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Created September 8, 2017 07:04
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slides-dem
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# import pandas"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# pd.read_csv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic = pd.read_csv(\"titanic/titanic.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# head"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>pclass</th>\n",
" <th>survived</th>\n",
" <th>name</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>ticket</th>\n",
" <th>fare</th>\n",
" <th>cabin</th>\n",
" <th>embarked</th>\n",
" <th>boat</th>\n",
" <th>body</th>\n",
" <th>home.dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Allen, Miss. Elisabeth Walton</td>\n",
" <td>female</td>\n",
" <td>29.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24160</td>\n",
" <td>211.337494</td>\n",
" <td>B5</td>\n",
" <td>Southampton</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>St Louis, MO</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Allison, Master. Hudson Trevor</td>\n",
" <td>male</td>\n",
" <td>0.9167</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>113781</td>\n",
" <td>151.550003</td>\n",
" <td>C22 C26</td>\n",
" <td>Southampton</td>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ / Chesterville, ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Allison, Miss. Helen Loraine</td>\n",
" <td>female</td>\n",
" <td>2.0000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>113781</td>\n",
" <td>151.550003</td>\n",
" <td>C22 C26</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ / Chesterville, ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Allison, Mr. Hudson Joshua Crei</td>\n",
" <td>male</td>\n",
" <td>30.0000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>113781</td>\n",
" <td>151.550003</td>\n",
" <td>C22 C26</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>135.0</td>\n",
" <td>Montreal, PQ / Chesterville, ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Allison, Mrs. Hudson J C (Bessi</td>\n",
" <td>female</td>\n",
" <td>25.0000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>113781</td>\n",
" <td>151.550003</td>\n",
" <td>C22 C26</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ / Chesterville, ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Anderson, Mr. Harry</td>\n",
" <td>male</td>\n",
" <td>48.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19952</td>\n",
" <td>26.549999</td>\n",
" <td>E12</td>\n",
" <td>Southampton</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Andrews, Miss. Kornelia Theodos</td>\n",
" <td>female</td>\n",
" <td>63.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>13502</td>\n",
" <td>77.958298</td>\n",
" <td>D7</td>\n",
" <td>Southampton</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>Hudson, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Andrews, Mr. Thomas Jr</td>\n",
" <td>male</td>\n",
" <td>39.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112050</td>\n",
" <td>0.000000</td>\n",
" <td>A36</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Belfast, NI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Appleton, Mrs. Edward Dale (Cha</td>\n",
" <td>female</td>\n",
" <td>53.0000</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>11769</td>\n",
" <td>51.479198</td>\n",
" <td>C101</td>\n",
" <td>Southampton</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>Bayside, Queens, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17609</td>\n",
" <td>49.504200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>Montevideo, Uruguay</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id pclass survived name sex age \\\n",
"0 1 1st 1 Allen, Miss. Elisabeth Walton female 29.0000 \n",
"1 2 1st 1 Allison, Master. Hudson Trevor male 0.9167 \n",
"2 3 1st 0 Allison, Miss. Helen Loraine female 2.0000 \n",
"3 4 1st 0 Allison, Mr. Hudson Joshua Crei male 30.0000 \n",
"4 5 1st 0 Allison, Mrs. Hudson J C (Bessi female 25.0000 \n",
"5 6 1st 1 Anderson, Mr. Harry male 48.0000 \n",
"6 7 1st 1 Andrews, Miss. Kornelia Theodos female 63.0000 \n",
"7 8 1st 0 Andrews, Mr. Thomas Jr male 39.0000 \n",
"8 9 1st 1 Appleton, Mrs. Edward Dale (Cha female 53.0000 \n",
"9 10 1st 0 Artagaveytia, Mr. Ramon male 71.0000 \n",
"\n",
" sibsp parch ticket fare cabin embarked boat body \\\n",
"0 0 0 24160 211.337494 B5 Southampton 2 NaN \n",
"1 1 2 113781 151.550003 C22 C26 Southampton 11 NaN \n",
"2 1 2 113781 151.550003 C22 C26 Southampton NaN NaN \n",
"3 1 2 113781 151.550003 C22 C26 Southampton NaN 135.0 \n",
"4 1 2 113781 151.550003 C22 C26 Southampton NaN NaN \n",
"5 0 0 19952 26.549999 E12 Southampton 3 NaN \n",
"6 1 0 13502 77.958298 D7 Southampton 10 NaN \n",
"7 0 0 112050 0.000000 A36 Southampton NaN NaN \n",
"8 2 0 11769 51.479198 C101 Southampton D NaN \n",
"9 0 0 PC 17609 49.504200 NaN Cherbourg NaN 22.0 \n",
"\n",
" home.dest \n",
"0 St Louis, MO \n",
"1 Montreal, PQ / Chesterville, ON \n",
"2 Montreal, PQ / Chesterville, ON \n",
"3 Montreal, PQ / Chesterville, ON \n",
"4 Montreal, PQ / Chesterville, ON \n",
"5 New York, NY \n",
"6 Hudson, NY \n",
"7 Belfast, NI \n",
"8 Bayside, Queens, NY \n",
"9 Montevideo, Uruguay "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# row selector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## H2"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>pclass</th>\n",
" <th>survived</th>\n",
" <th>name</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>ticket</th>\n",
" <th>fare</th>\n",
" <th>cabin</th>\n",
" <th>embarked</th>\n",
" <th>boat</th>\n",
" <th>body</th>\n",
" <th>home.dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Astor, Col. John Jacob</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17757</td>\n",
" <td>227.524994</td>\n",
" <td>C62 C64</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>124.0</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Astor, Mrs. John Jacob (Madelei</td>\n",
" <td>female</td>\n",
" <td>18.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17757</td>\n",
" <td>227.524994</td>\n",
" <td>C62 C64</td>\n",
" <td>Cherbourg</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Aubart, Mme. Leontine Pauline</td>\n",
" <td>female</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17477</td>\n",
" <td>69.300003</td>\n",
" <td>B35</td>\n",
" <td>Cherbourg</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>Paris, France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Barber, Miss. Ellen \\\"Nellie\\\"</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19877</td>\n",
" <td>78.849998</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry W</td>\n",
" <td>male</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27042</td>\n",
" <td>30.000000</td>\n",
" <td>A23</td>\n",
" <td>Southampton</td>\n",
" <td>B</td>\n",
" <td>NaN</td>\n",
" <td>Hessle, Yorks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Baumann, Mr. John D</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17318</td>\n",
" <td>25.924999</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Baxter, Mr. Quigg Edmond</td>\n",
" <td>male</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>PC 17558</td>\n",
" <td>247.520798</td>\n",
" <td>B58 B60</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Baxter, Mrs. James (Helene DeLa</td>\n",
" <td>female</td>\n",
" <td>50.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>PC 17558</td>\n",
" <td>247.520798</td>\n",
" <td>B58 B60</td>\n",
" <td>Cherbourg</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bazzani, Miss. Albina</td>\n",
" <td>female</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11813</td>\n",
" <td>76.291702</td>\n",
" <td>D15</td>\n",
" <td>Cherbourg</td>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Beattie, Mr. Thomson</td>\n",
" <td>male</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13050</td>\n",
" <td>75.241699</td>\n",
" <td>C6</td>\n",
" <td>Cherbourg</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>Winnipeg, MN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id pclass survived name sex age sibsp \\\n",
"10 11 1st 0 Astor, Col. John Jacob male 47.0 1 \n",
"11 12 1st 1 Astor, Mrs. John Jacob (Madelei female 18.0 1 \n",
"12 13 1st 1 Aubart, Mme. Leontine Pauline female 24.0 0 \n",
"13 14 1st 1 Barber, Miss. Ellen \\\"Nellie\\\" female 26.0 0 \n",
"14 15 1st 1 Barkworth, Mr. Algernon Henry W male 80.0 0 \n",
"15 16 1st 0 Baumann, Mr. John D male NaN 0 \n",
"16 17 1st 0 Baxter, Mr. Quigg Edmond male 24.0 0 \n",
"17 18 1st 1 Baxter, Mrs. James (Helene DeLa female 50.0 0 \n",
"18 19 1st 1 Bazzani, Miss. Albina female 32.0 0 \n",
"19 20 1st 0 Beattie, Mr. Thomson male 36.0 0 \n",
"\n",
" parch ticket fare cabin embarked boat body \\\n",
"10 0 PC 17757 227.524994 C62 C64 Cherbourg NaN 124.0 \n",
"11 0 PC 17757 227.524994 C62 C64 Cherbourg 4 NaN \n",
"12 0 PC 17477 69.300003 B35 Cherbourg 9 NaN \n",
"13 0 19877 78.849998 NaN Southampton 6 NaN \n",
"14 0 27042 30.000000 A23 Southampton B NaN \n",
"15 0 PC 17318 25.924999 NaN Southampton NaN NaN \n",
"16 1 PC 17558 247.520798 B58 B60 Cherbourg NaN NaN \n",
"17 1 PC 17558 247.520798 B58 B60 Cherbourg 6 NaN \n",
"18 0 11813 76.291702 D15 Cherbourg 8 NaN \n",
"19 0 13050 75.241699 C6 Cherbourg A NaN \n",
"\n",
" home.dest \n",
"10 New York, NY \n",
"11 New York, NY \n",
"12 Paris, France \n",
"13 NaN \n",
"14 Hessle, Yorks \n",
"15 New York, NY \n",
"16 Montreal, PQ \n",
"17 Montreal, PQ \n",
"18 NaN \n",
"19 Winnipeg, MN "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic[titanic.age < 10]\n",
"titanic[titanic.pclass == \"1st\"]\n",
"titanic[(titanic.age < 30) & (titanic.age > 10)]\n",
"titanic[(titanic.pclass == \"1st\") | (titanic.pclass == \"2nd\")]\n",
"titanic.iloc[10:20]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1309, 15)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.columns\n",
"titanic.index\n",
"titanic.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>29.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.9167</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>48.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>63.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>39.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>53.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>71.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>47.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>18.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>24.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>26.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>80.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>24.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>50.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>32.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>36.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>37.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>47.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>26.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>42.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>29.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>25.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>19.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>35.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>28.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1279</th>\n",
" <td>14.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1280</th>\n",
" <td>22.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1281</th>\n",
" <td>22.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1282</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1283</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1284</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1285</th>\n",
" <td>32.5000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>38.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1287</th>\n",
" <td>51.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1288</th>\n",
" <td>18.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1289</th>\n",
" <td>21.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1290</th>\n",
" <td>47.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1291</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1292</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1293</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>28.5000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1295</th>\n",
" <td>21.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1296</th>\n",
" <td>27.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1297</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1298</th>\n",
" <td>36.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>27.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1300</th>\n",
" <td>15.0000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>45.5000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1303</th>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1304</th>\n",
" <td>14.5000</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1305</th>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1306</th>\n",
" <td>26.5000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td>27.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1308</th>\n",
" <td>29.0000</td>\n",
" <td>male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex\n",
"0 29.0000 female\n",
"1 0.9167 male\n",
"2 2.0000 female\n",
"3 30.0000 male\n",
"4 25.0000 female\n",
"5 48.0000 male\n",
"6 63.0000 female\n",
"7 39.0000 male\n",
"8 53.0000 female\n",
"9 71.0000 male\n",
"10 47.0000 male\n",
"11 18.0000 female\n",
"12 24.0000 female\n",
"13 26.0000 female\n",
"14 80.0000 male\n",
"15 NaN male\n",
"16 24.0000 male\n",
"17 50.0000 female\n",
"18 32.0000 female\n",
"19 36.0000 male\n",
"20 37.0000 male\n",
"21 47.0000 female\n",
"22 26.0000 male\n",
"23 42.0000 female\n",
"24 29.0000 female\n",
"25 25.0000 male\n",
"26 25.0000 male\n",
"27 19.0000 female\n",
"28 35.0000 female\n",
"29 28.0000 male\n",
"... ... ...\n",
"1279 14.0000 female\n",
"1280 22.0000 male\n",
"1281 22.0000 male\n",
"1282 NaN male\n",
"1283 NaN male\n",
"1284 NaN male\n",
"1285 32.5000 male\n",
"1286 38.0000 female\n",
"1287 51.0000 male\n",
"1288 18.0000 male\n",
"1289 21.0000 male\n",
"1290 47.0000 female\n",
"1291 NaN male\n",
"1292 NaN male\n",
"1293 NaN male\n",
"1294 28.5000 male\n",
"1295 21.0000 male\n",
"1296 27.0000 male\n",
"1297 NaN male\n",
"1298 36.0000 male\n",
"1299 27.0000 male\n",
"1300 15.0000 female\n",
"1301 45.5000 male\n",
"1302 NaN male\n",
"1303 NaN male\n",
"1304 14.5000 female\n",
"1305 NaN female\n",
"1306 26.5000 male\n",
"1307 27.0000 male\n",
"1308 29.0000 male\n",
"\n",
"[1309 rows x 2 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic[[\"age\",\"sex\"]]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"id int64\n",
"pclass object\n",
"survived int64\n",
"name object\n",
"sex object\n",
"age float64\n",
"sibsp int64\n",
"parch int64\n",
"ticket object\n",
"fare float64\n",
"cabin object\n",
"embarked object\n",
"boat object\n",
"body float64\n",
"home.dest object\n",
"dtype: object"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.describe()\n",
"titanic.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"titanic[\"survived\"] = titanic.survived.astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic[\"new_col\"] = titanic.survived.astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"titanic[\"new_col2\"] = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"matplotlib.style.use('ggplot')\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: Int64Index([ 2, 3, 4, 7, 9, 10, 15, 16, 19, 25,\n",
" ...\n",
" 1298, 1299, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308],\n",
" dtype='int64', length=809),\n",
" 1: Int64Index([ 0, 1, 5, 6, 8, 11, 12, 13, 14, 17,\n",
" ...\n",
" 1254, 1256, 1257, 1258, 1260, 1261, 1277, 1286, 1290, 1300],\n",
" dtype='int64', length=500)}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x258606df748>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEdCAYAAAACUaxyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nETGcgl5ExHD/D06fr7RuAQ92AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25860734550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic.groupby(\"survived\").size().plot()\n",
"titanic.groupby(\"survived\").size().plot(kind=\"bar\")\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000025860DDBFD0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000025860E4FB70>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000025860DEEF60>], dtype=object)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AKSkpuOaaa7BmzZpOT2LBwkU8BBQCIv7EE08AAHw+X/v/AUAQBAwaNAhXXXVV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\nDodhuIhzOBwOw3AR53A4HIb5/0XlJBR7lpkLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25860dd2e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic.groupby([\"survived\",\"sex\",\"pclass\"]).size()\n",
"titanic.groupby([\"survived\",\"sex\",\"pclass\"]).size().unstack()\n",
"\n",
"# titanic.groupby([\"survived\",\"sex\",\"pclass\"]).size().unstack([\"sex\"])\n",
"titanic.groupby([\"survived\",\"pclass\"]).size().unstack([\"pclass\"]).plot.pie(subplots=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tw711 = pd.read_csv(\"tw711/711_stores_df.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "No group keys passed!",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-63-9c31bd2a2a68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtw711\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[0;32m 4414\u001b[0m return groupby(self, by=by, axis=axis, level=level, as_index=as_index,\n\u001b[0;32m 4415\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4416\u001b[1;33m **kwargs)\n\u001b[0m\u001b[0;32m 4417\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4418\u001b[0m def asfreq(self, freq, method=None, how=None, normalize=False,\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(obj, by, **kwds)\u001b[0m\n\u001b[0;32m 1697\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'invalid type: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1699\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1700\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[0;32m 390\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m mutated=self.mutated)\n\u001b[0m\u001b[0;32m 393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 394\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[1;34m(obj, key, axis, level, sort, mutated)\u001b[0m\n\u001b[0;32m 2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2715\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2716\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No group keys passed!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2718\u001b[0m \u001b[1;31m# create the internals grouper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: No group keys passed!"
]
}
],
"source": [
"tw711.groupby([])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index([False, True], dtype='object', name='survived')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.groupby([\"survived\",\"sex\"]).size().unstack().index\n",
"titanic.groupby([\"survived\",\"sex\"]).size().unstack().columns\n",
"titanic.groupby([\"survived\",\"sex\"]).size().unstack()\n",
"titanic.groupby([\"survived\",\"sex\"]).size().unstack().plot(kind=\"bar\",stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['female', 'male'], dtype='object', name='sex')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.groupby([\"survived\",\"sex\"]).size().unstack().columns"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>sex</th>\n",
" <th>female</th>\n",
" <th>male</th>\n",
" </tr>\n",
" <tr>\n",
" <th>survived</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>127</td>\n",
" <td>682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>339</td>\n",
" <td>161</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"sex female male\n",
"survived \n",
"False 127 682\n",
"True 339 161"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.groupby([\"survived\",\"sex\"]).size().unstack()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>sex</th>\n",
" <th>female</th>\n",
" <th>male</th>\n",
" </tr>\n",
" <tr>\n",
" <th>survived</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>127</td>\n",
" <td>682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>339</td>\n",
" <td>161</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"sex female male\n",
"survived \n",
"False 127 682\n",
"True 339 161"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.groupby([\"survived\",\"sex\"]).size().unstack()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2585fc64908>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7CzBMMdzzYqL2OXlMa5t5Bf0nTj75ZM466yxef/11nE4nyWQSl8tFMpmkoqICONiDHx0d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\nETGcgl5ExHD/D06fr7RuAQ92AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2585fcc1940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic.groupby(\"survived\").size().plot(kind=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2585ea41128>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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L4pbRiNvGIvzVvNNK01BDSbcgNZS0e3LXY5F7d9undzyZDlf9Ce3uhxFRnetc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2585e6eafd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic.plot()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2585e713048>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Pn//5n2PXLqkLxzYbVb/R/WXELVaWwft3JweVHtAlQ/eSC0eGbn75f8P4H30CO558WkS/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\nnc5h7tp+3jD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"text/plain": [
"<matplotlib.figure.Figure at 0x2585e23acf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lottery.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lottery = pd.read_csv(\"lottery/lottery_df.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>X1</th>\n",
" <th>X2</th>\n",
" <th>X3</th>\n",
" <th>X4</th>\n",
" <th>X5</th>\n",
" <th>X6</th>\n",
" <th>S</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>105/08/12</td>\n",
" <td>8</td>\n",
" <td>35</td>\n",
" <td>43</td>\n",
" <td>33</td>\n",
" <td>20</td>\n",
" <td>42</td>\n",
" <td>10</td>\n",
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" <td>37</td>\n",
" <td>35</td>\n",
" <td>17</td>\n",
" <td>45</td>\n",
" <td>30</td>\n",
" <td>41</td>\n",
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" <td>105/08/05</td>\n",
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" <td>20</td>\n",
" <td>22</td>\n",
" <td>28</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>105/07/29</td>\n",
" <td>28</td>\n",
" <td>9</td>\n",
" <td>27</td>\n",
" <td>15</td>\n",
" <td>30</td>\n",
" <td>41</td>\n",
" <td>13</td>\n",
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" <td>105/07/26</td>\n",
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" <td>41</td>\n",
" <td>19</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>15</td>\n",
" <td>37</td>\n",
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" <td>17</td>\n",
" <td>10</td>\n",
" <td>33</td>\n",
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" <td>49</td>\n",
" <td>27</td>\n",
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" <td>33</td>\n",
" <td>45</td>\n",
" <td>13</td>\n",
" <td>39</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>105/07/15</td>\n",
" <td>29</td>\n",
" <td>24</td>\n",
" <td>25</td>\n",
" <td>35</td>\n",
" <td>33</td>\n",
" <td>38</td>\n",
" <td>49</td>\n",
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" <td>105/07/12</td>\n",
" <td>42</td>\n",
" <td>31</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>25</td>\n",
" <td>34</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>105/07/08</td>\n",
" <td>13</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>19</td>\n",
" <td>35</td>\n",
" <td>23</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>105/07/05</td>\n",
" <td>9</td>\n",
" <td>17</td>\n",
" <td>40</td>\n",
" <td>6</td>\n",
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" <td>47</td>\n",
" <td>24</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>105/07/01</td>\n",
" <td>41</td>\n",
" <td>34</td>\n",
" <td>20</td>\n",
" <td>43</td>\n",
" <td>24</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
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" <td>46</td>\n",
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" <td>8</td>\n",
" <td>39</td>\n",
" <td>7</td>\n",
" <td>38</td>\n",
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" <tr>\n",
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" <td>105/06/24</td>\n",
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" <td>14</td>\n",
" <td>39</td>\n",
" <td>41</td>\n",
" <td>27</td>\n",
" <td>28</td>\n",
" <td>18</td>\n",
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" <td>2</td>\n",
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" <td>17</td>\n",
" <td>21</td>\n",
" <td>31</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>20</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>105/06/14</td>\n",
" <td>33</td>\n",
" <td>12</td>\n",
" <td>37</td>\n",
" <td>35</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>105/06/10</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>48</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>105/06/07</td>\n",
" <td>5</td>\n",
" <td>39</td>\n",
" <td>31</td>\n",
" <td>8</td>\n",
" <td>44</td>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>105/06/03</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>41</td>\n",
" <td>33</td>\n",
" <td>27</td>\n",
" <td>44</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>105/05/31</td>\n",
" <td>1</td>\n",
" <td>46</td>\n",
" <td>32</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>45</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>105/05/27</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>13</td>\n",
" <td>39</td>\n",
" <td>9</td>\n",
" <td>16</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>105/05/24</td>\n",
" <td>8</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>39</td>\n",
" <td>6</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>105/05/20</td>\n",
" <td>40</td>\n",
" <td>25</td>\n",
" <td>43</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>30</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>105/05/17</td>\n",
" <td>2</td>\n",
" <td>42</td>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>23</td>\n",
" <td>34</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>105/05/13</td>\n",
" <td>10</td>\n",
" <td>31</td>\n",
" <td>35</td>\n",
" <td>38</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>105/05/10</td>\n",
" <td>36</td>\n",
" <td>38</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>105/05/06</td>\n",
" <td>26</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" <td>42</td>\n",
" <td>39</td>\n",
" <td>37</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>105/05/03</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>43</td>\n",
" <td>41</td>\n",
" <td>33</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",
" </tr>\n",
" <tr>\n",
" <th>258</th>\n",
" <td>103/04/01</td>\n",
" <td>46</td>\n",
" <td>18</td>\n",
" <td>29</td>\n",
" <td>30</td>\n",
" <td>47</td>\n",
" <td>22</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>259</th>\n",
" <td>103/03/28</td>\n",
" <td>9</td>\n",
" <td>18</td>\n",
" <td>23</td>\n",
" <td>36</td>\n",
" <td>47</td>\n",
" <td>35</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>103/03/25</td>\n",
" <td>19</td>\n",
" <td>25</td>\n",
" <td>47</td>\n",
" <td>42</td>\n",
" <td>30</td>\n",
" <td>23</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td>103/03/21</td>\n",
" <td>47</td>\n",
" <td>26</td>\n",
" <td>46</td>\n",
" <td>8</td>\n",
" <td>31</td>\n",
" <td>18</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>262</th>\n",
" <td>103/03/18</td>\n",
" <td>26</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>39</td>\n",
" <td>12</td>\n",
" <td>28</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>103/03/14</td>\n",
" <td>47</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>41</td>\n",
" <td>27</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>103/03/11</td>\n",
" <td>40</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>103/03/07</td>\n",
" <td>38</td>\n",
" <td>26</td>\n",
" <td>6</td>\n",
" <td>37</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>103/03/04</td>\n",
" <td>43</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>37</td>\n",
" <td>26</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>103/02/28</td>\n",
" <td>17</td>\n",
" <td>24</td>\n",
" <td>13</td>\n",
" <td>18</td>\n",
" <td>32</td>\n",
" <td>28</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>103/02/25</td>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>46</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>103/02/21</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>23</td>\n",
" <td>40</td>\n",
" <td>28</td>\n",
" <td>48</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>103/02/18</td>\n",
" <td>47</td>\n",
" <td>19</td>\n",
" <td>6</td>\n",
" <td>26</td>\n",
" <td>32</td>\n",
" <td>20</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>103/02/14</td>\n",
" <td>24</td>\n",
" <td>10</td>\n",
" <td>48</td>\n",
" <td>33</td>\n",
" <td>20</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>103/02/11</td>\n",
" <td>15</td>\n",
" <td>31</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>44</td>\n",
" <td>22</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>103/02/07</td>\n",
" <td>22</td>\n",
" <td>5</td>\n",
" <td>28</td>\n",
" <td>11</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>103/02/04</td>\n",
" <td>19</td>\n",
" <td>46</td>\n",
" <td>32</td>\n",
" <td>12</td>\n",
" <td>42</td>\n",
" <td>8</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>103/02/03</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>103/02/02</td>\n",
" <td>14</td>\n",
" <td>45</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>19</td>\n",
" <td>13</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>103/02/01</td>\n",
" <td>36</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>103/01/31</td>\n",
" <td>38</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" <td>49</td>\n",
" <td>48</td>\n",
" <td>27</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>103/01/30</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" <td>49</td>\n",
" <td>48</td>\n",
" <td>45</td>\n",
" <td>44</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>103/01/28</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>7</td>\n",
" <td>26</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>103/01/24</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>29</td>\n",
" <td>41</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>103/01/21</td>\n",
" <td>22</td>\n",
" <td>43</td>\n",
" <td>12</td>\n",
" <td>44</td>\n",
" <td>38</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>103/01/17</td>\n",
" <td>20</td>\n",
" <td>29</td>\n",
" <td>13</td>\n",
" <td>15</td>\n",
" <td>26</td>\n",
" <td>36</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>103/01/14</td>\n",
" <td>11</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>45</td>\n",
" <td>41</td>\n",
" <td>21</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>103/01/10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>17</td>\n",
" <td>30</td>\n",
" <td>37</td>\n",
" <td>32</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>103/01/07</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>25</td>\n",
" <td>36</td>\n",
" <td>21</td>\n",
" <td>35</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>103/01/03</td>\n",
" <td>11</td>\n",
" <td>35</td>\n",
" <td>21</td>\n",
" <td>18</td>\n",
" <td>37</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>288 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Date X1 X2 X3 X4 X5 X6 S\n",
"0 105/08/12 8 35 43 33 20 42 10\n",
"1 105/08/09 15 37 35 17 45 30 41\n",
"2 105/08/05 27 40 2 35 22 1 33\n",
"3 105/08/02 15 4 20 22 28 29 1\n",
"4 105/07/29 28 9 27 15 30 41 13\n",
"5 105/07/26 35 41 19 22 9 15 37\n",
"6 105/07/22 17 10 33 2 49 27 15\n",
"7 105/07/19 7 46 40 33 45 13 39\n",
"8 105/07/15 29 24 25 35 33 38 49\n",
"9 105/07/12 42 31 46 43 25 34 5\n",
"10 105/07/08 13 10 9 19 35 23 38\n",
"11 105/07/05 9 17 40 6 15 47 24\n",
"12 105/07/01 41 34 20 43 24 8 32\n",
"13 105/06/28 25 46 45 8 39 7 38\n",
"14 105/06/24 17 14 39 41 27 28 18\n",
"15 105/06/21 36 27 2 49 35 34 26\n",
"16 105/06/17 17 21 31 10 7 20 49\n",
"17 105/06/14 33 12 37 35 13 16 18\n",
"18 105/06/10 23 4 48 22 15 13 3\n",
"19 105/06/07 5 39 31 8 44 23 19\n",
"20 105/06/03 10 3 41 33 27 44 7\n",
"21 105/05/31 1 46 32 5 2 45 43\n",
"22 105/05/27 2 24 13 39 9 16 41\n",
"23 105/05/24 8 21 13 33 39 6 45\n",
"24 105/05/20 40 25 43 10 5 30 26\n",
"25 105/05/17 2 42 3 20 23 34 40\n",
"26 105/05/13 10 31 35 38 22 3 37\n",
"27 105/05/10 36 38 22 3 4 8 7\n",
"28 105/05/06 26 48 1 42 39 37 41\n",
"29 105/05/03 40 1 7 17 43 41 33\n",
".. ... .. .. .. .. .. .. ..\n",
"258 103/04/01 46 18 29 30 47 22 6\n",
"259 103/03/28 9 18 23 36 47 35 41\n",
"260 103/03/25 19 25 47 42 30 23 16\n",
"261 103/03/21 47 26 46 8 31 18 20\n",
"262 103/03/18 26 40 35 39 12 28 16\n",
"263 103/03/14 47 8 40 41 27 2 28\n",
"264 103/03/11 40 20 15 16 13 43 14\n",
"265 103/03/07 38 26 6 37 18 30 16\n",
"266 103/03/04 43 8 40 35 37 26 6\n",
"267 103/02/28 17 24 13 18 32 28 37\n",
"268 103/02/25 13 9 5 3 46 12 4\n",
"269 103/02/21 11 12 23 40 28 48 9\n",
"270 103/02/18 47 19 6 26 32 20 35\n",
"271 103/02/14 24 10 48 33 20 19 1\n",
"272 103/02/11 15 31 21 6 44 22 41\n",
"273 103/02/07 22 5 28 11 21 12 25\n",
"274 103/02/04 19 46 32 12 42 8 28\n",
"275 103/02/03 2 16 27 30 17 1 48\n",
"276 103/02/02 14 45 3 24 19 13 5\n",
"277 103/02/01 36 37 13 19 9 6 27\n",
"278 103/01/31 38 17 23 49 48 27 19\n",
"279 103/01/30 9 22 49 48 45 44 8\n",
"280 103/01/28 2 30 7 26 5 1 24\n",
"281 103/01/24 4 20 29 41 45 1 36\n",
"282 103/01/21 22 43 12 44 38 15 9\n",
"283 103/01/17 20 29 13 15 26 36 37\n",
"284 103/01/14 11 49 2 45 41 21 26\n",
"285 103/01/10 7 19 17 30 37 32 3\n",
"286 103/01/07 1 7 25 36 21 35 39\n",
"287 103/01/03 11 35 21 18 37 20 8\n",
"\n",
"[288 rows x 8 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lottery"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"creditcard = pd.read_csv(\"./credit_card/CreditCards.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>資料月份</th>\n",
" <th>金額單位</th>\n",
" <th>金融機構名稱</th>\n",
" <th>流通卡數</th>\n",
" <th>有效卡數</th>\n",
" <th>當月發卡數</th>\n",
" <th>當月停卡數</th>\n",
" <th>循環信用餘額</th>\n",
" <th>未到期分期付款餘額</th>\n",
" <th>當月簽帳金額</th>\n",
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" <th>逾期三個月以上帳款占應收帳款餘額含催收款之比率</th>\n",
" <th>逾期六個月以上帳款占應收帳款餘額含催收款之比率</th>\n",
" <th>備抵呆帳提足率</th>\n",
" <th>當月轉銷呆帳金額</th>\n",
" <th>當年度轉銷呆帳金額累計至資料月份</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>201611</td>\n",
" <td>新臺幣千元 ,卡</td>\n",
" <td>臺灣銀行</td>\n",
" <td>232084.0</td>\n",
" <td>111037.0</td>\n",
" <td>2060.0</td>\n",
" <td>1403.0</td>\n",
" <td>227764.0</td>\n",
" <td>9141.0</td>\n",
" <td>635976.0</td>\n",
" <td>1323.0</td>\n",
" <td>0.37</td>\n",
" <td>0.08</td>\n",
" <td>531.90</td>\n",
" <td>1226.0</td>\n",
" <td>10211.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>201611</td>\n",
" <td>新臺幣千元 ,卡</td>\n",
" <td>臺灣土地銀行</td>\n",
" <td>231633.0</td>\n",
" <td>128021.0</td>\n",
" <td>3324.0</td>\n",
" <td>988.0</td>\n",
" <td>286575.0</td>\n",
" <td>52501.0</td>\n",
" <td>865876.0</td>\n",
" <td>1102.0</td>\n",
" <td>0.43</td>\n",
" <td>0.28</td>\n",
" <td>1000.64</td>\n",
" <td>1728.0</td>\n",
" <td>16166.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201611</td>\n",
" <td>新臺幣千元 ,卡</td>\n",
" <td>合作金庫商業銀行</td>\n",
" <td>447070.0</td>\n",
" <td>270979.0</td>\n",
" <td>13897.0</td>\n",
" <td>3707.0</td>\n",
" <td>643011.0</td>\n",
" <td>236095.0</td>\n",
" <td>2592231.0</td>\n",
" <td>3406.0</td>\n",
" <td>0.36</td>\n",
" <td>0.31</td>\n",
" <td>290.39</td>\n",
" <td>7776.0</td>\n",
" <td>42112.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201611</td>\n",
" <td>新臺幣千元 ,卡</td>\n",
" <td>第一商業銀行</td>\n",
" <td>949771.0</td>\n",
" <td>638761.0</td>\n",
" <td>10289.0</td>\n",
" <td>10740.0</td>\n",
" <td>1442600.0</td>\n",
" <td>1193942.0</td>\n",
" <td>4433394.0</td>\n",
" <td>18204.0</td>\n",
" <td>0.17</td>\n",
" <td>0.00</td>\n",
" <td>1836.20</td>\n",
" <td>5986.0</td>\n",
" <td>62750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>201611</td>\n",
" <td>新臺幣千元 ,卡</td>\n",
" <td>華南商業銀行</td>\n",
" <td>919883.0</td>\n",
" <td>669791.0</td>\n",
" <td>43145.0</td>\n",
" <td>9308.0</td>\n",
" <td>814346.0</td>\n",
" <td>1916067.0</td>\n",
" <td>4474420.0</td>\n",
" <td>1720.0</td>\n",
" <td>0.16</td>\n",
" <td>0.00</td>\n",
" <td>489.54</td>\n",
" <td>0.0</td>\n",
" <td>36400.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 資料月份 金額單位 金融機構名稱 流通卡數 有效卡數 當月發卡數 當月停卡數 \\\n",
"0 201611 新臺幣千元 ,卡 臺灣銀行 232084.0 111037.0 2060.0 1403.0 \n",
"1 201611 新臺幣千元 ,卡 臺灣土地銀行 231633.0 128021.0 3324.0 988.0 \n",
"2 201611 新臺幣千元 ,卡 合作金庫商業銀行 447070.0 270979.0 13897.0 3707.0 \n",
"3 201611 新臺幣千元 ,卡 第一商業銀行 949771.0 638761.0 10289.0 10740.0 \n",
"4 201611 新臺幣千元 ,卡 華南商業銀行 919883.0 669791.0 43145.0 9308.0 \n",
"\n",
" 循環信用餘額 未到期分期付款餘額 當月簽帳金額 當月預借現金金額 逾期三個月以上帳款占應收帳款餘額含催收款之比率 \\\n",
"0 227764.0 9141.0 635976.0 1323.0 0.37 \n",
"1 286575.0 52501.0 865876.0 1102.0 0.43 \n",
"2 643011.0 236095.0 2592231.0 3406.0 0.36 \n",
"3 1442600.0 1193942.0 4433394.0 18204.0 0.17 \n",
"4 814346.0 1916067.0 4474420.0 1720.0 0.16 \n",
"\n",
" 逾期六個月以上帳款占應收帳款餘額含催收款之比率 備抵呆帳提足率 當月轉銷呆帳金額 當年度轉銷呆帳金額累計至資料月份 \n",
"0 0.08 531.90 1226.0 10211.0 \n",
"1 0.28 1000.64 1728.0 16166.0 \n",
"2 0.31 290.39 7776.0 42112.0 \n",
"3 0.00 1836.20 5986.0 62750.0 \n",
"4 0.00 489.54 0.0 36400.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"creditcard.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>資料月份</th>\n",
" <th>流通卡數</th>\n",
" <th>有效卡數</th>\n",
" <th>當月發卡數</th>\n",
" <th>當月停卡數</th>\n",
" <th>循環信用餘額</th>\n",
" <th>未到期分期付款餘額</th>\n",
" <th>當月簽帳金額</th>\n",
" <th>當月預借現金金額</th>\n",
" <th>逾期三個月以上帳款占應收帳款餘額含催收款之比率</th>\n",
" <th>逾期六個月以上帳款占應收帳款餘額含催收款之比率</th>\n",
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" <th>當月轉銷呆帳金額</th>\n",
" <th>當年度轉銷呆帳金額累計至資料月份</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2996.000000</td>\n",
" <td>2.996000e+03</td>\n",
" <td>2.996000e+03</td>\n",
" <td>2996.000000</td>\n",
" <td>2.996000e+03</td>\n",
" <td>2.996000e+03</td>\n",
" <td>2.270000e+03</td>\n",
" <td>2.996000e+03</td>\n",
" <td>2996.000000</td>\n",
" <td>2996.000000</td>\n",
" <td>2996.000000</td>\n",
" <td>2993.000000</td>\n",
" <td>2996.000000</td>\n",
" <td>2.996000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>201302.319760</td>\n",
" <td>9.679885e+05</td>\n",
" <td>6.188266e+05</td>\n",
" <td>12768.680908</td>\n",
" <td>9.507385e+03</td>\n",
" <td>3.714946e+06</td>\n",
" <td>2.070225e+06</td>\n",
" <td>4.476430e+06</td>\n",
" <td>64897.861816</td>\n",
" <td>0.444953</td>\n",
" <td>0.121278</td>\n",
" <td>1352.615209</td>\n",
" <td>13477.039720</td>\n",
" <td>9.079654e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>198.650265</td>\n",
" <td>1.312594e+06</td>\n",
" <td>9.062258e+05</td>\n",
" <td>26527.141123</td>\n",
" <td>2.625821e+04</td>\n",
" <td>5.688075e+06</td>\n",
" <td>3.282676e+06</td>\n",
" <td>6.842430e+06</td>\n",
" <td>130715.566305</td>\n",
" <td>0.454693</td>\n",
" <td>0.278380</td>\n",
" <td>6893.184273</td>\n",
" <td>25724.087155</td>\n",
" <td>1.766923e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>201001.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>3.500000e+01</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>2.630000e+02</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-288069.000000</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>201109.000000</td>\n",
" <td>1.465728e+05</td>\n",
" <td>7.181900e+04</td>\n",
" <td>899.250000</td>\n",
" <td>8.877500e+02</td>\n",
" <td>3.012618e+05</td>\n",
" <td>2.750075e+04</td>\n",
" <td>4.250135e+05</td>\n",
" <td>1019.500000</td>\n",
" <td>0.150000</td>\n",
" <td>0.000000</td>\n",
" <td>316.940000</td>\n",
" <td>458.750000</td>\n",
" <td>5.799750e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>201306.000000</td>\n",
" <td>4.163040e+05</td>\n",
" <td>2.193615e+05</td>\n",
" <td>4423.500000</td>\n",
" <td>3.670500e+03</td>\n",
" <td>9.069745e+05</td>\n",
" <td>3.981480e+05</td>\n",
" <td>1.524247e+06</td>\n",
" <td>8579.500000</td>\n",
" <td>0.290000</td>\n",
" <td>0.000000</td>\n",
" <td>653.920000</td>\n",
" <td>4530.500000</td>\n",
" <td>2.317200e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>201503.000000</td>\n",
" <td>9.451208e+05</td>\n",
" <td>6.393918e+05</td>\n",
" <td>13544.500000</td>\n",
" <td>1.139850e+04</td>\n",
" <td>5.048222e+06</td>\n",
" <td>2.940345e+06</td>\n",
" <td>4.240157e+06</td>\n",
" <td>84748.250000</td>\n",
" <td>0.660000</td>\n",
" <td>0.120000</td>\n",
" <td>1294.250000</td>\n",
" <td>15944.250000</td>\n",
" <td>9.639850e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>201611.000000</td>\n",
" <td>6.608277e+06</td>\n",
" <td>4.142569e+06</td>\n",
" <td>485859.000000</td>\n",
" <td>1.123050e+06</td>\n",
" <td>4.281890e+07</td>\n",
" <td>1.678376e+07</td>\n",
" <td>4.084536e+07</td>\n",
" <td>858444.000000</td>\n",
" <td>7.000000</td>\n",
" <td>6.540000</td>\n",
" <td>349705.880000</td>\n",
" <td>353498.000000</td>\n",
" <td>2.030737e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 資料月份 流通卡數 有效卡數 當月發卡數 當月停卡數 \\\n",
"count 2996.000000 2.996000e+03 2.996000e+03 2996.000000 2.996000e+03 \n",
"mean 201302.319760 9.679885e+05 6.188266e+05 12768.680908 9.507385e+03 \n",
"std 198.650265 1.312594e+06 9.062258e+05 26527.141123 2.625821e+04 \n",
"min 201001.000000 0.000000e+00 3.500000e+01 0.000000 1.000000e+00 \n",
"25% 201109.000000 1.465728e+05 7.181900e+04 899.250000 8.877500e+02 \n",
"50% 201306.000000 4.163040e+05 2.193615e+05 4423.500000 3.670500e+03 \n",
"75% 201503.000000 9.451208e+05 6.393918e+05 13544.500000 1.139850e+04 \n",
"max 201611.000000 6.608277e+06 4.142569e+06 485859.000000 1.123050e+06 \n",
"\n",
" 循環信用餘額 未到期分期付款餘額 當月簽帳金額 當月預借現金金額 \\\n",
"count 2.996000e+03 2.270000e+03 2.996000e+03 2996.000000 \n",
"mean 3.714946e+06 2.070225e+06 4.476430e+06 64897.861816 \n",
"std 5.688075e+06 3.282676e+06 6.842430e+06 130715.566305 \n",
"min 0.000000e+00 0.000000e+00 2.630000e+02 0.000000 \n",
"25% 3.012618e+05 2.750075e+04 4.250135e+05 1019.500000 \n",
"50% 9.069745e+05 3.981480e+05 1.524247e+06 8579.500000 \n",
"75% 5.048222e+06 2.940345e+06 4.240157e+06 84748.250000 \n",
"max 4.281890e+07 1.678376e+07 4.084536e+07 858444.000000 \n",
"\n",
" 逾期三個月以上帳款占應收帳款餘額含催收款之比率 逾期六個月以上帳款占應收帳款餘額含催收款之比率 備抵呆帳提足率 \\\n",
"count 2996.000000 2996.000000 2993.000000 \n",
"mean 0.444953 0.121278 1352.615209 \n",
"std 0.454693 0.278380 6893.184273 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.150000 0.000000 316.940000 \n",
"50% 0.290000 0.000000 653.920000 \n",
"75% 0.660000 0.120000 1294.250000 \n",
"max 7.000000 6.540000 349705.880000 \n",
"\n",
" 當月轉銷呆帳金額 當年度轉銷呆帳金額累計至資料月份 \n",
"count 2996.000000 2.996000e+03 \n",
"mean 13477.039720 9.079654e+04 \n",
"std 25724.087155 1.766923e+05 \n",
"min -288069.000000 0.000000e+00 \n",
"25% 458.750000 5.799750e+03 \n",
"50% 4530.500000 2.317200e+04 \n",
"75% 15944.250000 9.639850e+04 \n",
"max 353498.000000 2.030737e+06 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"creditcard.describe()"
]
},
{
"cell_type": "code",
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"outputs": [],
"source": []
},
{
"cell_type": "code",
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{
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"outputs": [
{
"data": {
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>112050</td>\n",
" <td>0.000000</td>\n",
" <td>A36</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Belfast, NI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Appleton, Mrs. Edward Dale (Cha</td>\n",
" <td>female</td>\n",
" <td>53.0000</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>11769</td>\n",
" <td>51.479198</td>\n",
" <td>C101</td>\n",
" <td>Southampton</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>Bayside, Queens, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Artagaveytia, Mr. Ramon</td>\n",
" <td>male</td>\n",
" <td>71.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17609</td>\n",
" <td>49.504200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>Montevideo, Uruguay</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Astor, Col. John Jacob</td>\n",
" <td>male</td>\n",
" <td>47.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17757</td>\n",
" <td>227.524994</td>\n",
" <td>C62 C64</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>124.0</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Astor, Mrs. John Jacob (Madelei</td>\n",
" <td>female</td>\n",
" <td>18.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17757</td>\n",
" <td>227.524994</td>\n",
" <td>C62 C64</td>\n",
" <td>Cherbourg</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Aubart, Mme. Leontine Pauline</td>\n",
" <td>female</td>\n",
" <td>24.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17477</td>\n",
" <td>69.300003</td>\n",
" <td>B35</td>\n",
" <td>Cherbourg</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>Paris, France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Barber, Miss. Ellen \\\"Nellie\\\"</td>\n",
" <td>female</td>\n",
" <td>26.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19877</td>\n",
" <td>78.849998</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Barkworth, Mr. Algernon Henry W</td>\n",
" <td>male</td>\n",
" <td>80.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27042</td>\n",
" <td>30.000000</td>\n",
" <td>A23</td>\n",
" <td>Southampton</td>\n",
" <td>B</td>\n",
" <td>NaN</td>\n",
" <td>Hessle, Yorks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Baumann, Mr. John D</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17318</td>\n",
" <td>25.924999</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Baxter, Mr. Quigg Edmond</td>\n",
" <td>male</td>\n",
" <td>24.0000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>PC 17558</td>\n",
" <td>247.520798</td>\n",
" <td>B58 B60</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Baxter, Mrs. James (Helene DeLa</td>\n",
" <td>female</td>\n",
" <td>50.0000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>PC 17558</td>\n",
" <td>247.520798</td>\n",
" <td>B58 B60</td>\n",
" <td>Cherbourg</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>Montreal, PQ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bazzani, Miss. Albina</td>\n",
" <td>female</td>\n",
" <td>32.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11813</td>\n",
" <td>76.291702</td>\n",
" <td>D15</td>\n",
" <td>Cherbourg</td>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Beattie, Mr. Thomson</td>\n",
" <td>male</td>\n",
" <td>36.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13050</td>\n",
" <td>75.241699</td>\n",
" <td>C6</td>\n",
" <td>Cherbourg</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>Winnipeg, MN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mr. Richard Leonard</td>\n",
" <td>male</td>\n",
" <td>37.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11751</td>\n",
" <td>52.554199</td>\n",
" <td>D35</td>\n",
" <td>Southampton</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mrs. Richard Leonard</td>\n",
" <td>female</td>\n",
" <td>47.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11751</td>\n",
" <td>52.554199</td>\n",
" <td>D35</td>\n",
" <td>Southampton</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.000000</td>\n",
" <td>C148</td>\n",
" <td>Cherbourg</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>New York, NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bidois, Miss. Rosalie</td>\n",
" <td>female</td>\n",
" <td>42.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17757</td>\n",
" <td>227.524994</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bird, Miss. Ellen</td>\n",
" <td>female</td>\n",
" <td>29.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17483</td>\n",
" <td>221.779205</td>\n",
" <td>C97</td>\n",
" <td>Southampton</td>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1st</td>\n",
" <td>0</td>\n",
" <td>Birnbaum, Mr. Jakob</td>\n",
" <td>male</td>\n",
" <td>25.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13905</td>\n",
" <td>26.000000</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>148.0</td>\n",
" <td>San Francisco, CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bishop, Mr. Dickinson H</td>\n",
" <td>male</td>\n",
" <td>25.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>11967</td>\n",
" <td>91.079201</td>\n",
" <td>B49</td>\n",
" <td>Cherbourg</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>Dowagiac, MI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bishop, Mrs. Dickinson H (Helen</td>\n",
" <td>female</td>\n",
" <td>19.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>11967</td>\n",
" <td>91.079201</td>\n",
" <td>B49</td>\n",
" <td>Cherbourg</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>Dowagiac, MI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bissette, Miss. Amelia</td>\n",
" <td>female</td>\n",
" <td>35.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17760</td>\n",
" <td>135.633301</td>\n",
" <td>C99</td>\n",
" <td>Southampton</td>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>1st</td>\n",
" <td>1</td>\n",
" <td>Bjornstrom-Steffansson, Mr. Mau</td>\n",
" <td>male</td>\n",
" <td>28.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>110564</td>\n",
" <td>26.549999</td>\n",
" <td>C52</td>\n",
" <td>Southampton</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>Stockholm, Sweden / Washington,</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1279</th>\n",
" <td>1280</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Vestrom, Miss. Hulda Amanda Ado</td>\n",
" <td>female</td>\n",
" <td>14.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>350406</td>\n",
" <td>7.854200</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1280</th>\n",
" <td>1281</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Vovk, Mr. Janko</td>\n",
" <td>male</td>\n",
" <td>22.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349252</td>\n",
" <td>7.895800</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1281</th>\n",
" <td>1282</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Waelens, Mr. Achille</td>\n",
" <td>male</td>\n",
" <td>22.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345767</td>\n",
" <td>9.000000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Antwerp, Belgium / Stanton, OH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1282</th>\n",
" <td>1283</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>359309</td>\n",
" <td>8.050000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1283</th>\n",
" <td>1284</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Warren, Mr. Charles William</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>C.A. 49867</td>\n",
" <td>7.550000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1284</th>\n",
" <td>1285</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Webber, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/OQ 3101316</td>\n",
" <td>8.050000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1285</th>\n",
" <td>1286</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wenzel, Mr. Linhart</td>\n",
" <td>male</td>\n",
" <td>32.5000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345775</td>\n",
" <td>9.500000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>298.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>1287</td>\n",
" <td>3rd</td>\n",
" <td>1</td>\n",
" <td>Whabee, Mrs. George Joseph (Sha</td>\n",
" <td>female</td>\n",
" <td>38.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2688</td>\n",
" <td>7.229200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1287</th>\n",
" <td>1288</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Widegren, Mr. Carl/Charles Pete</td>\n",
" <td>male</td>\n",
" <td>51.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>347064</td>\n",
" <td>7.750000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1288</th>\n",
" <td>1289</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wiklund, Mr. Jakob Alfred</td>\n",
" <td>male</td>\n",
" <td>18.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3101267</td>\n",
" <td>6.495800</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>314.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1289</th>\n",
" <td>1290</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wiklund, Mr. Karl Johan</td>\n",
" <td>male</td>\n",
" <td>21.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3101266</td>\n",
" <td>6.495800</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1290</th>\n",
" <td>1291</td>\n",
" <td>3rd</td>\n",
" <td>1</td>\n",
" <td>Wilkes, Mrs. James (Ellen Needs</td>\n",
" <td>female</td>\n",
" <td>47.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>363272</td>\n",
" <td>7.000000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1291</th>\n",
" <td>1292</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Willer, Mr. Aaron (\\\"Abi Weller\\\"</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3410</td>\n",
" <td>8.712500</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1292</th>\n",
" <td>1293</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Willey, Mr. Edward</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>S.O./P.P. 751</td>\n",
" <td>7.550000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1293</th>\n",
" <td>1294</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Williams, Mr. Howard Hugh \\\"Harr</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A/5 2466</td>\n",
" <td>8.050000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>1295</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Williams, Mr. Leslie</td>\n",
" <td>male</td>\n",
" <td>28.5000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>54636</td>\n",
" <td>16.100000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1295</th>\n",
" <td>1296</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Windelov, Mr. Einar</td>\n",
" <td>male</td>\n",
" <td>21.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/OQ 3101317</td>\n",
" <td>7.250000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1296</th>\n",
" <td>1297</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wirz, Mr. Albert</td>\n",
" <td>male</td>\n",
" <td>27.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>315154</td>\n",
" <td>8.662500</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>131.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1297</th>\n",
" <td>1298</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wiseman, Mr. Phillippe</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A/4. 34244</td>\n",
" <td>7.250000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1298</th>\n",
" <td>1299</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Wittevrongel, Mr. Camille</td>\n",
" <td>male</td>\n",
" <td>36.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345771</td>\n",
" <td>9.500000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>1300</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Yasbeck, Mr. Antoni</td>\n",
" <td>male</td>\n",
" <td>27.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2659</td>\n",
" <td>14.454200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1300</th>\n",
" <td>1301</td>\n",
" <td>3rd</td>\n",
" <td>1</td>\n",
" <td>Yasbeck, Mrs. Antoni (Selini Al</td>\n",
" <td>female</td>\n",
" <td>15.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2659</td>\n",
" <td>14.454200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>1302</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Youseff, Mr. Gerious</td>\n",
" <td>male</td>\n",
" <td>45.5000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2628</td>\n",
" <td>7.225000</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>312.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>1303</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Yousif, Mr. Wazli</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2647</td>\n",
" <td>7.225000</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1303</th>\n",
" <td>1304</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Yousseff, Mr. Gerious</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2627</td>\n",
" <td>14.458300</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1304</th>\n",
" <td>1305</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Zabour, Miss. Hileni</td>\n",
" <td>female</td>\n",
" <td>14.5000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2665</td>\n",
" <td>14.454200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>328.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1305</th>\n",
" <td>1306</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Zabour, Miss. Thamine</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2665</td>\n",
" <td>14.454200</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1306</th>\n",
" <td>1307</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Zakarian, Mr. Mapriededer</td>\n",
" <td>male</td>\n",
" <td>26.5000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2656</td>\n",
" <td>7.225000</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>304.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td>1308</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Zakarian, Mr. Ortin</td>\n",
" <td>male</td>\n",
" <td>27.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2670</td>\n",
" <td>7.225000</td>\n",
" <td>NaN</td>\n",
" <td>Cherbourg</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1308</th>\n",
" <td>1309</td>\n",
" <td>3rd</td>\n",
" <td>0</td>\n",
" <td>Zimmerman, Mr. Leo</td>\n",
" <td>male</td>\n",
" <td>29.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>315082</td>\n",
" <td>7.875000</td>\n",
" <td>NaN</td>\n",
" <td>Southampton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 pclass survived name sex \\\n",
"0 1 1st 1 Allen, Miss. Elisabeth Walton female \n",
"1 2 1st 1 Allison, Master. Hudson Trevor male \n",
"2 3 1st 0 Allison, Miss. Helen Loraine female \n",
"3 4 1st 0 Allison, Mr. Hudson Joshua Crei male \n",
"4 5 1st 0 Allison, Mrs. Hudson J C (Bessi female \n",
"5 6 1st 1 Anderson, Mr. Harry male \n",
"6 7 1st 1 Andrews, Miss. Kornelia Theodos female \n",
"7 8 1st 0 Andrews, Mr. Thomas Jr male \n",
"8 9 1st 1 Appleton, Mrs. Edward Dale (Cha female \n",
"9 10 1st 0 Artagaveytia, Mr. Ramon male \n",
"10 11 1st 0 Astor, Col. John Jacob male \n",
"11 12 1st 1 Astor, Mrs. John Jacob (Madelei female \n",
"12 13 1st 1 Aubart, Mme. Leontine Pauline female \n",
"13 14 1st 1 Barber, Miss. Ellen \\\"Nellie\\\" female \n",
"14 15 1st 1 Barkworth, Mr. Algernon Henry W male \n",
"15 16 1st 0 Baumann, Mr. John D male \n",
"16 17 1st 0 Baxter, Mr. Quigg Edmond male \n",
"17 18 1st 1 Baxter, Mrs. James (Helene DeLa female \n",
"18 19 1st 1 Bazzani, Miss. Albina female \n",
"19 20 1st 0 Beattie, Mr. Thomson male \n",
"20 21 1st 1 Beckwith, Mr. Richard Leonard male \n",
"21 22 1st 1 Beckwith, Mrs. Richard Leonard female \n",
"22 23 1st 1 Behr, Mr. Karl Howell male \n",
"23 24 1st 1 Bidois, Miss. Rosalie female \n",
"24 25 1st 1 Bird, Miss. Ellen female \n",
"25 26 1st 0 Birnbaum, Mr. Jakob male \n",
"26 27 1st 1 Bishop, Mr. Dickinson H male \n",
"27 28 1st 1 Bishop, Mrs. Dickinson H (Helen female \n",
"28 29 1st 1 Bissette, Miss. Amelia female \n",
"29 30 1st 1 Bjornstrom-Steffansson, Mr. Mau male \n",
"... ... ... ... ... ... \n",
"1279 1280 3rd 0 Vestrom, Miss. Hulda Amanda Ado female \n",
"1280 1281 3rd 0 Vovk, Mr. Janko male \n",
"1281 1282 3rd 0 Waelens, Mr. Achille male \n",
"1282 1283 3rd 0 Ware, Mr. Frederick male \n",
"1283 1284 3rd 0 Warren, Mr. Charles William male \n",
"1284 1285 3rd 0 Webber, Mr. James male \n",
"1285 1286 3rd 0 Wenzel, Mr. Linhart male \n",
"1286 1287 3rd 1 Whabee, Mrs. George Joseph (Sha female \n",
"1287 1288 3rd 0 Widegren, Mr. Carl/Charles Pete male \n",
"1288 1289 3rd 0 Wiklund, Mr. Jakob Alfred male \n",
"1289 1290 3rd 0 Wiklund, Mr. Karl Johan male \n",
"1290 1291 3rd 1 Wilkes, Mrs. James (Ellen Needs female \n",
"1291 1292 3rd 0 Willer, Mr. Aaron (\\\"Abi Weller\\\" male \n",
"1292 1293 3rd 0 Willey, Mr. Edward male \n",
"1293 1294 3rd 0 Williams, Mr. Howard Hugh \\\"Harr male \n",
"1294 1295 3rd 0 Williams, Mr. Leslie male \n",
"1295 1296 3rd 0 Windelov, Mr. Einar male \n",
"1296 1297 3rd 0 Wirz, Mr. Albert male \n",
"1297 1298 3rd 0 Wiseman, Mr. Phillippe male \n",
"1298 1299 3rd 0 Wittevrongel, Mr. Camille male \n",
"1299 1300 3rd 0 Yasbeck, Mr. Antoni male \n",
"1300 1301 3rd 1 Yasbeck, Mrs. Antoni (Selini Al female \n",
"1301 1302 3rd 0 Youseff, Mr. Gerious male \n",
"1302 1303 3rd 0 Yousif, Mr. Wazli male \n",
"1303 1304 3rd 0 Yousseff, Mr. Gerious male \n",
"1304 1305 3rd 0 Zabour, Miss. Hileni female \n",
"1305 1306 3rd 0 Zabour, Miss. Thamine female \n",
"1306 1307 3rd 0 Zakarian, Mr. Mapriededer male \n",
"1307 1308 3rd 0 Zakarian, Mr. Ortin male \n",
"1308 1309 3rd 0 Zimmerman, Mr. Leo male \n",
"\n",
" age sibsp parch ticket fare cabin \\\n",
"0 29.0000 0 0 24160 211.337494 B5 \n",
"1 0.9167 1 2 113781 151.550003 C22 C26 \n",
"2 2.0000 1 2 113781 151.550003 C22 C26 \n",
"3 30.0000 1 2 113781 151.550003 C22 C26 \n",
"4 25.0000 1 2 113781 151.550003 C22 C26 \n",
"5 48.0000 0 0 19952 26.549999 E12 \n",
"6 63.0000 1 0 13502 77.958298 D7 \n",
"7 39.0000 0 0 112050 0.000000 A36 \n",
"8 53.0000 2 0 11769 51.479198 C101 \n",
"9 71.0000 0 0 PC 17609 49.504200 NaN \n",
"10 47.0000 1 0 PC 17757 227.524994 C62 C64 \n",
"11 18.0000 1 0 PC 17757 227.524994 C62 C64 \n",
"12 24.0000 0 0 PC 17477 69.300003 B35 \n",
"13 26.0000 0 0 19877 78.849998 NaN \n",
"14 80.0000 0 0 27042 30.000000 A23 \n",
"15 NaN 0 0 PC 17318 25.924999 NaN \n",
"16 24.0000 0 1 PC 17558 247.520798 B58 B60 \n",
"17 50.0000 0 1 PC 17558 247.520798 B58 B60 \n",
"18 32.0000 0 0 11813 76.291702 D15 \n",
"19 36.0000 0 0 13050 75.241699 C6 \n",
"20 37.0000 1 1 11751 52.554199 D35 \n",
"21 47.0000 1 1 11751 52.554199 D35 \n",
"22 26.0000 0 0 111369 30.000000 C148 \n",
"23 42.0000 0 0 PC 17757 227.524994 NaN \n",
"24 29.0000 0 0 PC 17483 221.779205 C97 \n",
"25 25.0000 0 0 13905 26.000000 NaN \n",
"26 25.0000 1 0 11967 91.079201 B49 \n",
"27 19.0000 1 0 11967 91.079201 B49 \n",
"28 35.0000 0 0 PC 17760 135.633301 C99 \n",
"29 28.0000 0 0 110564 26.549999 C52 \n",
"... ... ... ... ... ... ... \n",
"1279 14.0000 0 0 350406 7.854200 NaN \n",
"1280 22.0000 0 0 349252 7.895800 NaN \n",
"1281 22.0000 0 0 345767 9.000000 NaN \n",
"1282 NaN 0 0 359309 8.050000 NaN \n",
"1283 NaN 0 0 C.A. 49867 7.550000 NaN \n",
"1284 NaN 0 0 SOTON/OQ 3101316 8.050000 NaN \n",
"1285 32.5000 0 0 345775 9.500000 NaN \n",
"1286 38.0000 0 0 2688 7.229200 NaN \n",
"1287 51.0000 0 0 347064 7.750000 NaN \n",
"1288 18.0000 1 0 3101267 6.495800 NaN \n",
"1289 21.0000 1 0 3101266 6.495800 NaN \n",
"1290 47.0000 1 0 363272 7.000000 NaN \n",
"1291 NaN 0 0 3410 8.712500 NaN \n",
"1292 NaN 0 0 S.O./P.P. 751 7.550000 NaN \n",
"1293 NaN 0 0 A/5 2466 8.050000 NaN \n",
"1294 28.5000 0 0 54636 16.100000 NaN \n",
"1295 21.0000 0 0 SOTON/OQ 3101317 7.250000 NaN \n",
"1296 27.0000 0 0 315154 8.662500 NaN \n",
"1297 NaN 0 0 A/4. 34244 7.250000 NaN \n",
"1298 36.0000 0 0 345771 9.500000 NaN \n",
"1299 27.0000 1 0 2659 14.454200 NaN \n",
"1300 15.0000 1 0 2659 14.454200 NaN \n",
"1301 45.5000 0 0 2628 7.225000 NaN \n",
"1302 NaN 0 0 2647 7.225000 NaN \n",
"1303 NaN 0 0 2627 14.458300 NaN \n",
"1304 14.5000 1 0 2665 14.454200 NaN \n",
"1305 NaN 1 0 2665 14.454200 NaN \n",
"1306 26.5000 0 0 2656 7.225000 NaN \n",
"1307 27.0000 0 0 2670 7.225000 NaN \n",
"1308 29.0000 0 0 315082 7.875000 NaN \n",
"\n",
" embarked boat body home.dest \n",
"0 Southampton 2 NaN St Louis, MO \n",
"1 Southampton 11 NaN Montreal, PQ / Chesterville, ON \n",
"2 Southampton NaN NaN Montreal, PQ / Chesterville, ON \n",
"3 Southampton NaN 135.0 Montreal, PQ / Chesterville, ON \n",
"4 Southampton NaN NaN Montreal, PQ / Chesterville, ON \n",
"5 Southampton 3 NaN New York, NY \n",
"6 Southampton 10 NaN Hudson, NY \n",
"7 Southampton NaN NaN Belfast, NI \n",
"8 Southampton D NaN Bayside, Queens, NY \n",
"9 Cherbourg NaN 22.0 Montevideo, Uruguay \n",
"10 Cherbourg NaN 124.0 New York, NY \n",
"11 Cherbourg 4 NaN New York, NY \n",
"12 Cherbourg 9 NaN Paris, France \n",
"13 Southampton 6 NaN NaN \n",
"14 Southampton B NaN Hessle, Yorks \n",
"15 Southampton NaN NaN New York, NY \n",
"16 Cherbourg NaN NaN Montreal, PQ \n",
"17 Cherbourg 6 NaN Montreal, PQ \n",
"18 Cherbourg 8 NaN NaN \n",
"19 Cherbourg A NaN Winnipeg, MN \n",
"20 Southampton 5 NaN New York, NY \n",
"21 Southampton 5 NaN New York, NY \n",
"22 Cherbourg 5 NaN New York, NY \n",
"23 Cherbourg 4 NaN NaN \n",
"24 Southampton 8 NaN NaN \n",
"25 Cherbourg NaN 148.0 San Francisco, CA \n",
"26 Cherbourg 7 NaN Dowagiac, MI \n",
"27 Cherbourg 7 NaN Dowagiac, MI \n",
"28 Southampton 8 NaN NaN \n",
"29 Southampton D NaN Stockholm, Sweden / Washington, \n",
"... ... ... ... ... \n",
"1279 Southampton NaN NaN NaN \n",
"1280 Southampton NaN NaN NaN \n",
"1281 Southampton NaN NaN Antwerp, Belgium / Stanton, OH \n",
"1282 Southampton NaN NaN NaN \n",
"1283 Southampton NaN NaN NaN \n",
"1284 Southampton NaN NaN NaN \n",
"1285 Southampton NaN 298.0 NaN \n",
"1286 Cherbourg C NaN NaN \n",
"1287 Southampton NaN NaN NaN \n",
"1288 Southampton NaN 314.0 NaN \n",
"1289 Southampton NaN NaN NaN \n",
"1290 Southampton NaN NaN NaN \n",
"1291 Southampton NaN NaN NaN \n",
"1292 Southampton NaN NaN NaN \n",
"1293 Southampton NaN NaN NaN \n",
"1294 Southampton NaN 14.0 NaN \n",
"1295 Southampton NaN NaN NaN \n",
"1296 Southampton NaN 131.0 NaN \n",
"1297 Southampton NaN NaN NaN \n",
"1298 Southampton NaN NaN NaN \n",
"1299 Cherbourg C NaN NaN \n",
"1300 Cherbourg NaN NaN NaN \n",
"1301 Cherbourg NaN 312.0 NaN \n",
"1302 Cherbourg NaN NaN NaN \n",
"1303 Cherbourg NaN NaN NaN \n",
"1304 Cherbourg NaN 328.0 NaN \n",
"1305 Cherbourg NaN NaN NaN \n",
"1306 Cherbourg NaN 304.0 NaN \n",
"1307 Cherbourg NaN NaN NaN \n",
"1308 Southampton NaN NaN NaN \n",
"\n",
"[1309 rows x 15 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(\"https://raw.githubusercontent.com/haven-jeon/introduction_to_most_usable_pkgs_in_project/master/bicdata/data/titanic.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv(\"https://raw.githubusercontent.com/bokeh/bokeh/master/bokeh/sampledata/auto-mpg.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"mpg.csv\",index=False, encoding=\"utf8\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 磁碟區 E 中的磁碟沒有標籤。\n",
" 磁碟區序號: 8CDB-4137\n",
"\n",
" E:\\c3h3\\SampleCodes\\Day2 的目錄\n",
"\n",
"2017/09/08 上午 11:07 <DIR> .\n",
"2017/09/08 上午 11:07 <DIR> ..\n",
"2017/09/08 上午 10:49 <DIR> .ipynb_checkpoints\n",
"2017/09/08 上午 09:08 <DIR> cloud_agent\n",
"2017/09/08 上午 09:08 <DIR> credit_card\n",
"2017/09/08 上午 09:08 <DIR> expedia_hotel\n",
"2017/09/07 下午 04:39 21,332 ggplot.ipynb\n",
"2017/09/08 上午 11:04 102,189 livedemo_practice.ipynb\n",
"2017/09/08 上午 09:08 <DIR> lottery\n",
"2017/09/07 下午 04:39 83,800 lottery.ipynb\n",
"2017/09/08 上午 09:08 <DIR> lvr_land\n",
"2017/09/08 上午 11:07 19,367 mpg.csv\n",
"2017/09/07 下午 04:39 1,703,936 mydb.sqlite\n",
"2017/09/08 上午 10:49 96,180 pandas_load_dataset.ipynb\n",
"2017/09/07 下午 04:39 184,634 pandas_pivot_unpivot.ipynb\n",
"2017/09/07 下午 04:39 443,141 pandas_ploting_basic.ipynb\n",
"2017/09/07 下午 04:39 12,958 pandas_sql.ipynb\n",
"2017/09/07 下午 04:39 53,464 pandas_sqlite.ipynb\n",
"2017/09/07 下午 04:39 19,960 plotly_basic.ipynb\n",
"2017/09/07 下午 04:39 3,184 plotly_setup.ipynb\n",
"2017/09/08 上午 09:08 <DIR> pxmart\n",
"2017/09/07 下午 04:39 9,889 pxmart.ipynb\n",
"2017/09/08 上午 09:08 <DIR> salary_data\n",
"2017/09/08 上午 09:08 <DIR> stock_major\n",
"2017/09/07 下午 04:39 159,859 stock_major.ipynb\n",
"2017/09/07 下午 04:39 181 test_ggplot.py\n",
"2017/09/07 下午 04:39 19,572 test-pandas-plot.ipynb\n",
"2017/09/08 上午 09:08 <DIR> titanic\n",
"2017/09/08 上午 09:08 <DIR> tw711\n",
" 16 個檔案 2,933,646 位元組\n",
" 13 個目錄 20,376,080,384 位元組可用\n"
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