communicate-data-findings-slide-deck.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualizing FordGoBike Trip Data\n",
    "## by Abdulrahman Alyahya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Investigation Overview\n",
    "\n",
    "> In this investigation, I wanted to look at to the customer behaviour characteristics, I  focused on time, age, gender, duration, and user type to analyze and understand the usage of bike When, Who, How?\n",
    "\n",
    "## Dataset Overview\n",
    "\n",
    "> The forgobike trip data are 3,177,270 rides that happend in 2017, 2018 and 2019 (until May)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# suppress warnings from final output\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "df = pd.read_csv('forgobike-tripdata-master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>age_category</th>\n",
       "      <th>start_time_month</th>\n",
       "      <th>start_time_monthly</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>end_time_hour</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75284</td>\n",
       "      <td>2018-01-31 22:52:35.239</td>\n",
       "      <td>2018-02-01 19:47:19.824</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Mission Dolores Park</td>\n",
       "      <td>37.76142</td>\n",
       "      <td>-122.426435</td>\n",
       "      <td>285.0</td>\n",
       "      <td>Webster St at O'Farrell St</td>\n",
       "      <td>37.783521</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31-40</td>\n",
       "      <td>January</td>\n",
       "      <td>18-01</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>1254.733333</td>\n",
       "      <td>2.488001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec               start_time                 end_time  \\\n",
       "0         75284  2018-01-31 22:52:35.239  2018-02-01 19:47:19.824   \n",
       "\n",
       "   start_station_id    start_station_name  start_station_latitude  \\\n",
       "0             120.0  Mission Dolores Park                37.76142   \n",
       "\n",
       "   start_station_longitude  end_station_id            end_station_name  \\\n",
       "0              -122.426435           285.0  Webster St at O'Farrell St   \n",
       "\n",
       "   end_station_latitude     ...      bike_share_for_all_trip  member_age  \\\n",
       "0             37.783521     ...                           No        33.0   \n",
       "\n",
       "  age_category  start_time_month start_time_monthly start_time_day  \\\n",
       "0        31-40           January              18-01      Wednesday   \n",
       "\n",
       "   start_time_hour end_time_hour duration_min distance_km  \n",
       "0               22            19  1254.733333    2.488001  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\", font_scale=1.25, rc={'figure.figsize':(20,7)})\n",
    "base_color = sns.color_palette()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Visualization 1)\n",
    "\n",
    "> In our dataset there are two type of user: <br>\n",
    "    - Subscriber\n",
    "    - Customer\n",
    "  In the next visualization I will show the percentage of bike rides for all members user type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_points = df.shape[0]\n",
    "\n",
    "max_count_age = df['user_type'].value_counts().max()\n",
    "max_prop_age = max_count_age / n_points\n",
    "\n",
    "tick_props_age = np.arange(0, max_prop_age, 0.05)\n",
    "tick_names_age = ['{:0.2f}'.format(v) for v in tick_props_age]\n",
    "\n",
    "sns.countplot(data=df, x='user_type', color=base_color)\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.yticks(tick_props_age * n_points, tick_names_age)\n",
    "ax.set_ylabel('Proportion')\n",
    "ax.set_xlabel('User Type')\n",
    "ax.set_title(\"Percentage of Bike Rides for Members User Type\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Visualization 2)\n",
    "\n",
    "> Based on our data we have three gender male, female and other. In the next visualization I will show the number of bike rides for each day based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1005.96x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm = sns.catplot(data=df, x='start_time_day', hue='member_gender', kind='count',aspect=20.7/8.27, palette='viridis_r')\n",
    "\n",
    "lm.set_axis_labels('Day', 'Rides Count')\n",
    "lm._legend.set_title('Gender')\n",
    "lm.fig.suptitle('The Monthly Usage of Bike Rides Per Gender', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Visualization 3)\n",
    "\n",
    "In this Visualization I will show number of bike rides over time based on age category.<br>\n",
    "I divided members age into 5 categories.\n",
    "- 11-20\n",
    "- 21-30\n",
    "- 31-40\n",
    "- 41-50\n",
    "- 51-60 <br>\n",
    "\n",
    "So, I will show number of bike rides per category over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_order = ['11-20', '21-30', '31-40', '41-50', '51-60']\n",
    "ax = sns.countplot(x='start_time_monthly', hue='age_category', hue_order=my_order,\n",
    "                   data=df.sort_values(by=['start_time_monthly']))\n",
    "\n",
    "ax.legend().set_title('Age')\n",
    "ax.set_ylabel('Rides Count')\n",
    "ax.set_xlabel('Date (Year-Month)')\n",
    "ax.set_title(\"The Monthly Usage of Bike Rides\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "> Once you're ready to finish your presentation, check your output by using\n",
    "nbconvert to export the notebook and set up a server for the slides. From the\n",
    "terminal or command line, use the following expression:\n",
    "> > `jupyter nbconvert communicate-data-findings-slide-deck.ipynb --to slides --post serve --template output_toggle`\n",
    "\n",
    "> This should open a tab in your web browser where you can scroll through your\n",
    "presentation. Sub-slides can be accessed by pressing 'down' when viewing its parent\n",
    "slide. Make sure you remove all of the quote-formatted guide notes like this one\n",
    "before you finish your presentation!"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}