{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3136fdb8-97c4-47d7-9b0a-61529c747e4d", "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "\n", "#set up strings\n", "carnum = 'carnum'\n", "start = 'start'\n", "end = 'end'\n", "columns = [\n", " carnum,\n", " start,\n", " end,\n", " 'distance',\n", " 'gas'\n", "]\n", "mileage = 'mileage'\n", "triptime = 'time'\n", "speed = 'speed'" ] }, { "cell_type": "code", "execution_count": 2, "id": "1e7a8579-893c-42d2-94fc-73c2ada83d74", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumstartenddistancegas
0732023-11-30 04:57:522023-11-30 05:48:1598.8636204.842427
1732023-11-30 05:00:252023-11-30 06:48:4060.95221410.008699
2732023-11-30 11:58:032023-11-30 12:21:1144.8618556.668674
3732023-11-30 19:55:542023-11-30 22:37:0626.6796835.725053
4732023-11-30 10:55:572023-11-30 13:03:4956.4497033.550615
..................
680562023-11-30 05:41:402023-11-30 06:52:2329.2056018.796899
681562023-11-30 00:34:242023-11-30 02:53:0062.3602215.515841
682562023-11-30 19:02:122023-11-30 20:34:31111.7592548.484224
683562023-11-30 02:01:122023-11-30 02:41:5845.3958296.123383
684562023-11-30 07:17:092023-11-30 07:48:4020.9699457.003565
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685 rows × 5 columns

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" ], "text/plain": [ " carnum start end distance gas\n", "0 73 2023-11-30 04:57:52 2023-11-30 05:48:15 98.863620 4.842427\n", "1 73 2023-11-30 05:00:25 2023-11-30 06:48:40 60.952214 10.008699\n", "2 73 2023-11-30 11:58:03 2023-11-30 12:21:11 44.861855 6.668674\n", "3 73 2023-11-30 19:55:54 2023-11-30 22:37:06 26.679683 5.725053\n", "4 73 2023-11-30 10:55:57 2023-11-30 13:03:49 56.449703 3.550615\n", ".. ... ... ... ... ...\n", "680 56 2023-11-30 05:41:40 2023-11-30 06:52:23 29.205601 8.796899\n", "681 56 2023-11-30 00:34:24 2023-11-30 02:53:00 62.360221 5.515841\n", "682 56 2023-11-30 19:02:12 2023-11-30 20:34:31 111.759254 8.484224\n", "683 56 2023-11-30 02:01:12 2023-11-30 02:41:58 45.395829 6.123383\n", "684 56 2023-11-30 07:17:09 2023-11-30 07:48:40 20.969945 7.003565\n", "\n", "[685 rows x 5 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#read file into dataframe\n", "df = pd.read_csv('cardata.csv',names=columns,parse_dates=['start','end'])\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "id": "efcec18b-cdc9-4676-ace6-c982f3eafcac", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumstartenddistancegastime
0732023-11-30 04:57:522023-11-30 05:48:1598.8636204.8424270 days 00:50:23
1732023-11-30 05:00:252023-11-30 06:48:4060.95221410.0086990 days 01:48:15
2732023-11-30 11:58:032023-11-30 12:21:1144.8618556.6686740 days 00:23:08
3732023-11-30 19:55:542023-11-30 22:37:0626.6796835.7250530 days 02:41:12
4732023-11-30 10:55:572023-11-30 13:03:4956.4497033.5506150 days 02:07:52
.....................
680562023-11-30 05:41:402023-11-30 06:52:2329.2056018.7968990 days 01:10:43
681562023-11-30 00:34:242023-11-30 02:53:0062.3602215.5158410 days 02:18:36
682562023-11-30 19:02:122023-11-30 20:34:31111.7592548.4842240 days 01:32:19
683562023-11-30 02:01:122023-11-30 02:41:5845.3958296.1233830 days 00:40:46
684562023-11-30 07:17:092023-11-30 07:48:4020.9699457.0035650 days 00:31:31
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685 rows × 6 columns

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" ], "text/plain": [ " carnum start end distance gas \\\n", "0 73 2023-11-30 04:57:52 2023-11-30 05:48:15 98.863620 4.842427 \n", "1 73 2023-11-30 05:00:25 2023-11-30 06:48:40 60.952214 10.008699 \n", "2 73 2023-11-30 11:58:03 2023-11-30 12:21:11 44.861855 6.668674 \n", "3 73 2023-11-30 19:55:54 2023-11-30 22:37:06 26.679683 5.725053 \n", "4 73 2023-11-30 10:55:57 2023-11-30 13:03:49 56.449703 3.550615 \n", ".. ... ... ... ... ... \n", "680 56 2023-11-30 05:41:40 2023-11-30 06:52:23 29.205601 8.796899 \n", "681 56 2023-11-30 00:34:24 2023-11-30 02:53:00 62.360221 5.515841 \n", "682 56 2023-11-30 19:02:12 2023-11-30 20:34:31 111.759254 8.484224 \n", "683 56 2023-11-30 02:01:12 2023-11-30 02:41:58 45.395829 6.123383 \n", "684 56 2023-11-30 07:17:09 2023-11-30 07:48:40 20.969945 7.003565 \n", "\n", " time \n", "0 0 days 00:50:23 \n", "1 0 days 01:48:15 \n", "2 0 days 00:23:08 \n", "3 0 days 02:41:12 \n", "4 0 days 02:07:52 \n", ".. ... \n", "680 0 days 01:10:43 \n", "681 0 days 02:18:36 \n", "682 0 days 01:32:19 \n", "683 0 days 00:40:46 \n", "684 0 days 00:31:31 \n", "\n", "[685 rows x 6 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#compute trip times\n", "df[triptime] = df.end - df.start\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "6a1526cd-c9b6-431e-9b89-508cb44e53f8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumdistancegastime
07398.8636204.8424270 days 00:50:23
17360.95221410.0086990 days 01:48:15
27344.8618556.6686740 days 00:23:08
37326.6796835.7250530 days 02:41:12
47356.4497033.5506150 days 02:07:52
...............
6805629.2056018.7968990 days 01:10:43
6815662.3602215.5158410 days 02:18:36
68256111.7592548.4842240 days 01:32:19
6835645.3958296.1233830 days 00:40:46
6845620.9699457.0035650 days 00:31:31
\n", "

685 rows × 4 columns

\n", "
" ], "text/plain": [ " carnum distance gas time\n", "0 73 98.863620 4.842427 0 days 00:50:23\n", "1 73 60.952214 10.008699 0 days 01:48:15\n", "2 73 44.861855 6.668674 0 days 00:23:08\n", "3 73 26.679683 5.725053 0 days 02:41:12\n", "4 73 56.449703 3.550615 0 days 02:07:52\n", ".. ... ... ... ...\n", "680 56 29.205601 8.796899 0 days 01:10:43\n", "681 56 62.360221 5.515841 0 days 02:18:36\n", "682 56 111.759254 8.484224 0 days 01:32:19\n", "683 56 45.395829 6.123383 0 days 00:40:46\n", "684 56 20.969945 7.003565 0 days 00:31:31\n", "\n", "[685 rows x 4 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get rid of start and end - no longer needed\n", "df.drop(columns=[start,end],inplace=True)\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "a1083c0e-8af9-4a73-869d-6ab18c27c222", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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distancegastime
carnum
1693.39675378.1103100 days 12:52:05
2981.044936100.6543070 days 18:20:12
3456.63835446.2091410 days 08:14:21
4392.06301644.4881160 days 10:02:49
5777.30921994.4530470 days 19:19:18
............
93577.79873375.8101270 days 16:47:56
941172.460418107.9001251 days 02:12:58
971581.524745130.3864751 days 05:07:45
981086.025420121.2206340 days 13:33:35
99394.13010235.0150680 days 09:46:12
\n", "

64 rows × 3 columns

\n", "
" ], "text/plain": [ " distance gas time\n", "carnum \n", "1 693.396753 78.110310 0 days 12:52:05\n", "2 981.044936 100.654307 0 days 18:20:12\n", "3 456.638354 46.209141 0 days 08:14:21\n", "4 392.063016 44.488116 0 days 10:02:49\n", "5 777.309219 94.453047 0 days 19:19:18\n", "... ... ... ...\n", "93 577.798733 75.810127 0 days 16:47:56\n", "94 1172.460418 107.900125 1 days 02:12:58\n", "97 1581.524745 130.386475 1 days 05:07:45\n", "98 1086.025420 121.220634 0 days 13:33:35\n", "99 394.130102 35.015068 0 days 09:46:12\n", "\n", "[64 rows x 3 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get totals by car\n", "tots = df.groupby(carnum).sum(numeric_only=False) # need numeric_only=False so the time column is included\n", "tots" ] }, { "cell_type": "code", "execution_count": 6, "id": "e2e20634-6e9d-4fdd-858a-f99dd2e1c252", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumdistancegastimemileagespeed
01693.39675378.1103100 days 12:52:058.87714853.885123
12981.044936100.6543070 days 18:20:129.74667653.501814
23456.63835446.2091410 days 08:14:219.88199255.422881
34392.06301644.4881160 days 10:02:498.81275839.023110
45777.30921994.4530470 days 19:19:188.22958340.229926
.....................
5993577.79873375.8101270 days 16:47:567.62165634.395057
60941172.460418107.9001251 days 02:12:5810.86616444.722896
61971581.524745130.3864751 days 05:07:4512.12951554.293512
62981086.025420121.2206340 days 13:33:358.95908180.092011
6399394.13010235.0150680 days 09:46:1211.25601440.340850
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64 rows × 6 columns

\n", "
" ], "text/plain": [ " carnum distance gas time mileage speed\n", "0 1 693.396753 78.110310 0 days 12:52:05 8.877148 53.885123\n", "1 2 981.044936 100.654307 0 days 18:20:12 9.746676 53.501814\n", "2 3 456.638354 46.209141 0 days 08:14:21 9.881992 55.422881\n", "3 4 392.063016 44.488116 0 days 10:02:49 8.812758 39.023110\n", "4 5 777.309219 94.453047 0 days 19:19:18 8.229583 40.229926\n", ".. ... ... ... ... ... ...\n", "59 93 577.798733 75.810127 0 days 16:47:56 7.621656 34.395057\n", "60 94 1172.460418 107.900125 1 days 02:12:58 10.866164 44.722896\n", "61 97 1581.524745 130.386475 1 days 05:07:45 12.129515 54.293512\n", "62 98 1086.025420 121.220634 0 days 13:33:35 8.959081 80.092011\n", "63 99 394.130102 35.015068 0 days 09:46:12 11.256014 40.340850\n", "\n", "[64 rows x 6 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#compute mileage and speed\n", "tots[mileage] = tots.distance / tots.gas\n", "tots[speed] = tots.distance / tots.time.dt.total_seconds() * 3600\n", "tots.reset_index(inplace=True)\n", "tots" ] }, { "cell_type": "code", "execution_count": 7, "id": "1cfc9b34-5902-415f-ba0c-cab4d120a738", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
carnumdistancegastimemileagespeed
1626554.40695433.5966120 days 04:06:5616.501871134.710113
\n", "
" ], "text/plain": [ " carnum distance gas time mileage speed\n", "16 26 554.406954 33.596612 0 days 04:06:56 16.501871 134.710113" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#show car with best mileage\n", "tots[tots.mileage == tots.mileage.max()]" ] }, { "cell_type": "code", "execution_count": 8, "id": "21280939-c8f8-46d0-9cdc-e4487534a9f9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumdistancegastime
01999
12141414
23555
34777
45141414
...............
5993101010
6094161616
6197202020
6298151515
6399666
\n", "

64 rows × 4 columns

\n", "
" ], "text/plain": [ " carnum distance gas time\n", "0 1 9 9 9\n", "1 2 14 14 14\n", "2 3 5 5 5\n", "3 4 7 7 7\n", "4 5 14 14 14\n", ".. ... ... ... ...\n", "59 93 10 10 10\n", "60 94 16 16 16\n", "61 97 20 20 20\n", "62 98 15 15 15\n", "63 99 6 6 6\n", "\n", "[64 rows x 4 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#show car with the most trips\n", "trips = df.groupby(carnum).count().reset_index()\n", "trips" ] }, { "cell_type": "code", "execution_count": 9, "id": "ed60416d-a1b3-494d-8b1d-21e0d424c15e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
carnumdistancegastime
2849303030
\n", "
" ], "text/plain": [ " carnum distance gas time\n", "28 49 30 30 30" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips[trips.distance == trips.distance.max()]" ] }, { "cell_type": "code", "execution_count": 10, "id": "3e6b964d-a051-4167-bca0-a53ab778748c", "metadata": {}, "outputs": [], "source": [ "summary=tots[[carnum,speed,mileage]]\n", "summary.to_excel('car_results.xlsx',index=False)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "8e0c2ff2-ddb1-4bc5-bc0d-b3de22eb1c9d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumdistancegastime
07398.8636204.8424270 days 00:50:23
17360.95221410.0086990 days 01:48:15
27344.8618556.6686740 days 00:23:08
37326.6796835.7250530 days 02:41:12
47356.4497033.5506150 days 02:07:52
...............
6805629.2056018.7968990 days 01:10:43
6815662.3602215.5158410 days 02:18:36
68256111.7592548.4842240 days 01:32:19
6835645.3958296.1233830 days 00:40:46
6845620.9699457.0035650 days 00:31:31
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685 rows × 4 columns

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carnumdistancegastimemileagespeed
01693.39675378.1103100 days 12:52:058.87714853.885123
12981.044936100.6543070 days 18:20:129.74667653.501814
23456.63835446.2091410 days 08:14:219.88199255.422881
34392.06301644.4881160 days 10:02:498.81275839.023110
45777.30921994.4530470 days 19:19:188.22958340.229926
.....................
5993577.79873375.8101270 days 16:47:567.62165634.395057
60941172.460418107.9001251 days 02:12:5810.86616444.722896
61971581.524745130.3864751 days 05:07:4512.12951554.293512
62981086.025420121.2206340 days 13:33:358.95908180.092011
6399394.13010235.0150680 days 09:46:1211.25601440.340850
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64 rows × 6 columns

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Bar Chart comparing the mileage of the top 20 cars\n", "\n", "top20=tots.sort_values('mileage',ascending=False)[:20]\n", "top20['carnum'] = top20.carnum.astype('str') # needed since if x is numeric, it will use the number as the coordinates. and since car numbers aren't sequential, it leaves gaps\n", "import plotly.express as px\n", "\n", "barchart=px.bar(\n", " top20,\n", " x='carnum',\n", " y='mileage',\n", " \n", ")\n", "barchart" ] }, { "cell_type": "code", "execution_count": 14, "id": "a6e728f9-b465-4d68-90c0-d2a45874a5fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "49" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Line Chart showing the distance of each trip for the car with the most trips\n", "#first find the top car using the code from previous homework\n", "topcar = trips[trips.distance == trips.distance.max()].iloc[0].loc['carnum']\n", "topcar" ] }, { "cell_type": "code", "execution_count": 15, "id": "ff2d79a8-4cbe-4151-b2d1-6a9d691323fb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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carnumdistancegastimetripnum
04973.0389015.9364010 days 00:41:551
149113.5942437.4949830 days 01:09:432
24967.1186833.3800640 days 01:28:333
34997.3364778.2654270 days 01:05:524
449115.74912910.7301610 days 01:38:155
549119.8274658.9680320 days 01:14:236
64948.9815866.3638230 days 00:14:047
749106.54458110.0931810 days 00:20:068
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104945.9362845.5361470 days 01:05:1411
114953.07305111.8407640 days 02:13:0812
1249100.8959635.1318390 days 00:39:2613
134947.8064149.1917880 days 00:33:2214
144963.4305936.9489790 days 02:31:1915
154936.7728832.8819280 days 00:41:0516
164999.7329437.8998300 days 01:34:1917
174957.83140010.1885950 days 01:49:5318
184995.01221910.7239830 days 02:44:4619
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204963.63179711.3114140 days 01:11:5921
214924.0816154.1991200 days 01:57:2922
224954.0605466.9824710 days 00:13:2323
234948.8173112.1477150 days 01:55:3824
244952.7697146.9240480 days 01:44:4425
254962.1293024.4634840 days 00:49:0826
264974.84517211.6494830 days 01:33:0627
2749100.3824537.5057120 days 00:43:5228
284969.6104342.3153900 days 00:36:5429
294994.8187028.7419020 days 02:22:1630
\n", "
" ], "text/plain": [ " carnum distance gas time tripnum\n", "0 49 73.038901 5.936401 0 days 00:41:55 1\n", "1 49 113.594243 7.494983 0 days 01:09:43 2\n", "2 49 67.118683 3.380064 0 days 01:28:33 3\n", "3 49 97.336477 8.265427 0 days 01:05:52 4\n", "4 49 115.749129 10.730161 0 days 01:38:15 5\n", "5 49 119.827465 8.968032 0 days 01:14:23 6\n", "6 49 48.981586 6.363823 0 days 00:14:04 7\n", "7 49 106.544581 10.093181 0 days 00:20:06 8\n", "8 49 62.657442 6.436208 0 days 00:38:31 9\n", "9 49 24.743508 10.837144 0 days 02:06:25 10\n", "10 49 45.936284 5.536147 0 days 01:05:14 11\n", "11 49 53.073051 11.840764 0 days 02:13:08 12\n", "12 49 100.895963 5.131839 0 days 00:39:26 13\n", "13 49 47.806414 9.191788 0 days 00:33:22 14\n", "14 49 63.430593 6.948979 0 days 02:31:19 15\n", "15 49 36.772883 2.881928 0 days 00:41:05 16\n", "16 49 99.732943 7.899830 0 days 01:34:19 17\n", "17 49 57.831400 10.188595 0 days 01:49:53 18\n", "18 49 95.012219 10.723983 0 days 02:44:46 19\n", "19 49 64.524515 11.154344 0 days 01:05:41 20\n", "20 49 63.631797 11.311414 0 days 01:11:59 21\n", "21 49 24.081615 4.199120 0 days 01:57:29 22\n", "22 49 54.060546 6.982471 0 days 00:13:23 23\n", "23 49 48.817311 2.147715 0 days 01:55:38 24\n", "24 49 52.769714 6.924048 0 days 01:44:44 25\n", "25 49 62.129302 4.463484 0 days 00:49:08 26\n", "26 49 74.845172 11.649483 0 days 01:33:06 27\n", "27 49 100.382453 7.505712 0 days 00:43:52 28\n", "28 49 69.610434 2.315390 0 days 00:36:54 29\n", "29 49 94.818702 8.741902 0 days 02:22:16 30" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#then filter the dataframe to just that car\n", "topcartrips = df[df.carnum == topcar].reset_index(drop=True)\n", "#add a column for index number\n", "topcartrips['tripnum']=range(1,len(topcartrips)+1)\n", "topcartrips" ] }, { "cell_type": "code", "execution_count": 16, "id": "9c35224c-db12-4f4d-98ce-1894d441ed13", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "tripnum=%{x}
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# finally draw the chart\n", "linechart=px.line(\n", " topcartrips,\n", " x='tripnum',\n", " y='distance'\n", ")\n", "linechart.update_layout(title=f'Trips for car {topcar}')" ] }, { "cell_type": "code", "execution_count": 17, "id": "c6256e43-94ca-41e6-b789-a967768790a0", "metadata": {}, "outputs": [], "source": [ "from dash import Dash,dcc,html,dash_table as dt\n", "\n", "app = Dash(__name__)\n", "\n", "app.layout = html.Div([\n", " html.H1(\"Gas Mileage Analysis\"),\n", " html.Div([\n", " dcc.Graph(figure=barchart)\n", " ],\n", " style = {\n", " 'display':'inline-block',\n", " 'width':'50%'\n", " }),\n", " html.Div([\n", " dcc.Graph(figure=linechart)\n", " ],\n", " style = {\n", " 'display':'inline-block',\n", " 'width':'50%'\n", " }),\n", " html.Div([\n", " dt.DataTable(\n", " data = summary.to_dict('records'),\n", " columns = [{'name':c,'id':c} for c in summary.columns]\n", " )\n", " ],\n", " style = {\n", " 'display':'inline-block',\n", " 'width':'100%'\n", " })\n", "])\n", "\n", "# Run app and display result in tab\n", "app.run_server(jupyter_mode='jupyterlab')" ] }, { "cell_type": "code", "execution_count": null, "id": "9aee3155-f9f0-4862-8c85-a34963800f60", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }