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hygull/Pandas-users.ipynb

Created November 19, 2018 06:32
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Pandas, to_datetime(), concat(), plot(), isnull(), loc[a:b, c:d]
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Pandas-users.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rahul Yadav</td>\n",
" <td>36</td>\n",
" <td>500000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>New Delhi (Delhi)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" <td>55</td>\n",
" <td>550000.0</td>\n",
" <td>Java developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rishikesh Agrawani</td>\n",
" <td>26</td>\n",
" <td>200000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Anupam Shukla</td>\n",
" <td>27</td>\n",
" <td>250000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Robery Griesemer</td>\n",
" <td>44</td>\n",
" <td>600000.0</td>\n",
" <td>JavaScript developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gudal Sharma</td>\n",
" <td>24</td>\n",
" <td>100000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Quinton Clark</td>\n",
" <td>35</td>\n",
" <td>45000.0</td>\n",
" <td>Java developer</td>\n",
" <td>New City</td>\n",
" <td>UAE</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Niyati Jain</td>\n",
" <td>24</td>\n",
" <td>80000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Rajim</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Malinikesh Agrawani</td>\n",
" <td>22</td>\n",
" <td>NaN</td>\n",
" <td>Student</td>\n",
" <td>Kondagaon</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hira Dewangan</td>\n",
" <td>24</td>\n",
" <td>50000.0</td>\n",
" <td>Python developer</td>\n",
" <td>New Raipur</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Hemkesh Agrawani</td>\n",
" <td>24</td>\n",
" <td>NaN</td>\n",
" <td>Student</td>\n",
" <td>Kondagaon</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Larry Page</td>\n",
" <td>45</td>\n",
" <td>6000000.0</td>\n",
" <td>Computer Scientist</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession \\\n",
"0 Rahul Yadav 36 500000.0 PHP developer \n",
"1 James Gosling 55 550000.0 Java developer \n",
"2 Rishikesh Agrawani 26 200000.0 Python developer \n",
"3 Anupam Shukla 27 250000.0 PHP developer \n",
"4 Robery Griesemer 44 600000.0 JavaScript developer \n",
"5 Gudal Sharma 24 100000.0 Python developer \n",
"6 Quinton Clark 35 45000.0 Java developer \n",
"7 Niyati Jain 24 80000.0 Python developer \n",
"8 Malinikesh Agrawani 22 NaN Student \n",
"9 Hira Dewangan 24 50000.0 Python developer \n",
"10 Hemkesh Agrawani 24 NaN Student \n",
"11 Larry Page 45 6000000.0 Computer Scientist \n",
"\n",
" Address Country Sex \n",
"0 New Delhi (Delhi) India Male \n",
"1 Newyork USA Male \n",
"2 Raipur (CG) India Male \n",
"3 Raipur (CG) India Male \n",
"4 Newyork USA Male \n",
"5 Raipur (CG) India Female \n",
"6 New City UAE Male \n",
"7 Rajim India Female \n",
"8 Kondagaon India Female \n",
"9 New Raipur India Male \n",
"10 Kondagaon India Male \n",
"11 Newyork USA Male "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users = pd.read_csv(\"Users.csv\"); # Reading data from csv named Users.csv (Manually written, not copied from Internet)\n",
"users"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Fullname 12\n",
"Age 12\n",
"Salary 10\n",
"Profession 12\n",
"Address 12\n",
"Country 12\n",
"Sex 12\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.count() # Non-NA elements in Series"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 7\n",
"1 7\n",
"2 7\n",
"3 7\n",
"4 7\n",
"5 7\n",
"6 7\n",
"7 7\n",
"8 6\n",
"9 7\n",
"10 6\n",
"11 7\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.count(axis=\"columns\") # Non-NA values in each row"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Fullname 12\n",
"Age 12\n",
"Salary 10\n",
"Profession 12\n",
"Address 12\n",
"Country 12\n",
"Sex 12\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.count(axis=\"index\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Fullname 0\n",
"Age 0\n",
"Salary 2\n",
"Profession 0\n",
"Address 0\n",
"Country 0\n",
"Sex 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.isnull().sum() # NA values in columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users[\"Salary\"].isnull().values.any() # Is any Salary equals NA"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users[\"Salary\"].isnull().sum() # Count of salaries with NA values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Fullname', 'Age', 'Salary', 'Profession', 'Address', 'Country', 'Sex'], dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Fullname False\n",
"Age False\n",
"Salary True\n",
"Profession False\n",
"Address False\n",
"Country False\n",
"Sex False\n",
"dtype: bool"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.isnull().any() "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Salary'], dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"null_columns = users.columns[users.isnull().any()] # Which columns have Na values\n",
"null_columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>Salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>500000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>550000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>250000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>600000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>100000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>45000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>80000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>50000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>6000000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Salary\n",
"0 500000.0\n",
"1 550000.0\n",
"2 200000.0\n",
"3 250000.0\n",
"4 600000.0\n",
"5 100000.0\n",
"6 45000.0\n",
"7 80000.0\n",
"8 NaN\n",
"9 50000.0\n",
"10 NaN\n",
"11 6000000.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users[null_columns]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 500000.0\n",
"1 550000.0\n",
"2 200000.0\n",
"3 250000.0\n",
"4 600000.0\n",
"5 100000.0\n",
"6 45000.0\n",
"7 80000.0\n",
"8 NaN\n",
"9 50000.0\n",
"10 NaN\n",
"11 6000000.0\n",
"Name: Salary, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"salaries = users[\"Salary\"] # Getting Salary column\n",
"salaries"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8375000.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users[\"Salary\"].sum() # Adding Non-NA salariesb"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d68af5dd8>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"salaries.plot() # Plotting Salaries"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d6940f940>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HTi215wdbfRo4Y2jcCmD/HPUVs9RH6fFTqmpLVa2uqtXLli1b0AuUJM3fqGGyHTh0R9YG4Pqh+qXtjqs1wMPtFNUOYG2Sk9uF97XAjrbskSRr2l1cl87Y1kJ6SJImZOlcA5J8Evg14JQk0wzuytoMXJvkMuB+4OI2/AbgDcBe4FHgLQBVdTDJ+4Bdbdx7q+rQRf23Mrhj7ETg8+3BQntIkiZnzjCpqjcfZtH5s4wt4PLDbGcrsHWW+hTw8lnq319oD0nSZPgJeElSN8NEktTNMJEkdTNMJEndDBNJUjfDRJLUzTCRJHUzTCRJ3eb80KL0s+7D//ymkda7/D+/bsx7Ih2/PDKRJHXzyGQS3vOcEdd7eLz7IUlj4pGJJKmbRybAyk2fG2m9+za/ccx7IknHJo9MJEndDBNJUjfDRJLUzTCRJHUzTCRJ3QwTSVI3w0SS1M3PmWjsdr/kpSOt99K7d495TyQdLR6ZSJK6eWRyHPilbb800np3bLhjzHsi6cnKIxNJUjfDRJLU7ZgNkyTrktyTZG+STZPeH0k6nh2T10ySLAE+DPw6MA3sSrK9qr412T3T8eA//OPfGGm93//UZ8e8J+P3nve856iupyePYzJMgHOBvVV1L0CSa4D1gGGiJ5XpTf99pPVWbP7VMe/Jk8PPf+m2kdb77mvPHvOePPkcq6e5lgP7huanW02SNAGpqknvw4IluRi4oKr+aZv/XeDcqnr7jHEbgY1t9sXAPSO0OwX4Xsfu2s9+T4Ze9jt++72gqpbNNehYPc01DZwxNL8C2D9zUFVtAbb0NEoyVVWre7ZhP/sd673sZ7+5HKunuXYBq5K8MMkJwCXA9gnvkyQdt47JI5OqeizJ24AdwBJga1XdNeHdkqTj1jEZJgBVdQNww1Fo1XWazH72e5L0sp/9juiYvAAvSfrZcqxeM5Ek/QwxTCRJ3Y7ZayaLJclLGHyafjlQDG453l5V/uWmBUpyLlBVtSvJWcA64O52veto9L+6qi49Gr3UZ+iuzP1V9edJfhv4FWA3sKWq/t9Ed1Bz8prJkCTvAt4MXMPgsyww+AzLJcA1VbV5Uvs2Li0slwM3V9UPh+rrquoLY+xzBXAhgzcsO4HzgC8Drwd2VNX7x9Wr9Zt5a3iA1wI3AVTVb46z3yz9/wGDr/m5s6q+uAjbPw/YXVV/neREYBNwDoOvEPp3VfXwmPu9A/hMVe2bc/B4+n2Cwf8rTwd+ADwT+DPgfAa/pzYsQs+/B/wWg8+sPQbsAT457p/l8cIwGZLkL4GXzXwX1N413VVVq47ivrylqj425m2+A7icwbu9s4F3VtX1bdk3quqcMfa6o/V4GvBdYMXQL8Kbq+qXx9Wr9fsGg1+sH2VwRBngkwzeCFBVfzHmfl+vqnPb9D9j8HP9DLAW+K/jfuOR5C7gFe22+C3Ao8B1DH7ZvqKq/tGY+z0M/A3wVwx+jp+uqgPj7DGj3+1V9ctJlgL/G3h+VT2eJMA3F+H/l3cA/xD4C+ANwG3AQwzC5V9U1ZfH2e+4UFU+2gO4m8FXB8ysvwC45yjvy/2LsM07gGe26ZXAFINAAbh1zL1unW26zd+2CK/tKcC/ZHAUdHar3buI/32GX98uYFmbfgZwxyL02z00/Y2j8PO8tf1M1wJXAQeALwAbgGctQr87gROAk4FHgOe2+s8Nv/Yx9rsDWNKmnw58uU2fOe5/C227zwE2t98x32+P3a120rj7zbEvn1+M7XrN5Il+D7gxyR7+7oskzwReBLxt3M2S3H64RcBp4+7H4B/PDwGq6r4kvwZcl+QFrec4/TjJ06vqUeBVh4pJngP8ZMy9qKqfAB9M8un2/ACLe03wKUlOZvALN9XetVfV3yR5bBH63Tl0tPrNJKurairJLwKLcT2h2s/0i8AXkzyVwWnLNwP/Hpjzu5oW6CoGv2iXAP8G+HSSe4E1DE47L4alwOMMjp6fBVBV97fXOm7XMjjl+mtV9V2AJD/PIJw/zeDPaYxNksOdZQiDMwZj52muGZI8hcG57+UMfvDTwK6qenwRej0AXMDg8PoJi4D/WVXPH3O/m4B/VVW3DdWWAluB36mqJWPs9bSq+tEs9VOA06tqUf/AfJI3Aq+uqj9YpO3fxyAUw+C02q9U1XeTPBP4SlWN9R9sC+EPAb/K4Mv6zmHwhmcf8I6q+uaY+91aVa88zLITq+r/jLNf2+7zAapqf5KTGFxfu7+qvr4Ivd4JXAZ8DXgN8IGq+liSZcCfVtVrxtzvnqp68UKXdfR7nMEpvNneJK6pqhPH2Q8Mk4lKchXwsar6yizL/qSqfnvM/VYAjx16ZzRj2aur6n+Ms9/xKMnTgdOq6tuLtP1nAb/A4F31dFU9sEh9frGq/nIxtv2zIsnLgJcyuGni7kXu9UXgz4Fth/6bJTkN+CfAr1fV68fc707gt6pqzyzL9lXVGbOs1tfTMJGkxdVOiW5i8LGDU1v5AQZfULu5qmaenejt9yYG1+5+6s9uJLmoqv7LOPuBYSJJE7UYd25Oop9hIkkTlOT+qjrzWO/n3VyStMiO9p2bE7hT1DCRpKPgNI5w5+aToJ9hIklHwWcZfGD4tpkLknz5SdDPayaSpH5+Bb0kqZthIknqZphIkroZJpKkboaJJKnb/wcUGSpOEPSBCgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"salaries.plot.bar() # Bar chart for the Salaries"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d6949b5f8>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7eG2O0j3bP8bI1PxN6+dJzQEvPTqoS7lUR2iE7fWV5ORkd0NRUsoJXURyRaRDRH6e+HyTiLSISKeIvCQiWd/RY4yhpcu6/vPrNa3xTpdD5+Pb/Q80LZHQrw3q0rKLcpeJ2QXevTrOTgv6z5OWs0L/BnDuus//C/AtY8wWYAT4g3QGloqLQ5MMTczaIqE3+jyIwIWBtblCP/TuEHesL8NfdutOo/cGdWnZRbnLqd4IMYNlHS6QYkIXkTrgk8C3E58L8DDww8RdngEeyUSAt3JtfosNEnpRfi513uI1OQJgam6B4z0jPHjb4t0t1yvMy2VbnQ7qUu7T0WvNhMXrpbpC/1vgz4BY4vNqIGKMWUh8fgmoTXNsS2rtClPjKbx2bqXV1uqQrpauMHPR2JL186RdgSod1KVcpyM0QqOvlIqS7G8oSloyoYvIp4ABY8zx629e5K6Lbv8TkSdFpE1E2gYHB1cY5uJau8Ls3VSV9YlmN9Pk83BxcILoGtsJebhziIK8HHanuBegOeDVQV3KVYwxdIQilsxvuV4qK/QDwKdFpBv4PvFSy98ClSKSl7hPHdC/2IONMU8ZY5qNMc0+39JvyVPVG56iLzJti3JLUpPfw+xCjL6RaatDyar4dv+qRbf7L0YHdSm3CYWnGJ6cs7R+DikkdGPMN40xdcaYIPBl4FfGmK8ArwNfSNztceAnGYtyEXaqnyetxdOLro7N8O7ViWuHWaTCW1pAo69UTzBSrpE8ocjKDhdYXR/6nwN/KiLnidfU/z49IaWmtStMRXE+t68ry+bL3lLjGjxf9NAS2/1vplkHdSkX6QiNUFKQy20W56NlJXRjzK+NMZ9KfHzRGLPHGNNkjPmiMSarO0Vau8PsDlZZ1sC/GG9pAdWlBWssoQ9S4yngjvXL+0beFfQyNrOwJruClPu0hyJsq6sk1+J85MidogNjM3QNTVo2LvdWGtfQ6UWxmOGN80Pc31Sz7B+syQuo2o+unG56Lsq5y2OW18/BoQm9xYb186Qmf7x10Rj3lxLOXRljaGKO+5dZbgEIVpfooC7lCmf6R1mIGcvr5+DQhN7aFaa0IJe7NpZbHcoHNPk8jE7PMzw5Z3UoGffeuNzUL4gmiQi7dFCXcoH2xPfwdl2hr0xrV5hdwSrycu0X/lo6vehQ5xC3rytj3QoPFmkOxgd1DYzPpDkypbKnIxShoaqEGk+h1aE4L6GPTM7xztVxW9bPYe0M6ZqZj9LaHeb+FazOk3YF4v+Gx7WOrhzKGEN7aISdNlidgwMTemu3fevnABsriigpyHV9Qm/tCjO3kPp2/8XcXVseH9SlZRflUP2jMwyMz1q+QzTJeQm9K0xhXo4lJ2qnQkRo9Ln/9KJDnYMU5Oawd1P1ip9DB3Upp0ueUGSHDhdwaELf0VBJYV5q28yt0OT3uP7A6EOdQzQHvRQXrO7fYVegirN9o0zP6aAu5TwdoQiFeTls3WCPBg1HJfTxmXnO9o+yZxWrwmxo9JXSPzrD5OzC0nd2oIHxGd6+Mr6q+nnS7qCXhZjh5CUd1KWcpz00wr11FeTbpEHDHlGkqK1nhJjBthdEk9w+0+WN8/F2xQdX0H9+o+SgLm1fVE4zuxDlbN+Ybern4LCE3toVJi9HbNHAfytuT+iH3h2iqrSAO9PwNrOypIAmv0cHdSnHeat/jLlozDYdLuDAhH5vXcWq67aZFqguJS9HXNnpYozh0PkhDqxgu//NNCc2GOmgLuUkyQmLukJfgem5KKcuRWxfPwfIz82hobrElQn9navjDI7Prqpd8Ua7AvFBXZ0u/PtS7tUeGmFjRdGKN9ZlgmMSekdohPmosX39PKnJpcfRrWa7/800Jwd16VwX5SAdoQg7AvZZnYODEnpLV5gciY9ddYImv4ee4Snmo7Gl7+wgv+0cosnvYUNFcdqeMzmoS3eMKqcYGJuhLzLNjnr71M/BQQm9tSvMnRvLKS+y7gDW5Wjye1iIGXqGp6wOJW1m5qO0XBxe1ulEqUgO6tINRsop2m1YPweHJPS5hRjtoRH2BO1fP09y40yX4z0jzC7EePC29CZ0iM9HD4V1UJdyho7eEfJzxXYTXx2R0E9dijC7ELPt/JbFbPa5r3Xxt52D5OfKqrb730yylKZlF+UEHaEId22sSPlg9GxxREK384EWN+MpzGNDRZGrVuiHO4fY2eCltDAv7c9998YKCnVQl3KA+WiMU5citpnfcj1HJHSA+xqrqSotsDqMZWnyu2dI19DELGf7x3jwttXvDl1MQV4O2+oqNaEr23vnyjgz8zHb1c/BIQn9jx9q4sU/3Gd1GMvW6IsP6XLDcXTJ7f7pviB6vV1Brw7qUraXnLBopx2iSUsmdBEpEpFWETkpImdF5D8mbv+uiHSJyInEr+2ZD9dZGv0eJueiXB51/oW+Q51DVJbkc3dt5sYWNwfig7pO9OqgLmVf7aEIvrJCaivT17qbLqms0GeBh40x24DtwMdEJLlc/vfGmO2JXycyFqVDNfnc0elijOFQ5yAHGmvITdN2/8W8N6hLNxgp++oIjbCjvhKRzP1fWKklE7qJS2ak/MQv59cQssAtQ7rOD0xwdSy92/0XU1lSwBa/R+voyrbCk3N0D0+x02Y7RJNSqqGLSK6InAAGgFeNMS2JP/rPInJKRL4lItafkGozNZ4CKorzHb9C/21iu3865p8vpTnopd0hg7reOD/Et1591+owVBZdO6HIZjtEk1JK6MaYqDFmO1AH7BGRu4FvAncAu4Eq4M8Xe6yIPCkibSLSNjg4mKawnUFEaPI7f6bL4c5BNteUUuctyfhr7QpUOWJQlzGG//Szt/hvr3Vytn/U6nBUlnSEIuTmCPfY9AjMZXW5GGMiwK+BjxljLifKMbPA08CemzzmKWNMszGm2efLTMubnTX6Sh1dcpldiHL0Yjgrq3OIXxgF+w/qau0K887VcQCeO9JjcTQqWzp6R9i6oYySgvTvxUiHVLpcfCJSmfi4GPgI8LaIbEjcJsAjwJlMBupUTX4PQxNzRKbmrA5lRdp7IkzPR3kgDacTpSJQXUKNx/6Dup490kNFcT6f3VHLj0/0MTo1b3VIKsOiMcPJ3lF21Nuzfg6prdA3AK+LyCngGPEa+s+BF0TkNHAaqAH+MnNhOpfTL4we6hwkL0fYtzk7u3SdMKjryugMvzx7hS/trucPH9jMzHyMHxzvtToslWGdA+NMzC7Ycodo0pLvG4wxp4Adi9z+cEYicpkmXxkQ7xTZFXDO6IKkw+eH2NFQSVkWp1w2B6r45dmrDIzN4LfR4QFJL7aGiBrDV/cGaKguYXfQy7NHevj6gU1pO8VJ2U/yhCI7H4HpiJ2iTlbrLaYgL8eRF0bDk3Oc7hvNWrklKTmoy46r9LmFGN9rDfHQ7X4aquMXiR/bHyQUnuI3766ti/5rTUdoBG9JPoHqzDcHrJQm9AzLzRE215Q6MqG/cX4IY7LTrni9a4O6bFhHf+XsFQbHZzm4P3Dtto/etR5fWSHPHum2LC6Vee2hCDsavLbcUJSkCT0L4kO6Jq0OY9kOdw5RXpTHvRnc7r+YgrwcttVX2nLH6HNHuglUl/Ch6961FOTl8OieBn797iA9w877d1ZLG52e5/zAhG37z5M0oWdBk99D78gUM/POGTqV3O5/X2MNebnZ/zZpDng52z9mq0Fdb/WPcax7hIP7Ah+olT+6t4FcEZ4/qi2MbnQyMV/IrjtEkzShZ0GT34MxcNFBq/SLQ5P0j87wQAZOJ0pFc9B+g7qeO9pNUX4OX9xV/4E/W1dexEfvXs9Lx3pt9UNIpUd7aAQRuNemG4qSNKFnQWNySJeDWhcPJS7wPdBkzWawZCeBXcouo1Pz/Kijj0e211JRsnjHz2P7AozNLPDTk31Zjk5lWkcowm3+sqx2e62EJvQs2FRTSo44a+ri4fNDBKpLrnVyZJvdBnX94HgvM/Ox910MvdGeTVXcsb6MZ97sccUMfBUXS7xT3Bmwd/0cNKFnRVF+LvVVJY7ZXDS3EOPIheGMT1dcSnPQy3EbDOqKxQzPHe2hOeDlro03f8stIhzcH+Cty2O0h+zxg0it3sWhSUan5229QzRJE3qWNCVOL3KCjtAIk3NR7reo3JK0K1DF+MwC7w6MWxrHbzsH6Rme4rH7gkve95HttZQV5fHMm3px1C2uTVi08Q7RJE3oWdLo93BxaJKoA8bCHj4/RG6OsL+x2tI4dic3GFncj/7skR5qPIV87K71S963tDCPL+yq4/87c5mBceefVKWgozdCWVHetWthdqYJPUuafB7mFmL0hqesDmVJv+0cYltdBRXF1l4AaqgqocZTyHEL6+ih4Slef2eAR/c2UJCX2n+Xg/sCzEcN32/V+S5u0N4zwvb6SkeMddCEniWNDhnSFZma4/SlSNa3+y9GRGgOeC0dpft8Sw85Ijy6pyHlx2z2eXhgSw0vtoSYj8YyGJ3KtInZBd69Os4OG89vuZ4m9CxJTl20e6fLmxeGiRksvyCa1Bz00hueZmAs++WL6bkoLx3r5aN3rWN9xfKGhD2+P8iVsRlefetqhqJT2XDqUoSYgZ0OqJ+DJvSsqSjOx1dWaPuEfqhziLLCPLbZZIvzroB1g7p+drKf0el5HtsfXPZjH7rDT21lsc53cbjkhMXtNvn/sBRN6FnU6Cu19eai5Hb/fY3V5Fuw3X8xd1k0qMsYwzNHurltnYe9m5Y/9jg3J97CePRimHeuWNulo1auIzTCZl8plSUFVoeSEnv8r10jkueL2nXTSc/wFJdGpnnQJuUWeG9QV7br6O2hCGf7x3hsf3DF0/V+r7megrwcnjvand7gVFYYY+gIRWw9//xGmtCzqMnnYXxmgcGJWatDWdShzsR2fxtcEL3e7mB8UNfU3ELWXvO5I92UFebx2R21K36OqtICPr1tIy+39zE2o0fUOU1veJrhyTlH9J8naULPoib/e6cX2dFvO4eo8xbbboB/c6CKaBYHdQ2Oz/KL05f5/K46SgtXdxjwY/sDTM1Fefn4pTRFp7IludvXCTtEkzShZ1GjvxTAljtG56Mxjl4Y5oEtPtsN8L82qCtLdfSXjoWYj5pbzm1J1b11lWyvr+TZozrfxWk6QiOUFORy+/oyq0NJmSb0LFpfXoSnMM+WK/STvRHGZxds0654vYqSfG5bl51BXQvRGC+0hHhgS03adgY+tj/AxcFJ3jg/nJbnU9nR0RthW10luQ7YUJSkCT2LRIRGX6ktTy861DlEjsB9Fm/3v5ldgSraQ5kf1PUv565yeXSGg/tWvzpP+sQ9G6guLeCZI91pe06VWTPzUd7qH3NU/RxSSOgiUiQirSJyUkTOish/TNy+SURaRKRTRF4SEWf09VisMdHpYjeHOge5p67Stu1ZzQFvVgZ1PfNmD7WVxXx467q0PWdRfi5f2l3Pa+eucmnE/qMfFJzuG2UhZhyzQzQplRX6LPCwMWYbsB34mIjsA/4L8C1jzBZgBPiDzIXpHk1+D1fGZhi3UdfD6PQ8Jy+N2qpd8UbNWRjU1Xl1nCMXh/nKvoa0v83+SmLF/0JLKK3PqzLDSRMWr7dkQjdxySVlfuKXAR4Gfpi4/RngkYxE6DLJuqydyi5HLgwTjRnub7JvQs/GoK5nj/RQkJfDl5o/eMTcatVWFvORret46Vivo86WXavaeyLXvuecJKUauojkisgJYAB4FbgARIwxycbgS8DKG3bXkORMFzt1uhzqHKS0INfWby9FhN1BL8e6M7PBaHxmnpfbL/GpezdQnaH/xI/fFyQ8OccvTl3OyPOr9DDG0B4acdzqHFJM6MaYqDFmO1AH7AG2Lna3xR4rIk+KSJuItA0ODq48UpcIVJWQnyu2GgFw+PwQ+xurUx4Pa5VdAS+XRqa5moFBXS+39zE5F+XxFcxtSdV9jdU0+kp59qgefmFnl0dnGBifddQO0aRl/Q82xkSAXwP7gEoRSe66qAP6b/KYp4wxzcaYZp/PXjsQrZCXm0OwutQ2F0ZDw1P0DE/ZutyS1ByMz1RJdx3dGMOzR7rZVleR0aFkIsJj+4Oc7I1wMkubpNTytTu0fg6pdbn4RKQy8XEx8BHgHPA68IXE3R4HfpKpIN2m0UaUEbTTAAARlElEQVTH0R06n9juf5v9f9jetbGcovyctM91efPCMBcGJ1c0VXG5PrezltKCXJ49oqt0u+oIRSjMy+GO9eVWh7JsqazQNwCvi8gp4BjwqjHm58CfA38qIueBauDvMxemuzT5PfSEp5hbsP7wg0PvDrGxoojNNaVWh7Kk/NwcttVVpv3C6LNHuqkqLeCT925I6/Mupqwon8/trONnp/oJT85l/PXU8nWERri3rsL2JcjFpNLlcsoYs8MYc68x5m5jzH9K3H7RGLPHGNNkjPmiMcaeE6dsqMnvIRoz9Axb2+myEI3x5oUhW273v5nmNA/q6otM8+pbV/nS7nqK8nPT8pxLObg/wNxCjJeO6RF1djO7EOVM35itGwRuxXk/glzALqcXneobZWxmgftt3H9+o3QP6nqxJV76+Mre1I+YW63b1pWxf3M1zx/tccSh4WvJW/1jzEVj7HDIgRY30oRugc2+eHnD6oR+uHMIETjggAuiSekc1DW7EOX7rb18eOs66rzZnTD52P4AfZFpfvX2QFZfV91a8oSinQFdoasUlRTkUVtZbHnr4qHOQe6praCq1J7b/RdTUZLP7evKOJaGOvo/nb7M8OQcj6VhquJy/c6d69hQUaRH1NlMR2+EjRVFrCtf3hmydqEJ3SKNfg8XLEzo4zPztIcijmhXvNGuoJeOnpFVlyueebOHzb5SDjRm/+8gLzeHR/c0cKhzyNLvA/V+7T0jjq2fgyZ0yzT5PFwYmMz49MCbOXoxTDRmbHc6USqaA17GZxd49+rKB3WduhThRG+Eg/sC5Fg0HvXLexrIzxWe0xZGWxgYm6EvMu3I/vMkTegWafJ7mJ6P0j86bcnrH+ocpDg/l50B533zNgcSG4xWUXZ59kgPJQW5fH5XXbrCWjZfWSGfuGcD/3j8EpOz2TteTy2uI3GhXVfoatkaLb4werhziH2bqyjMy06rXjrVVxXjKyvk+ArnuoxMzvHTk/18dkct5UX5aY5ueR7bH2R8doEfdfRZGoeK7xDNzxXu2ui8DUVJmtAtYmXr4qWRKS4OTXK/A8stEN9C3xzwrniF/lJbL3MLsazsDF3KzoZK7tpYznNH9Ig6q3WEIty5sSJr+xEyQRO6Rao9hXhL8i0Zo3u4cwjA1vPPl7LSQV3RmOH5oz3s3VRli7MiRYTH9wd55+o4LV2ZmSSplrYQjXHqUoSdDq6fgyZ0SzX5rZnpcqhziHXlhdfeJTjR7hUO6nr97QEujUzz+H3BDES1Mv/Tto1UFOfrxVELvX1lnJn5mKPr56AJ3VKNPk/We9GjMcPh887a7r+YOzeWU5yfu+z56M8e7WFdeSG/c2f6jphbreKC+BF1r5y9wpXR9I8GVku7dkKRQ3eIJmlCt1CT30N4ci6rQ5rO9I0yOj3PAw4ut0BiUFd9xbIGdV0cnOC37w7ylb0B8nPt9a3/1b0BYsbwYqseUWeFjlAEX1khdd5iq0NZFXt9V68xjcnTizK8Sp+Zj3K8J8y3D13k//r5W4CztvvfTHOgircuj6Xc8vf80RD5ucKX96T/iLnVaqgu4aHb/bzYErLFFM61pj00wo76Ske/awXIW/ouKlOafO91uiRrwqtljKF7eIoTvSN0hOKbZ97qH2MhsYGptrKYP3pws+POSlzMrqCX6OuGk70R7lviB9TU3AI/ON7Lx+/egL/Mntu6D+4P8MTTx3jl7BU+vW2j1eGsGeHJObqHp/jS7uwNaMsUTegWqq0spig/Z1Wti6NT85y4FKEjNMKJ3ngCj0zNA1BSkMu9dRX84YOb2V5fyY76SvwOnVGxmJ0NXkTiG4yWSug/7uhnfGbBkrktqfrQFh+B6hKefbNbE3oWneiNl+2c3uECmtAtlZMjbK7xpJzQ56Mx3rkyTkdohI5E8r6YaHsUgS1+Dx+9cz3bGyrZ0VDJFn8ZuRZta8+GiuJ8bvOXLdmPnjxi7s4N5eyy8RS9nBzh4L4Af/mLc5ztH+WujRVWh7QmtPdEyM0R7qlz/t+3JnSLNfk9184wvJ4xhsujM5zofW/1fbpvlJn5eH21xlPA9novn99Zx476Su6pq6DM4l2PVtgV9PKzE/1EY+amP7yOdY/w9pVx/upz99i+RvrFXfX89T+/w3NHevirz99rdThrQkfvCHesL6OkwPnp0PlfgcM1+T387FQ/wxOzdA5MJOre8fr3wHj8EKiCvBzu3ljOo3sC7GioZHt9JXXeYtsnp2zYHfTyYkuId6+Os3XD4lu2nznSTXlRHp/ZXpvd4FagoiSfz+6o5UcdfXzz41upKFl7P6SzKRoznOwd5bM77P+9kQpN6BZr8nswBnb95b9cuy1YXcJ9jdXsaPCyvb6SrRvKHXm+YTZcG9TVHV40oV8dm+GXZ67wtfuCFBc4Y0v3wX1Bvtfayw+O9/I/P7DZ6nBc7fzABBOzC46esHg9TegWO9BUw+d21lLnLWFHfSXb6isddeCE1eq8xfjLCmnrGeHgIrNZXmwJETWGr+6z78XQG925sZzdQS/PHe3h6wc2WTbedy1IljudvkM0SRO6xSqK8/mb39tudRiOJSI0B72LjgCYW4jxYmuID93mI1hTakF0K3dwf5B//b0OftM5yEO3+60Ox7U6QiN4S/IJVmf3CMJMWfJ9vIjUi8jrInJORM6KyDcSt/8HEekTkROJX5/IfLhKfdCuQBV9kekPbJv/5dkrDI7P8rgNpiou18fuWo+vrFDnu2RYRyjCjgava65HpVKYXQD+nTFmK7AP+GMRuTPxZ98yxmxP/PqnjEWp1C00J1oR23reP9fluSM9NFSV8KHbnDcmuCAvh9/f08Dr7wwQGp6yOhxXGp2ep3NgwvHzW663ZEI3xlw2xrQnPh4HzgHuuCSsXCE5qOv6ssu5y2O0doctPWJutb6yt4FcEZ5v0VV6Jpx0wQlFN1pW64SIBIEdQEvipj8RkVMi8h0Rcc/finKUxQZ1PXukh8K8HL7YbN0Rc6u1rryIj961npeO9TI9F7U6HNfpCEUQgW31zt9QlJRyQhcRD/CPwL8xxowBfwc0AtuBy8B/vcnjnhSRNhFpGxwcTEPISn3Q7uB7g7pGp+f5cUcfj2yvpbLE2R1Dj+0PMDo9z89O9lsdiuu0h0a4zV/mqg15KSV0EcknnsxfMMa8DGCMuWqMiRpjYsD/APYs9lhjzFPGmGZjTLPP57xapnKGXQFvYpNIhB8ev8T0fJSDNp7bkqo9m6q4fV0Zzxzp1iPq0igWM5zojbim/zwplS4XAf4eOGeM+Zvrbt9w3d0+C5xJf3hKpWZnID6oq7U7zHNHutkV8HJ3rfPfSosIj90X4Gz/GO2hiNXhuEbX8CSj0/PsdFH9HFJboR8ADgIP39Ci+H+LyGkROQU8BPzbTAaq1K2UF+Vz+7oynj3SQ/fwlK2nKi7XI9trKSvM49kj3VaH4hrtPckNRe5aoS+5scgYcxhYrE1A2xSVrewKeHmhJUSNp5CP371h6Qc4RGlhHl9oruP5oz3875+8E1+Z82fZW62jN0JZUR6NPueeq7sY3SmqXKM5GE/ov7+n3nWzbw7uC/D0G9188+VT/K8fvZ071i8+iMyJzg9M8N03uzjek72SUmh4kp0Br2NbWm9GE7pyjQ9vXcfBfQGeOLDJ6lDSbrPPwzc+vIX/9zcX+JdzA9zXWM0TBzbx8B1+R868j8UMv3l3kO+80cWhziEKcnPY31idtR/Edd5iR833SZVk88p5c3OzaWtry9rrKeU2I5NzfO9YiOeO9HB5dIaGqhIe2x/g93bXU+6A9ruJ2QX+8fglnnmzm4tDk/jLCjm4L8Dv721wxbGImSIix40xzUveTxO6Us6zEI3xy7NXefqNLtp6RigtyOULu+p4/L4gm21YFw4NT/HdN7v5QVsv47MLbKuv5OsHgnz87g2uK49lgiZ0pdaI05dGefqNLn52qp/5qOFf3e7jiQObeHBLjaVDp4wxHLkwzHfe6Oa1t6+SK8LH79nAEweCrmsXzDRN6EqtMQPjM7xwNMQLLT0MTczR6Cvlawc28fmdtVk9Xm1mPsqPO/r47pvdvH1lnKrSAh7d08BX9wVYX+GeQ8qzSRO6UmvU7EKUX5y6zNNvdHO6b5Tyojy+vKeBx/YHqPNmbu735dFpnjvSw/daQ4xMzXPH+jK+fmATn96+kaJ8Z5wWZVea0JVa44wxHO8Z4ek3unnl7BWMMfzunev52oEgezdVpaUcY4yhPTTCd97o5pUz8df4yNZ1PHFgE/s2p+c1VOoJXdsWlXKp+GlOVTQHq+iPTPPskR6+fyzEK2evsHVDOU8cCPLpbStbPc8txPjF6X6efqObU5dGKSvK4+sHgjy2P0h9lTtO/3EiXaErtYZMz0X58Yk+nn6ji3evTlBdWsCje+P17XXlS9e3B8dneaGlhxdaQgyOz7LZV8oT9wX53M46Sgt1fZgpWnJRSt2UMYY3Lwzz9BtdvPb2ALkifCLRgbLYgQ9n+kb5zhtd/PzkZeaisWudNA801bhut6UdaclFKXVTIsKBphoONNXQMzzJM2/28A9tvfz0ZD/b6yt54kCQj961ntfODfDdN7s41j1CSUEuX95Tz+P3BV03A8UtdIWulALiuzh/2NbLd9/spnt4irwcYSFmqPMW87X7gnyxuZ6KYvvvRnUjXaErpZbFU5jH1w5s4rH9QX7z7iD/cu4qD97m4yNb1zlyXsxapAldKfU+OTnCQ3f4eegOv9WhqGXSIQpKKeUSmtCVUsolNKErpZRLaEJXSimX0ISulFIuoQldKaVcQhO6Ukq5hCZ0pZRyiaxu/ReRQaBnhQ+vAYbSGI7duPnr06/Nudz89TnpawsYY3xL3SmrCX01RKQtlVkGTuXmr0+/Nudy89fnxq9NSy5KKeUSmtCVUsolnJTQn7I6gAxz89enX5tzufnrc93X5pgaulJKqVtz0gpdKaXULTgioYvIx0TkHRE5LyJ/YXU86SIi9SLyuoicE5GzIvINq2NKNxHJFZEOEfm51bGkm4hUisgPReTtxL/hfqtjShcR+beJ78kzIvI9EVn6BGkbE5HviMiAiJy57rYqEXlVRDoTv3/wMFWHsX1CF5Fc4P8BPg7cCfy+iNxpbVRpswD8O2PMVmAf8Mcu+tqSvgGcszqIDPlvwCvGmDuAbbjk6xSRWuBfA83GmLuBXODL1ka1at8FPnbDbX8BvGaM2QK8lvjc0Wyf0IE9wHljzEVjzBzwfeAzFseUFsaYy8aY9sTH48QTQq21UaWPiNQBnwS+bXUs6SYi5cCDwN8DGGPmjDERa6NKqzygWETygBKg3+J4VsUY81sgfMPNnwGeSXz8DPBIVoPKACck9Fqg97rPL+GipJckIkFgB9BibSRp9bfAnwExqwPJgM3AIPB0oqT0bREptTqodDDG9AF/DYSAy8CoMeafrY0qI9YZYy5DfHEFOP7MPSck9MVOp3VVa46IeIB/BP6NMWbM6njSQUQ+BQwYY45bHUuG5AE7gb8zxuwAJnHBW3aARC35M8AmYCNQKiJftTYqlQonJPRLQP11n9fh8Ld/1xORfOLJ/AVjzMtWx5NGB4BPi0g38TLZwyLyvLUhpdUl4JIxJvmO6ofEE7wbfAToMsYMGmPmgZeB+yyOKROuisgGgMTvAxbHs2pOSOjHgC0isklECohfnPmpxTGlhYgI8RrsOWPM31gdTzoZY75pjKkzxgSJ/5v9yhjjmlWeMeYK0Csitydu+jDwloUhpVMI2CciJYnv0Q/jkgu+N/gp8Hji48eBn1gYS1rkWR3AUowxCyLyJ8AviV9t/44x5qzFYaXLAeAgcFpETiRu+9+MMf9kYUwqdf8L8EJioXEReMLieNLCGNMiIj8E2ol3YnXg8F2VIvI94F8BNSJyCfg/gb8C/kFE/oD4D7EvWhdheuhOUaWUcgknlFyUUkqlQBO6Ukq5hCZ0pZRyCU3oSinlEprQlVLKJTShK6WUS2hCV0opl9CErpRSLvH/A/10bX2bFFqfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"users[\"Age\"].plot() # Plotting age of users"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d694db358>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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S/+6zXve4zwBIsrOqTmDwfsuDSb48gVqXAZcA/wa8EvhAko9W1RTwqSSv7Lne/UmeN9/7FlDvcQbTSXMN1s5Kckyf9eAQDPTFVlVXAB9N8qU57vt4krf1WGsFsGfv6GC/+85O8s991TqSVdWxwLIk/z6hxz8e+BkGo8sdSXZNqM5zk3xtEo99qKiqFwKnM3gj+74J17oR+AKwce+/WVUtA34NOC/Ja3qudw/wpiTb5rjvoSTPnOOwhdU80gNd0pGhm55bx+C06FO6zbsYfKjg+iT7v0pfaL03M3gv5yc+MryqLkryd33WAwNdkno/o+1g1TPQJR3xqurBJKcd7vUOtbNcJGkiFvmMtkWvBwa6pCPHMp7gjLYG6hnoko4YNzC4sO+u/e+oqs0N1HMOXZJa4cfnSlIjDHRJaoSBLkmNMNAlqREGuiQ14n8BC03ZW/rU91MAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"users[\"Age\"].plot.bar() # Bar chart for ages"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d69573ac8>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"users[\"Age\"].plot.area()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x19d69474940>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"users.plot.scatter('Salary', 'Age') # For Dataframes"
]
},
{
"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>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rahul Yadav</td>\n",
" <td>36</td>\n",
" <td>500000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>New Delhi (Delhi)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" <td>55</td>\n",
" <td>550000.0</td>\n",
" <td>Java developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rishikesh Agrawani</td>\n",
" <td>26</td>\n",
" <td>200000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Anupam Shukla</td>\n",
" <td>27</td>\n",
" <td>250000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Robery Griesemer</td>\n",
" <td>44</td>\n",
" <td>600000.0</td>\n",
" <td>JavaScript developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gudal Sharma</td>\n",
" <td>24</td>\n",
" <td>100000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Quinton Clark</td>\n",
" <td>35</td>\n",
" <td>45000.0</td>\n",
" <td>Java developer</td>\n",
" <td>New City</td>\n",
" <td>UAE</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Niyati Jain</td>\n",
" <td>24</td>\n",
" <td>80000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Rajim</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Malinikesh Agrawani</td>\n",
" <td>22</td>\n",
" <td>NaN</td>\n",
" <td>Student</td>\n",
" <td>Kondagaon</td>\n",
" <td>India</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hira Dewangan</td>\n",
" <td>24</td>\n",
" <td>50000.0</td>\n",
" <td>Python developer</td>\n",
" <td>New Raipur</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Hemkesh Agrawani</td>\n",
" <td>24</td>\n",
" <td>NaN</td>\n",
" <td>Student</td>\n",
" <td>Kondagaon</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Larry Page</td>\n",
" <td>45</td>\n",
" <td>6000000.0</td>\n",
" <td>Computer Scientist</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession \\\n",
"0 Rahul Yadav 36 500000.0 PHP developer \n",
"1 James Gosling 55 550000.0 Java developer \n",
"2 Rishikesh Agrawani 26 200000.0 Python developer \n",
"3 Anupam Shukla 27 250000.0 PHP developer \n",
"4 Robery Griesemer 44 600000.0 JavaScript developer \n",
"5 Gudal Sharma 24 100000.0 Python developer \n",
"6 Quinton Clark 35 45000.0 Java developer \n",
"7 Niyati Jain 24 80000.0 Python developer \n",
"8 Malinikesh Agrawani 22 NaN Student \n",
"9 Hira Dewangan 24 50000.0 Python developer \n",
"10 Hemkesh Agrawani 24 NaN Student \n",
"11 Larry Page 45 6000000.0 Computer Scientist \n",
"\n",
" Address Country Sex \n",
"0 New Delhi (Delhi) India Male \n",
"1 Newyork USA Male \n",
"2 Raipur (CG) India Male \n",
"3 Raipur (CG) India Male \n",
"4 Newyork USA Male \n",
"5 Raipur (CG) India Female \n",
"6 New City UAE Male \n",
"7 Rajim India Female \n",
"8 Kondagaon India Female \n",
"9 New Raipur India Male \n",
"10 Kondagaon India Male \n",
"11 Newyork USA Male "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Fullname</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rahul Yadav</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rishikesh Agrawani</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Anupam Shukla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Robery Griesemer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gudal Sharma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Quinton Clark</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Niyati Jain</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Malinikesh Agrawani</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hira Dewangan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Hemkesh Agrawani</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Larry Page</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname\n",
"0 Rahul Yadav\n",
"1 James Gosling\n",
"2 Rishikesh Agrawani\n",
"3 Anupam Shukla\n",
"4 Robery Griesemer\n",
"5 Gudal Sharma\n",
"6 Quinton Clark\n",
"7 Niyati Jain\n",
"8 Malinikesh Agrawani\n",
"9 Hira Dewangan\n",
"10 Hemkesh Agrawani\n",
"11 Larry Page"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.loc[:, [\"Fullname\"]] # Show Fullname of all users"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" <td>55</td>\n",
" <td>550000.0</td>\n",
" <td>Java developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Robery Griesemer</td>\n",
" <td>44</td>\n",
" <td>600000.0</td>\n",
" <td>JavaScript developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Larry Page</td>\n",
" <td>45</td>\n",
" <td>6000000.0</td>\n",
" <td>Computer Scientist</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession Address Country \\\n",
"1 James Gosling 55 550000.0 Java developer Newyork USA \n",
"4 Robery Griesemer 44 600000.0 JavaScript developer Newyork USA \n",
"11 Larry Page 45 6000000.0 Computer Scientist Newyork USA \n",
"\n",
" Sex \n",
"1 Male \n",
"4 Male \n",
"11 Male "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.loc[users[\"Country\"] == \"USA\"] # Finding users from Dubai"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Quinton Clark</td>\n",
" <td>35</td>\n",
" <td>45000.0</td>\n",
" <td>Java developer</td>\n",
" <td>New City</td>\n",
" <td>UAE</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession Address Country Sex\n",
"6 Quinton Clark 35 45000.0 Java developer New City UAE Male"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.loc[users[\"Country\"] == \"UAE\"] # Finding users from UAE"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.loc[users[\"Country\"] == \"USA\"][\"Country\"].count() # Number of users from USA"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2018-05-06', '2018-05-10', '2018-05-07'], dtype='datetime64[ns]', freq=None)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.to_datetime([2, 6, 3], unit=\"D\", origin=pd.Timestamp(\"2018/05/04\"))\n",
"dates"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dob</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2018-05-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-05-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-05-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dob\n",
"0 2018-05-06\n",
"1 2018-05-10\n",
"2 2018-05-07"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = pd.DataFrame(dates, columns=[\"dob\"])\n",
"d"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .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>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rahul Yadav</td>\n",
" <td>36</td>\n",
" <td>500000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>New Delhi (Delhi)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" <td>55</td>\n",
" <td>550000.0</td>\n",
" <td>Java developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rishikesh Agrawani</td>\n",
" <td>26</td>\n",
" <td>200000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession Address \\\n",
"0 Rahul Yadav 36 500000.0 PHP developer New Delhi (Delhi) \n",
"1 James Gosling 55 550000.0 Java developer Newyork \n",
"2 Rishikesh Agrawani 26 200000.0 Python developer Raipur (CG) \n",
"\n",
" Country Sex \n",
"0 India Male \n",
"1 USA Male \n",
"2 India Male "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users2 = users.loc[0:2, :]\n",
"users2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>Fullname</th>\n",
" <th>Age</th>\n",
" <th>Salary</th>\n",
" <th>Profession</th>\n",
" <th>Address</th>\n",
" <th>Country</th>\n",
" <th>Sex</th>\n",
" <th>dob</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rahul Yadav</td>\n",
" <td>36</td>\n",
" <td>500000.0</td>\n",
" <td>PHP developer</td>\n",
" <td>New Delhi (Delhi)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" <td>2018-05-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>James Gosling</td>\n",
" <td>55</td>\n",
" <td>550000.0</td>\n",
" <td>Java developer</td>\n",
" <td>Newyork</td>\n",
" <td>USA</td>\n",
" <td>Male</td>\n",
" <td>2018-05-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rishikesh Agrawani</td>\n",
" <td>26</td>\n",
" <td>200000.0</td>\n",
" <td>Python developer</td>\n",
" <td>Raipur (CG)</td>\n",
" <td>India</td>\n",
" <td>Male</td>\n",
" <td>2018-05-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fullname Age Salary Profession Address \\\n",
"0 Rahul Yadav 36 500000.0 PHP developer New Delhi (Delhi) \n",
"1 James Gosling 55 550000.0 Java developer Newyork \n",
"2 Rishikesh Agrawani 26 200000.0 Python developer Raipur (CG) \n",
"\n",
" Country Sex dob \n",
"0 India Male 2018-05-06 \n",
"1 USA Male 2018-05-10 \n",
"2 India Male 2018-05-07 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat([users2, d], axis=1) # Adding 1 more column"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
Users.csv
Fullname Age Salary Profession Address Country Sex
Rahul Yadav 36 500000 PHP developer New Delhi (Delhi) India Male
James Gosling 55 550000 Java developer Newyork USA Male
Rishikesh Agrawani 26 200000 Python developer Raipur (CG) India Male
Anupam Shukla 27 250000 PHP developer Raipur (CG) India Male
Robery Griesemer 44 600000 JavaScript developer Newyork USA Male
Gudal Sharma 24 100000 Python developer Raipur (CG) India Female
Quinton Clark 35 45000 Java developer New City UAE Male
Niyati Jain 24 80000 Python developer Rajim India Female
Malinikesh Agrawani 22 Student Kondagaon India Female
Hira Dewangan 24 50000 Python developer New Raipur India Male
Hemkesh Agrawani 24 Student Kondagaon India Male
Larry Page 45 6000000 Computer Scientist Newyork USA Male
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