json 绘制泰坦尼克数据：由Begüm领导的Schloss实验室代码审查任务
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting the Titanic Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Siblings/Spouses Aboard</th>\n",
" <th>Parents/Children Aboard</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Mr. Owen Harris Braund</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Miss. Laina Heikkinen</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Mr. William Henry Allen</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Name \\\n",
"0 0 3 Mr. Owen Harris Braund \n",
"1 1 1 Mrs. John Bradley (Florence Briggs Thayer) Cum... \n",
"2 1 3 Miss. Laina Heikkinen \n",
"3 1 1 Mrs. Jacques Heath (Lily May Peel) Futrelle \n",
"4 0 3 Mr. William Henry Allen \n",
"\n",
" Sex Age Siblings/Spouses Aboard Parents/Children Aboard Fare \n",
"0 male 22.0 1 0 7.2500 \n",
"1 female 38.0 1 0 71.2833 \n",
"2 female 26.0 0 0 7.9250 \n",
"3 female 35.0 1 0 53.1000 \n",
"4 male 35.0 0 0 8.0500 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"titanic_data = pd.read_csv('titanic.csv')\n",
"titanic_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate family size"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Siblings/Spouses Aboard</th>\n",
" <th>Parents/Children Aboard</th>\n",
" <th>Fare</th>\n",
" <th>family_size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Mr. Owen Harris Braund</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Miss. Laina Heikkinen</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Mr. William Henry Allen</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Name \\\n",
"0 0 3 Mr. Owen Harris Braund \n",
"1 1 1 Mrs. John Bradley (Florence Briggs Thayer) Cum... \n",
"2 1 3 Miss. Laina Heikkinen \n",
"3 1 1 Mrs. Jacques Heath (Lily May Peel) Futrelle \n",
"4 0 3 Mr. William Henry Allen \n",
"\n",
" Sex Age Siblings/Spouses Aboard Parents/Children Aboard Fare \\\n",
"0 male 22.0 1 0 7.2500 \n",
"1 female 38.0 1 0 71.2833 \n",
"2 female 26.0 0 0 7.9250 \n",
"3 female 35.0 1 0 53.1000 \n",
"4 male 35.0 0 0 8.0500 \n",
"\n",
" family_size \n",
"0 2 \n",
"1 2 \n",
"2 1 \n",
"3 2 \n",
"4 1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"family_sizes = list()\n",
"for index, row in titanic_data.iterrows():\n",
" family_sizes.append(row['Siblings/Spouses Aboard'] + row['Parents/Children Aboard'] + 1)\n",
"titanic_data['family_size'] = family_sizes\n",
"titanic_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make pretty plots"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"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": [
"sns.barplot(x='Sex', y='Survived', data=titanic_data, ci=None)\n",
"plt.savefig('sex_survival.svg', format='svg')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"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": [
"sns.barplot(x='Pclass', y='Survived', data=titanic_data, hue='Sex', ci=None)\n",
"plt.savefig('class_survival.svg', format='svg')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"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": [
"sns.barplot(x='family_size', y='Survived', data=titanic_data, hue='Pclass', ci=None)\n",
"plt.savefig('family_size_survival.svg', format='svg')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(ncols=1, nrows=2, sharex=True)\n",
"for sex, axis in (('male', 1), ('female', 0)):\n",
" sns.barplot(x='family_size', y='Survived', data=titanic_data.loc[titanic_data['Sex'] == sex], hue='Pclass', ax=ax[axis], ci=None)\n",
" ax[axis].set(title=sex)\n",
" if sex == 'female':\n",
" ax[axis].set(xlabel='')\n",
"plt.savefig('family_size_sex_survival.svg', format='svg')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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