0.949606 0.968240 1.000000 \n",
"2000 0.930588 0.963443 0.966265 ... 0.973534 0.942900 0.969211 \n",
"2001 0.901719 0.947252 0.964192 ... 0.964142 0.935073 0.943708 \n",
"2002 0.834993 0.926268 0.927709 ... 0.965126 0.924394 0.898854 \n",
"2003 0.937192 0.957167 0.977522 ... 0.948528 0.929735 0.911372 \n",
"2004 0.956383 0.946136 0.966473 ... 0.974752 0.964548 0.955283 \n",
"2005 0.942899 0.969510 0.986435 ... 0.947337 0.941978 0.929218 \n",
"2006 0.982676 0.941783 0.974004 ... 0.961810 0.976090 0.963987 \n",
"\n",
"year 2000 2001 2002 2003 2004 2005 2006 \n",
"year \n",
"1949 0.945821 0.949552 0.918213 0.975277 0.971641 0.987455 0.974490 \n",
"1950 0.958600 0.906434 0.878226 0.929568 0.933543 0.900428 0.947021 \n",
"1951 0.968900 0.924609 0.864014 0.952932 0.971115 0.946616 0.986767 \n",
"1952 0.951140 0.953170 0.903300 0.945131 0.944775 0.986555 0.951781 \n",
"1953 0.966482 0.947469 0.884853 0.962570 0.977620 0.963773 0.991853 \n",
"1954 0.959077 0.929715 0.887171 0.980068 0.986436 0.960221 0.992831 \n",
"1955 0.927315 0.953645 0.947226 0.920892 0.948935 0.944265 0.969116 \n",
"1956 0.930588 0.901719 0.834993 0.937192 0.956383 0.942899 0.982676 \n",
"1957 0.963443 0.947252 0.926268 0.957167 0.946136 0.969510 0.941783 \n",
"1958 0.966265 0.964192 0.927709 0.977522 0.966473 0.986435 0.974004 \n",
"1959 0.959352 0.979020 0.980658 0.919108 0.951717 0.936442 0.948662 \n",
"1960 0.961449 0.917164 0.877776 0.960716 0.962270 0.951674 0.967339 \n",
"1961 0.960616 0.954859 0.969823 0.927705 0.963333 0.930895 0.957506 \n",
"1962 0.959491 0.973046 0.923053 0.960509 0.943741 0.986267 0.973657 \n",
"1963 0.947895 0.924909 0.888317 0.979236 0.950308 0.964327 0.964328 \n",
"1964 0.962973 0.937318 0.882428 0.970000 0.972766 0.977872 0.985798 \n",
"1965 0.946226 0.910626 0.871594 0.958380 0.982621 0.949383 0.990242 \n",
"1966 0.969820 0.971050 0.948432 0.954698 0.962609 0.949078 0.979433 \n",
"1967 0.948673 0.930125 0.904840 0.956684 0.960392 0.933952 0.975788 \n",
"1968 0.964636 0.917016 0.903006 0.945811 0.969220 0.924146 0.967869 \n",
"1969 0.964426 0.943076 0.887014 0.969590 0.956943 0.960158 0.990264 \n",
"1970 0.983449 0.963677 0.931091 0.981823 0.987399 0.970842 0.988318 \n",
"1971 0.945488 0.958113 0.917272 0.970558 0.971383 0.991007 0.971654 \n",
"1972 0.967628 0.910495 0.880718 0.964934 0.971409 0.931399 0.968629 \n",
"1973 0.981195 0.946093 0.944966 0.938086 0.955545 0.928352 0.947163 \n",
"1974 0.946567 0.901861 0.895149 0.935502 0.956870 0.910894 0.946068 \n",
"1975 0.955493 0.959841 0.932645 0.943056 0.960383 0.953644 0.973148 \n",
"1976 0.955063 0.929977 0.886810 0.970375 0.967450 0.948907 0.974272 \n",
"1977 0.968232 0.957173 0.947051 0.958699 0.959321 0.944206 0.966304 \n",
"1978 0.924520 0.964606 0.954503 0.918706 0.940147 0.928297 0.985533 \n",
"1979 0.940968 0.925370 0.912821 0.974005 0.974710 0.957409 0.973021 \n",
"1980 0.981242 0.940268 0.896144 0.949363 0.962366 0.957006 0.975519 \n",
"1981 0.958332 0.940945 0.920683 0.965981 0.972258 0.967685 0.963238 \n",
"1982 0.952360 0.923100 0.877104 0.984042 0.977902 0.951664 0.984144 \n",
"1983 0.966043 0.963176 0.912794 0.975063 0.968770 0.980768 0.980418 \n",
"1984 0.944948 0.966859 0.926829 0.950987 0.955164 0.971091 0.983241 \n",
"1985 0.959461 0.942488 0.887696 0.975184 0.984041 0.969635 0.989663 \n",
"1986 0.946522 0.953029 0.907487 0.953994 0.965175 0.959348 0.976244 \n",
"1987 0.962458 0.909104 0.908165 0.946739 0.954901 0.923629 0.950520 \n",
"1988 0.971440 0.964360 0.959164 0.954974 0.979503 0.954457 0.973551 \n",
"1989 0.953824 0.957073 0.972427 0.908777 0.952094 0.927080 0.957811 \n",
"1990 0.984113 0.959404 0.965323 0.935661 0.964333 0.920404 0.942921 \n",
"1991 0.972670 0.964607 0.925710 0.968715 0.976590 0.980586 0.989602 \n",
"1992 0.953767 0.951920 0.956514 0.947090 0.984343 0.941732 0.958688 \n",
"1993 0.946018 0.922876 0.909700 0.934496 0.948452 0.929671 0.954126 \n",
"1994 0.948911 0.976582 0.917401 0.946036 0.964879 0.975920 0.978448 \n",
"1995 0.956667 0.956077 0.970308 0.899663 0.937548 0.919154 0.969338 \n",
"1996 0.956161 0.947586 0.905412 0.977445 0.958623 0.968900 0.985703 \n",
"1997 0.973534 0.964142 0.965126 0.948528 0.974752 0.947337 0.961810 \n",
"1998 0.942900 0.935073 0.924394 0.929735 0.964548 0.941978 0.976090 \n",
"1999 0.969211 0.943708 0.898854 0.911372 0.955283 0.929218 0.963987 \n",
"2000 1.000000 0.969893 0.929979 0.946657 0.962733 0.947933 0.962612 \n",
"2001 0.969893 1.000000 0.963334 0.933810 0.947630 0.956674 0.961347 \n",
"2002 0.929979 0.963334 1.000000 0.885885 0.923089 0.891745 0.922826 \n",
"2003 0.946657 0.933810 0.885885 1.000000 0.973246 0.974325 0.970291 \n",
"2004 0.962733 0.947630 0.923089 0.973246 1.000000 0.954920 0.981520 \n",
"2005 0.947933 0.956674 0.891745 0.974325 0.954920 1.000000 0.971098 \n",
"2006 0.962612 0.961347 0.922826 0.970291 0.981520 0.971098 1.000000 \n",
"\n",
"[58 rows x 58 columns]"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(table.groupby(['year', 'month']).mean()['TMPMN'].\n",
" unstack().T.corr())"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/user/prj/oldhse-2010-11/repo/2017-18/icef-python'"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATwAAAD8CAYAAADqmhgGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAGlRJREFUeJzt3X+QVeWd5/H3R1ARGAXSBhBESUEycXCCbkdjTJEsgmGMJXHXSbAqM+iaYmYrJmoyM9F1KlLOZovsTo3O1ljZdCnqTCxNJM6kZwZ/gg7jJrq0kQ2C60jQaBMUEdEYo9jy3T/u6dTt27e5t+95+t7LPZ9X1ak+v+73PJcf336e85zzPIoIzMyK4IhWF8DMrFmc8MysMJzwzKwwnPDMrDCc8MysMJzwzKwwnPDMbMxIWitpj6SnRjguSf9T0g5JP5V0etmxlZKezZaVKcrjhGdmY+k2YNkhjv8eMD9bVgHfBpA0DbgOOBM4A7hO0tS8hXHCM7MxExGbgH2HOGU58LdR8hgwRdJM4NPAgxGxLyJeAx7k0ImzLuPzBhiNiVJMSRDnhEmTEkQBjj02TZzxCf8Yp+b+JVYipYmz71D/Vuv35sT3J4kz+eAbSeJwRLrf9a+/NzlJnGOOyR+jv/959u3bm+svf54Ub9V57m7YBrxdtqsnInpGcblZwItl2/3ZvpH259LUhDeFUp01r9WnnpogCrB0aZo4XV1p4gBcdFGaOKmS8N13Jwmz6dQvJYmz6O0HksRhwoQ0cYD1by5KEmfBgvwxzj+/O3eMt4A/qvPc1fB2ROS/aJO4SWtmQ4hSYqhnSWAXcGLZ9uxs30j7c3HCM7MhRKnpV8+SQC/wh1lv7ceA1yNiN3A/cK6kqVlnxbnZvlya2qQ1s8NDqpqQpDuBTwFdkvop9bweCRAR/wtYD5wH7KDUmr40O7ZP0l8Am7NQ10dE7hvKTnhmNkyqhBcRF9c4HkDVG7wRsRZYm6goQM7vJWmZpGeyhwavTlUoM2udJt/Da6qGyyxpHHATpQcHTwEulnRKqoKZWes44Q13BrAjInZGxAHgLkoPEZrZYayTa3h57uFVezDwzMqTJK0ie/zuuBwXM7PmGdfqAoyRMe+0yJ667gE4QfIEGmZtTjjhVTMmDwaaWesdjs3VeuT5XpuB+ZLmSjoKWEHpIUIzO4z5Hl4VETEg6XJKTz+PA9ZGxLZkJTOzljkck1k9ct3Di4j1lJ6UNrMOMfhqWSfq1O9lZjm4hmdmhaBs6UROeGY2jB9LSeCESZOSDN65+rHHEpQGVh95ZJI4yQbtBJiSYkxoYGAgTZyXX04SZvJZScLAgkRjTe7dmyYOcN6EF5LEOTBjTu4YKf5JD/bSdiLX8MxsGCc8MysE99KaWaG4hmdmheB7eGZWKJ36WEqnJnIzy2FcnUs9ao2MLukGSVuy5d8k7S879l7Zsdzv6ruGZ2ZDpOy0KBsZfSmlMTM3S+qNiO2D50TEVWXnfxk4rSzEryNiYaLiuIZnZkMlHi1ltCOjXwzc2WDRa3LCM7NhEia8aiOjz6p2oqSTgLnAxrLdEyT1SXpM0mdH9SWqcJPWzIYZRU2oS1Jf2XZPNsp5I1YA6yLivbJ9J0XELkkfADZK2hoRP2swvhOemQ01ysdS9kbEod73G83I6CuomKM2InZlP3dKeoTS/b2GE56btGY2jOpc6lDXyOiSfhuYCvy4bN9USUdn613A2cD2ys+Ohmt4ZjaEgETDaow4Mrqk64G+iBhMfiuAuyKifKKvDwPfkXSQUuVsTXnvbiOc8MxsmJRNv2ojo0fENyq2V1f53I+A/MMrlXHCM7Mh/GqZmRWKE56ZFYJreKkceywsXZo7TKqRilf/67+miTNjRpI4ACxblibO5s1p4iQazfn0eW8libP9+WlJ4syYkSZOSnufzx/jnXfyxwAnPDMrCA8AamaF4hqemRWC7+GZWaE44ZlZYTjhmVkhdHKnRcOJXNKJkh6WtF3SNklXpCyYmbVOwsED2kqeRD4AfC0ifiLpt4AnJD2Y9+VeM2stUf98FYebhhNeROwGdmfrv5T0NKWRTJ3wzA5zvod3CJJOpjQw3+NVjq0CVgHMOe64FJczszHUyY+l5P5ekiYDPwCujIg3Ko9HRE9EdEdE9/ETJ+a9nJk1QcI5LdpKrhqepCMpJbs7IuKeNEUys1bq5F7ahr+XJAG3AE9HxF+lK5KZtZKbtNWdDfwBsLhsZvDzEpXLzFrITdoKEfEoh+ejOGZWw+GYzOrRqd/LzBo02KRNVcOTtEzSM5J2SLq6yvFLJL1S1lL8YtmxlZKezZaVOb9ax96bNLMcUtWEJI0DbgKWAv3AZkm9VV5Q+F5EXF7x2WnAdUA3EJRebuiNiNcaLU9zE9748dDVlT9OolF4U41UvPruu5PEAVj9N3+TJtCFF6aJc999ScJs2v+7SeIsWjjsyafGDAykiQOwd2+SMNPmzcsdY8KE/OVI3Et7BrAjInYCSLoLWE59Lyh8GngwIvZln30QWAbc2Whh3KQ1s2Ek1bUAXZL6ypZVFaFmAS+Wbfdn+yr9R0k/lbRO0omj/Gzd3KQ1s6GkUmusHu++uzciunNe8R+BOyPiHUl/BNwOLM4ZsyrX8MxsuPHj61tq2wWcWLY9O9v3GxHxakQMTj90M/Dv6v3saDnhmdlQgzW8NAlvMzBf0lxJRwErgN6hl9PMss0LgKez9fuBcyVNlTQVODfb1zA3ac1sqCOOqL/345e/POThiBiQdDmlRDUOWBsR2yRdD/RFRC/wFUkXUBpybh9wSfbZfZL+glLSBLh+sAOjUU54ZjbUaO7h1SEi1gPrK/Z9o2z9GuCaET67FlibqixOeGY2XMKE104681uZWeMS1/DaSWd+KzNrnBOemRWGE56ZFYaU5h21NuSEZ2ZDuYZnZoXhhGdmheGEZ2aF4YRnZoXihGdmhTCad2kPM054ZjaUm7SJTJ2aZnj2KVPyxwBYtixJmGTDsgOrp09PEmfxv0SSOIuWLEkTZ8umJHEYWJAmzr33pomT0D1PfTB3jP37ExTECc/MCsMJz8wKxQnPzArBnRZmVhhu0ppZYXRwwss9iY+kcZKelPRPKQpkZm0g3SQ+SFom6RlJOyRdXeX4VyVtz+al3SDppLJj70naki29lZ8d9dfKGwC4gtIsQ8cmiGVmrZawhidpHHATsJTSRNqbJfVGxPay054EuiPiLUn/GfjvwOezY7+OiIVJCkPOGp6k2cBnKM0laWadIO00jWcAOyJiZ0QcAO4ClpefEBEPR8Rb2eZjlOafHRN50/iNwJ8BvzXSCZJWAasA5syalfNyZjbmRtdL2yWpr2y7JyJ6yrZnAS+WbfcDZx4i3mVA+VPhE7L4A8CaiPiHegtWTcMJT9L5wJ6IeELSp0Y6L/vyPQDdH/lImsf/zWxs1d+k3RsR3SkuKekLQDfwybLdJ0XELkkfADZK2hoRP2v0GnlqeGcDF0g6D5gAHCvpuxHxhRwxzazV0vbS7gJOLNuene2ruKSWANcCn4yIdwb3R8Su7OdOSY8ApwENJ7yG7+FFxDURMTsiTgZWABud7Mw6QNp7eJuB+ZLmSjqKUq4Y0tsq6TTgO8AFEbGnbP9USUdn612UKlnlnR2j1pkP25hZ4xLW8CJiQNLlwP3AOGBtRGyTdD3QFxG9wP8AJgN3SwJ4ISIuAD4MfEfSQUqVszUVvbujluRbRcQjwCMpYplZiyWetSwi1gPrK/Z9o2y96pA8EfEj4NRkBcE1PDOr1MFvWnTmtzKzxjnhmVlhOOElkuoPcmAgfwyAzZvTxLnwwjRxSDdS8cZPKkmcRZdemiQOV16ZJMwLb05LEmfO3LlJ4gAs/vOPJ4mz8crcr4ry30gw5LETnpkVhhOemRVG4l7aduKEZ2ZDuYZnZoXhhGdmheGEZ2aF4oRnZoXgGp6ZFYanaTSzwnANz8wKxQnPzArBNTwzKwwnPDMrDHdamFmhdGgNL9dE3GbWgdJO4oOkZZKekbRD0tVVjh8t6XvZ8cclnVx27Jps/zOSPp33q3VmGjezxiW8hydpHHATsJTSJNybJfVWTMZzGfBaRMyTtAL4FvB5SadQmuXsd4ATgIckfTAi3mu0PK7hmdlQaWt4ZwA7ImJnRBwA7gKWV5yzHLg9W18HnKPS9GXLgbsi4p2IeA7YkcVrWHNrePv2wd1354/z8sv5YwBcdFGaOPfdlyYOsGhJ1QmcRh8n0UjFq2+9NU2cvr4kcebceGOSOCltfOhgmkAvdeePMWlS/hijq+F1SSr/y+2JiJ6y7VnAi2Xb/cCZFTF+c042rePrwPuy/Y9VfHZWvQWrxk1aMxsiAg4M1N342xsRCTJ1czjhmdkQEemmjQF2ASeWbc/O9lU7p1/SeOA44NU6PzsqvodnZkMMJrx6ljpsBuZLmivpKEqdEJWzFfUCK7P1i4CNERHZ/hVZL+5cYD7wf/J8N9fwzGyIlDW87J7c5cD9wDhgbURsk3Q90BcRvcAtwN9J2gHso5QUyc77PrAdGAC+lKeHFpzwzKyKhE1aImI9sL5i3zfK1t8Gfn+Ez34T+GaqsuRKeJKmADcDC4AA/lNE/DhFwcysNRLfw2sreWt4fw3cFxEXZe3ziQnKZGYtdPAgvP12q0sxNhpOeJKOAxYBlwBkDxUeSFMsM2sV1/Cqmwu8Atwq6SPAE8AVEfGr8pMkrQJWAcyZOjXH5cysWTo14eV5LGU8cDrw7Yg4DfgVMOzF4IjoiYjuiOg+fvLkHJczs2ZI/FhKW8lTw+sH+iPi8Wx7HVUSnpkdXtykrSIiXpL0oqQPRcQzwDmUnpcxs8OYOy1G9mXgjqyHdieQ5o11M2sp1/CqiIgtwGHz4rCZ1eYmrZkVhhOemRWGE56ZFYYTXiJvTnw/m079Uu44k89KUBjg9HlvJYmzaf/vJokDsGjLpjSBrrwySZhUIxWv3ro1TZwtW5LEYfr0NHEAHn00TZxPfCJ/jCOPzB0iwr20ZlYQruGZWWE44ZlZYTjhmVlhOOGZWaF0asLzJD5mNsTgu7T1LHlImibpQUnPZj+HjR8naaGkH0vaJumnkj5fduw2Sc9J2pItC2td0wnPzIZo4vBQVwMbImI+sIHqoy29BfxhRPwOsAy4MZtaYtCfRsTCbKn5zJKbtGY2RBPv4S0HPpWt3w48Anx9aFni38rWfyFpD3A8sL+RC7qGZ2bDjKKG1yWpr2xZNYrLTI+I3dn6S8AhnwaXdAZwFPCzst3fzJq6N0g6utYFXcMzsyFGWcPbGxEjjpgk6SFgRpVD1w69ZoSkOEScmcDfASsj4mC2+xpKifIooIdS7fD6QxXWCc/Mhkg5AGhELBnpmKSXJc2MiN1ZQtszwnnHAv8MXBsRj5XFHqwdviPpVuBPapXHTVozG6KJnRa9wMpsfSXww8oTssGF/x7424hYV3FsZvZTwGeBp2pd0DU8MxumSZ0Wa4DvS7oM+DnwOQBJ3cAfR8QXs32LgPdJuiT73CVZj+wdko4HBGwB/rjWBZ3wzGyIZvXSRsSrlObCqdzfB3wxW/8u8N0RPr94tNd0wjOzIfxqmZkVhhOemRWGBwBNZPLBN1j09gP5Ay1IM1Ha9uenJYmzaOEbSeIAMLAgSZgX3kzz3ebceGOSOKlGKl79ta+liXPVVUniAPDRj6aJ09+fP8aBA7lDuIZnZoXhhGdmheGEZ2aF4YRnZoXhTgszKwzX8EYg6SpKT0QHsBW4NCI69HeDWTF0csJrePAASbOArwDdEbEAGAesSFUwM2uNJg4e0HR5m7TjgWMkvQtMBH6Rv0hm1kqdXMNrOOFFxC5Jfwm8APwaeCAihj1VnI2Augpgzvvf3+jlzKyJOjXh5WnSTqU0Jv1c4ARgkqQvVJ4XET0R0R0R3ccfd1zjJTWzpmjWrGWtkGcA0CXAcxHxSkS8C9wDfDxNscysVXwPr7oXgI9JmkipSXsO0JekVGbWMr6HV0VEPC5pHfATYAB4ktJEGmZ2mHPCqyIirgOuS1QWM2sDnVzD8yQ+ZjZEs+7hSZom6UFJz2Y/p45w3nuStmRLb9n+uZIel7RD0veyCX8OyQnPzIZoYi/t1cCGiJgPbMi2q/l1RCzMlgvK9n8LuCEi5gGvAZfVuqATnpkN06Re2uXA7dn67ZSmWqxLNjXjYmBw6sa6Pt/cwQOOOAImTMgfZ+/e/DGAGTPSjAqc9IbHvfcmCTNn7twkcZKZPj1JmFQjFa++4YYkcQBWnzNs4q2G7DvrM7ljDBxRs1VX0yjv4XVJKn86oyci6u28nF42mfZLwEj/SCZk1xgA1kTEPwDvA/ZHxGBJ+4FZtS7o0VLMbJiIg/WeujciRpxzQdJDwIwqh64der0ISTFCmJOyN7s+AGyUtBV4vd4ClnPCM7MKAbyXJlLEkpGOSXpZ0syI2C1pJrBnhBi7sp87JT0CnAb8AJgiaXxWy5sN7KpVHt/DM7MKARyoc8mlF1iZra8Eflh5gqSpko7O1ruAs4HtERHAw8BFh/p8JSc8M6viYJ1LLmuApZKepfSq6hoASd2Sbs7O+TDQJ+n/UkpwayJie3bs68BXJe2gdE/vlloXdJPWzCqka9Ie8ioRr1J6JbVyfx+lgYWJiB8Bp47w+Z3AGaO5phOemVVoTsJrBSc8M6vCCc/MCsE1PDMrjADebXUhxoQTnplVcA3PzArFCc/MCsE1PDMrlNwPFbclJzwzq+AanpkVxuC7tJ3HCc/MKriGZ2aF4nt4ZlYIruEl8fp7k1n/5qLccc6b8EKC0iSUaMj5lBb/+ceTxNn4UKLf9I8+mibORz+aJEyqYdkBVp9/fpI45/7vkQb8rV+CiXUyTnhmVgjutDCzwgh8D8/MCsRNWjMrhM7ttKg5p4WktZL2SHqqbN80SQ9Kejb7OXVsi2lmzTOY8OpZGldPHpH07yVtKVvelvTZ7Nhtkp4rO7aw1jXrmcTnNmBZxb6rgQ0RMR/YkG2bWcdoyiQ+NfNIRDwcEQsjYiGwGHgLeKDslD8dPB4RW2pdsGbCi4hNwL6K3cuB27P124HP1opjZoeLpk3TONo8chFwb0S81egFG52mcXpE7M7WXwKmN1oAM2s3zWnSMvo8sgK4s2LfNyX9VNINg/PXHkruTouICEkjPjEpaRWwCuD44+fkvZyZNUXdyaxLUl/Zdk9E9AxuSHoImFHlc9eWb9SRR2ZSmq7x/rLd11BKlEcBPZTmqb3+UIVtNOG9LGlmROzOCrJnpBOzL98DMH9+d/5Hyc1sjI3qOby9EdE9YqSIJSMdk1R3HgE+B/x9RPxmso2y2uE7km4F/qRWYRtt0vYCK7P1lcAPG4xjZm2naU3a0eSRi6lozmZJEkmidP/vqSqfG6Kex1LuBH4MfEhSv6TLgDXAUknPAkuybTPrGE1JeFXziKRuSTcPniTpZOBE4F8qPn+HpK3AVqAL+K+1LlizSRsRF49wKN3b12bWRprzLm1EvEqVPBIRfcAXy7afB2ZVOW/xaK/pNy3MrILfpTWzQunMV8uc8MysQue+S+uEZ2YVnPCSOOYYWLAgf5wDM9I8wLz3+SRhmDZvXppAwD1PfTBJnI1X9iaJw0sjPmI1Op/4RJo4/f1Jwuw76zNJ4kCakYoBHjhbuWO8kaAcHgDUzArGnRZmVghu0ppZoTjhmVkhuIZnZoXie3hmVggHcS+tmRWIm7RmVgi+h2dmheJ7eGZWCK7hmVmhOOGZWSG4l9bMCsU1PDMrhM4d8bjRWcvMrKON/SQ+kn5f0jZJByWNOA6ZpGWSnpG0Q9LVZfvnSno82/89SUfVuqYTnplVaNo0jU8B/wHYNNIJksYBNwG/B5wCXCzplOzwt4AbImIe8BpwWa0LOuGZWYUA3q1zyXGViKcj4pkap50B7IiInRFxALgLWJ7NRbsYWJeddzuluWkPqan38LZufWLvSSfp5zVO6wL2NqM8dXJ5amu3MhW5PCflD/H6/fCPXXWePEFSX9l2T0T05C/Db8wCXizb7gfOBN4H7I+IgbL9w6ZyrNTUhBcRx9c6R1JfRCQaVzw/l6e2diuTy5NPRCxLFUvSQ8CMKoeujYgfprpOvdxLa2ZjJiKW5AyxCzixbHt2tu9VYIqk8Vktb3D/Ifkenpm1s83A/KxH9ihgBdAbEQE8DFyUnbcSqFljbMeEl7L9n4LLU1u7lcnlOQxIulBSP3AW8M+S7s/2nyBpPUBWe7scuB94Gvh+RGzLQnwd+KqkHZTu6d1S85qlRGlm1vnasYZnZjYmnPDMrDDaJuGN9PpIC8tzoqSHJW3PXn+5otVlgtKT55KelPRPbVCWKZLWSfp/kp6WdFaLy3NV9nf1lKQ7JU1oQRnWStoj6amyfdMkPSjp2ezn1GaXy0raIuHVeH2kVQaAr0XEKcDHgC+1QZkArqB087Yd/DVwX0T8NvARWlguSbOArwDdEbEAGEepR6/ZbgMqn2O7GtgQEfOBDdm2tUBbJDxGeH2klQWKiN0R8ZNs/ZeU/jPXfJJ7LEmaDXwGuLmV5cjKchywiKxnLCIORMT+1paK8cAxksYDE4FfNLsAEbEJ2FexezmlV5+gzlegbGy0S8Kr9vpIS5NLOUknA6cBj7e2JNwI/BntMXbPXOAV4NasiX2zpEmtKkxE7AL+EngB2A28HhEPtKo8FaZHxO5s/SVgeisLU2TtkvDalqTJwA+AKyPijRaW43xgT0Q80aoyVBgPnA58OyJOA35FC5tq2X2x5ZQS8QnAJElfaFV5RpI9MOtnwVqkXRLeSK+PtJSkIykluzsi4p4WF+ds4AJJz1Nq8i+W9N0Wlqcf6I+IwVrvOkoJsFWWAM9FxCsR8S5wD/DxFpan3MuSZgJkP/e0uDyF1S4Jr+rrI60sUDb8zC3A0xHxV60sC0BEXBMRsyPiZEp/PhsjomU1mIh4CXhR0oeyXecA21tVHkpN2Y9Jmpj93Z1D+3Tu9FJ69QnqfAXKxkZbDB4QEQOSBl8fGQesLXt9pFXOBv4A2CppS7bvv0TE+haWqd18Gbgj+yW1E7i0VQWJiMclrQN+QqmH/Ula8EqXpDuBTwFd2WtT1wFrgO9Lugz4OfC5ZpfLSvxqmZkVRrs0ac3MxpwTnpkVhhOemRWGE56ZFYYTnpkVhhOemRWGE56ZFcb/B9jOsTu3ykcNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b2c6208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(corr, cmap='seismic', vmin=-1,\n",
" vmax=1)\n",
"plt.colorbar()\n",
"plt.savefig(\"corr.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment