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June 4, 2017 18:35
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Boxplot\n", | |
"月ごと、年ごとの散歩の距離の分布を可視化する。\n", | |
"\n", | |
"bobplot(箱ひげ図)を使うと外れ値、中央値、(外れ値以外の)最大値、最小値、第1四分位点、第3四分位点を一望してだいたいの分布の様子がわかる。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import psycopg2 as pg\n", | |
"import pandas.io.sql as psql\n", | |
"%matplotlib inline\n", | |
"\n", | |
"sql = '''\n", | |
"select \n", | |
" Extract(year from date) as year, \n", | |
" Extract(month from date) as month, \n", | |
" length\n", | |
"from walks\n", | |
"'''\n", | |
"\n", | |
"with pg.connect(host=\"localhost\", user=\"postgres\", database=\"postgres\") as conn:\n", | |
" df = psql.read_sql(sql, conn)\n", | |
"\n", | |
"df['year'] = df['year'].astype(int)\n", | |
"df['month'] = df['month'].astype(int)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 年ごとのBoxplot\n", | |
"2012年に大きな変化が起きていることがわかる、一気に距離が伸びた。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x103020160>" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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5nSHWVwNXAB9tWnYLcElm7o6I9cAlwNpJ7t+fmQ9PK+UcsXDhQi6++GI2bNiw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\nJAD+fxpnk9cWwaDoAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x10538c4e0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"df.boxplot(column=\"length\", by=\"year\", figsize=(16, 5))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 月ごとのBoxplot\n", | |
"8月が有意に歩けていないことがわかる。1月もなぜか伸び悩んでいる。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x105f9c4e0>" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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mM33hAPAV4KCI2BIRK4AzIuI7EfFtoBv438W2+0bE5QCZuQ14C3AlMAxclJk3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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x105f93828>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"df.boxplot(column=\"length\", by=\"month\", figsize=(12, 5))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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