uber_pickups_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uber dataset analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project's goal is to analyze data about Uber rides while using various data visualization frameworks that are available for Python. Data on Uber rides in New York from April to September of 2014 are included in the dataset. The information was gathered by FiveThirtyEight and is accessible on [Kaggle](https://www.kaggle.com/datasets/fivethirtyeight/uber-pickups-in-new-york-city?select=uber-raw-data-aug14.csv).\n",
    "\n",
    "The variables in the dataset are:\n",
    "\n",
    "| name | descripiton |\n",
    "| -- | -- |\n",
    "| Date/Time | The date and hour of Uber pickup |\n",
    "| Lat | The Latitude of the Uber pickup |\n",
    "| Lon | The longitude of the Uber pickup |\n",
    "| Base | The LTC base company code affiliated with the Uber pickup |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "apr_data = pd.read_csv('./data/uber-raw-data-apr14.csv') # Pickups in april\n",
    "may_data = pd.read_csv('./data/uber-raw-data-may14.csv') # Pickups in may\n",
    "jun_data = pd.read_csv('./data/uber-raw-data-jun14.csv') # pickups in june\n",
    "jul_data = pd.read_csv('./data/uber-raw-data-jul14.csv') # pickups in july\n",
    "aug_data = pd.read_csv('./data/uber-raw-data-aug14.csv') # pickups in august\n",
    "sep_data = pd.read_csv('./data/uber-raw-data-sep14.csv') # pickups in september\n",
    "\n",
    "# Concat all the subsets in just one variable\n",
    "data = pd.concat([apr_data, may_data, jun_data, jul_data, aug_data, sep_data])\n",
    "\n",
    "del apr_data, may_data, jun_data, jul_data, aug_data, sep_data # Deleting the unnecessary variables to clean the memory\n",
    "\n",
    "data['Date/Time'] = pd.to_datetime(data['Date/Time'], format='%m/%d/%Y %H:%M:%S') # String to datetime\n",
    "data['day'] = data['Date/Time'].dt.day # Extract the day\n",
    "data['month'] = data['Date/Time'].dt.month # Extract the month\n",
    "data['year'] = data['Date/Time'].dt.year # Extract the year\n",
    "data['day_of_week'] = data['Date/Time'].dt.day_of_week # Extract the day of the week\n",
    "data['hour'] = data['Date/Time'].dt.hour # Extract the hour\n",
    "data['minute'] = data['Date/Time'].dt.minute # Extract the minute\n",
    "data['second'] = data['Date/Time'].dt.second # Extract the second\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_names = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}\n",
    "months_names = {4:'April', 5:'May', 6:'June', 7:'July', 8:'August', 9:'September'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by the hours in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_data = data.groupby('hour')['hour'].count().to_frame().rename(columns={'hour':'Total'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 6), dpi=100)\n",
    "fig.fontsize = 20\n",
    "ax.plot(hour_data, color='#09091a')\n",
    "ax.set_xlim(0, 23)\n",
    "ax.set_xticks(range(24))\n",
    "ax.set_ylim(0, hour_data.Total.max() + 5000)\n",
    "ax.spines[['top', 'right']].set_visible(False)\n",
    "ax.set_xlabel('Hour', fontsize=14, color='#222233')\n",
    "plt.suptitle('Number of trips during the day', fontsize=18, color='#09091a',\n",
    "             x=0.123, y=1.05, ha='left')\n",
    "ax.set_title('Most trips in 2014 took place after 15:00', \n",
    "             fontsize=14, loc='left', color='#1fbad6', y=1.1, ha='left')\n",
    "\n",
    "ax.vlines(15, ymin=0, ymax=hour_data.Total.max(), color='#c0c0c8')\n",
    "ax.text(15.3, hour_data.loc[15] / 1.2, '2,521,360 trips \\nwere recorded between\\n 15:00 and 23:00 hours.', color='#222233')\n",
    "ax.text(14.8, hour_data.loc[15] / 3, '2,288,433 trips \\nwere recorded between\\n 00:00 and 15:00.',\n",
    "        horizontalalignment='right', color='#222233')\n",
    "ax.plot(17, hour_data.loc[17], 'o', color='#222233')\n",
    "ax.text(17, hour_data.loc[17] * 1.02, 'Pickups peak at 17:00 with 336,190 trips.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by hour and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_hour_month = pd.crosstab(data.hour, data.month)\n",
    "data_hour_month = data_hour_month.rename(columns=months_names)\n",
    "data_hour_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(11, 6), dpi=100)\n",
    "data_hour_month.plot(kind='bar', stacked=True, color=['#d9d9d9', '#999999', '#747474', '#5d5d5d', '#3f3f3f', '#1fbad6'], ax=ax)\n",
    "ax.spines[['top', 'right']].set_visible(False)\n",
    "ax.set_xlabel('Hour', fontsize=14, color='#222233')\n",
    "plt.legend(title='Month')\n",
    "plt.suptitle('Number of trips by month and hour', fontsize=18, color='#09091a',\n",
    "             x=0.123, y=1.05, ha='left')\n",
    "ax.set_title('In the month of September, more trips were registered', \n",
    "             fontsize=14, loc='left', color='#1fbad6', y=1.1, ha='left')\n",
    "ax.text(17, hour_data.loc[17], 'Peak hours are \\nthe same every month.',\n",
    "        horizontalalignment='right', color='#222233')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by day and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_avg = round(data.day.value_counts().mean(), 0)\n",
    "daily_trips = data.day.value_counts()\n",
    "days_above_avg = daily_trips[daily_trips > trips_avg].to_frame().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = []\n",
    "\n",
    "for i in range(32):\n",
    "    if i == 29:\n",
    "        palette.append('#1fbad6')\n",
    "    elif i == 30:\n",
    "        palette.append('#d9d9d9')\n",
    "    elif i+1 in days_above_avg.index:\n",
    "        palette.append('#3f3f3f')\n",
    "    else:\n",
    "        palette.append('#999999')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10, 6),\n",
    "            'axes.facecolor':'white',\n",
    "            'figure.facecolor':'white'})\n",
    "ax = sns.countplot(data=data, x='day', palette=palette)\n",
    "ax.set_xlabel('Hour', fontsize=12)\n",
    "ax.set_ylabel('')\n",
    "plt.suptitle('Number of trips by day and month', fontsize=18, color='#09091a',\n",
    "             x=0.123, y=1.05, ha='left')\n",
    "ax.set_title('17 out of 31 days are above average trips.', \n",
    "             fontsize=14, loc='left', color='#1fbad6', y=1.1, ha='left')\n",
    "ax.hlines(trips_avg, xmin=-0.5, xmax=31, ls='--', colors='k')\n",
    "ax.text(31, trips_avg, f\"Average = {int(trips_avg)}\", va='center')\n",
    "ax.text(30, daily_trips.loc[31], f\"{daily_trips.loc[31]} trips\")\n",
    "ax.text(29, daily_trips.loc[30], f\"{daily_trips.loc[30]} trips\", color='#1fbad6', weight='bold')\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by week day and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.copy()\n",
    "data2 = data2.replace({'month': months_names, 'day_of_week': days_names})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "palette = ['#0d47a1', '#1565c0', '#1976d2', '#1e88e5', '#2196f3', '#42a5f5', '#64b5f6', '#90caf9']\n",
    "\n",
    "px.histogram(data2, x='month', color='day_of_week', barmode='group',\n",
    "             labels = {'month':'Months', 'day_of_week':'Day of week'},\n",
    "             title = 'Trips by week day and month',\n",
    "             color_discrete_sequence = palette,\n",
    "             category_orders = {'day_of_week': ['Monday', 'Tuesday', 'Wednesday', 'Quinta', 'Thursday', 'Saturday', 'Sunday']}\n",
    "             ).update_layout(yaxis_title = '',\n",
    "                             plot_bgcolor = 'rgb(255, 255, 255)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import ggplot\n",
    "from plotnine import *\n",
    "import plotnine as p9\n",
    "\n",
    "trips = data.groupby('month')['month'].count().to_frame().rename(columns={'month':'Total'}).reset_index()\n",
    "palette = ('#2d9dff', '#2d9dff', '#2d9dff', '#2d9dff', '#2d9dff', '#2d9dff')\n",
    "\n",
    "p9.options.figure_size = (10, 6)\n",
    "\n",
    "ggplot(trips)\\\n",
    "    + aes(x='month', y='Total', fill='factor(month)')\\\n",
    "    + geom_col()\\\n",
    "    + coord_flip()\\\n",
    "    + geom_text(\n",
    "        aes(label = 'Total'),\n",
    "        ha = 'right'\n",
    "    )\\\n",
    "    + labs(\n",
    "        y = 'Trips',\n",
    "        x = 'Months'  ,\n",
    "        title = 'Trips by month'     \n",
    "    )\\\n",
    "    + theme_minimal()\\\n",
    "    + theme(legend_position='none')\\\n",
    "    + scale_x_continuous(breaks=list(range(4, 10)), labels=['April', 'May', 'June', 'July', 'August', 'September'])\\\n",
    "    + scale_fill_manual(values=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_trips = data.groupby('Base')['Base'].count().to_frame().rename(columns={'Base':'Total'}).reset_index()\n",
    "base_trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "\n",
    "bars = alt.Chart(base_trips, title='Trips by Base').mark_bar().encode(\n",
    "    x='Total',\n",
    "    y=\"Base\"\n",
    ")\n",
    "\n",
    "text = bars.mark_text(\n",
    "    align='right',\n",
    "    baseline='middle',\n",
    "    dx=-3, color='#ffffff'\n",
    ").encode(\n",
    "    text='Total'\n",
    ")\n",
    "\n",
    "(bars + text).properties(height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by base and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_base_trips = pd.crosstab(data.Base, data.month)\n",
    "month_base_trips = month_base_trips.rename(columns=months_names)\n",
    "month_base_trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import show\n",
    "from bokeh.models import ColumnDataSource, FactorRange\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "x = [(base, mes) for base in month_base_trips.index.values[:] for mes in month_base_trips.columns]\n",
    "counts = [month_base_trips.loc[base, mes] for base in month_base_trips.index.values[:] for mes in month_base_trips.columns]\n",
    "source = ColumnDataSource(data=dict(x=x, counts=counts))\n",
    "\n",
    "p = figure(x_range=FactorRange(*x), plot_height=350, title=\"Trips by base and month\",\n",
    "           toolbar_location=None, tools=\"\")\n",
    "\n",
    "p.vbar(x='x', top='counts', width=0.9, source=source)\n",
    "\n",
    "p.y_range.start = 0\n",
    "p.x_range.range_padding = 0.1\n",
    "p.xaxis.major_label_orientation = 1\n",
    "p.xgrid.grid_line_color = None\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trips by Base and day of week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.copy()\n",
    "data2 = data2.replace({'month': months_names, 'day_of_week': days_names})\n",
    "base_days_week_trips = pd.crosstab(data2.Base, data2.day_of_week)\n",
    "base_days_week_trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygal\n",
    "from pygal.style import LightenStyle\n",
    "dark_lighten_style = LightenStyle('#336676')\n",
    "\n",
    "bar_chart = pygal.Bar(style=dark_lighten_style, height=250)\n",
    "bar_chart.title = 'Trips by Base and day of week'\n",
    "bar_chart.x_labels = base_days_week_trips.index.values[:]\n",
    "for column in ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']:\n",
    "    bar_chart.add(column, base_days_week_trips[column])\n",
    "bar_chart.render_to_file('trips_base_week_day.svg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap: trips by hour and day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips = pd.crosstab(data.hour, data.day) / 1_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "im = ax.imshow(trips, cmap=plt.get_cmap(\"Blues\", 13), vmin=0, vmax=13)\n",
    "\n",
    "ax.set_xticks(np.arange(len(trips.columns)), labels=trips.columns, fontsize=10)\n",
    "ax.set_yticks(np.arange(len(trips.index)), labels=trips.index, fontsize=10)\n",
    "ax.set_title(\"Trips by hour and day\", fontsize=20)\n",
    "\n",
    "cbar = ax.figure.colorbar(im, ticks=np.arange(14), fraction=0.035, ax=ax)\n",
    "cbar.ax.set_ylabel(\"Trips in thounsands\", rotation=-90, va=\"bottom\", fontsize=12)\n",
    "\n",
    "ax.spines[:].set_visible(False)\n",
    "\n",
    "ax.set_xticks(np.arange(trips.shape[1]+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(trips.shape[0]+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "ax.set_xlabel('Day', fontsize=12)\n",
    "ax.set_ylabel('Hour', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap: Trips by month and day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "trips = pd.crosstab(data.month, data.day) / 1_000\n",
    "corridas_plot = trips.rename(index=months_names)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 7))\n",
    "sns.heatmap(trips, \n",
    "            vmin=0, \n",
    "            vmax=45, \n",
    "            cmap=plt.get_cmap(\"Blues\", 9), \n",
    "            ax=ax, \n",
    "            linewidths=2)\n",
    "ax.set_title('Trips by month and day', fontsize=20)\n",
    "ax.set_xlabel('Day', fontsize=12)\n",
    "ax.set_ylabel('', fontsize=12)\n",
    "ax.collections[0].colorbar.set_label('Trips in thousands', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap: Trips by month and week day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips = pd.crosstab(data.month, data.day_of_week) / 1_000\n",
    "trips = trips.rename(index=months_names, columns=days_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "plot = go.Heatmap(z = trips.values[:],\n",
    "                  x = trips.columns,\n",
    "                  y = trips.index,\n",
    "                  colorscale = 'Blues',\n",
    "                  xgap = 2,\n",
    "                  ygap = 2,\n",
    "                  zmin = 0,\n",
    "                  zmax = 165,\n",
    "                  colorbar = dict(title='Trips in thousands')\n",
    "                )\n",
    "\n",
    "layout = go.Layout(title = 'Trips by month and week day')\n",
    "\n",
    "fig = go.Figure(data=plot, layout=layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap: Trips by Base and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips = data.groupby(['Base', 'month'])['hour'].count().reset_index().rename(columns={'hour':'Total'})\n",
    "trips = trips.replace({'month':months_names})\n",
    "trips['Total'] /= 1000\n",
    "trips['Total'] = trips['Total'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "import plotnine as p9\n",
    "\n",
    "p9.options.figure_size = (10, 6)\n",
    "\n",
    "ggplot(trips)\\\n",
    "    + aes(x='month', y='Base', fill='Total')\\\n",
    "    + geom_tile(aes(width=.95, height=.95))\\\n",
    "    + geom_text(aes(label='Total'), size=10)\\\n",
    "    + labs(\n",
    "        y = 'Base',\n",
    "        x = ''  ,\n",
    "        title = 'Trips by Base and month'     \n",
    "    )\\\n",
    "    + theme_minimal()\\\n",
    "    + scale_fill_gradient(low='#cbe7ff', high='#08306b')\\\n",
    "    + scale_x_discrete(limits=('April', 'May', 'June', 'July', 'August', 'September'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap: Trips by base and week day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips = data.groupby(['Base', 'day_of_week'])['hour'].count().reset_index().rename(columns={'hour':'Total'})\n",
    "trips = trips.replace({'day_of_week':days_names})\n",
    "trips['Total'] /= 1000\n",
    "trips['Total'] = trips['Total'].round(2)\n",
    "trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.Chart(trips, title='Trips by Base and week day').mark_rect().encode(\n",
    "    x=alt.X('day_of_week', axis=alt.Axis(title='Week day'), sort=['Monday', 'Tuesday', 'Wednesday', 'Thursday',\n",
    "                                                                  'Friday', 'Saturday', 'Sunday']),\n",
    "    y='Base',\n",
    "    color=alt.Color('Total', scale=alt.Scale(scheme='blues')),\n",
    ").properties(height=300, width=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maps of Uber rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.tile_providers import get_provider, WIKIMEDIA\n",
    "from bokeh.io import output_notebook, show\n",
    "from pyproj import Proj, transform\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "inProj = Proj(init='epsg:3857')\n",
    "outProj = Proj(init='epsg:4326')\n",
    "\n",
    "lons, lats = [], []\n",
    "for lon, lat in list(set(zip(data[\"Lon\"], data[\"Lat\"]))):\n",
    "    x, y = transform(outProj, inProj, lon, lat)\n",
    "    lons.append(x)\n",
    "    lats.append(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_map = pd.DataFrame([])\n",
    "\n",
    "data_map[\"MercatorX\"] = lons\n",
    "data_map[\"MercatorY\"] = lats\n",
    "\n",
    "data_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "wikimedia = get_provider(WIKIMEDIA)\n",
    "\n",
    "ny_lon1, ny_lat1 = transform(outProj, inProj, -73.7, 40.58)\n",
    "ny_lon2, ny_lat2 = transform(outProj, inProj, -74.15, 40.92)\n",
    "\n",
    "p = figure(plot_width=900, plot_height=700,\n",
    "           x_range=(ny_lon1, ny_lon2), y_range=(ny_lat1, ny_lat2),\n",
    "           x_axis_type=\"mercator\", y_axis_type=\"mercator\",\n",
    "           title=\"Uber rides in NY\")\n",
    "\n",
    "p.add_tile(wikimedia)\n",
    "\n",
    "p.circle(x=\"MercatorX\", y=\"MercatorY\",\n",
    "         size=2,\n",
    "         fill_color=\"dodgerblue\", line_color=\"dodgerblue\",\n",
    "         fill_alpha=0.3,\n",
    "         source=data_map)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map Uber rides by base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from cartopy import crs as ccrs\n",
    "from cartopy import feature as cfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the domain for defining the plot region.\n",
    "latN = 40.92\n",
    "latS = 40.58\n",
    "lonW = -74.15\n",
    "lonE = -73.7\n",
    "cLat = (latN + latS)/2\n",
    "cLon = (lonW + lonE )/2\n",
    "\n",
    "base_colors = {'B02512':'red', 'B02598':'green', 'B02617':'blue', 'B02682':'yellow', 'B02764':'gray'}\n",
    "bases = data.Base.unique()\n",
    "\n",
    "proj = ccrs.LambertConformal(central_longitude=cLon, central_latitude=cLat)\n",
    "res = '10m' # Coarsest and quickest to display; other options are '10m' (slowest), '50m', 1110m.\n",
    "fig = plt.figure(figsize=(18, 12))\n",
    "ax = plt.subplot(1 ,1, 1, projection=proj)\n",
    "ax.set_extent ([lonW, lonE, latS, latN])\n",
    "ax.add_feature (cfeature.OCEAN.with_scale(res))\n",
    "ax.add_feature(cfeature.COASTLINE.with_scale(res))\n",
    "ax.set_title ('New York Map on Uber rides during 2014 (Apr-Sep) by Base')\n",
    "\n",
    "for base in bases:\n",
    "    lat = data.query(f'Base == \"{base}\"').Lat\n",
    "    lon = data.query(f'Base == \"{base}\"').Lon\n",
    "    ax.scatter(lon, lat, s=9, c=base_colors[base], \n",
    "               edgecolor=None, alpha=0.75, \n",
    "               transform=ccrs.PlateCarree(), label=base)\n",
    "    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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