{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of Bike Trips in 2019" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "Import all libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting chart_studio\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/b9/3f/d2f3f506ba1aaf109f549f8b01d1483cd3e324c5ebe6b206acee66efdf46/chart_studio-1.0.0-py3-none-any.whl (76kB)\n", "\u001b[K 100% |████████████████████████████████| 81kB 18.3MB/s ta 0:00:01\n", "\u001b[?25hRequirement already satisfied: requests in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from chart_studio) (2.20.0)\n", "Requirement already satisfied: plotly in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from chart_studio) (4.2.1)\n", "Requirement already satisfied: retrying>=1.3.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from chart_studio) (1.3.3)\n", "Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from chart_studio) (1.11.0)\n", "Requirement already satisfied: idna<2.8,>=2.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->chart_studio) (2.6)\n", "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->chart_studio) (3.0.4)\n", "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->chart_studio) (1.23)\n", "Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->chart_studio) (2019.9.11)\n", "Installing collected packages: chart-studio\n", "Successfully installed chart-studio-1.0.0\n", "\u001b[33mYou are using pip version 10.0.1, however version 19.3.1 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install chart_studio" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import chart_studio.plotly as py\n", "import plotly.graph_objects as go\n", "from plotly import tools\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download\n", "Get all available trip data for 2019." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[1/10]: https://s3.amazonaws.com/tripdata/201901-citibike-tripdata.csv.zip --> 201901-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201901-citibike-tripdata.csv.zip\n", " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 39.5M 100 39.5M 0 0 34.9M 0 0:00:01 0:00:01 --:--:-- 34.9M\n", "\n", "[2/10]: https://s3.amazonaws.com/tripdata/201902-citibike-tripdata.csv.zip --> 201902-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201902-citibike-tripdata.csv.zip\n", "100 38.5M 100 38.5M 0 0 42.5M 0 --:--:-- --:--:-- --:--:-- 60.5M\n", "\n", "[3/10]: https://s3.amazonaws.com/tripdata/201903-citibike-tripdata.csv.zip --> 201903-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201903-citibike-tripdata.csv.zip\n", "100 54.5M 100 54.5M 0 0 52.5M 0 0:00:01 0:00:01 --:--:-- 52.1M\n", "\n", "[4/10]: https://s3.amazonaws.com/tripdata/201904-citibike-tripdata.csv.zip --> 201904-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201904-citibike-tripdata.csv.zip\n", "100 72.1M 100 72.1M 0 0 49.1M 0 0:00:01 0:00:01 --:--:-- 65.1M\n", "\n", "[5/10]: https://s3.amazonaws.com/tripdata/201905-citibike-tripdata.csv.zip --> 201905-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201905-citibike-tripdata.csv.zip\n", "100 78.3M 100 78.3M 0 0 45.3M 0 0:00:01 0:00:01 --:--:-- 48.1M\n", "\n", "[6/10]: https://s3.amazonaws.com/tripdata/201906-citibike-tripdata.csv.zip --> 201906-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201906-citibike-tripdata.csv.zip\n", "100 86.8M 100 86.8M 0 0 75.1M 0 0:00:01 0:00:01 --:--:-- 81.2M\n", "\n", "[7/10]: https://s3.amazonaws.com/tripdata/201907-citibike-tripdata.csv.zip --> 201907-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201907-citibike-tripdata.csv.zip\n", "100 89.0M 100 89.0M 0 0 90.9M 0 --:--:-- --:--:-- --:--:-- 92.9M\n", "\n", "[8/10]: https://s3.amazonaws.com/tripdata/201908-citibike-tripdata.csv.zip --> 201908-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201908-citibike-tripdata.csv.zip\n", "100 95.3M 100 95.3M 0 0 81.4M 0 0:00:01 0:00:01 --:--:-- 97.0M\n", "\n", "[9/10]: https://s3.amazonaws.com/tripdata/201909-citibike-tripdata.csv.zip --> 201909-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201909-citibike-tripdata.csv.zip\n", "100 99.3M 100 99.3M 0 0 82.1M 0 0:00:01 0:00:01 --:--:-- 86.3M\n", "\n", "[10/10]: https://s3.amazonaws.com/tripdata/201910-citibike-tripdata.csv.zip --> 201910-citibike-tripdata.csv.zip\n", "--_curl_--https://s3.amazonaws.com/tripdata/201910-citibike-tripdata.csv.zip\n", "100 85.0M 100 85.0M 0 0 65.6M 0 0:00:01 0:00:01 --:--:-- 86.0M\n", "Archive: 201908-citibike-tripdata.csv.zip\n", " inflating: 201908-citibike-tripdata.csv \n", "\n", "Archive: 201907-citibike-tripdata.csv.zip\n", " inflating: 201907-citibike-tripdata.csv \n", "\n", "Archive: 201910-citibike-tripdata.csv.zip\n", " inflating: 201910-citibike-tripdata.csv \n", "\n", "Archive: 201905-citibike-tripdata.csv.zip\n", " inflating: 201905-citibike-tripdata.csv \n", "\n", "Archive: 201909-citibike-tripdata.csv.zip\n", " inflating: 201909-citibike-tripdata.csv \n", "\n", "Archive: 201901-citibike-tripdata.csv.zip\n", " inflating: 201901-citibike-tripdata.csv \n", "\n", "Archive: 201903-citibike-tripdata.csv.zip\n", " inflating: 201903-citibike-tripdata.csv \n", "\n", "Archive: 201906-citibike-tripdata.csv.zip\n", " inflating: 201906-citibike-tripdata.csv \n", "\n", "Archive: 201904-citibike-tripdata.csv.zip\n", " inflating: 201904-citibike-tripdata.csv \n", "\n", "Archive: 201902-citibike-tripdata.csv.zip\n", " inflating: 201902-citibike-tripdata.csv \n", " creating: __MACOSX/\n", " inflating: __MACOSX/._201902-citibike-tripdata.csv \n", "\n", "10 archives were successfully processed.\n" ] } ], "source": [ "!curl -O \"https://s3.amazonaws.com/tripdata/2019[01-10]-citibike-tripdata.csv.zip\"\n", "!unzip -o \"*.zip\"\n", "!rm *.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create DataFrame\n", "Read 2019 trip data into a DataFrame." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tripdurationstarttimestoptimestart station idstart station namestart station latitudestart station longitudeend station idend station nameend station latitudeend station longitudebikeidusertypebirth yeargender
201901-citibike-tripdata.csv03202019-01-01 00:01:47.40102019-01-01 00:07:07.58103160.0Central Park West & W 76 St40.778968-73.9737473283.0W 89 St & Columbus Ave40.788221-73.97041615839Subscriber19711
13162019-01-01 00:04:43.73602019-01-01 00:10:00.6080519.0Pershing Square North40.751873-73.977706518.0E 39 St & 2 Ave40.747804-73.97344232723Subscriber19641
25912019-01-01 00:06:03.99702019-01-01 00:15:55.43803171.0Amsterdam Ave & W 82 St40.785247-73.9766733154.0E 77 St & 3 Ave40.773142-73.95856227451Subscriber19871
327192019-01-01 00:07:03.54502019-01-01 00:52:22.6500504.01 Ave & E 16 St40.732219-73.9816563709.0W 15 St & 6 Ave40.738046-73.99643021579Subscriber19901
43032019-01-01 00:07:35.94502019-01-01 00:12:39.5020229.0Great Jones St40.727434-73.993790503.0E 20 St & Park Ave40.738274-73.98752035379Subscriber19791
\n", "
" ], "text/plain": [ " tripduration starttime \\\n", "201901-citibike-tripdata.csv 0 320 2019-01-01 00:01:47.4010 \n", " 1 316 2019-01-01 00:04:43.7360 \n", " 2 591 2019-01-01 00:06:03.9970 \n", " 3 2719 2019-01-01 00:07:03.5450 \n", " 4 303 2019-01-01 00:07:35.9450 \n", "\n", " stoptime start station id \\\n", "201901-citibike-tripdata.csv 0 2019-01-01 00:07:07.5810 3160.0 \n", " 1 2019-01-01 00:10:00.6080 519.0 \n", " 2 2019-01-01 00:15:55.4380 3171.0 \n", " 3 2019-01-01 00:52:22.6500 504.0 \n", " 4 2019-01-01 00:12:39.5020 229.0 \n", "\n", " start station name \\\n", "201901-citibike-tripdata.csv 0 Central Park West & W 76 St \n", " 1 Pershing Square North \n", " 2 Amsterdam Ave & W 82 St \n", " 3 1 Ave & E 16 St \n", " 4 Great Jones St \n", "\n", " start station latitude \\\n", "201901-citibike-tripdata.csv 0 40.778968 \n", " 1 40.751873 \n", " 2 40.785247 \n", " 3 40.732219 \n", " 4 40.727434 \n", "\n", " start station longitude end station id \\\n", "201901-citibike-tripdata.csv 0 -73.973747 3283.0 \n", " 1 -73.977706 518.0 \n", " 2 -73.976673 3154.0 \n", " 3 -73.981656 3709.0 \n", " 4 -73.993790 503.0 \n", "\n", " end station name end station latitude \\\n", "201901-citibike-tripdata.csv 0 W 89 St & Columbus Ave 40.788221 \n", " 1 E 39 St & 2 Ave 40.747804 \n", " 2 E 77 St & 3 Ave 40.773142 \n", " 3 W 15 St & 6 Ave 40.738046 \n", " 4 E 20 St & Park Ave 40.738274 \n", "\n", " end station longitude bikeid usertype \\\n", "201901-citibike-tripdata.csv 0 -73.970416 15839 Subscriber \n", " 1 -73.973442 32723 Subscriber \n", " 2 -73.958562 27451 Subscriber \n", " 3 -73.996430 21579 Subscriber \n", " 4 -73.987520 35379 Subscriber \n", "\n", " birth year gender \n", "201901-citibike-tripdata.csv 0 1971 1 \n", " 1 1964 1 \n", " 2 1987 1 \n", " 3 1990 1 \n", " 4 1979 1 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files = !ls *.csv\n", "df = pd.concat([pd.read_csv(f, header=0, skip_blank_lines=True) for f in files], keys=files)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tripdurationstart station idstart station latitudestart station longitudeend station idend station latitudeend station longitudebikeidbirth yeargender
count1.811778e+071.811760e+071.811778e+071.811778e+071.811760e+071.811778e+071.811778e+071.811778e+071.811778e+071.811778e+07
mean9.975303e+021.663640e+034.073724e+01-7.398201e+011.655954e+034.073693e+01-7.398221e+012.931836e+041.980243e+031.162605e+00
std1.070981e+041.475535e+033.049230e-021.977536e-021.475292e+033.037217e-021.986180e-027.402559e+031.208459e+015.421212e-01
min6.100000e+017.200000e+014.065540e+01-7.402535e+017.200000e+014.065540e+01-7.408364e+011.452900e+041.857000e+030.000000e+00
25%3.670000e+023.820000e+024.071755e+01-7.399530e+013.800000e+024.071749e+01-7.399595e+012.516500e+041.970000e+031.000000e+00
50%6.260000e+025.140000e+024.073782e+01-7.398518e+015.130000e+024.073705e+01-7.398565e+013.067700e+041.983000e+031.000000e+00
75%1.101000e+033.295000e+034.075725e+01-7.397121e+013.295000e+034.075715e+01-7.397137e+013.440700e+041.990000e+031.000000e+00
max3.812666e+063.891000e+034.086900e+01-7.387800e+013.891000e+034.086900e+01-7.387800e+014.206800e+042.003000e+032.000000e+00
\n", "
" ], "text/plain": [ " tripduration start station id start station latitude \\\n", "count 1.811778e+07 1.811760e+07 1.811778e+07 \n", "mean 9.975303e+02 1.663640e+03 4.073724e+01 \n", "std 1.070981e+04 1.475535e+03 3.049230e-02 \n", "min 6.100000e+01 7.200000e+01 4.065540e+01 \n", "25% 3.670000e+02 3.820000e+02 4.071755e+01 \n", "50% 6.260000e+02 5.140000e+02 4.073782e+01 \n", "75% 1.101000e+03 3.295000e+03 4.075725e+01 \n", "max 3.812666e+06 3.891000e+03 4.086900e+01 \n", "\n", " start station longitude end station id end station latitude \\\n", "count 1.811778e+07 1.811760e+07 1.811778e+07 \n", "mean -7.398201e+01 1.655954e+03 4.073693e+01 \n", "std 1.977536e-02 1.475292e+03 3.037217e-02 \n", "min -7.402535e+01 7.200000e+01 4.065540e+01 \n", "25% -7.399530e+01 3.800000e+02 4.071749e+01 \n", "50% -7.398518e+01 5.130000e+02 4.073705e+01 \n", "75% -7.397121e+01 3.295000e+03 4.075715e+01 \n", "max -7.387800e+01 3.891000e+03 4.086900e+01 \n", "\n", " end station longitude bikeid birth year gender \n", "count 1.811778e+07 1.811778e+07 1.811778e+07 1.811778e+07 \n", "mean -7.398221e+01 2.931836e+04 1.980243e+03 1.162605e+00 \n", "std 1.986180e-02 7.402559e+03 1.208459e+01 5.421212e-01 \n", "min -7.408364e+01 1.452900e+04 1.857000e+03 0.000000e+00 \n", "25% -7.399595e+01 2.516500e+04 1.970000e+03 1.000000e+00 \n", "50% -7.398565e+01 3.067700e+04 1.983000e+03 1.000000e+00 \n", "75% -7.397137e+01 3.440700e+04 1.990000e+03 1.000000e+00 \n", "max -7.387800e+01 4.206800e+04 2.003000e+03 2.000000e+00 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find Most Popular Destinations\n", "Group by `end station name` and plot top ten." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "type": "bar", "x": [ "Pershing Square North", "E 17 St & Broadway", "Broadway & E 22 St", "8 Ave & W 31 St", "West St & Chambers St", "Broadway & E 14 St", "W 21 St & 6 Ave", "12 Ave & W 40 St", "Christopher St & Greenwich St", "W 20 St & 11 Ave", "Broadway & W 60 St", "Lafayette St & E 8 St", "8 Ave & W 33 St", "W 41 St & 8 Ave", "South End Ave & Liberty St", "Broadway & W 25 St", "Cleveland Pl & Spring St", "W 38 St & 8 Ave", "Cooper Square & Astor Pl", "E 24 St & Park Ave S", "W 31 St & 7 Ave", "E 47 St & Park Ave", "Grand St & Elizabeth St", "1 Ave & E 16 St", "Broadway & W 41 St", "W 18 St & 6 Ave", "W 33 St & 7 Ave", "Great Jones St", "Carmine St & 6 Ave", "North Moore St & Greenwich St" ], "y": [ 136454, 108881, 107808, 106930, 105577, 100448, 94733, 94324, 90737, 88763, 88610, 85373, 82313, 78642, 77514, 76945, 75789, 75233, 74790, 74457, 74283, 72273, 72149, 72013, 71881, 71716, 71659, 69594, 69062, 68923 ] } ], "layout": { "template": { "data": { "bar": 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"#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Most Popular Destinations" } } }, "text/html": [ "
\n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_end_station = df.groupby(['end station name']).size().nlargest(30).reset_index(name='trips')\n", "bar_end_station = go.Bar(x=df_end_station['end station name'],\n", " y=df_end_station['trips'])\n", "layout_end_station = go.Layout(title='Most Popular Destinations')\n", "fig_end_station = go.Figure(data=[bar_end_station],\n", " layout=layout_end_station)\n", "iplot(fig_end_station)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find Most Popular Starts\n", "Group by `start station name` and plot top ten." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "type": "bar", "x": [ "Pershing Square North", "E 17 St & Broadway", "8 Ave & W 31 St", "Broadway & E 22 St", "Broadway & E 14 St", "West St & Chambers St", "W 21 St & 6 Ave", "Broadway & W 60 St", "12 Ave & W 40 St", "Christopher St & Greenwich St", "W 20 St & 11 Ave", "8 Ave & W 33 St", "Lafayette St & E 8 St", "W 41 St & 8 Ave", "Broadway & W 25 St", "South End Ave & Liberty St", "Cleveland Pl & Spring St", "W 38 St & 8 Ave", "W 31 St & 7 Ave", "E 24 St & Park Ave S", "E 13 St & Avenue A", "Broadway & W 41 St", "1 Ave & E 16 St", "W 33 St & 7 Ave", "Grand St & Elizabeth St", "E 47 St & Park Ave", "Cooper Square & Astor Pl", "W 18 St & 6 Ave", "Central Park S & 6 Ave", "Grand Army Plaza & Central Park S" ], "y": [ 137277, 106519, 105820, 100096, 99516, 96750, 94621, 91506, 91330, 90241, 86511, 83077, 82329, 78488, 78355, 77121, 75616, 75269, 74386, 74185, 72973, 72891, 72692, 72583, 71660, 71659, 71128, 71043, 69545, 69542 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], 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"#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Most Popular Starts" } } }, "text/html": [ "
\n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_start_station = df.groupby(['start station name']).size().nlargest(30).reset_index(name='trips')\n", "bar_start_station = go.Bar(x=df_start_station['start station name'],\n", " y=df_start_station['trips'])\n", "layout_start_station = go.Layout(title='Most Popular Starts')\n", "fig_start_station = go.Figure(data=[bar_start_station],\n", " layout=layout_start_station)\n", "iplot(fig_start_station)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Bins for Part of Day\n", "Label `part of day` to see if certain hours have more riders." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tripdurationstarttimestoptimestart station idstart station namestart station latitudestart station longitudeend station idend station nameend station latitudeend station longitudebikeidusertypebirth yeargenderpart of day
201901-citibike-tripdata.csv03202019-01-01 00:01:47.4012019-01-01 00:07:07.58103160.0Central Park West & W 76 St40.778968-73.9737473283.0W 89 St & Columbus Ave40.788221-73.97041615839Subscriber19711night
13162019-01-01 00:04:43.7362019-01-01 00:10:00.6080519.0Pershing Square North40.751873-73.977706518.0E 39 St & 2 Ave40.747804-73.97344232723Subscriber19641night
25912019-01-01 00:06:03.9972019-01-01 00:15:55.43803171.0Amsterdam Ave & W 82 St40.785247-73.9766733154.0E 77 St & 3 Ave40.773142-73.95856227451Subscriber19871night
327192019-01-01 00:07:03.5452019-01-01 00:52:22.6500504.01 Ave & E 16 St40.732219-73.9816563709.0W 15 St & 6 Ave40.738046-73.99643021579Subscriber19901night
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" ], "text/plain": [ " tripduration starttime \\\n", "201901-citibike-tripdata.csv 0 320 2019-01-01 00:01:47.401 \n", " 1 316 2019-01-01 00:04:43.736 \n", " 2 591 2019-01-01 00:06:03.997 \n", " 3 2719 2019-01-01 00:07:03.545 \n", " 4 303 2019-01-01 00:07:35.945 \n", "\n", " stoptime start station id \\\n", "201901-citibike-tripdata.csv 0 2019-01-01 00:07:07.5810 3160.0 \n", " 1 2019-01-01 00:10:00.6080 519.0 \n", " 2 2019-01-01 00:15:55.4380 3171.0 \n", " 3 2019-01-01 00:52:22.6500 504.0 \n", " 4 2019-01-01 00:12:39.5020 229.0 \n", "\n", " start station name \\\n", "201901-citibike-tripdata.csv 0 Central Park West & W 76 St \n", " 1 Pershing Square North \n", " 2 Amsterdam Ave & W 82 St \n", " 3 1 Ave & E 16 St \n", " 4 Great Jones St \n", "\n", " start station latitude \\\n", "201901-citibike-tripdata.csv 0 40.778968 \n", " 1 40.751873 \n", " 2 40.785247 \n", " 3 40.732219 \n", " 4 40.727434 \n", "\n", " start station longitude end station id \\\n", "201901-citibike-tripdata.csv 0 -73.973747 3283.0 \n", " 1 -73.977706 518.0 \n", " 2 -73.976673 3154.0 \n", " 3 -73.981656 3709.0 \n", " 4 -73.993790 503.0 \n", "\n", " end station name end station latitude \\\n", "201901-citibike-tripdata.csv 0 W 89 St & Columbus Ave 40.788221 \n", " 1 E 39 St & 2 Ave 40.747804 \n", " 2 E 77 St & 3 Ave 40.773142 \n", " 3 W 15 St & 6 Ave 40.738046 \n", " 4 E 20 St & Park Ave 40.738274 \n", "\n", " end station longitude bikeid usertype \\\n", "201901-citibike-tripdata.csv 0 -73.970416 15839 Subscriber \n", " 1 -73.973442 32723 Subscriber \n", " 2 -73.958562 27451 Subscriber \n", " 3 -73.996430 21579 Subscriber \n", " 4 -73.987520 35379 Subscriber \n", "\n", " birth year gender part of day \n", "201901-citibike-tripdata.csv 0 1971 1 night \n", " 1 1964 1 night \n", " 2 1987 1 night \n", " 3 1990 1 night \n", " 4 1979 1 night " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def bin_part_of_day(hour):\n", " if hour < 2:\n", " return 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"gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Rides by Part of Day" } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_partofday = df.groupby(['part of day']).size().reset_index(name='trips')\n", "bar_partofday = go.Bar(x=df_partofday['part of day'],\n", " y=df_partofday['trips'])\n", "layout_partofday = go.Layout(title='Rides by Part of Day')\n", "fig_partofday = go.Figure(data=[bar_partofday],\n", " layout=layout_partofday)\n", "iplot(fig_partofday)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot by weekend\n", "Plot to see if there rides by time of day differs during weekdays and weekends." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "type": "bar", "x": [ "afternoon", "before dawn", "evening", "midday", "morning", "night" ], "y": [ 3696778, 180166, 3309305, 2303472, 3351487, 651580 ] } ], "layout": { 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Weekend Rides by Part of Day" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['weekday'] = df['starttime'].apply(lambda x: x.dayofweek < 5)\n", "df_weekday_partofday = df.groupby(['weekday','part of day']).size().reset_index(name='trips')\n", "\n", "df_wdyonly = df_weekday_partofday[df_weekday_partofday['weekday']]\n", "df_wkdonly = df_weekday_partofday[df_weekday_partofday['weekday'] != True]\n", "\n", "bar_df_wdyonly = go.Bar(x=df_wdyonly['part of day'],\n", " y=df_wdyonly['trips'])\n", "layout_wdyonly = go.Layout(title='Weekday Rides by Part of Day')\n", "fig_wdyonly = go.Figure(data=[bar_df_wdyonly],\n", " layout=layout_wdyonly)\n", "\n", "bar_df_wkdonly = go.Bar(x=df_wkdonly['part of day'],\n", " y=df_wkdonly['trips'])\n", "layout_wkdonly = go.Layout(title='Weekend Rides by Part of Day')\n", "fig_wkdonly = go.Figure(data=[bar_df_wkdonly],\n", " layout=layout_wkdonly)\n", "\n", "iplot(fig_wdyonly)\n", "iplot(fig_wkdonly)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find Most Popular Month by Rides\n", "Chart rides by month." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "type": "bar", "x": [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct" ], "y": [ 967287, 943744, 1327960, 1766094, 1924563, 2125370, 2181064, 2344224, 2444900, 2092573 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": 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"gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": 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\n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['month of year'] = df['starttime'].apply(lambda x: x.month)\n", "df['month name'] = df['starttime'].apply(lambda x: x.strftime('%b'))\n", "df_monthofyr = df.groupby(['month of year','month name']).size().reset_index(name='trips')\n", "bar_monthofyr = go.Bar(x=df_monthofyr['month name'],\n", " y=df_monthofyr['trips'])\n", "layout_monthofyr = go.Layout(title='Rides by Month')\n", "fig_monthofyr = go.Figure(data=[bar_monthofyr],\n", " layout=layout_monthofyr)\n", "iplot(fig_monthofyr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "conda_python3", "language": "python", "name": "conda_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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }