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nishimotz/201230-python.ipynb

Created December 30, 2020 12:08
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「アンナほえたワン」を再集計した
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201230-python.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# すごい広島 with Python\n",
"\n",
"## 2020-12-30 @nishimotz / @24motz\n",
"\n",
"### 「アンナほえたワン」を再集計した\n",
"\n",
"過去の報告\n",
"\n",
"* https://gist.github.com/nishimotz/8a989398c1fca4ddcfdb10745676e4e2 → 2020年6月。今回は不要な処理を削って、データを追加\n",
"* https://www.slideshare.net/nishimotz/200429-python\n",
"* https://www.slideshare.net/nishimotz/191030-annawithpython"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 入力データ\n",
"* 記録時刻（約5分周期。時刻はUTC）, 直前の5分間にほえた回数\n",
"\n",
"dogbarks_xxx.csv\n",
"\n",
"```\n",
"2019-10-27 12:26:44+00:00,18\n",
"2019-10-27 22:45:05+00:00,3\n",
"2019-10-27 23:34:57+00:00,17\n",
"```\n",
"\n",
"* 200を超える値はシステムの誤動作（後述）\n",
"* 稼働しなかった日は欠損データ（後述）"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 第1列を datetime として読み込んで index にする\n",
"* 各列を datetime, barks と名付ける"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"filename = \"dogbarks_201230.csv\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(filename, parse_dates=[0], index_col=0, names=['datetime','barks'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"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>barks</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-12-29 05:28:09+00:00</th>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-29 06:23:00+00:00</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 01:14:51+00:00</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 03:34:28+00:00</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 06:04:03+00:00</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks\n",
"datetime \n",
"2020-12-29 05:28:09+00:00 33\n",
"2020-12-29 06:23:00+00:00 31\n",
"2020-12-30 01:14:51+00:00 4\n",
"2020-12-30 03:34:28+00:00 22\n",
"2020-12-30 06:04:03+00:00 3"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* インデックス列を UTC から JST に変換する\n",
"* インデックス列の名前を localtime にする"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df.index = df.index.tz_convert('Asia/Tokyo')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>barks</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-12-29 14:28:09+09:00</th>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-29 15:23:00+09:00</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 10:14:51+09:00</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 12:34:28+09:00</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 15:04:03+09:00</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks\n",
"datetime \n",
"2020-12-29 14:28:09+09:00 33\n",
"2020-12-29 15:23:00+09:00 31\n",
"2020-12-30 10:14:51+09:00 4\n",
"2020-12-30 12:34:28+09:00 22\n",
"2020-12-30 15:04:03+09:00 3"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df.index = df.index.set_names('localtime')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 200を超える値を除外する\n",
"\n",
"* 2019年10月ごろシステムが誤動作していた\n",
"* 不正な値を削除したい\n",
"* 5分間に 200 を超える場合は誤動作とみなす\n",
"* 200以下の場合だけを残して df を更新する"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df = df[ df['barks'] <= 200 ]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>barks</th>\n",
" </tr>\n",
" <tr>\n",
" <th>localtime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-12-29 14:28:09+09:00</th>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-29 15:23:00+09:00</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 10:14:51+09:00</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 12:34:28+09:00</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-30 15:04:03+09:00</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks\n",
"localtime \n",
"2020-12-29 14:28:09+09:00 33\n",
"2020-12-29 15:23:00+09:00 31\n",
"2020-12-30 10:14:51+09:00 4\n",
"2020-12-30 12:34:28+09:00 22\n",
"2020-12-30 15:04:03+09:00 3"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>barks</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3510.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>19.029915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>20.371373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>193.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks\n",
"count 3510.000000\n",
"mean 19.029915\n",
"std 20.371373\n",
"min 2.000000\n",
"25% 5.000000\n",
"50% 11.000000\n",
"75% 25.000000\n",
"max 193.000000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 合計が0の日を除外する\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 最初と最後の月は日数が少ない。稼働しなかった日（欠損データ）もある。\n",
"* リサンプリングで1日の sum を作り、合計が0の日を除外する。\n",
"* 本当に 0 だった（記録がなかった）日も除外している（が、ほとんどない）"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"ser_d = df['barks'].resample('D').sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"ser_d = ser_d[ ser_d > 0 ]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"localtime\n",
"2019-10-27 00:00:00+09:00 18\n",
"2019-10-28 00:00:00+09:00 372\n",
"2019-10-29 00:00:00+09:00 176\n",
"2019-10-30 00:00:00+09:00 337\n",
"2019-10-31 00:00:00+09:00 569\n",
"Name: barks, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_d.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"localtime\n",
"2020-12-03 00:00:00+09:00 127\n",
"2020-12-06 00:00:00+09:00 57\n",
"2020-12-07 00:00:00+09:00 24\n",
"2020-12-08 00:00:00+09:00 3\n",
"2020-12-09 00:00:00+09:00 44\n",
"2020-12-11 00:00:00+09:00 33\n",
"2020-12-12 00:00:00+09:00 8\n",
"2020-12-13 00:00:00+09:00 69\n",
"2020-12-18 00:00:00+09:00 7\n",
"2020-12-19 00:00:00+09:00 13\n",
"2020-12-20 00:00:00+09:00 97\n",
"2020-12-21 00:00:00+09:00 16\n",
"2020-12-23 00:00:00+09:00 13\n",
"2020-12-24 00:00:00+09:00 129\n",
"2020-12-25 00:00:00+09:00 4\n",
"2020-12-26 00:00:00+09:00 74\n",
"2020-12-27 00:00:00+09:00 30\n",
"2020-12-28 00:00:00+09:00 63\n",
"2020-12-29 00:00:00+09:00 158\n",
"2020-12-30 00:00:00+09:00 29\n",
"Name: barks, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_d.tail(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1日にほえた回数"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18,10))\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(ser_d.index, ser_d, label='barks')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 1週間、1か月ごとにリサンプリング"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ser_d_w = ser_d.resample('W').agg('mean')\n",
"fig = plt.figure(figsize=(18,10))\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(ser_d_w.index, ser_d_w, label='barks', width=5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"ser_d_m = ser_d.resample('M').agg('mean')\n",
"ser_d_m.index = ser_d_m.index.map(lambda t: t.replace(day=1))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nkvxykpdU1fVZ/sjG8SRvSpIxxtGqujvJg0meTnLrGOPUYoYOAAAAbAWrxokxxutWWPzes6x/e5LbNzIoAAAAYPvYyK91AAAAAGyYOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAqx3dAwAAABZr6cCh7iGs6vjBvd1DABo5cwIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQKtV40RVva+qnqyqz88su7Sq7q2qR6brS2b+dltVHauqh6vqxkUNHAAAANga1nLmxPuT3HTasgNJDo8xdic5PN1PVV2bZF+S66Zt3l1VF81ttAAAAMCWs2qcGGP8YZKvn7b45iR3TrfvTPLKmeV3jTGeGmN8OcmxJDfMaawAAADAFrTe75y4YozxeJJM15dPy69M8ujMeiemZQAAAAArmvcXYtYKy8aKK1bdUlVHqurIyZMn5zwMAAAA4EKx3jjxRFXtSpLp+slp+YkkV8+sd1WSx1bawRjjjjHGnjHGnp07d65zGAAAAMCFbr1x4p4k+6fb+5N8dGb5vqq6uKquSbI7yX0bGyIAAACwle1YbYWq+mCSlyS5rKpOJPnlJAeT3F1Vb0zylSSvTZIxxtGqujvJg0meTnLrGOPUgsYOAAAAbAGrxokxxuvO8KeXnWH925PcvpFBAQAAANvHvL8QEwAAAOCciBMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFY7ugcAAADnw9KBQ91DWNXxg3u7hwDQwpkTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWu3oHgAAAOfP0oFD3UNY1fGDe7uHAMB55swJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQKsN/ZRoVR1P8s0kp5I8PcbYU1WXJvmtJEtJjif5R2OMP9/YMAEAAICtah5nTvz9Mcb1Y4w90/0DSQ6PMXYnOTzdBwAAAFjRIj7WcXOSO6fbdyZ55QKOAQAAAGwRG/pYR5KR5L9X1UjyG2OMO5JcMcZ4PEnGGI9X1eUbHSQAAACczdKBQ91DWNXxg3u7h7BpbTROvHiM8dgUIO6tqi+sdcOquiXJLUnyvOc9b4PDAAAAAC5UG/pYxxjjsen6ySQfSXJDkieqaleSTNdPnmHbO8YYe8YYe3bu3LmRYQAAAAAXsHXHiar63qp6zjO3k/zDJJ9Pck+S/dNq+5N8dKODBAAAALaujXys44okH6mqZ/bzX8YYv19Vf5zk7qp6Y5KvJHntxocJAAAAbFXrjhNjjD9N8iMrLP9akpdtZFAAAADA9rGInxIFAAAAWDNxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABa7egeAABAh6UDh7qHsKrjB/d2DwEAzgtnTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWokTAAAAQCtxAgAAAGglTgAAAACtxAkAAACglTgBAAAAtBInAAAAgFbiBAAAANBKnAAAAABaiRMAAABAK3ECAAAAaCVOAAAAAK3ECQAAAKCVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArcQJAAAAoJU4AQAAALQSJwAAAIBW4gQAAADQSpwAAAAAWi0sTlTVTVX1cFUdq6oDizoOAAAAcGFbSJyoqouS/FqSn0pybZLXVdW1izgWAAAAcGFb1JkTNyQ5Nsb40zHG/0tyV5KbF3QsAAAA4AK2qDhxZZJHZ+6fmJYBAAAA/BU1xpj/Tqtem+TGMcbPTfdfn+SGMcYvzKxzS5Jbprs/kOTh03ZzWZI/m/vg2MzM+fZivrcPc739mPPtxXxvX+Z+ezHf28/snD9/jLFzkQfbsaD9nkhy9cz9q5I8NrvCGOOOJHecaQdVdWSMsWcxw2MzMufbi/nePsz19mPOtxfzvX2Z++3FfG8/53vOF/Wxjj9Osruqrqmqv5ZkX5J7FnQsAAAA4AK2kDMnxhhPV9Wbk/y3JBcled8Y4+gijgUAAABc2Bb1sY6MMT6W5GMb2MUZP/LBlmXOtxfzvX2Y6+3HnG8v5nv7Mvfbi/nefs7rnC/kCzEBAAAA1mpR3zkBAAAAsCZzixNVdXVVfaKqHqqqo1X1lmn5pVV1b1U9Ml1fMi1/7rT+t6rqXaft6x9X1Wen/bz9LMf80ar6XFUdq6r/WFU1LX9+VR2e9vEHVXXVvB4n39E057dX1aNV9a3Tlv+9qvp0VT1dVa9ZxOPd7tYx3y+vqvun1+j9VfXSmX2t+Npd4Zhneo2b7wXaZHP989PyB6rqk1V17fl4DraTTTbfb6iqk9N8P1BVP3c+noPtZpPN+Ttn5vuLVfUX5+M52K422dx7v75gTfPtvXqjOc/5inO5wjHn9359jDGXS5JdSf7OdPs5Sb6Y5Nokb09yYFp+IMmvTLe/N8lPJPn5JO+a2c9zk3wlyc7p/p1JXnaGY96X5MeTVJLfS/JT0/LfTrJ/uv3SJP95Xo/TpX3OXzQd91unLV9K8sNJ/lOS13Q/N1vxso75fmGS759u/1CSr87sa8XX7grHPNNr3Hxvn7n+mzPr/HSS3+9+frbaZZPN9xtm//vgsvXn/LR1fiHLX6Le/hxt1ctmmvt4v75V59t79a0z5yvO5QrHnNv79UU+MR9N8vIkDyfZNfNkPXzaem/IX/0fqj+W5OMz91+f5N1neOK/MHP/dUl+Y7p9NMlV0+1K8o3uf1C2w2XRc37aPlZ8kSR5v3/hba75npZXkq8lufhsr93Ttll1PfO9feZ6ZvnvdT8fW/3SOd+n//fBZevP+Wnr/e8kL+9+PrbTpfn17v36Fpvv07b3Xn0TXNY752uZy5l9ze39+kK+c6KqlrJcYT6V5IoxxuNJMl1fvsrmx5L8YFUtVdWOJK9McvUK612Z5MTM/RPTsiT5TJJXT7dfleQ5VfXcc38krNV5mnM2iXXM96uT/MkY46mc/bU7a63rsUCbYa6r6taq+lKWq/8/W+9jYXWbYb6TvHo6zft3qsp/CxZsk8x5qur5Sa5J8j/W8zg4d5tg7r1fP4/O03yziWxwztdqrv9szD1OVNWzk3woyVvHGN841+3HGH+e5J8k+a0k/zPJ8SRPr3SolTafrv95kp+sqj9J8pNJvnqGfTAH53HO2QTOdb6r6rokv5LkTc8sWmG1scKyta7HgmyWuR5j/NoY4wVJ/kWSf7XaOFifTTLfv5tkaYzxw0k+nuWP+bEgm2TOn7Evye+MMU6tNg42bpPMvffr58l5nG82iTnM+ZoPtcKydf+zMdc4UVXPyvKT8IExxoenxU9U1a7p77uSPLnafsYYvzvG+LtjjB/P8ikoj1TVRfWdL0z6t1muMrNfnHNVksem7R8bY/zMGOOFSf7ltOz/zulhMuM8zznNznW+py+3+kiSnx1jfGlavOJr91xe4yzeJp3ru7J8ZhVztlnme4zxtZn/x+Y9SX50no+T79gscz5jX5IPzufRcTabZe69Xz8/zvN8swnMac7PtO+Fvl+f5691VJL3JnlojPGOmT/dk2T/dHt/lj/3stq+Lp+uL0nyT5P85hjj1Bjj+unyr6fTUb5ZVS+ajv2zz+y7qi6rqmce221J3jeHh8hpzvecz3f0nKtzne+q+r4kh5LcNsb4X8+sfKbX7rm8xlmszTTXVbV75vh7kzyyiMe8nW2y+d41c/yfTvLQIh7zdreZ5nza/w8kuSTJHy3qMbNsM8299+uLd77n+zw8JFYxrzk/k4W/Xx/z+7KNn8jyKRyfTfLAdHlFln+J4XCW31AeTnLpzDbHk3w9ybeyXF2unZZ/MMmD02XfWY65J8nnk3wpybuS1LT8NdPxvpjkN3Pal3q4XNBz/vZpu29P12+blv/YdP8vs/xFLke7n5+tdjnX+c7y6fd/ObPuA0kun/624mt3hWOe6TVuvrfPXP+HLH9p2gNJPpHkuu7nZ6tdNtl8/7tpvj8zzfcPdj8/W/GymeZ8+tvbkhzsfl62w2UzzX28X9+q8+29+taZ8xXncoVjzu39+jMbAgAAALRYyK91AAAAAKyVOAEAAAC0EicAAACAVuIEAAAA0EqcAAAAAFqJEwAAAEArcQIAAABoJU4AAAAArf4/DB+AVk/LID4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18,10))\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(ser_d_m.index, ser_d_m, label='barks', width=20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 時間帯別に集計\n",
"* 最初の df を作るところからやり直す\n",
"* date と同じように hour という列を作る方法"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(filename, parse_dates=[0], index_col=0, names=['datetime','barks'])\n",
"df = df[ df['barks'] <= 200 ]\n",
"df.index = df.index.tz_convert('Asia/Tokyo')\n",
"df.index = df.index.set_names('localtime')\n",
"df['date'] = df.index.date\n",
"df['hour'] = df.index.hour"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>barks</th>\n",
" <th>date</th>\n",
" <th>hour</th>\n",
" </tr>\n",
" <tr>\n",
" <th>localtime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-10-27 21:26:44+09:00</th>\n",
" <td>18</td>\n",
" <td>2019-10-27</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-10-28 07:45:05+09:00</th>\n",
" <td>3</td>\n",
" <td>2019-10-28</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-10-28 08:34:57+09:00</th>\n",
" <td>17</td>\n",
" <td>2019-10-28</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-10-28 09:19:50+09:00</th>\n",
" <td>19</td>\n",
" <td>2019-10-28</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-10-28 09:34:47+09:00</th>\n",
" <td>39</td>\n",
" <td>2019-10-28</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks date hour\n",
"localtime \n",
"2019-10-27 21:26:44+09:00 18 2019-10-27 21\n",
"2019-10-28 07:45:05+09:00 3 2019-10-28 7\n",
"2019-10-28 08:34:57+09:00 17 2019-10-28 8\n",
"2019-10-28 09:19:50+09:00 19 2019-10-28 9\n",
"2019-10-28 09:34:47+09:00 39 2019-10-28 9"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" <th>count</th>\n",
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" <th>std</th>\n",
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" <th>50%</th>\n",
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" <tr>\n",
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" <td>3.0</td>\n",
" <td>3.50</td>\n",
" <td>4.0</td>\n",
" <td>5.50</td>\n",
" <td>7.0</td>\n",
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" <td>3.0</td>\n",
" <td>64.000000</td>\n",
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" <td>6.50</td>\n",
" <td>10.0</td>\n",
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" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.0</td>\n",
" <td>3.00</td>\n",
" <td>3.0</td>\n",
" <td>3.00</td>\n",
" <td>3.0</td>\n",
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" <th>4</th>\n",
" <td>14.0</td>\n",
" <td>23.428571</td>\n",
" <td>50.255829</td>\n",
" <td>3.0</td>\n",
" <td>4.25</td>\n",
" <td>7.0</td>\n",
" <td>12.75</td>\n",
" <td>193.0</td>\n",
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" <th>5</th>\n",
" <td>5.0</td>\n",
" <td>10.200000</td>\n",
" <td>6.797058</td>\n",
" <td>3.0</td>\n",
" <td>6.00</td>\n",
" <td>8.0</td>\n",
" <td>14.00</td>\n",
" <td>20.0</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>20.0</td>\n",
" <td>7.450000</td>\n",
" <td>8.438477</td>\n",
" <td>2.0</td>\n",
" <td>3.00</td>\n",
" <td>4.0</td>\n",
" <td>5.00</td>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>94.0</td>\n",
" <td>17.106383</td>\n",
" <td>17.334451</td>\n",
" <td>2.0</td>\n",
" <td>5.00</td>\n",
" <td>10.5</td>\n",
" <td>20.00</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>263.0</td>\n",
" <td>21.007605</td>\n",
" <td>22.688059</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>13.0</td>\n",
" <td>26.00</td>\n",
" <td>139.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>351.0</td>\n",
" <td>20.253561</td>\n",
" <td>21.585209</td>\n",
" <td>2.0</td>\n",
" <td>5.00</td>\n",
" <td>13.0</td>\n",
" <td>28.00</td>\n",
" <td>132.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>340.0</td>\n",
" <td>20.411765</td>\n",
" <td>20.447526</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>12.0</td>\n",
" <td>29.00</td>\n",
" <td>134.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>273.0</td>\n",
" <td>18.956044</td>\n",
" <td>17.807743</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>12.0</td>\n",
" <td>25.00</td>\n",
" <td>106.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>178.0</td>\n",
" <td>20.050562</td>\n",
" <td>20.292848</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>12.0</td>\n",
" <td>27.75</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>257.0</td>\n",
" <td>19.217899</td>\n",
" <td>23.706852</td>\n",
" <td>2.0</td>\n",
" <td>5.00</td>\n",
" <td>11.0</td>\n",
" <td>26.00</td>\n",
" <td>184.0</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>270.0</td>\n",
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" <td>24.317586</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>17.0</td>\n",
" <td>33.00</td>\n",
" <td>137.0</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>265.0</td>\n",
" <td>17.215094</td>\n",
" <td>16.314004</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>12.0</td>\n",
" <td>23.00</td>\n",
" <td>99.0</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>314.0</td>\n",
" <td>21.235669</td>\n",
" <td>21.794169</td>\n",
" <td>3.0</td>\n",
" <td>6.00</td>\n",
" <td>12.0</td>\n",
" <td>28.00</td>\n",
" <td>124.0</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>368.0</td>\n",
" <td>18.214674</td>\n",
" <td>17.294692</td>\n",
" <td>2.0</td>\n",
" <td>6.00</td>\n",
" <td>13.0</td>\n",
" <td>25.00</td>\n",
" <td>144.0</td>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>231.0</td>\n",
" <td>14.766234</td>\n",
" <td>14.647062</td>\n",
" <td>2.0</td>\n",
" <td>5.00</td>\n",
" <td>9.0</td>\n",
" <td>20.00</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>130.0</td>\n",
" <td>14.530769</td>\n",
" <td>17.223649</td>\n",
" <td>2.0</td>\n",
" <td>5.00</td>\n",
" <td>9.0</td>\n",
" <td>17.00</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>61.0</td>\n",
" <td>13.426230</td>\n",
" <td>14.298554</td>\n",
" <td>3.0</td>\n",
" <td>5.00</td>\n",
" <td>9.0</td>\n",
" <td>19.00</td>\n",
" <td>82.0</td>\n",
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" <th>21</th>\n",
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" <td>3.0</td>\n",
" <td>4.00</td>\n",
" <td>7.0</td>\n",
" <td>9.25</td>\n",
" <td>40.0</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>20.0</td>\n",
" <td>10.100000</td>\n",
" <td>10.046733</td>\n",
" <td>3.0</td>\n",
" <td>4.50</td>\n",
" <td>5.5</td>\n",
" <td>12.00</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>7.0</td>\n",
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" <td>1.889822</td>\n",
" <td>2.0</td>\n",
" <td>3.50</td>\n",
" <td>5.0</td>\n",
" <td>6.00</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" barks \n",
" count mean std min 25% 50% 75% max\n",
"hour \n",
"0 1.0 3.000000 NaN 3.0 3.00 3.0 3.00 3.0\n",
"1 3.0 4.666667 2.081666 3.0 3.50 4.0 5.50 7.0\n",
"2 3.0 64.000000 99.654403 3.0 6.50 10.0 94.50 179.0\n",
"3 2.0 3.000000 0.000000 3.0 3.00 3.0 3.00 3.0\n",
"4 14.0 23.428571 50.255829 3.0 4.25 7.0 12.75 193.0\n",
"5 5.0 10.200000 6.797058 3.0 6.00 8.0 14.00 20.0\n",
"6 20.0 7.450000 8.438477 2.0 3.00 4.0 5.00 34.0\n",
"7 94.0 17.106383 17.334451 2.0 5.00 10.5 20.00 70.0\n",
"8 263.0 21.007605 22.688059 2.0 6.00 13.0 26.00 139.0\n",
"9 351.0 20.253561 21.585209 2.0 5.00 13.0 28.00 132.0\n",
"10 340.0 20.411765 20.447526 2.0 6.00 12.0 29.00 134.0\n",
"11 273.0 18.956044 17.807743 2.0 6.00 12.0 25.00 106.0\n",
"12 178.0 20.050562 20.292848 2.0 6.00 12.0 27.75 120.0\n",
"13 257.0 19.217899 23.706852 2.0 5.00 11.0 26.00 184.0\n",
"14 270.0 24.203704 24.317586 2.0 6.00 17.0 33.00 137.0\n",
"15 265.0 17.215094 16.314004 2.0 6.00 12.0 23.00 99.0\n",
"16 314.0 21.235669 21.794169 3.0 6.00 12.0 28.00 124.0\n",
"17 368.0 18.214674 17.294692 2.0 6.00 13.0 25.00 144.0\n",
"18 231.0 14.766234 14.647062 2.0 5.00 9.0 20.00 80.0\n",
"19 130.0 14.530769 17.223649 2.0 5.00 9.0 17.00 145.0\n",
"20 61.0 13.426230 14.298554 3.0 5.00 9.0 19.00 82.0\n",
"21 40.0 9.125000 8.190637 3.0 4.00 7.0 9.25 40.0\n",
"22 20.0 10.100000 10.046733 3.0 4.50 5.5 12.00 46.0\n",
"23 7.0 4.714286 1.889822 2.0 3.50 5.0 6.00 7.0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['hour']).describe()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"df_by_hour = df.pivot_table(index='hour', aggfunc=np.sum)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18,10))\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(df_by_hour.index, df_by_hour['barks'], label='barks', width=0.5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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