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test.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import pandas as pd\n",
"from datetime import date, datetime\n",
"from dateutil.relativedelta import relativedelta\n",
"\n",
"from acrclient import Client\n",
"\n",
"# set up an ACRCloud client\n",
"client = Client(bearer_token=os.getenv(\"ACR_BEARER_TOKEN\"))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"project_id=11691 stream_id='s-qXuJARB'\n"
]
}
],
"source": [
"# get project id and stream id\n",
"project_id = [p.id for p in client.list_bm_cs_projects()][0]\n",
"stream_id = [s.stream_id for s in client.list_bm_cs_projects_streams(project_id=project_id)][0]\n",
"print(f\"{project_id=} {stream_id=}\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>date</th>\n",
" <th>stream_id</th>\n",
" <th>name</th>\n",
" <th>region</th>\n",
" <th>code</th>\n",
" <th>stream_type</th>\n",
" <th>created_at</th>\n",
" <th>state_data.total_time</th>\n",
" <th>state_data.valid_time</th>\n",
" <th>state_data.state_list</th>\n",
" <th>state_data.timeoffset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-01</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>82739</td>\n",
" <td>[{'code': 0, 'start': '2021-06-01 01:01:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-02</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2021-06-02 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-03</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2021-06-03 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-04</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2021-06-04 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-05</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2021-06-05 00:00:00', '...</td>\n",
" <td>-61</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",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-15</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2023-02-15 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-16</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86130</td>\n",
" <td>[{'code': 0, 'start': '2023-02-16 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-17</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>85798</td>\n",
" <td>[{'code': 0, 'start': '2023-02-17 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-18</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2023-02-18 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-19</td>\n",
" <td>s-qXuJARB</td>\n",
" <td>rabe-hd.mp3</td>\n",
" <td>eu-west-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2018-06-18T17:18:33.000000Z</td>\n",
" <td>86400</td>\n",
" <td>86399</td>\n",
" <td>[{'code': 0, 'start': '2023-02-19 00:00:00', '...</td>\n",
" <td>-61</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>629 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" date stream_id name region code stream_type \\\n",
"0 2021-06-01 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2021-06-02 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2021-06-03 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2021-06-04 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2021-06-05 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
".. ... ... ... ... ... ... \n",
"0 2023-02-15 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2023-02-16 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2023-02-17 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2023-02-18 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"0 2023-02-19 s-qXuJARB rabe-hd.mp3 eu-west-1 0 1 \n",
"\n",
" created_at state_data.total_time state_data.valid_time \\\n",
"0 2018-06-18T17:18:33.000000Z 86400 82739 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
".. ... ... ... \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86130 \n",
"0 2018-06-18T17:18:33.000000Z 86400 85798 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"0 2018-06-18T17:18:33.000000Z 86400 86399 \n",
"\n",
" state_data.state_list state_data.timeoffset \n",
"0 [{'code': 0, 'start': '2021-06-01 01:01:00', '... -61 \n",
"0 [{'code': 0, 'start': '2021-06-02 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2021-06-03 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2021-06-04 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2021-06-05 00:00:00', '... -61 \n",
".. ... ... \n",
"0 [{'code': 0, 'start': '2023-02-15 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2023-02-16 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2023-02-17 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2023-02-18 00:00:00', '... -61 \n",
"0 [{'code': 0, 'start': '2023-02-19 00:00:00', '... -61 \n",
"\n",
"[629 rows x 11 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load data from ACRCloud\n",
"\n",
"start = date(2021, 6, 1)\n",
"end = datetime.now().date()\n",
"sus = start\n",
"\n",
"df = pd.DataFrame()\n",
"state_list_df = pd.DataFrame()\n",
"\n",
"while sus < end:\n",
" start_date = sus.strftime(\"%Y%m%d\")\n",
" data = client.json(\n",
" f\"/api/bm-cs-state_list/{project_id}\",\n",
" params={\n",
" \"timeoffset\": -61,\n",
" \"start_date\": start_date,\n",
" \"end_date\": start_date,\n",
" },\n",
" )\n",
" assert data.get(\"total\", -1) == 1\n",
"\n",
" sus_df = pd.json_normalize(data.get(\"data\"))\n",
" sus_df.insert(0, \"date\", sus)\n",
" df = pd.concat([df, sus_df[sus_df[\"state_data.total_time\"] > 0]])\n",
"\n",
" sus = sus + relativedelta(days=+1)\n",
"\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>date</th>\n",
" <th>code</th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" <th>duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-06-14</td>\n",
" <td>6.0</td>\n",
" <td>2021-06-14 03:30:56</td>\n",
" <td>2021-06-14 03:45:45</td>\n",
" <td>889.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-07-04</td>\n",
" <td>12.0</td>\n",
" <td>2021-07-04 21:07:41</td>\n",
" <td>2021-07-04 21:10:01</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-07-31</td>\n",
" <td>12.0</td>\n",
" <td>2021-07-31 16:57:18</td>\n",
" <td>2021-07-31 16:57:38</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-08-05</td>\n",
" <td>6.0</td>\n",
" <td>2021-08-05 00:49:13</td>\n",
" <td>2021-08-05 00:49:31</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2021-08-05</td>\n",
" <td>6.0</td>\n",
" <td>2021-08-05 13:28:39</td>\n",
" <td>2021-08-05 13:30:19</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023-02-16</td>\n",
" <td>6.0</td>\n",
" <td>2023-02-16 13:42:54</td>\n",
" <td>2023-02-16 13:46:44</td>\n",
" <td>230.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023-02-16</td>\n",
" <td>6.0</td>\n",
" <td>2023-02-16 13:55:11</td>\n",
" <td>2023-02-16 13:55:50</td>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023-02-17</td>\n",
" <td>6.0</td>\n",
" <td>2023-02-17 11:49:53</td>\n",
" <td>2023-02-17 11:52:53</td>\n",
" <td>180.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023-02-17</td>\n",
" <td>6.0</td>\n",
" <td>2023-02-17 12:09:40</td>\n",
" <td>2023-02-17 12:12:40</td>\n",
" <td>180.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2023-02-17</td>\n",
" <td>6.0</td>\n",
" <td>2023-02-17 12:22:28</td>\n",
" <td>2023-02-17 12:26:29</td>\n",
" <td>241.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>68 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" date code start end duration\n",
"2 2021-06-14 6.0 2021-06-14 03:30:56 2021-06-14 03:45:45 889.0\n",
"2 2021-07-04 12.0 2021-07-04 21:07:41 2021-07-04 21:10:01 140.0\n",
"2 2021-07-31 12.0 2021-07-31 16:57:18 2021-07-31 16:57:38 20.0\n",
"1 2021-08-05 6.0 2021-08-05 00:49:13 2021-08-05 00:49:31 18.0\n",
"5 2021-08-05 6.0 2021-08-05 13:28:39 2021-08-05 13:30:19 100.0\n",
".. ... ... ... ... ...\n",
"2 2023-02-16 6.0 2023-02-16 13:42:54 2023-02-16 13:46:44 230.0\n",
"4 2023-02-16 6.0 2023-02-16 13:55:11 2023-02-16 13:55:50 39.0\n",
"2 2023-02-17 6.0 2023-02-17 11:49:53 2023-02-17 11:52:53 180.0\n",
"4 2023-02-17 6.0 2023-02-17 12:09:40 2023-02-17 12:12:40 180.0\n",
"6 2023-02-17 6.0 2023-02-17 12:22:28 2023-02-17 12:26:29 241.0\n",
"\n",
"[68 rows x 5 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate df for state_list\n",
"\n",
"state_list_df = pd.DataFrame()\n",
"_tmp = df[[\"date\", \"state_data.state_list\"]].rename(\n",
" columns={\"state_data.state_list\": \"state\"}\n",
")\n",
"for t in _tmp.iterrows():\n",
" (_, t) = t\n",
" jdf = pd.json_normalize(t.state)\n",
" jdf.insert(0, \"date\", t.date)\n",
" state_list_df = pd.concat([state_list_df, jdf])\n",
"\n",
"state_list_df = state_list_df[state_list_df.code != 0]\n",
"state_list_df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Daily Uptime'}, xlabel='date'>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# calculate and plot uptimes per day\n",
"df[\"uptime\"] = (df[\"state_data.valid_time\"] / df[\"state_data.total_time\"])*100\n",
"df.plot(x=\"date\", y=\"uptime\", title=\"Daily Uptime\", rot=90, figsize=(14,5,))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Total Error Duration on Days with Error'}, xlabel='date'>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"per_date = pd.DataFrame()\n",
"summed = state_list_df[[\"date\", \"duration\"]].groupby(by=\"date\")\n",
"for v in summed:\n",
" date, f = v\n",
" per_date = pd.concat(\n",
" [\n",
" per_date,\n",
" pd.DataFrame(\n",
" [\n",
" {\n",
" \"date\": date,\n",
" \"duration\": f.duration.sum(),\n",
" }\n",
" ]\n",
" ),\n",
" ]\n",
" )\n",
"per_date.plot.bar(x=\"date\", y=\"duration\", title=\"Total Error Duration on Days with Error\", rot=90, figsize=(14,5,))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>date</th>\n",
" <th>duration</th>\n",
" <th>in minutes</th>\n",
" <th>in hours</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-01-01</td>\n",
" <td>46080.0</td>\n",
" <td>768.000000</td>\n",
" <td>12.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-14</td>\n",
" <td>889.0</td>\n",
" <td>14.816667</td>\n",
" <td>0.246944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-10-06</td>\n",
" <td>854.0</td>\n",
" <td>14.233333</td>\n",
" <td>0.237222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-17</td>\n",
" <td>601.0</td>\n",
" <td>10.016667</td>\n",
" <td>0.166944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-01-18</td>\n",
" <td>474.0</td>\n",
" <td>7.900000</td>\n",
" <td>0.131667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-12-16</td>\n",
" <td>373.0</td>\n",
" <td>6.216667</td>\n",
" <td>0.103611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-09-19</td>\n",
" <td>350.0</td>\n",
" <td>5.833333</td>\n",
" <td>0.097222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-05-21</td>\n",
" <td>300.0</td>\n",
" <td>5.000000</td>\n",
" <td>0.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-10-09</td>\n",
" <td>282.0</td>\n",
" <td>4.700000</td>\n",
" <td>0.078333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-02-16</td>\n",
" <td>269.0</td>\n",
" <td>4.483333</td>\n",
" <td>0.074722</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date duration in minutes in hours\n",
"0 2022-01-01 46080.0 768.000000 12.800000\n",
"0 2021-06-14 889.0 14.816667 0.246944\n",
"0 2022-10-06 854.0 14.233333 0.237222\n",
"0 2023-02-17 601.0 10.016667 0.166944\n",
"0 2022-01-18 474.0 7.900000 0.131667\n",
"0 2022-12-16 373.0 6.216667 0.103611\n",
"0 2022-09-19 350.0 5.833333 0.097222\n",
"0 2022-05-21 300.0 5.000000 0.083333\n",
"0 2022-10-09 282.0 4.700000 0.078333\n",
"0 2023-02-16 269.0 4.483333 0.074722"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# top ten error days\n",
"per_date[\"in minutes\"] = (per_date[\"duration\"]/60)\n",
"per_date[\"in hours\"] = (per_date[\"in minutes\"]/60)\n",
"per_date[[\"date\", \"duration\", \"in minutes\", \"in hours\"]].sort_values(by=\"duration\", ascending=False).head(10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.11.1"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "e13b8f3c5f32bf9fa87941b2645b8833d80ae8584e3849380a675c44d329ad8b"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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