{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import plotly.express as px\n", "import datetime\n", "\n" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "january = pd.read_csv('tmp/2020-01/body.csv')\n", "march19 = pd.read_csv('tmp/2019-03/body.csv')\n", "march20 = pd.read_csv('tmp/2020-03/body.csv')\n", "april19 = pd.read_csv('tmp/2019-04/body.csv')\n", "april20 = pd.read_csv('tmp/2020-04/body.csv')\n", "\n", "january['timestamp'] = pd.to_datetime(january['data_as_of'])\n", "march19['timestamp'] = pd.to_datetime(march19['data_as_of'])\n", "march20['timestamp'] = pd.to_datetime(march20['data_as_of'])\n", "april19['timestamp'] = pd.to_datetime(april19['data_as_of'])\n", "april20['timestamp'] = pd.to_datetime(april20['data_as_of'])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 996684 entries, 0 to 996683\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 996684 non-null int64 \n", " 1 speed 996684 non-null float64 \n", " 2 travel_time 996684 non-null int64 \n", " 3 data_as_of 996684 non-null object \n", " 4 owner 996684 non-null object \n", " 5 borough 996684 non-null object \n", " 6 link_name 996684 non-null object \n", " 7 timestamp 996684 non-null datetime64[ns]\n", "dtypes: datetime64[ns](1), float64(1), int64(2), object(4)\n", "memory usage: 60.8+ MB\n" ] } ], "source": [ "january['timestamp'] = pd.to_datetime(january['data_as_of'])\n", "january.info()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 993 entries, 782269 to 920168\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 993 non-null int64 \n", " 1 speed 993 non-null float64 \n", " 2 travel_time 993 non-null int64 \n", " 3 data_as_of 993 non-null object \n", " 4 owner 993 non-null object \n", " 5 borough 993 non-null object \n", " 6 link_name 993 non-null object \n", " 7 timestamp 993 non-null datetime64[ns]\n", "dtypes: datetime64[ns](1), float64(1), int64(2), object(4)\n", "memory usage: 69.8+ KB\n" ] } ], "source": [ "# BQE N Atlantic Ave - BKN Bridge Manhattan Side\n", "brooklynBridge = january[(january['id'] == 149) & (january['timestamp'] >= pd.to_datetime('2020-01-21')) & (january['timestamp'] < pd.to_datetime('2020-01-28'))]\n", "brooklynBridge.info()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverlabel": { "namelength": 0 }, "hovertemplate": "timestamp=%{x}
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" ], "text/plain": [ " hourFloor speed\n", "0 2020-01-21 00:00:00 38.520000\n", "1 2020-01-21 01:00:00 43.802500\n", "2 2020-01-21 02:00:00 43.880000\n", "3 2020-01-21 03:00:00 45.482000\n", "4 2020-01-21 04:00:00 45.854000\n", ".. ... ...\n", "163 2020-01-27 19:00:00 30.336667\n", "164 2020-01-27 20:00:00 33.756667\n", "165 2020-01-27 21:00:00 35.322857\n", "166 2020-01-27 22:00:00 36.284000\n", "167 2020-01-27 23:00:00 39.141667\n", "\n", "[168 rows x 2 columns]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get average speed by hour\n", "\n", "# floor the timestamp to the preceding hour\n", "brooklynBridge['hourFloor'] = brooklynBridge['timestamp'].dt.floor('H')\n", "\n", "hourly_avg = brooklynBridge.groupby('hourFloor').speed.mean()\n", "\n", "\n", "hourly_avg = hourly_avg.to_frame()\n", "\n", "hourly_avg.reset_index(inplace=True)\n", "\n", "hourly_avg\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverlabel": { "namelength": 0 }, "hovertemplate": "hourFloor=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make a line chart showing speed x timestamp\n", "fig = px.line(hourly_avg, x=\"hourFloor\", y=\"speed\", title='Hourly Average Speed, BQE N Atlantic Ave - BKN Bridge Manhattan Side')\n", "fig.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/chriswhong/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverlabel": { "namelength": 0 }, "hovertemplate": "hourFloor=%{x}
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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Hourly Average Speed, BQE N Atlantic Ave - BKN Bridge Manhattan Side" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "hourFloor" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "speed" } } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filter for highway segment id and date range SIE E CLOVE ROAD - FINGERBOARD ROAD\n", "\n", "def speedchart(df, title):\n", " chartdata = df\n", " \n", " chartdata['hourFloor'] = chartdata['timestamp'].dt.floor('3H')\n", "\n", " hourly_avg = chartdata.groupby('hourFloor').speed.mean()\n", "\n", "\n", " hourly_avg = hourly_avg.to_frame()\n", "\n", " hourly_avg.reset_index(inplace=True)\n", " \n", " fig = px.line(hourly_avg, x=\"hourFloor\", y=\"speed\", title='Average Speed Across 3-Hour Intervals', height=300)\n", " fig.show()\n", "\n", "\n", "SIE19 = april19[(april19['id'] == 376)]\n", "SIE20 = april20[(april20['id'] == 376)]\n", "\n", "\n", "speedchart(SIE19, 'BQE N Atlantic Ave - BKN Bridge Manhattan Side')\n", "\n", "\n", "speedchart(SIE20, 'BQE N Atlantic Ave - BKN Bridge Manhattan Side')\n" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "speedchart() takes 1 positional argument but 2 were given", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mspeedchart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSIE19\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'SIE E CLOVE ROAD - FINGERBOARD ROAD'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: speedchart() takes 1 positional argument but 2 were given" ] } ], "source": [ "SIE19 = march19[(march19['id'] == 376)]\n", "SIE20 = march20[(march20['id'] == 376)]\n", "\n", "\n", "speedchart(SIE19, 'SIE E CLOVE ROAD - FINGERBOARD ROAD')\n", "\n", "\n", "speedchart(SIE20, 'SIE E CLOVE ROAD - FINGERBOARD ROAD')" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }