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proelbtn/2020-07-30.ipynb

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2020-07-30.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"vignettes = pd.read_csv(\"vignettes.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>self</th>\n",
" <th>alison</th>\n",
" <th>jane</th>\n",
" <th>moses</th>\n",
" <th>china</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>776</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>777</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>780</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>781 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" self alison jane moses china age\n",
"0 1 5 5 2 0 31\n",
"1 1 1 5 5 0 54\n",
"2 2 3 1 1 0 50\n",
"3 2 4 2 1 0 22\n",
"4 2 3 3 3 0 52\n",
".. ... ... ... ... ... ...\n",
"776 3 3 3 4 1 55\n",
"777 3 5 3 2 1 25\n",
"778 5 5 4 4 1 36\n",
"779 3 5 5 5 1 50\n",
"780 2 3 3 2 1 23\n",
"\n",
"[781 rows x 6 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vignettes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 問1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"china_data = vignettes[vignettes[\"china\"] == 1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.6219081272084805"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"china_data[\"self\"].sum() / china_data.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 問2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"mexico_data = vignettes[vignettes[\"china\"] == 0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.8253012048192772"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mexico_data[\"self\"].sum() / mexico_data.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 問3"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"cross = pd.crosstab(vignettes.china, vignettes.self, normalize=\"index\")\n",
"cross.index = [\"mexico\", \"china\"]\n",
"cross.columns = [\"1:No\", \"2:A Little\", \"3:Some\", \"4:Much\", \"5:Almost All\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>1:No</th>\n",
" <th>2:A Little</th>\n",
" <th>3:Some</th>\n",
" <th>4:Much</th>\n",
" <th>5:Almost All</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mexico</th>\n",
" <td>0.514056</td>\n",
" <td>0.291165</td>\n",
" <td>0.110442</td>\n",
" <td>0.024096</td>\n",
" <td>0.060241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>china</th>\n",
" <td>0.250883</td>\n",
" <td>0.229682</td>\n",
" <td>0.265018</td>\n",
" <td>0.155477</td>\n",
" <td>0.098940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1:No 2:A Little 3:Some 4:Much 5:Almost All\n",
"mexico 0.514056 0.291165 0.110442 0.024096 0.060241\n",
"china 0.250883 0.229682 0.265018 0.155477 0.098940"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"cross.plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 問4"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"159"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"china_data[china_data[\"self\"] < china_data[\"moses\"]].shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 問5"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"124"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mexico_data[mexico_data[\"self\"] < mexico_data[\"moses\"]].shape[0]"
]
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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