Bike_Share_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2016 US Bike Share Activity Snapshot\n",
    "\n",
    "## Table of Contents\n",
    "- [Introduction](#intro)\n",
    "- [Posing Questions](#pose_questions)\n",
    "- [Data Collection and Wrangling](#wrangling)\n",
    "  - [Condensing the Trip Data](#condensing)\n",
    "- [Exploratory Data Analysis](#eda)\n",
    "  - [Statistics](#statistics)\n",
    "  - [Visualizations](#visualizations)\n",
    "- [Performing Your Own Analysis](#eda_continued)\n",
    "- [Conclusions](#conclusions)\n",
    "\n",
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles for short trips, typically 30 minutes or less. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.\n",
    "\n",
    "In this project, I performed an exploratory analysis on data provided by [Motivate](https://www.motivateco.com/), a bike-share system provider for many major cities in the United States. I compared the system usage between three large cities: New York City, Chicago, and Washington, DC. I also saw if there are any differences within each system for those users that are registered, regular users and those users that are short-term, casual users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pose_questions'></a>\n",
    "## Posing Questions\n",
    "\n",
<<<<<<< HEAD
    "Before looking at the bike sharing data, I started by asking questions I wanted to understand about the bike share data. Consider, for example, if I were working for Motivate. What kinds of information would I want to know about in order to make smarter business decisions? If I were a user of the bike-share service, what factors might influence how I would want to use the service?"
||||||| merged common ancestors
    "Before looking at the bike sharing data, you should start by asking questions you might want to understand about the bike share data. Consider, for example, if you were working for Motivate. What kinds of information would you want to know about in order to make smarter business decisions? If you were a user of the bike-share service, what factors might influence how you would want to use the service?\n",
    "\n",
    "**Question 1**: Write at least two questions related to bike sharing that you think could be answered by data.\n",
    "\n",
    "\n",
    "> **Tip**: If you double click on this cell, you will see the text change so that all of the formatting is removed. This allows you to edit this block of text. This block of text is written using [Markdown](http://daringfireball.net/projects/markdown/syntax), which is a way to format text using headers, links, italics, and many other options using a plain-text syntax. You will also use Markdown later in the Nanodegree program. Use **Shift** + **Enter** or **Shift** + **Return** to run the cell and show its rendered form."
=======
    "Before looking at the bike sharing data, you should start by asking questions you might want to understand about the bike share data. Consider, for example, if you were working for Motivate. What kinds of information would you want to know about in order to make smarter business decisions? If you were a user of the bike-share service, what factors might influence how you would want to use the service?\n",
    "\n",
    "**Question 1**: Write at least two questions related to bike sharing that you think could be answered by data.\n"
>>>>>>> 5b798f57a0f553c42358d24defdb95f3d0c7a0f5
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1:** What the peak days, weekdays or weekends, people use bikeshare system? What the trip duration average in terms of Subscribers or Customers? All of three cities follow the same pattern?\n",
    "\n",
    "**Q1:** Which season has the more trips duration in average? All of three cities follow the same pattern also?\n",
    "\n",
    "**Answering these questions let Motivate rethink in the number of bicycles during  days of the week or seasons, and give it a chance to put pricing policy to activate the both subscribers and customers markets.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Collection and Wrangling\n",
    "\n",
    "It's time to collect and explore our data. In this project, I focused on the record of individual trips taken in 2016 from our selected cities: New York City, Chicago, and Washington, DC. Each of these cities has a page where I could freely download the trip data:\n",
    "\n",
    "- New York City (Citi Bike): [Link](https://www.citibikenyc.com/system-data)\n",
    "- Chicago (Divvy): [Link](https://www.divvybikes.com/system-data)\n",
    "- Washington, DC (Capital Bikeshare): [Link](https://www.capitalbikeshare.com/system-data)\n",
    "\n",
    "If you visit these pages, you will notice that each city has a different way of delivering its data. Chicago updates with new data twice a year, Washington DC is quarterly, and New York City is monthly. **However, you do not need to download the data yourself.** The data has already been collected for you in the `/data/` folder of the project files. While the original data for 2016 is spread among multiple files for each city, the files in the `/data/` folder collect all of the trip data for the year into one file per city. Some data wrangling of inconsistencies in timestamp format within each city has already been performed for you. In addition, a random 2% sample of the original data is taken to make the exploration more manageable. \n",
    "\n",
    "However, there is still a lot of data for us to investigate, so it's a good idea to start off by looking at one entry from each of the cities we're going to analyze. I ran the first code cell below to load some packages and functions that I'll be using in my analysis. Then, I completed the second code cell to print out the first trip recorded from each of the cities (the second line of each data file).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import all necessary packages and functions.\n",
    "import csv # read and write csv files\n",
    "from datetime import datetime # operations to parse dates\n",
    "from pprint import pprint # use to print data structures like dictionaries in\n",
    "                          # a nicer way than the base print function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: NYC\n",
      "OrderedDict([('tripduration', '839'),\n",
      "             ('starttime', '1/1/2016 00:09:55'),\n",
      "             ('stoptime', '1/1/2016 00:23:54'),\n",
      "             ('start station id', '532'),\n",
      "             ('start station name', 'S 5 Pl & S 4 St'),\n",
      "             ('start station latitude', '40.710451'),\n",
      "             ('start station longitude', '-73.960876'),\n",
      "             ('end station id', '401'),\n",
      "             ('end station name', 'Allen St & Rivington St'),\n",
      "             ('end station latitude', '40.72019576'),\n",
      "             ('end station longitude', '-73.98997825'),\n",
      "             ('bikeid', '17109'),\n",
      "             ('usertype', 'Customer'),\n",
      "             ('birth year', ''),\n",
      "             ('gender', '0')])\n",
      "\n",
      "City: Chicago\n",
      "OrderedDict([('trip_id', '9080545'),\n",
      "             ('starttime', '3/31/2016 23:30'),\n",
      "             ('stoptime', '3/31/2016 23:46'),\n",
      "             ('bikeid', '2295'),\n",
      "             ('tripduration', '926'),\n",
      "             ('from_station_id', '156'),\n",
      "             ('from_station_name', 'Clark St & Wellington Ave'),\n",
      "             ('to_station_id', '166'),\n",
      "             ('to_station_name', 'Ashland Ave & Wrightwood Ave'),\n",
      "             ('usertype', 'Subscriber'),\n",
      "             ('gender', 'Male'),\n",
      "             ('birthyear', '1990')])\n",
      "\n",
      "City: Washington\n",
      "OrderedDict([('Duration (ms)', '427387'),\n",
      "             ('Start date', '3/31/2016 22:57'),\n",
      "             ('End date', '3/31/2016 23:04'),\n",
      "             ('Start station number', '31602'),\n",
      "             ('Start station', 'Park Rd & Holmead Pl NW'),\n",
      "             ('End station number', '31207'),\n",
      "             ('End station', 'Georgia Ave and Fairmont St NW'),\n",
      "             ('Bike number', 'W20842'),\n",
      "             ('Member Type', 'Registered')])\n"
     ]
    }
   ],
   "source": [
    "def print_first_point(filename):\n",
    "    \"\"\"\n",
    "    This function prints and returns the first data point (second row) from\n",
    "    a csv file that includes a header row.\n",
    "    \"\"\"\n",
    "    # print city name for reference\n",
    "    city = filename.split('-')[0].split('/')[-1]\n",
    "    print('\\nCity: {}'.format(city))\n",
    "    \n",
    "    with open(filename, 'r') as f_in:\n",
    "        ## TODO: Use the csv library to set up a DictReader object. ##\n",
    "        ## see https://docs.python.org/3/library/csv.html           ##\n",
    "        trip_reader = csv.DictReader(f_in)\n",
    "        \n",
    "        ## TODO: Use a function on the DictReader object to read the     ##\n",
    "        ## first trip from the data file and store it in a variable.     ##\n",
    "        ## see https://docs.python.org/3/library/csv.html#reader-objects ##\n",
    "        first_trip = next(trip_reader)\n",
    "                \n",
    "        ## TODO: Use the pprint library to print the first trip. ##\n",
    "        ## see https://docs.python.org/3/library/pprint.html     ##\n",
    "        pprint(first_trip)\n",
    "        \n",
    "    # output city name and first trip for later testing\n",
    "    return (city, first_trip)\n",
    "\n",
    "# list of files for each city\n",
    "data_files = ['./data/NYC-CitiBike-2016.csv',\n",
    "              './data/Chicago-Divvy-2016.csv',\n",
    "              './data/Washington-CapitalBikeshare-2016.csv',]\n",
    "\n",
    "# print the first trip from each file, store in dictionary\n",
    "example_trips = {}\n",
    "for data_file in data_files:\n",
    "    city, first_trip = print_first_point(data_file)\n",
    "    example_trips[city] = first_trip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything has been filled out correctly, you should see below the printout of each city name (which has been parsed from the data file name) that the first trip has been parsed in the form of a dictionary. When you set up a `DictReader` object, the first row of the data file is normally interpreted as column names. Every other row in the data file will use those column names as keys, as a dictionary is generated for each row.\n",
    "\n",
    "This will be useful since we can refer to quantities by an easily-understandable label instead of just a numeric index. For example, if we have a trip stored in the variable `row`, then we would rather get the trip duration from `row['duration']` instead of `row[0]`.\n",
    "\n",
    "<a id='condensing'></a>\n",
    "### Condensing the Trip Data\n",
    "\n",
    "I noticed in the printout that each city provides different information. Even where the information is the same, the column names and formats are sometimes different. To make things as simple as possible when I get to the actual exploration, I should trim and clean the data. Cleaning the data makes sure that the data formats across the cities are consistent, while trimming focuses only on the parts of the data I'm most interested in to make the exploration easier to work with.\n",
    "\n",
    "You will generate new data files with five values of interest for each trip: trip duration, starting month, starting hour, day of the week, and user type. Each of these may require additional wrangling depending on the city:\n",
    "\n",
    "- **Duration**: This has been given to us in seconds (New York, Chicago) or milliseconds (Washington). A more natural unit of analysis will be if all the trip durations are given in terms of minutes.\n",
    "- **Month**, **Hour**, **Day of Week**: Ridership volume is likely to change based on the season, time of day, and whether it is a weekday or weekend. Use the start time of the trip to obtain these values. The New York City data includes the seconds in their timestamps, while Washington and Chicago do not. The [`datetime`](https://docs.python.org/3/library/datetime.html) package will be very useful here to make the needed conversions.\n",
    "- **User Type**: It is possible that users who are subscribed to a bike-share system will have different patterns of use compared to users who only have temporary passes. Washington divides its users into two types: 'Registered' for users with annual, monthly, and other longer-term subscriptions, and 'Casual', for users with 24-hour, 3-day, and other short-term passes. The New York and Chicago data uses 'Subscriber' and 'Customer' for these groups, respectively. For consistency, you will convert the Washington labels to match the other two.\n",
    "\n",
    "\n",
    "**Question 3a**: Complete the helper functions in the code cells below to address each of the cleaning tasks described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def duration_in_mins(datum, city):\n",
    "    \"\"\"\n",
    "    Takes as input a dictionary containing info about a single trip (datum) and\n",
    "    its origin city (city) and returns the trip duration in units of minutes.\n",
    "    \n",
    "    Remember that Washington is in terms of milliseconds while Chicago and NYC\n",
    "    are in terms of seconds. \n",
    "    \n",
    "    HINT: The csv module reads in all of the data as strings, including numeric\n",
    "    values. You will need a function to convert the strings into an appropriate\n",
    "    numeric type when making your transformations.\n",
    "    see https://docs.python.org/3/library/functions.html\n",
    "    \"\"\"\n",
    "    \n",
    "     \n",
    "    if city == \"Washington\":\n",
    "            return  int(datum[\"Duration (ms)\"]) / 60000 # 1 Minute = 60 SECOND * 1000 MS = 60000 ms\n",
    "    else:\n",
    "            return  int(datum[\"tripduration\"]) / 60     # 1 Minute = 60 SECOND\n",
    "    \n",
    "\n",
    "\n",
    "# Some tests to check that your code works. There should be no output if all of\n",
    "# the assertions pass. The `example_trips` dictionary was obtained from when\n",
    "# you printed the first trip from each of the original data files.\n",
    "tests = {'NYC': 13.9833,\n",
    "         'Chicago': 15.4333,\n",
    "         'Washington': 7.1231}\n",
    "\n",
    "for city in tests:\n",
    "    assert abs(duration_in_mins(example_trips[city], city) - tests[city]) < .001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_of_trip(datum, city):\n",
    "    \"\"\"\n",
    "    Takes as input a dictionary containing info about a single trip (datum) and\n",
    "    its origin city (city) and returns the month, hour, and day of the week in\n",
    "    which the trip was made.\n",
    "    \n",
    "    Remember that NYC includes seconds, while Washington and Chicago do not.\n",
    "    \n",
    "    HINT: You should use the datetime module to parse the original date\n",
    "    strings into a format that is useful for extracting the desired information.\n",
    "    see https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior\n",
    "    \"\"\"\n",
    "    \n",
    "    # YOUR CODE HERE\n",
    "    if city == \"NYC\":\n",
    "        dt = datetime.strptime(datum[\"starttime\"], '%m/%d/%Y %H:%M:%S') #includes ms at the end\n",
    "        month = dt.month\n",
    "        hour  = dt.hour\n",
    "        day_of_week = datetime.strftime(dt,\"%A\")                   \n",
    "    elif city == \"Chicago\":\n",
    "        dt = datetime.strptime(datum[\"starttime\"], '%m/%d/%Y %H:%M')\n",
    "        month = dt.month\n",
    "        hour  = dt.hour\n",
    "        day_of_week = datetime.strftime(dt,\"%A\")         \n",
    "    else:\n",
    "        dt = datetime.strptime(datum[\"Start date\"], '%m/%d/%Y %H:%M')\n",
    "        month = dt.month\n",
    "        hour  = dt.hour\n",
    "        day_of_week = datetime.strftime(dt,\"%A\")\n",
    "    return (month, hour, day_of_week)\n",
    "    \n",
    "# Some tests to check that your code works. There should be no output if all of\n",
    "# the assertions pass. The `example_trips` dictionary was obtained from when\n",
    "# you printed the first trip from each of the original data files.\n",
    "tests = {'NYC': (1, 0, 'Friday'),\n",
    "         'Chicago': (3, 23, 'Thursday'),\n",
    "         'Washington': (3, 22, 'Thursday')}\n",
    "\n",
    "for city in tests:\n",
    "    assert time_of_trip(example_trips[city], city) == tests[city]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def type_of_user(datum, city):\n",
    "    \"\"\"\n",
    "    Takes as input a dictionary containing info about a single trip (datum) and\n",
    "    its origin city (city) and returns the type of system user that made the\n",
    "    trip.\n",
    "    \n",
    "    Remember that Washington has different category names compared to Chicago\n",
    "    and NYC. \n",
    "    \"\"\"\n",
    "    \n",
    "    # YOUR CODE HERE\n",
    "    if city == \"Washington\":\n",
    "        if datum[\"Member Type\"] == \"Registered\":\n",
    "            user_type = 'Subscriber'\n",
    "        else:\n",
    "            user_type = 'Customer'\n",
    "    else:\n",
    "        user_type = datum['usertype']\n",
    "        \n",
    "    return user_type\n",
    "\n",
    "# Some tests to check that your code works. There should be no output if all of\n",
    "# the assertions pass. The `example_trips` dictionary was obtained from when\n",
    "# you printed the first trip from each of the original data files.\n",
    "tests = {'NYC': 'Customer',\n",
    "         'Chicago': 'Subscriber',\n",
    "         'Washington': 'Subscriber'}\n",
    "\n",
    "for city in tests:\n",
    "    assert type_of_user(example_trips[city], city) == tests[city]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3b**: Now, use the helper functions you wrote above to create a condensed data file for each city consisting only of the data fields indicated above. In the `/examples/` folder, you will see an example datafile from the [Bay Area Bike Share](http://www.bayareabikeshare.com/open-data) before and after conversion. Make sure that your output is formatted to be consistent with the example file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def condense_data(in_file, out_file, city):\n",
    "    \"\"\"\n",
    "    This function takes full data from the specified input file\n",
    "    and writes the condensed data to a specified output file. The city\n",
    "    argument determines how the input file will be parsed.\n",
    "    \n",
    "    HINT: See the cell below to see how the arguments are structured!\n",
    "    \"\"\"\n",
    "    \n",
    "    with open(out_file, 'w') as f_out, open(in_file, 'r') as f_in:\n",
    "        # set up csv DictWriter object - writer requires column names for the\n",
    "        # first row as the \"fieldnames\" argument\n",
    "        \n",
    "        out_colnames = ['duration', 'month', 'hour', 'day_of_week', 'user_type']        \n",
    "        trip_writer = csv.DictWriter(f_out, fieldnames = out_colnames)\n",
    "        trip_writer.writeheader()\n",
    "        \n",
    "        ## TODO: set up csv DictReader object ##\n",
    "        trip_reader = csv.DictReader(f_in)\n",
    "        \n",
    "        # collect data from and process each row\n",
    "        for row in trip_reader:\n",
    "            # set up a dictionary to hold the values for the cleaned and trimmed\n",
    "            # data point\n",
    "            new_point = {}\n",
    "\n",
    "            ## TODO: use the helper functions to get the cleaned data from  ##\n",
    "            ## the original data dictionaries.                              ##\n",
    "            ## Note that the keys for the new_point dictionary should match ##\n",
    "            ## the column names set in the DictWriter object above.         ##\n",
    "            \n",
    "            new_point['duration'] = duration_in_mins(row, city)            \n",
    "            new_point['month'], new_point[\"hour\"], new_point[\"day_of_week\"] = time_of_trip(row, city)\n",
    "            new_point['user_type'] = type_of_user(row, city)\n",
    "            \n",
    "            ## TODO: write the processed information to the output file.     ##\n",
    "            ## see https://docs.python.org/3/library/csv.html#writer-objects ##   \n",
    "            \n",
    "            trip_writer.writerow(new_point)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: Washington\n",
      "OrderedDict([('duration', '7.123116666666666'),\n",
      "             ('month', '3'),\n",
      "             ('hour', '22'),\n",
      "             ('day_of_week', 'Thursday'),\n",
      "             ('user_type', 'Subscriber')])\n",
      "\n",
      "City: Chicago\n",
      "OrderedDict([('duration', '15.433333333333334'),\n",
      "             ('month', '3'),\n",
      "             ('hour', '23'),\n",
      "             ('day_of_week', 'Thursday'),\n",
      "             ('user_type', 'Subscriber')])\n",
      "\n",
      "City: NYC\n",
      "OrderedDict([('duration', '13.983333333333333'),\n",
      "             ('month', '1'),\n",
      "             ('hour', '0'),\n",
      "             ('day_of_week', 'Friday'),\n",
      "             ('user_type', 'Customer')])\n"
     ]
    }
   ],
   "source": [
    "# Run this cell to check your work\n",
    "city_info = {'Washington': {'in_file': './data/Washington-CapitalBikeshare-2016.csv',\n",
    "                            'out_file': './data/Washington-2016-Summary.csv'},\n",
    "             'Chicago': {'in_file': './data/Chicago-Divvy-2016.csv',\n",
    "                         'out_file': './data/Chicago-2016-Summary.csv'},\n",
    "             'NYC': {'in_file': './data/NYC-CitiBike-2016.csv',\n",
    "                     'out_file': './data/NYC-2016-Summary.csv'}}\n",
    "\n",
    "for city, filenames in city_info.items():\n",
    "    condense_data(filenames['in_file'], filenames['out_file'], city)\n",
    "    print_first_point(filenames['out_file'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "Now that I have the data collected and wrangled, I'm ready to start exploring the data. In this section I wrote some code to compute descriptive statistics from the data. I also introduced to the  `matplotlib` library to create some basic histograms of the data.\n",
    "\n",
    "<a id='statistics'></a>\n",
    "### Statistics\n",
    "\n",
    "First, let's compute some basic counts. The first cell below contains a function that uses the csv module to iterate through a provided data file, returning the number of trips made by subscribers and customers. The second cell runs this function on the example Bay Area data in the `/examples/` folder. Modify the cells to answer the question below.\n",
    "\n",
    "**Question 4a**: Which city has the highest number of trips? Which city has the highest proportion of trips made by subscribers? Which city has the highest proportion of trips made by short-term customers?\n",
    "\n",
    "**4a answer**: \n",
    "\n",
    "1. New York City has the highest number of trips (276,798).\n",
    "\n",
    "2. New York City has the highest proportion of trips taken by subscribers (89%).\n",
    "\n",
    "3. Chicago has the highest proportion of trips taken by customers (24%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def number_of_trips(filename):\n",
    "    '''\n",
    "    This function reads in a file with trip data and reports the number of\n",
    "    trips made by subscribers, customers, and total overall.\n",
    "    '''\n",
    "    with open(filename, 'r') as f_in:\n",
    "        # set up csv reader object\n",
    "        reader = csv.DictReader(f_in)\n",
    "        \n",
    "        # initialize count variables\n",
    "        n_subscribers = 0\n",
    "        n_customers = 0\n",
    "        \n",
    "        # tally up ride types\n",
    "        for row in reader:\n",
    "            if row['user_type'] == 'Subscriber':\n",
    "                n_subscribers += 1\n",
    "            else:\n",
    "                n_customers += 1\n",
    "        \n",
    "        # compute total number of rides\n",
    "        n_total = n_subscribers + n_customers\n",
    "        \n",
    "        # return tallies as a tuple\n",
    "        return(n_subscribers, n_customers, n_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Modify this and the previous cell to answer Question 4a. Remember to run ##\n",
    "## the function on the cleaned data files you created from Question 3.      ##\n",
    "\n",
    "Washington_file = './data/Washington-2016-Summary.csv'\n",
    "Chicago_file = './data/Chicago-2016-Summary.csv'\n",
    "NYC_file = './data/NYC-2016-Summary.csv'\n",
    "\n",
    "Washington_result = number_of_trips(Washington_file)\n",
    "Chicago_result = number_of_trips(Chicago_file)\n",
    "NYC_result = number_of_trips(NYC_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYC has the highest number of trips: 276798\n"
     ]
    }
   ],
   "source": [
    "#Which city has the highest number of trips?\n",
    "highest_total = max((Washington_result[2]), (Chicago_result[2]), (NYC_result[2]))\n",
    "#print(highest_total)\n",
    "\n",
    "if (Washington_result[2]) == highest_total:\n",
    "    print(\"Washington has the highest number of trips: \" + str(highest_total))\n",
    "elif (Chicago_result[2]) == highest_total:\n",
    "    print(\"Chicago has the highest number of trips: \" + str(highest_total))\n",
    "else:\n",
    "    print(\"NYC has the highest number of trips: \" + str(highest_total))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYC has the highest proportion of trips made by subscribers: 88.83590199351151 %\n"
     ]
    }
   ],
   "source": [
    "#Which city has the highest proportion of trips made by subscribers? \n",
    "Washington_sub_prop = ((Washington_result[0]) / (Washington_result[2])) * 100 \n",
    "Chicago_sub_prop = ((Chicago_result[0]) / (Chicago_result[2])) * 100\n",
    "NYC_sub_prop = ((NYC_result[0]) / (NYC_result[2])) * 100\n",
    "highest_sub_prop = max(Washington_sub_prop, Chicago_sub_prop, NYC_sub_prop)\n",
    "if Washington_sub_prop  == highest_sub_prop:\n",
    "    print(\"Washington has the highest proportion of trips made by subscribers: \" + str(highest_sub_prop)+ \" %\")\n",
    "elif Chicago_sub_prop == highest_sub_prop:\n",
    "    print(\"Chicago has the highest proportion of trips made by subscribers: \" + str(highest_sub_prop)+ \" %\")\n",
    "else:\n",
    "    print(\"NYC has the highest proportion of trips made by subscribers: \" + str(highest_sub_prop) + \" %\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago has the highest proportion of trips made by short_term customers: 23.774798630269924 %\n"
     ]
    }
   ],
   "source": [
    "#Which city has the highest proportion of trips made by short_term customers?\n",
    "Washington_cust_prop = ((Washington_result[1]) / (Washington_result[2])) *100\n",
    "Chicago_cust_prop = ((Chicago_result[1]) / (Chicago_result[2])) * 100\n",
    "NYC_cust_prop = ((NYC_result[1]) / (NYC_result[2])) * 100\n",
    "highest_cust_prop = max(Washington_cust_prop, Chicago_cust_prop, NYC_cust_prop)\n",
    "if Washington_cust_prop  == highest_cust_prop:\n",
    "    print(\"Washington has the highest proportion of trips made by short_term customers: \" + str(highest_cust_prop)  + \" %\")\n",
    "elif Chicago_cust_prop == highest_cust_prop:\n",
    "    print(\"Chicago has the highest proportion of trips made by short_term customers: \" + str(highest_cust_prop) + \" %\")\n",
    "else:\n",
    "    print(\"NYC has the highest proportion of trips made by short_term customers: \" + str(highest_cust_prop) + \" %\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you will write your own code to continue investigating properties of the data.\n",
    "\n",
    "**Question 4b**: Bike-share systems are designed for riders to take short trips. Most of the time, users are allowed to take trips of 30 minutes or less with no additional charges, with overage charges made for trips of longer than that duration. What is the average trip length for each city? What proportion of rides made in each city are longer than 30 minutes?\n",
    "\n",
    "**4b answer**: \n",
    "\n",
    "1. **The average trip lengths** are 16.6, 15.8 and 18.9 minutes for Chicago, NYC and Washington respectively.\n",
    "\n",
    "2. **The proportion of rides that are longer than 30 minutes** are 8, 7 and 11 percent for Chicago, NYC and Washington, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago:\n",
      " Average trip length = 16.563629368787335 min\n",
      " Proportion of rides longer than 30 mins = 8.332062497400562 %\n",
      "NYC:\n",
      " Average trip length = 15.81259299802294 min\n",
      " Proportion of rides longer than 30 mins = 7.3024371563378345 %\n",
      "Washington:\n",
      " Average trip length = 18.93287355913721 min\n",
      " Proportion of rides longer than 30 mins = 10.83888671109369 %\n"
     ]
    }
   ],
   "source": [
    "## Use this and additional cells to answer Question 4b.                 ##\n",
    "##                                                                      ##\n",
    "## HINT: The csv module reads in all of the data as strings, including  ##\n",
    "## numeric values. You will need a function to convert the strings      ##\n",
    "## into an appropriate numeric type before you aggregate data.          ##\n",
    "## TIP: For the Bay Area example, the average trip length is 14 minutes ##\n",
    "## and 3.5% of trips are longer than 30 minutes.                        ##\n",
    "\n",
    "def total_duration(filename):\n",
    "    ''' \n",
    "    calculate the average trip lengths and the proportion of rides \n",
    "    that are longer than 30 minutes\n",
    "    '''\n",
    "    with open(filename, 'r') as dur_f:\n",
    "        reader = csv.DictReader(dur_f)\n",
    "        trips_count = 0      #Number of total number of trips\n",
    "        total_dur = 0        #Total time (duration) of all the trips (upto 30 minutes or less)\n",
    "        longer_trip = 0      #Total number trips longer than 30 mins\n",
    "        \n",
    "        for trip in reader:\n",
    "            trips_count += 1\n",
    "            total_dur += float(trip['duration'])\n",
    "            if float(trip['duration']) > 30:\n",
    "                longer_trip += 1\n",
    "            \n",
    "    avg_trip_length = total_dur / trips_count\n",
    "    proportion_longer_30 = (longer_trip / trips_count) * 100\n",
    "           \n",
    "    return (avg_trip_length, proportion_longer_30)\n",
    "\n",
    "data_files = ['./data/Chicago-2016-Summary.csv',\n",
    "              './data/NYC-2016-Summary.csv',\n",
    "              './data/Washington-2016-Summary.csv']\n",
    "for data_file in data_files:\n",
    "    td = total_duration(data_file)\n",
    "    city = data_file.split('-')[0].split('/')[-1]\n",
    "    print(city+':')\n",
    "    print(' Average trip length = '.format(city) + str(td[0]) + ' min')\n",
    "    print(' Proportion of rides longer than 30 mins = '.format(city) + str(td[1]) + ' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4c**: Dig deeper into the question of trip duration based on ridership. Choose one city. Within that city, which type of user takes longer rides on average: Subscribers or Customers?\n",
    "\n",
    "**4c answer**: \n",
    "\n",
    "**NYC** Subscribers rides are longer on average than Customers rides : 12.2 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: NYC\n",
      "Subscribers longer rides on average than Customers: 12.15345366175579 min\n"
     ]
    }
   ],
   "source": [
    "## Use this and additional cells to answer Question 4c. If you have    ##\n",
    "## not done so yet, consider revising some of your previous code to    ##\n",
    "## make use of functions for reusability.                              ##\n",
    "##                                                                     ##\n",
    "## TIP: For the Bay Area example data, you should find the average     ##\n",
    "## Subscriber trip duration to be 9.5 minutes and the average Customer ##\n",
    "## trip duration to be 54.6 minutes. Do the other cities have this     ##\n",
    "## level of difference?                                                ##\n",
    "\n",
    "def Longer_rides_avg(filename):\n",
    "    '''\n",
    "    to find which type of user (Subscribers or Customers) takes longer rides on average.\n",
    "    '''\n",
    "    city = filename.split('-')[0].split('/')[-1]\n",
    "    print('\\nCity: {}'.format(city))\n",
    "    with open(filename, 'r') as rides_f:\n",
    "        rider_duration = csv.DictReader(rides_f)\n",
    "        \n",
    "        trips_count = 0                #Number of total number of trips\n",
    "        subscriber_total_dur = 0       #Total time (duration) of trips by subscribers\n",
    "        customer_total_dur = 0         #Total time (duration) of trips by customers\n",
    "        \n",
    "        for trip in rider_duration:\n",
    "            trips_count += 1\n",
    "            if (trip['user_type']) == 'Subscriber':\n",
    "                subscriber_total_dur += float(trip['duration'])\n",
    "            else:\n",
    "                customer_total_dur += float(trip['duration'])\n",
    "        \n",
    "        avg_subscriber_dur = subscriber_total_dur / trips_count\n",
    "        avg_customer_dur = customer_total_dur / trips_count\n",
    "        \n",
    "        if avg_subscriber_dur > avg_customer_dur:\n",
    "            print('Subscribers longer rides on average than Customers: ' + str(avg_subscriber_dur) + ' min')\n",
    "        elif avg_subscriber_dur < avg_customer_dur:\n",
    "            print('Customers longer rides on average than Subscribers: ' + str(avg_customer_dur) + ' min')\n",
    "        else:\n",
    "            print('Customers rides on average equal Subscribers average: ' + str(avg_customer_dur) + ' min')\n",
    "    \n",
    "        return\n",
    "\n",
    "data_file = './data/NYC-2016-Summary.csv'\n",
    "Longer_rides_avg (data_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='visualizations'></a>\n",
    "### Visualizations\n",
    "\n",
    "The last set of values that you computed should have pulled up an interesting result. While the mean trip time for Subscribers is well under 30 minutes, the mean trip time for Customers is actually _above_ 30 minutes! It will be interesting for us to look at how the trip times are distributed. In order to do this, a new library will be introduced here, `matplotlib`. I loaded the library and to generate an example plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69beacb4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# this is a 'magic word' that allows for plots to be displayed\n",
    "# inline with the notebook. If you want to know more, see:\n",
    "# http://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "%matplotlib inline \n",
    "\n",
    "# example histogram, data taken from bay area sample\n",
    "data = [ 7.65,  8.92,  7.42,  5.50, 16.17,  4.20,  8.98,  9.62, 11.48, 14.33,\n",
    "        19.02, 21.53,  3.90,  7.97,  2.62,  2.67,  3.08, 14.40, 12.90,  7.83,\n",
    "        25.12,  8.30,  4.93, 12.43, 10.60,  6.17, 10.88,  4.78, 15.15,  3.53,\n",
    "         9.43, 13.32, 11.72,  9.85,  5.22, 15.10,  3.95,  3.17,  8.78,  1.88,\n",
    "         4.55, 12.68, 12.38,  9.78,  7.63,  6.45, 17.38, 11.90, 11.52,  8.63,]\n",
    "plt.hist(data, bins=10)\n",
    "plt.title('Distribution of Trip Durations')\n",
    "plt.xlabel('Duration (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I collected fifty trip times in a list, and passed this list as the first argument to the `.hist()` function. This function performs the computations and creates plotting objects for generating a histogram, but the plot is actually not rendered until the `.show()` function is executed. The `.title()` and `.xlabel()` functions provide some labeling for plot context.\n",
    "\n",
    "I used these functions to create a histogram of the trip times for the city you selected in question 4c. Didn't separate the Subscribers and Customers for now: just collected all of the trip times and plot them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69beb47d30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69bed58710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69beedbc88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Use this and additional cells to collect all of the trip times as a list ##\n",
    "## and then use pyplot functions to generate a histogram of trip times.     ##\n",
    "def get_trip_times(filename):\n",
    "    '''\n",
    "    Returns all of the trip times as a list to use pyplot functions to generate \n",
    "    a histogram of trip times.\n",
    "    '''\n",
    "    trip_times = []\n",
    "    with open(filename) as f_in:\n",
    "        reader = csv.DictReader(f_in)\n",
    "        for row in reader:\n",
    "            trip_times.append(float(row['duration']))\n",
    "    return trip_times\n",
    "\n",
    "data_files = ['./data/Chicago-2016-Summary.csv',\n",
    "              './data/NYC-2016-Summary.csv',\n",
    "              './data/Washington-2016-Summary.csv',]\n",
    "colors = ['red', 'blue', 'green']\n",
    "color_index = 0\n",
    "for data_file in data_files:\n",
    "    plt.gcf().clear()\n",
    "    trip_times = get_trip_times(data_file)\n",
    "    plt.hist(trip_times, bins = range(0, 90), color = colors[color_index])\n",
    "    color_index += 1\n",
    "    city = data_file.split('-')[0].split('/')[-1]\n",
    "    plt.title('Distribution of Trip Durations\\n{}'.format(city))\n",
    "    plt.xlabel('Duration (m)')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you followed the use of the `.hist()` and `.show()` functions exactly like in the example, you're probably looking at a plot that's completely unexpected. The plot consists of one extremely tall bar on the left, maybe a very short second bar, and a whole lot of empty space in the center and right. Take a look at the duration values on the x-axis. This suggests that there are some highly infrequent outliers in the data. Instead of reprocessing the data, you will use additional parameters with the `.hist()` function to limit the range of data that is plotted. Documentation for the function can be found [[here]](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist).\n",
    "\n",
    "**Question 5**: Use the parameters of the `.hist()` function to plot the distribution of trip times for the Subscribers in your selected city. Do the same thing for only the Customers. Add limits to the plots so that only trips of duration less than 75 minutes are plotted. As a bonus, set the plots up so that bars are in five-minute wide intervals. For each group, where is the peak of each distribution? How would you describe the shape of each distribution?\n",
    "\n",
    "**Q5 answer**: \n",
    "\n",
    "**1- For subscribers,** the peak of the distribution is in the **5 to 10 minute \"bucket\".** For **customers,** the peak of the distribution is in the **20 to 25 minute \"bucket\"** for **NYC only.** For each user type, the distribution is **unimodal**, with a **positive (right) skew**.\n",
    "\n",
    "**2-** The shape of the histogram for subscribers is unimodal with a skew to the right. For customers, the mode is further to the right than for subscribers, but the shape still remains positively skewed. The tail is much fatter for customers than for subscribers, as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be324400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAElCAYAAADtFjXiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAHgdJREFUeJzt3Xu0XWV57/Hvj4SbgBBIQEwigUFUwB4jbhGLpyIgBJTLGQcrHq1BYzPsoT1abRW8RRAvHFuxthUHFZtwEaR4IVIsxkCk4CGww0USEZMCkpiQi+EWQSDJc/543wUzi5W918re67L3+/uMscaa853vnPOZa801n3l9lyICMzMrzw7dDsDMzLrDCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBNBFkr4p6TPDNK1XSNooaUzuXyjpg8Mx7Ty9H0uaMVzTa2G+50taL+mRTs+7BJIOkrSx23EMxWhYhm5xAmgTSQ9JelrSk5Iek/RzSR+S9PxnHhEfiojPNzmt4waqExEPR8TuEbF5GGL/nKTL66Z/YkTMHeq0W4xjMvAx4NCIeNk26rxU0tckPZwT4PLcP36I8x70M+81eYdiY349K+m5Sv+PG40TEQ9ExO7bOb/jJG2pzGOlpO9Kev3QlmTQ+a6UdHStfyjLUDongPY6OSL2AA4Avgx8ArhkuGciaexwT7NHHAD8LiLWNhooaSdgAXAYMB14KfDHwO+AIzoVZDspaep3mncods8bwy8C3631R8SJDaY9HOvNw3l+ewBvApYDt1Y30K0Yxetyb4oIv9rwAh4CjqsrOwLYArwm988Bzs/d44HrgMeADcB/khL0ZXmcp4GNwMeBKUAAM4GHgZsrZWPz9BYCXwJuBx4HrgX2zsOOBlY2ipe0IX0WeC7P757K9D6Yu3cAPg38BlgLXArsmYfV4piRY1sPfGqAz2nPPP66PL1P5+kfl5d5S45jToNxPwisAXYfYPoBHFzp367PPNc/BVia6y8EDqn7/P4W+AXwe1Ki3w/4MfAk8FNgXKX+kcDP87TuAY6uDFsIfAG4NcdwMHAm8ECe1oPAewZZ/z4HXF5XdnD+PN6fv5sba2WVOrfkeffn9eYH1bjrpncc8FCD8m8Ct1XnWTf8FuDMynd4M/D1/B18DpgK3ERK5Ovz91Fbv66s+24+2mAZJuXvdQOwDPhAZdj5eRqX589yCXB4ZfgngVXAE8Cvqt/LaHx1PYDR+qJBAsjlDwN/kbvn8MLG6Ev5h7Njfv13QI2mxQsb2UuB3YBdaZwAfgu8Jtf5Xm2DwAAJIHc32ngs5IUE8AHSnt5BwO7A94HL6mL7lxzXa4FnqGws66Z7KSk57ZHH/TUwc1tx1o17FTB3kO9hoATQymf+StKG/W257sfzZ7BTpf5tpI3+RFJivBN4HbAzaWM7O9edSNq4nURKOG/L/RMqn/XDpCObsaQk+QTwqjx8f+CwQZa70XdYSwD/Crwkfz+NEsAK4NC83vyQBsk3191WAjge2AzsUj/9yjzOzN0fBDYBfwGMyTG9EjgW2AnYl5QI/64y/kq2Tpj1y3Ar8I95/oeTkshb8rDzScnjhDy/rwC35GGHkXZCXpb7DwQO6va2pJ0vnwLqvFXA3g3KnyP9sA+IiOci4j8jr4UD+FxE/D4int7G8MsiYklE/B74DPCntYvEQ/Qe4KuRzr1uBM4Bzqg7fD83Ip6OiHtIe7ivrZ9IjuVdwDkR8WREPAT8PfBnTcaxD7B6CMvRymf+LuDfI2J+RDwH/B1pY/XHlTr/GBFrIuK3pKOJRRFxV0Q8Q9qTfl2u917g+oi4PiK2RMR80h73SZVpzYmIpRGxibSB3AK8RtKuEbE6IpYOYblnR8RTA6w3cyPil3m9+Szpu1UL019FSmx7Nln/4Yi4KCI253Xm1xGxICKejXT670LgLc1MSNKBpCPtsyPiDxFxJynhVdepn0XEDZGul10GTMvlm0hJ4zBJYyPiwYh4oMllGJGcADpvIunQtN5XSHuUP5H0gKSzm5jWihaG/4a05zqki6PZy/P0qtMeS9r7ranetfMU6Uih3njSXl79tCY2GcfvSBvw7dXKZ77VMkfEFtLnW411TaX76Qb9tc/gAOCd+eaAxyQ9BryZrZfl+e8ub4jfBXwIWC3p3yW9usllbKTV9WZnGu+0bMtEUsJ6fHvikfQySVdL+q2kJ0hHbc2uty8H1ufPrKZ+napfN3cDiIj7STcdnAeslXSlpIY3H4wWTgAdJOkNpBXxlvpheQ/4YxFxEHAy8FFJx9YGb2OSgx0hTK50v4K0x7uedCrjJZW4xgATWpjuKtJGrDrtTWy9wWvG+hxT/bR+2+T4PwVOkLTbAHWeorKswPM/6BY/862WOe8RT24h1qoVpKOzvSqv3SLiy5U6W80/77G+jZQkfkU6xbZdmjiyrF9vnqHxTsu2/A/gjoj4A2ldQ1LD76AWUl3/BXmefxQRLyVd/9AA9atWAePr1omm16mIuDwijiKd/hlDOk04ajkBdEC+VfEdpHPWl0fEvQ3qvEPSwXnD8gTpHGrtls41pPPtrXqvpEPzj+884Jp82PtrYBdJb5e0I+nC686V8dYAUwa4++RK4K8lHSipesfJplaCy7FcDXxB0h6SDiBd1Lt84DGfdxlpY/o9Sa+WtIOkfSR9UlLtdMrdwP+SNEbSdCqnElr8zK8G3i7p2PyZfYy0kfp5K8ucXQ6cLOmEHNcuko6WNKlRZUn7STolb9SeIV38HPLtvgN4X/48dwPOBa4eLGnku5UmSTqXtMH+ZB70SH69Ny/rLLZO+I3sQUocj+dbgf+mbvg2fw8R8SDpdNoXJe0saRrpovcVg8wTSYdIequknUlHbE/T3s+565wA2utHkp4kbaQ+BXyVtDI2MpW0R7sR+H/ANyJiYR72JeDT+XRB/Y9hIJeRDp8fIZ3b/D8AEfE48L+Bb5H2jH5PurBW82/5/XeS7mww3W/nad9MuiPlD8BftRBX1V/l+T9AOjL6Tp7+oPK59eNIe8TzSRvx20mnCxblah8m7d0/Rrp28cPKJJr+zPPpgfeSLi6uz9M8OSKebXWBI2IFcCppI7mOtH78Ldv+Pe5ASjirSHvibyF9f+1yGSlJrSbtBX9kgLqvUHoIayPpMz8U+JOIuBGeP9r4c9KyriddsF20jWnVzCadx38cmEe6gaHqi8C5+btpFNu7SN/tI8A1wCcj4qZB5glpJ+j/5jgfAcaRdo5GLQ1+NGhmpZB0C/CtiJjT7Vis/XwEYGZWKCcAM7NC+RSQmVmhfARgZlYoJwAzs0I5AZjVkRSS7q0+B6H0vwRzJO0qaZmk99WNM1vSrbVx8j3+Nys1B75O0s8kndLpZTEbiBOAWWMvB86oL8zt58wEvippP0gPEJEeYJsZEVsknU56luJSUsuU+5Ha1Dm5Q7GbNcUXgc3qSArgbFJLlYdExCZJ5wOTIuLMXOefSS1V/imp4bfrIuLL+ani35AahvtKVxbArEk+AjBr7PukJ4vP3MbwTwBvID2lugupYTmAV5Ha0rmmzfGZDZkTgFljQWpC+7O5bZitB6ZmsM8iNXw2M174K8598vtQmqk26wgnALNtiIjrSX/MMmsbVZbWvUNqohqG1ky1WUc4AZgN7NOkhvxeMljF7H5S427/s20RmQ0TJwCzAeTWQe8l/cdxM/WDdEfQZyS9PzcFvoOkN0u6uI2hmrXMCcBscJ+mhX/EiohrSE0Sf4DUhPMa0n/RXtuW6My2k28DNTMrlI8AzMwK5QRgZlYoJwAzs0I5AZiZFWpstwMYyPjx42PKlCndDsPMbERZvHjx+oiYMFi9nk4AU6ZMob+/v9thmJmNKJJ+00w9nwIyMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrVE8/CWw9TBr+afq/Kcw6ykcAZmaF8hHAaNeOPXUzGxV8BGBmVignADOzQjkBmJkVygnAzKxQTSUASQ9JulfS3ZL6c9nekuZLWpbfx+VySfq6pOWSfiHp8Mp0ZuT6yyTNaM8imZlZM1o5AnhrREyLiL7cfzawICKmAgtyP8CJwNT8mgVcBClhALOBNwJHALNrScPMzDpvKKeATgXm5u65wGmV8ksjuQ3YS9L+wAnA/IjYEBGPAvOB6UOYv5mZDUGzCSCAn0haLGlWLtsvIlYD5Pd9c/lEYEVl3JW5bFvlW5E0S1K/pP5169Y1vyRmZtaSZh8EOyoiVknaF5gv6VcD1G305FEMUL51QcTFwMUAfX19bhvAzKxNmjoCiIhV+X0t8APSOfw1+dQO+X1trr4SmFwZfRKwaoByMzPrgkETgKTdJO1R6waOB5YA84DanTwzgGtz9zzgffluoCOBx/MpohuA4yWNyxd/j89lZmbWBc2cAtoP+IFSmzJjge9ExH9IugO4WtJM4GHgnbn+9cBJwHLgKeD9ABGxQdLngTtyvfMiYsOwLYmZmbVE0cNN8Pb19UV/f3+3wxjZRlJjcD28LpqNJJIWV27Z3yY/CWxmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRWqmT+Ft04YSf/da2ajgo8AzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUnwOw3tGuZyEi2jNdsxHORwBmZoVyAjAzK1TTCUDSGEl3Sbou9x8oaZGkZZK+K2mnXL5z7l+eh0+pTOOcXH6/pBOGe2HMzKx5rRwBfBi4r9J/AXBhREwFHgVm5vKZwKMRcTBwYa6HpEOBM4DDgOnANySNGVr4Zma2vZpKAJImAW8HvpX7BRwDXJOrzAVOy92n5n7y8GNz/VOBqyLimYh4EFgOHDEcC2FmZq1r9gjga8DHgS25fx/gsYjYlPtXAhNz90RgBUAe/niu/3x5g3HMzKzDBk0Akt4BrI2IxdXiBlVjkGEDjVOd3yxJ/ZL6161bN1h4Zma2nZo5AjgKOEXSQ8BVpFM/XwP2klR7jmASsCp3rwQmA+ThewIbquUNxnleRFwcEX0R0TdhwoSWF8jMzJozaAKIiHMiYlJETCFdxL0xIt4D3AScnqvNAK7N3fNyP3n4jRERufyMfJfQgcBU4PZhWxIzM2vJUJ4E/gRwlaTzgbuAS3L5JcBlkpaT9vzPAIiIpZKuBn4JbALOiojNQ5i/mZkNgaKHH5Pv6+uL/v7+bofRGf5LyPbp4XXcrB0kLY6IvsHq+UlgM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBjux3AiCN1OwIzs2HhIwAzs0I5AZiZFcoJwMysUIMmAEm7SLpd0j2Slko6N5cfKGmRpGWSvitpp1y+c+5fnodPqUzrnFx+v6QT2rVQZmY2uGaOAJ4BjomI1wLTgOmSjgQuAC6MiKnAo8DMXH8m8GhEHAxcmOsh6VDgDOAwYDrwDUljhnNhzMyseYMmgEg25t4d8yuAY4Brcvlc4LTcfWruJw8/VpJy+VUR8UxEPAgsB44YlqUwM7OWNXUNQNIYSXcDa4H5wH8Bj0XEplxlJTAxd08EVgDk4Y8D+1TLG4xTndcsSf2S+tetW9f6EpmZWVOaSgARsTkipgGTSHvthzSqlt8b3SgfA5TXz+viiOiLiL4JEyY0E56ZmW2Hlu4CiojHgIXAkcBekmoPkk0CVuXulcBkgDx8T2BDtbzBOGZm1mHN3AU0QdJeuXtX4DjgPuAm4PRcbQZwbe6el/vJw2+MiMjlZ+S7hA4EpgK3D9eCmJlZa5ppCmJ/YG6+Y2cH4OqIuE7SL4GrJJ0P3AVckutfAlwmaTlpz/8MgIhYKulq4JfAJuCsiNg8vItjZmbNUto57019fX3R39/f7TC25raARp4eXsfN2kHS4ojoG6yenwQ2MyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaHGdjsAs7aT2jPdiPZM16xDfARgZlYoJwAzs0I5AZiZFWrQBCBpsqSbJN0naamkD+fyvSXNl7Qsv4/L5ZL0dUnLJf1C0uGVac3I9ZdJmtG+xTIzs8E0cwSwCfhYRBwCHAmcJelQ4GxgQURMBRbkfoATgan5NQu4CFLCAGYDbwSOAGbXkoaZmXXeoAkgIlZHxJ25+0ngPmAicCowN1ebC5yWu08FLo3kNmAvSfsDJwDzI2JDRDwKzAemD+vSmJlZ01q6BiBpCvA6YBGwX0SshpQkgH1ztYnAispoK3PZtsrr5zFLUr+k/nXr1rUSnpmZtaDpBCBpd+B7wEci4omBqjYoiwHKty6IuDgi+iKib8KECc2GZ2ZmLWoqAUjakbTxvyIivp+L1+RTO+T3tbl8JTC5MvokYNUA5WZm1gXN3AUk4BLgvoj4amXQPKB2J88M4NpK+fvy3UBHAo/nU0Q3AMdLGpcv/h6fy8zMrAuaaQriKODPgHsl3Z3LPgl8Gbha0kzgYeCdedj1wEnAcuAp4P0AEbFB0ueBO3K98yJiw7AshZmZtUzRw+2Z9PX1RX9/f7fD2Fq72pWxkaeHfztWNkmLI6JvsHp+EtjMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMr1NhuB2A2YknDP82I4Z+m2Tb4CMDMrFBOAGZmhRo0AUj6tqS1kpZUyvaWNF/Ssvw+LpdL0tclLZf0C0mHV8aZkesvkzSjPYtjZmbNauYIYA4wva7sbGBBREwFFuR+gBOBqfk1C7gIUsIAZgNvBI4AZteShpmZdcegCSAibgY21BWfCszN3XOB0yrll0ZyG7CXpP2BE4D5EbEhIh4F5vPipGJmZh20vXcB7RcRqwEiYrWkfXP5RGBFpd7KXLat8vZqx10aZmajxHBfBG60xY0Byl88AWmWpH5J/evWrRvW4MzM7AXbmwDW5FM75Pe1uXwlMLlSbxKwaoDyF4mIiyOiLyL6JkyYsJ3hmZnZYLY3AcwDanfyzACurZS/L98NdCTweD5VdANwvKRx+eLv8bnMzMy6ZNBrAJKuBI4GxktaSbqb58vA1ZJmAg8D78zVrwdOApYDTwHvB4iIDZI+D9yR650XEfUXls3MrIMUPfzoeV9fX/T392//BHwR2EaaHv492sghaXFE9A1Wz08Cm5kVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5X8EM+sl7bp12beXWgM+AjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaF8oNgZiXwA2bWgI8AzMwK5QRgZlYoJwAzs0L5GoCZbT9fWxjRfARgZlYoJwAzs0I5AZiZFcrXAMys97Tj2oKvK7yIjwDMzArlIwAzK4PvWHoRHwGYmRXKRwBmZkMxgo8sfARgZlYoJwAzs0I5AZiZFarjCUDSdEn3S1ou6exOz9/MzJKOJgBJY4B/Bk4EDgXeLenQTsZgZmZJp48AjgCWR8QDEfEscBVwaodjMDMzOn8b6ERgRaV/JfDGagVJs4BZuXejpPuHML/xwPohjN9JIylWGFnxOtb2GUnxjqRYQRpKvAc0U6nTCaDRDbNb3ewaERcDFw/LzKT+iOgbjmm120iKFUZWvI61fUZSvCMpVuhMvJ0+BbQSmFzpnwSs6nAMZmZG5xPAHcBUSQdK2gk4A5jX4RjMzIwOnwKKiE2S/hK4ARgDfDsilrZxlsNyKqlDRlKsMLLidaztM5LiHUmxQgfiVYzgluzMzGz7+UlgM7NCOQGYmRVqVCaAXm9uQtK3Ja2VtKRStrek+ZKW5fdx3YyxRtJkSTdJuk/SUkkfzuU9F6+kXSTdLumeHOu5ufxASYtyrN/NNyD0DEljJN0l6brc35PxSnpI0r2S7pbUn8t6bj2okbSXpGsk/Sqvv2/qxXglvSp/prXXE5I+0olYR10CGCHNTcwBpteVnQ0siIipwILc3ws2AR+LiEOAI4Gz8ufZi/E+AxwTEa8FpgHTJR0JXABcmGN9FJjZxRgb+TBwX6W/l+N9a0RMq9yf3ovrQc0/AP8REa8GXkv6jHsu3oi4P3+m04DXA08BP6ATsUbEqHoBbwJuqPSfA5zT7bgaxDkFWFLpvx/YP3fvD9zf7Ri3Efe1wNt6PV7gJcCdpCfN1wNjG60f3X6RnoVZABwDXEd6WLIn4wUeAsbXlfXkegC8FHiQfKNLr8dbie944NZOxTrqjgBo3NzExC7F0or9ImI1QH7ft8vxvIikKcDrgEX0aLz5dMrdwFpgPvBfwGMRsSlX6bX14WvAx4EtuX8fejfeAH4iaXFusgV6dD0ADgLWAf+aT699S9Ju9G68NWcAV+butsc6GhPAoM1NWOsk7Q58D/hIRDzR7Xi2JSI2RzqUnkRqfPCQRtU6G1Vjkt4BrI2IxdXiBlV7Il7gqIg4nHR69SxJf9LtgAYwFjgcuCgiXgf8nh443TOQfK3nFODfOjXP0ZgARmpzE2sk7Q+Q39d2OZ7nSdqRtPG/IiK+n4t7Nl6AiHgMWEi6brGXpNpDj720PhwFnCLpIVLLuMeQjgh6Mt6IWJXf15LOUR9B764HK4GVEbEo919DSgi9Gi+kxHpnRKzJ/W2PdTQmgJHa3MQ8YEbunkE61951kgRcAtwXEV+tDOq5eCVNkLRX7t4VOI504e8m4PRcrSdiBYiIcyJiUkRMIa2nN0bEe+jBeCXtJmmPWjfpXPUSenA9AIiIR4AVkl6Vi44FfkmPxpu9mxdO/0AnYu32RY82XUg5Cfg16fzvp7odT4P4rgRWA8+R9lRmks79LgCW5fe9ux1njvXNpFMQvwDuzq+TejFe4L8Bd+VYlwCfzeUHAbcDy0mH1zt3O9YGsR8NXNer8eaY7smvpbXfVS+uB5WYpwH9eX34ITCuV+Ml3bTwO2DPSlnbY3VTEGZmhRqNp4DMzKwJTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgI5qkzbkFxaW5FdCPShq29VrSmZJeXun/1nA1LijpNEmfbXGcn/ZCC5Y2Ovg2UBvRJG2MiN1z977Ad0iNac1uYRpjImLzNoYtBP4mIvqHI966af8cOCUi1rcwzgxgUkR8YbjjsfL4CMBGjUhNFMwC/lLJmZL+qTZc0nWSjs7dGyWdJ2kR8CZJn5V0h6Qlki7O458O9AFX5KOMXSUtlNSXp/Hu3D7+EkkXVOazUdIX8hHJbZL2q49V0iuBZ2obf0lzJF2k9N8LD0h6i9L/RtwnaU5l1HmkJ0bNhswJwEaViHiAtF4P1nLibqTmuN8YEbcA/xQRb4iI1wC7Au+IiGtIT5K+J1J77U/XRs6nhS4gtd8zDXiDpNMq074t0v8S3Az8eYP5H0VqrrpqXJ7eXwM/Ai4EDgP+SNK0vHyPAjtL2qeJj8NsQE4ANho1alGz3mZSA3c1b1X6F657SRvhwwYZ/w3AwohYF6np5iuAWuuYz5La9gdYTPrvh3r7k5orrvpRpHOy9wJrIuLeiNhCanqhOo21wMsxG6Kxg1cxGzkkHUTauK8l/ZtZdSdnl0r3H2rn/SXtAnwD6IuIFZI+V1e34awGGPZcvHBxbTONf2dPA3vWlT2T37dUumv91Wnsksc3GxIfAdioIWkC8E3S6Zwg/YPVNEk7SJpMar64kdrGfn3+34PTK8OeBPZoMM4i4C2Sxue/IX038LMWwr0POLiF+sDzrbO+jLRsZkPiIwAb6XbN/wC2I2mP/zKg1mz1raS/BbyX1Dpo/Tl3IP13gKR/yfUeIjUpXjMH+Kakp0l/z1gbZ7Wkc0hNNwu4PiJaaa73ZuDvJalytNCM15OuL2watKbZIHwbqFmXSPoH0nn/n7Y4zryIWNC+yKwUPgVk1j1fJLUD34ol3vjbcPERgJlZoXwEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhfr/4OH/AwaedjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be3678d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Use this and additional cells to answer Question 5. ##\n",
    "filename = './data/NYC-2016-Summary.csv'\n",
    "def city_trip_times(filename):\n",
    "    '''\n",
    "    Returns the distribution of trip times for the Subscribers and Customers in NYC, using \n",
    "    the duration that less than 75 minutes, to use the parameters of the .hist() function \n",
    "    to plot it.\n",
    "    '''\n",
    "    sub_trip_times = []\n",
    "    cust_trip_times = []\n",
    "    with open(filename) as nyc_f:\n",
    "        reader = csv.DictReader(nyc_f)\n",
    "        for row in reader:\n",
    "            if (row['user_type']) == 'Subscriber' and (float(row['duration'])) < 75:\n",
    "                sub_trip_times.append(float(row['duration']))\n",
    "            elif (row['user_type']) == 'Customer' and (float(row['duration'])) < 75:\n",
    "                cust_trip_times.append(float(row['duration']))\n",
    "    \n",
    "    return (sub_trip_times, cust_trip_times)\n",
    "\n",
    "plt.gcf().clear()\n",
    "sub_trip_times,cust_trip_times = city_trip_times(filename) \n",
    "plt.hist(sub_trip_times, bins = range(0, 75, 5), color = 'green')\n",
    "#city = data_file.split('-')[0].split('/')[-1]\n",
    "plt.title('Distribution of Subscribers Trip Durations\\n NYC')\n",
    "plt.xlabel('Duration (m)')\n",
    "plt.show()\n",
    "\n",
    "plt.gcf().clear() \n",
    "plt.hist(cust_trip_times, bins = range(0, 75, 5), color = 'red')\n",
    "#city = data_file.split('-')[0].split('/')[-1]\n",
    "plt.title('Distribution of Customers Trip Durations\\n NYC')\n",
    "plt.xlabel('Duration (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='eda_continued'></a>\n",
    "## Performing Your Own Analysis\n",
    "\n",
    "So far, I've performed an initial exploration into the data available. I have compared the relative volume of trips made between three U.S. cities and the ratio of trips made by Subscribers and Customers. For one of these cities, I have investigated differences between Subscribers and Customers in terms of how long a typical trip lasts. I continued the exploration in a direction that I selected. Here are a few suggestions for questions to explore:\n",
    "\n",
    "- How does ridership differ by month or season? Which month / season has the highest ridership? Does the ratio of Subscriber trips to Customer trips change depending on the month or season?\n",
    "- Is the pattern of ridership different on the weekends versus weekdays? On what days are Subscribers most likely to use the system? What about Customers? Does the average duration of rides change depending on the day of the week?\n",
    "- During what time of day is the system used the most? Is there a difference in usage patterns for Subscribers and Customers?\n",
    "\n",
    "These questions which I posed in my answer to question 1 align with the bullet points above, this is a good opportunity to investigate one of them. As part of my investigation, for that,  I needed to create something other than a histogram, so I consulted the [Pyplot documentation](https://matplotlib.org/devdocs/api/pyplot_summary). In particular, because I wanted to plot values across a categorical variable (e.g. city, user type), so a bar chart was useful. [documentation page for `.bar()`](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar) includes links at the bottom of the page with examples for you to build off of for your own use.\n",
    "\n",
    "**Question 6**: Continue the investigation by exploring another question that could be answered by the data available. Document the question you want to explore below. Your investigation should involve at least two variables and should compare at least two groups. You should also use at least one visualization as part of your explorations.\n",
    "\n",
    "**Question 6a**:\n",
    "\n",
    "How does ridership differ by **season**? Which season has the highest ridership? Does the ratio of Subscriber trips to Customer trips change depending on the season?\n",
    "\n",
    "**6a: answer:**\n",
    "\n",
    "1- The bars below aggregate number of trips for each city based on whether the rides occur in four seasons. The bars are qualitatively very similar in each city for the same season of trips, with summer season the most common types of trips, for the three cities.\n",
    "\n",
    "2- It is reasonable to expect that the number of trips in summer is more trips than others for both user types and the three cities, and we find the winter in the all cities is less than other becaus the snow and the heavy rains are preventing the user ride the bikes.\n",
    "\n",
    "3- the bars show the ratio of Subscriber trips to Customer trips is approximatly constant in for seasons in the three cities, with small diference in NYC, that is reasonable to expect as explained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def season_ridership_by_user_type(filename):\n",
    "    '''\n",
    "    to calculate ridership difference by season (winter = Jan, Feb, Mar - Spring = Apr, May, Jun - \n",
    "    Summer = Jul, Aug, Sept - Autumn = Oct, Nov, Dec) and to know which season has the highest ridership. \n",
    "    How Does the ratio of Subscriber trips to Customer trips change depending on the season?\n",
    "    '''\n",
    "    with open(filename, 'r') as ridership_f:\n",
    "        ridership = csv.DictReader(ridership_f)\n",
    "        sub_win_count = 0\n",
    "        sub_spr_count = 0\n",
    "        sub_summ_count = 0\n",
    "        sub_aut_count = 0\n",
    "        cust_win_count = 0\n",
    "        cust_spr_count = 0\n",
    "        cust_summ_count = 0\n",
    "        cust_aut_count = 0\n",
    "        \n",
    "        for trip in ridership:\n",
    "            if (trip['user_type']) == 'Subscriber' and (int(trip['month']) == 1 or int(trip['month']) == 2 or int(trip['month']) == 3):\n",
    "                sub_win_count  += 1 \n",
    "            elif (trip['user_type']) == 'Subscriber' and (int(trip['month']) == 4 or int(trip['month']) == 5 or int(trip['month']) == 6):\n",
    "                sub_spr_count  += 1\n",
    "            elif (trip['user_type']) == 'Subscriber' and (int(trip['month']) == 7 or int(trip['month']) == 8 or int(trip['month']) == 9):\n",
    "                sub_summ_count  += 1\n",
    "            elif (trip['user_type']) == 'Subscriber' and (int(trip['month']) == 10 or int(trip['month']) == 11 or int(trip['month']) == 12):\n",
    "                sub_aut_count  += 1\n",
    "            elif (trip['user_type']) == 'Customer' and (int(trip['month']) == 1 or int(trip['month']) == 2 or int(trip['month']) == 3):\n",
    "                cust_win_count  += 1\n",
    "            elif (trip['user_type']) == 'Customer' and (int(trip['month']) == 4 or int(trip['month']) == 5 or int(trip['month']) == 6):\n",
    "                cust_spr_count  += 1\n",
    "            elif (trip['user_type']) == 'Customer' and (int(trip['month']) == 7 or int(trip['month']) == 8 or int(trip['month']) == 9):\n",
    "                cust_summ_count  += 1\n",
    "            elif (trip['user_type']) == 'Customer' and (int(trip['month']) == 10 or int(trip['month']) == 11 or int(trip['month']) == 12):\n",
    "                cust_aut_count  += 1\n",
    "    \n",
    "        return ([sub_win_count, sub_spr_count, sub_summ_count, sub_aut_count] , [cust_win_count, cust_spr_count, cust_summ_count, cust_aut_count])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_season_ridership(subscriber, customer, city):\n",
    "    '''\n",
    "    Plotting bar chart of 'season_ridership_by_user_type' function using two \n",
    "    categorical variables ('User Type' and 'Seasons')\n",
    "    '''\n",
    "    seasons = ['Winter', 'Spring', 'Summer', 'Autumn']\n",
    "    ypos = np.arange(len(seasons))\n",
    "    plt.gcf().clear()\n",
    "    plt.xticks(ypos, seasons)\n",
    "    plt.ylabel('Number of trips')\n",
    "    plt.title('Season Ridership\\n{}'.format(city))\n",
    "    plt.bar(ypos-0.2, subscriber, width = 0.4, label = 'Subscriber', color = '#000099')\n",
    "    plt.bar(ypos+0.2, customer, width = 0.4, label = 'Customer', color = 'red')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be481208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be17bc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEXCAYAAABlI9noAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xu8VVW5//HPV0DhiAoKeBDUjUqloiltb+UxksLrATMv2EUskyzr1K/LScuyvJvHVI7akZKELNGDmWSaegxESkEwRC4R5I2dqFyEQAMFnt8fcyxcbNfeLPaeay8WfN+v13rtuZ455pxjTrf7Ycwx5xiKCMzMzPKwXbUrYGZmWw8nFTMzy42TipmZ5cZJxczMcuOkYmZmuXFSMTOz3DipmG2hJO0laZWkdk2s/4GkOypw3HMkTW5m/YOShuV9XNs6OKlYTZN0tKQ/SVohaZmkP0o6rNr1Kkf6470uJY5/SHpG0smF9RHxUkR0joh11axnYxFxQkSMrnY9bMvkpGI1S9LOwP3AfwO7Ar2AHwJrqlmvzfRERHQGugC3AGMldankAZXx//tWEf7Fslr2HoCIuDMi1kXEPyPi4YiYWSgg6XOS5kp6XdJDkvYuWnejpIWplTBd0r8VrTtc0rS07lVJPy5aN1jSbEnLJU2UtH/RuhckfVPSzNR6uktSx02dSESsB34B7Aj0TfuqkxSS2qfvfSQ9JmmlpEeAbsX7kHRkarUtT62eAUXrJkq6QtIfgTeBfVJL6bm0v+clfarR/v4rXbfnJZ3QaF+fT8vnpNbhf6fz/YukgZs6X9t6OalYLfsrsE7SaEknSOpavFLSKcB3gFOB7sDjwJ1FRZ4CDiFr5fwK+N+iBHAjcGNE7AzsC9yd9vmetI+vpX0+APxW0vZF+z0DOB7oAxwMnLOpE0n9Jp8F3gZebKLYr4DpZMnkMmBDv4akXsDvgMvT+XwTuEdS96LtPwMMB3YCFgMjgBMiYifgg8CMorJHAPPSsX4E3CZJTdTrCOC5VPYS4NeSdt3UOdvWyUnFalZE/AM4Ggjgp8BiSeMl7Z6KfAG4KiLmRsRa4ErgkEJrJSLuiIilEbE2Iq4DdgDem7Z9G9hPUreIWBURT6b4mcDvIuKRiHgb+C+gE9kf5YIREfFyRCwDfkuWuJpypKTlwOq0r09HxGuNC0naCzgM+F5ErImISWnfBZ8GHoiIByJifUQ8AkwDTiwqc3tEzE7XYi2wHugnqVNELIqI2UVlX4yIn6b+nNFAT2B3SnsNuCEi3o6Iu8iS0UnNnLNtxZxUrKalhHFORPQG+gF7ADek1XsDN6bbQcuBZYDI+l6Q9I10a2xFWr8L79xSOpfs9tpfJD1V1IG+B0UtiXTbamFhn8krRctvAp2bOYUnI6IL0BUYD/xbE+X2AF6PiDeKYsUtmr2B0wvnms7naLJkULCwqN5vkCXI84FFkn4n6X2lziEi3kyLTZ3H32PjkWlfTPW1bZCTim01IuIvwO1kyQWyP6JfiIguRZ9OEfGn1H/ybbJbVV3TH/YVZEmHiJgfEWcBPYBrgHGSdgReJvsDDmSd3sCewN9bWfdVwJeAz0g6tESRRUDXVIeCvYqWFwK/aHSuO0bE1cWHaXTMhyLiY2SJ5y9krb2W6NXo1theZNfJtkFOKlazJL0vtTZ6p+97AmcBhVtV/wNcJOnAtH4XSaendTuR3QJaDLSX9H1g56J9f1pS99QSWZ7C68j6Vk6SNFBSB+AbZE+b/am15xMRS4GfAd8vse5FsttZP5S0vaSjgX8vKnIH8O+SjpPUTlJHSQMK16YxSbunBw52TPVflc6vJXoA/yGpQ7q++5P1Ndk2yEnFatlKsk7iKZLeIEsms8j+0BMR95K1MsZK+kdaV3iK6SHgQbLO/hfJ+jQWFu37eGC2pFVknfZDI2J1RMwj67/4b2AJ2R/2f4+It3I6pxuAEyUdXGLdJ9P5LiPrEB9TWBERC4EhZA8mLE7n8i2a/n98O7Lr9HLa34fJWkotMYXsibUlwBXAaSlB2jZInqTLzFpK0jnA5yPi6GrXxbYMbqmYmVlunFTMzCw3vv1lZma5cUvFzMxy46RiVmGNx/Aqsf47kn7W1vUyqwQnFdumSbpI0gONYvObiA2tRB0i4sqI+Hwe+0oDWn40j32ZtYSTim3rJgEfSgM6IulfgQ5A/0ax/VJZM2uGk4pt654iSyKFQR+PASaQDYpYHPtbRLysFg6Xn3xK0kuSlkj6btF2G2ZwLLpVNqyJsp3SqMyvp3HL/lNSQ1r3C7IhUn6rbOKv/0zx3IfqN2uKk4pt09Kb8FPIEgfp5+PA5EaxQitls4fLL3I02SjIA4HvF/9xL6GpspcAdcA+wMfI3u4vnMtngJfI3vDvHBE/qtRQ/WZNcVIxg8d4J4H8G1lSebxR7DFo8XD5BT9ME4k9AzwDvL+ZOjVV9gzgyoh4PSIayOZEaU7eQ/WbNctJxSxrhRytbJKv7hExn2yAyA+mWL9UpqXD5RdszpD4TZXdg43HKCteLiXvofrNmuWkYgZPkCWH4cAfYcMEYC+n2MsR8XwrhsvP0yKgeOThPRutb/w2c0WG6jdripOKbfMi4p9kw8p/ney2V8HkFCv0p7R0uPw83U02nH9XZVMIf7nR+lfJ+luKy1dkqH6zUpxUzDKPkbUwJhfFHk+xQlJp0XD5OdfzUqABeB74P2AcWZIouAq4OD3p9c02GKrfbCMe+8ushkn6Ilny+nC162IGbqmY1RRJPSV9SNJ2kt5Ldjvr3mrXy6yg5FhEZrbF2h64leydkuXAWOCWqtbIrIhvf5mZWW58+8vMzHKzzd3+6tatW9TV1VW7GmZmNWX69OlLIqL7psptc0mlrq6OadOmVbsaZmY1RdKLmy7l219mZpYjJxUzM8tNxZKKpFGSXpM0q1H8K5LmpfkdflQUv0jSgrTuuKL48Sm2QNKFRfE+kqakGfnuajSUt5mZVUEl+1RuB24CxhQCkj4CDAEOjog1knqk+AHAUOBAslFV/y/NAwFwM9m8EQ3AU5LGR8QcsgH7ro+IsZL+h2yE2J9U8HzMrMrefvttGhoaWL0679FvrKBjx4707t2bDh06tGj7iiWViJgkqa5R+IvA1RGxJpV5LcWHAGNT/HlJC4DD07oFEfEcgKSxwBBJc4FjgU+mMqOBH+CkYrZVa2hoYKeddqKuro5swGXLU0SwdOlSGhoa6NOnT4v20dZ9Ku8B/i3dtnpM0mEp3ouNB+ZrSLGm4rsByyNibaN4SZKGp2lepy1evDinUzGztrZ69Wp22203J5QKkcRuu+3WqpZgWyeV9kBX4EjgW8DdaX6HUr8h0YJ4SRExMiLqI6K+e/dNPmZtZlswJ5TKau31bev3VBqAX0c2NsxUSevJZs1rYOPJhnqTTS5EE/ElQBdJ7VNrpbi8mZlVSVsnld+Q9YVMTB3x25MliPHAryT9mKyjvi8wlaxF0ldSH7KZ6oYCn4yIkDQBOI1sQL1hwH1tfC5mVmXSyFz3FzG8rHJXXHEFv/rVr2jXrh3bbbcdt956K0cccUTJsj/4wQ/o3Lkz3/zmN3Or57Rp0xgzZgwjRoyoyP5bo2JJRdKdwACgm6QG4BJgFDAqPWb8FjAstVpmS7obmEM2s94FEbEu7efLZJMjtQNGRcTsdIhvA2MlXQ78GbitUuditiXI+w/o5ir3D+7W7oknnuD+++/n6aefZocddmDJkiW89VbbzXm2du1a6uvrqa+vb/W+IoKIYLvt8usJqVifSkScFRE9I6JDRPSOiNsi4q2I+HRE9IuI/hHxh6LyV0TEvhHx3oh4sCj+QES8J627oij+XEQcHhH7RcTphSfKzMwqadGiRXTr1o0ddtgBgG7durHHHntQV1fHkiVLgKwlMWDAgA3bPPPMMxx77LH07duXn/70pxv2c8wxx3DIIYfQr18/Hn88m8n697//Pf379+f9738/AwcOBLLWzvDhwxk0aBBnn302EydO5OSTT252/wDXXnsthx12GAcffDCXXHIJAC+88AL7778/X/rSl+jfvz8LFxY/C9V629zYX2ZmrTFo0CAuvfRS3vOe9/DRj36UM888kw9/uPmJN2fOnMmTTz7JG2+8waGHHspJJ53EnXfeyXHHHcd3v/td1q1bx5tvvsnixYs577zzmDRpEn369GHZsmUb9jF9+nQmT55Mp06dmDhx4ib3P2vWLObPn8/UqVOJCAYPHsykSZPYa6+9mDdvHj//+c+55Zb8p+JxUjEz2wydO3dm+vTpPP7440yYMIEzzzyTq6++utlthgwZQqdOnejUqRMf+chHmDp1Kocddhif+9znePvttznllFM45JBDmDhxIsccc8yGd0R23XXXDfsYPHgwnTp1Knv/kydP5uGHH+bQQw8FYNWqVcyfP5+99tqLvffemyOPPDKnK7IxJxUzs83Url07BgwYwIABAzjooIMYPXo07du3Z/369QDves+j8WO6kjjmmGOYNGkSv/vd7/jMZz7Dt771Lbp06dLkI7077rhjk/Uptf+I4KKLLuILX/jCRuteeOGFZvfVWh5Q0sxsM8ybN4/58+dv+D5jxgz23ntv6urqmD59OgD33HPPRtvcd999rF69mqVLlzJx4kQOO+wwXnzxRXr06MF5553Hueeey9NPP81RRx3FY489xvPPPw+w0e2v5pTa/3HHHceoUaNYtWoVAH//+9957bXXNrGn1nNLxcxqVjWeSFu1ahVf+cpXWL58Oe3bt2e//fZj5MiRzJ07l3PPPZcrr7zyXY8XH3744Zx00km89NJLfO9732OPPfZg9OjRXHvttXTo0IHOnTszZswYunfvzsiRIzn11FNZv349PXr04JFHHtlknUrtf4899mDu3LkcddRRQHbb7o477qBdu3YVuS4F29wc9fX19eFJuqwW+ZFimDt3Lvvvv3+1q7HVK3WdJU2PiE0+x+zbX2ZmlhsnFTMzy42TipmZ5cZJxczMcuOkYmZmuXFSMTOz3DipmFntkvL9lOmVV15h6NCh7LvvvhxwwAGceOKJ/PWvf92sqv/mN79hzpw5m3vGWzwnFTOzzRARfPzjH2fAgAH87W9/Y86cOVx55ZW8+uqrm7WfaiSVdevWVfwYfqPe2oxf3rOtwYQJE+jQoQPnn3/+hlhhMMiTTz6Z+++/H4Avf/nL1NfXc84553DhhRcyfvx42rdvz6BBgzj11FMZP348jz32GJdffjn33HMPK1eu5Pzzz+fNN99k3333ZdSoUXTt2pUBAwZw6KGHMn36dBYvXsyYMWO46qqrePbZZznzzDO5/PLLAbjjjjsYMWIEb731FkcccQS33HIL7dq1o3Pnznz961/noYce4rrrruPoo4+u6PVxS8XMbDPMmjWLD3zgA2WXX7ZsGffeey+zZ89m5syZXHzxxXzwgx9k8ODBXHvttcyYMYN9992Xs88+m2uuuYaZM2dy0EEH8cMf/nDDPrbffnsmTZrE+eefz5AhQ7j55puZNWsWt99+O0uXLmXu3Lncdddd/PGPf2TGjBm0a9eOX/7ylwC88cYb9OvXjylTplQ8oYBbKmZmFbXzzjvTsWNHPv/5z3PSSSdtNLlWwYoVK1i+fPmGeVmGDRvG6aefvmH94MGDATjooIM48MAD6dmzJwD77LMPCxcuZPLkyUyfPp3DDjsMgH/+85/06NEDyEZU/sQnPlHRcyzmpGJmthkOPPBAxo0b96548dD38M7w9+3bt2fq1Kk8+uijjB07lptuuok//OEP79q+OYVZJrfbbrsNy4Xva9euJSIYNmwYV1111bu27dixY8UHkSxWsdtfkkZJei3NR9943TclhaRu6bskjZC0QNJMSf2Lyg6TND99hhXFPyDp2bTNCDU1CYGZWY6OPfZY1qxZs9G0vU899RTr1q1jzpw5rFmzhhUrVvDoo48C2ajGK1as4MQTT+SGG25gxowZAOy0006sXLkSgF122YWuXbtumFL4F7/4xSZnkyw2cOBAxo0bt2Fo+2XLlvHiiy/mcr6bq5ItlduBm4AxxUFJewIfA14qCp8A9E2fI4CfAEdI2hW4BKgHApguaXxEvJ7KDAeeBB4AjgcexMy2HVUYZV0S9957L1/72te4+uqr6dixI3V1ddxwww2cccYZHHzwwfTt23fDjIsrV65kyJAhrF69mojg+uuvB2Do0KGcd955jBgxgnHjxjF69OgNHfX77LMPP//5z8uu0wEHHMDll1/OoEGDWL9+PR06dODmm29m7733rsg1aE5Fh76XVAfcHxH9imLjgMuA+4D6iFgi6VZgYkTcmcrMAwYUPhHxhRS/FZiYPhMi4n0pflZxueZ46Pvq8dNfrePr56Hv20rNDH0vaTDw94h4ptGqXsDCou8NKdZcvKFEvKnjDpc0TdK0xYsXt+IMzMysOW2WVCT9C/Bd4PulVpeIRQviJUXEyIioj4j67t27l1NdMzNrgbZsqewL9AGekfQC0Bt4WtK/krU09iwq2xt4eRPx3iXiZraV29Zmq21rrb2+bZZUIuLZiOgREXURUUeWGPpHxCvAeODs9BTYkcCKiFgEPAQMktRVUldgEPBQWrdS0pHpqa+zyfpozGwr1rFjR5YuXerEUiERwdKlS+nYsWOL91Gxp78k3UnW0d5NUgNwSUTc1kTxB4ATgQXAm8BnASJimaTLgKdSuUsjYlla/iLZE2adyJ768pNfZlu53r1709DQgPtGK6djx4707t170wWbULGkEhFnbWJ9XdFyABc0UW4UMKpEfBrQ791bmNnWqkOHDvTp06fa1bBmeOwvMzPLjZOKmZnlxknFzMxy46RiZma5cVIxM7PcOKmYmVlunFTMzCw3TipmZpYbJxUzM8uNk4qZmeXGScXMzHLjpGJmZrlxUjEzs9w4qZiZWW6cVMzMLDdOKmZmlpuKTdJlZrYlkUZW9fgRw6t6/LZSsZaKpFGSXpM0qyh2raS/SJop6V5JXYrWXSRpgaR5ko4rih+fYgskXVgU7yNpiqT5ku6StH2lzsXMzMpTydtftwPHN4o9AvSLiIOBvwIXAUg6ABgKHJi2uUVSO0ntgJuBE4ADgLNSWYBrgOsjoi/wOnBuBc/FzMzKULGkEhGTgGWNYg9HxNr09Umgd1oeAoyNiDUR8TywADg8fRZExHMR8RYwFhgiScCxwLi0/WjglEqdi5mZlaeaHfWfAx5My72AhUXrGlKsqfhuwPKiBFWIlyRpuKRpkqYtXrw4p+qbmVljVUkqkr4LrAV+WQiVKBYtiJcUESMjoj4i6rt377651TUzszK1+dNfkoYBJwMDI6KQCBqAPYuK9QZeTsul4kuALpLap9ZKcXkzM6uSNm2pSDoe+DYwOCLeLFo1HhgqaQdJfYC+wFTgKaBvetJre7LO/PEpGU0ATkvbDwPua6vzMDOz0ir5SPGdwBPAeyU1SDoXuAnYCXhE0gxJ/wMQEbOBu4E5wO+BCyJiXWqFfBl4CJgL3J3KQpacvi5pAVkfy22VOhczMytPxW5/RcRZJcJN/uGPiCuAK0rEHwAeKBF/juzpMDMz20J4mBYzM8uNk4qZmeXGScXMzHLjpGJmZrlxUjEzs9w4qZiZWW42mVQknS5pp7R8saRfS+pf+aqZmVmtKael8r2IWCnpaOA4shGBf1LZapmZWS0qJ6msSz9PAn4SEfcBnhDLzMzepZyk8ndJtwJnAA9I2qHM7czMbBtTTnI4g2zsreMjYjmwK/CtitbKzMxq0iaTShpN+AXgBElfAXpGxMOVrpiZmdWecp7++j5Z5/xuQDfg55IurnTFzMys9pQzSvFZwKERsRpA0tXA08DllayYmZnVnnL6VF4AOhZ93wH4W0VqY2ZmNa2clsoaYLakR8jmgf8YMFnSCICI+I8K1s/MzGpIOUnl3vQpmFiZqpiZWa3bZFKJiNEt2bGkUcDJwGsR0S/FdgXuAurIbqudERGvSxJwI3Ai8CZwTkQ8nbYZBhQeDLi8UB9JHwBuBzqRzQz51TR3vZmZVUmTfSqS7k4/n5U0s/GnjH3fDhzfKHYh8GhE9AUeTd8BTgD6ps9w0jAwKQldAhxBNnXwJZK6pm1+ksoWtmt8LDMza2PNtVS+mn6e3JIdR8QkSXWNwkOAAWl5NNmttG+n+JjU0nhSUhdJPVPZRyJiGUDq1zle0kRg54h4IsXHAKcAD7akrmZmlo8mk0pELJLUDrgtIj6a0/F2j4hFRfvvkeK9gIVF5RpSrLl4Q4l4SZKGk7Vq2GuvvVp5CmZm1pRmHymOiHXAm5J2qXA9VOrwLYiXFBEjI6I+Iuq7d+/ewiqamdmmlPP012rg2XTr6Y1CsIWPEr8qqWdqpfQEXkvxBmDPonK9gZdTfECj+MQU712ivJmZVVE5Lz/+DvgeMAmYnj7TWni88cCwtDwMuK8ofrYyRwIr0m2yh4BBkrqmDvpBwENp3UpJR6Ynx84u2peZmVVJOS2VLhFxY3FA0lebKlxU5k6yVkY3SQ1kT3FdDdwt6VzgJeD0VPwBsseJF5A9UvxZgIhYJuky4KlU7tJCpz3wRd55pPhB3ElvZlZ15SSVYWTvkBQ7p0RsIxFxVhOrBpYoG8AFTexnFDCqRHwa0K+5OpiZWdtqMqlIOgv4JNBH0viiVTsBSytdMTMzqz3NtVT+BCwiG+7+uqL4SqCclx/NzGwb09x7Ki8CLwJHtV11zMyslnmueTMzy42TipmZ5aa5jvpHI2KgpGsi4tttWaktlTSyqsePGF7V45uZbUpzHfU9JX0YGCxpLI2GRikMTW9mZlbQXFL5PtnQ9L2BHzdaF8CxlaqUmZnVpuae/hoHjJP0vYi4rA3rZGZmNaqcmR8vkzQYOCaFJkbE/ZWtlpmZ1aJNPv0l6SqyCbvmpM9XU8zMzGwj5Yz9dRJwSESsB5A0GvgzcFElK2ZmZrWn3PdUuhQtV3rCLjMzq1HltFSuAv4saQLZY8XH4FaKmZmVUE5H/Z2SJgKHkSWVb0fEK5WumJmZ1Z5yWiqkmRbHb7KgmZlt0zz2l5mZ5aYqSUXS/5M0W9IsSXdK6iipj6QpkuZLukvS9qnsDun7grS+rmg/F6X4PEnHVeNczMzsHc0mFUnbSZqV5wEl9QL+A6iPiH5AO2AocA1wfUT0BV4Hzk2bnAu8HhH7Adenckg6IG13IHA8cIukdnnW1czMNk+zSSW9m/KMpL1yPm57oJOk9sC/kM0weSwwLq0fDZySloek76T1AyUpxcdGxJqIeB5YAByecz3NzGwzlNNR3xOYLWkq8EYhGBGDW3LAiPi7pP8CXgL+CTwMTAeWR8TaVKwB6JWWewEL07ZrJa0AdkvxJ4t2XbyNmZlVQTlJ5Yd5HlBSV7JWRh9gOfC/wAklikZhkybWNRUvdczhwHCAvfbKu9FlZmYFm+yoj4jHgBeADmn5KaA1c6l8FHg+IhZHxNvAr4EPAl3S7TDIhtt/OS03AHsCpPW7AMuK4yW2aXwOIyOiPiLqu3fv3oqqm5lZc8oZUPI8sr6MW1OoF/CbVhzzJeBISf+S+kYGkg1UOQE4LZUZBtyXlsen76T1f4iISPGh6emwPkBfYGor6mVmZq1Uzu2vC8g6wKcARMR8ST1aesCImCJpHFlrZy3Z4JQjgd8BYyVdnmK3pU1uA34haQFZC2Vo2s9sSXeTJaS1wAURsa6l9TIzs9YrJ6msiYi3skbFhltQJfsuyhURlwCXNAo/R4mntyJiNXB6E/u5AriiNXUxM7P8lPPy42OSvkP2CPDHyDrWf1vZapmZWS0qJ6lcCCwGngW+ADwAXFzJSpmZWW0qZ5Ti9Wlirilkt73mpY5yMzOzjWwyqUg6Cfgf4G9k74b0kfSFiHiw0pUzM7PaUk5H/XXARyJiAYCkfcme1HJSMTOzjZTTp/JaIaEkzwGvVag+ZmZWw5psqUg6NS3OlvQAcDdZn8rpZG/Vm5mZbaS521//XrT8KvDhtLwY6FqxGpmZWc1qMqlExGfbsiJmZlb7ynn6qw/wFaCuuHxLh743M7OtVzlPf/2GbPyt3wLrK1sdMzOrZeUkldURMaLiNTEzs5pXTlK5UdIlZDM0rikEI6I1c6qYmdlWqJykchDwGbI55Au3vyJ9NzMz26CcpPJxYJ+IeKvSlTEzs9pWzhv1zwBdKl0RMzOrfeW0VHYH/iLpKTbuU/EjxWZmtpFykkrjGRrNzMxK2uTtr4h4rNSnNQeV1EXSOEl/kTRX0lGSdpX0iKT56WfXVFaSRkhaIGmmpP5F+xmWys+XNKw1dTIzs9bbZFKRtFLSP9JntaR1kv7RyuPeCPw+It4HvB+YSzbD5KMR0Rd4NH0HOAHomz7DgZ+keu1K1oo6gmxu+0sKicjMzKqjnJbKThGxc/p0BD4B3NTSA0raGTiG7C19IuKtiFgODAFGp2KjgVPS8hBgTGSeBLpI6gkcBzwSEcsi4nXgEeD4ltbLzMxar5ynvzYSEb+hde+o7EM20vHPJf1Z0s8k7QjsHhGL0jEWAT1S+V7AwqLtG1Ksqfi7SBouaZqkaYsXL25F1c3MrDnlDCh5atHX7YB6spcfW3PM/sBXImKKpBt551ZXySqUiEUz8XcHI0YCIwHq6+tbU3czM2tGOU9/Fc+rshZ4geyWVEs1AA0RMSV9H0eWVF6V1DMiFqXbW68Vld+zaPvewMspPqBRfGIr6mVmZq20yaSS97wqEfGKpIWS3hsR84CBwJz0GQZcnX7elzYZD3xZ0liyTvkVKfE8BFxZ1Dk/CLgoz7qamdnmaW464e83s11ExGWtOO5XgF9K2p5szvvPkt1au1vSucBLZNMWAzwAnAgsAN5MZYmIZZIu452pjS+NiGWtqJOZNUel7ji3ofCd61rQXEvljRKxHYFzgd2AFieViJhB1jfT2MASZQO4oIn9jAJGtbQeZmaWr+amE76usCxpJ+CrZK2EscB1TW1nZmbbrmb7VNILhl8HPkX27kj/9E6ImZnZuzTXp3ItcCrZo7gHRcSqNquVmZnVpOZefvwGsAdwMfBy0VAtK3MYpsXMzLZCzfWpbPbb9mZmtm1z4jAzs9w4qZiZWW6cVMzMLDdOKmZmlhsnFTMjLZmTAAAK8klEQVQzy42TipmZ5cZJxczMcuOkYmZmuXFSMTOz3DipmJlZbpxUzMwsN04qZmaWm6olFUntJP1Z0v3pex9JUyTNl3RXmmoYSTuk7wvS+rqifVyU4vMkHVedMzEzs4JqtlS+Cswt+n4NcH1E9AVeJ5u2mPTz9YjYD7g+lUPSAcBQ4EDgeOAWSe3aqO5Wi6Tqfsy2AVVJKpJ6AycBP0vfBRwLjEtFRgOnpOUh6Ttp/cBUfggwNiLWRMTzwALg8LY5AzMzK6VaLZUbgP8E1qfvuwHLI2Jt+t4A9ErLvYCFAGn9ilR+Q7zENhuRNFzSNEnTFi9enOd5mJlZkTZPKpJOBl6LiOnF4RJFYxPrmttm42DEyIioj4j67t27b1Z9zcysfE3O/FhBHwIGSzoR6AjsTNZy6SKpfWqN9AZeTuUbgD2BBkntgV2AZUXxguJtzMysCtq8pRIRF0VE74ioI+to/0NEfAqYAJyWig0D7kvL49N30vo/RESk+ND0dFgfoC8wtY1Ow8zMSqhGS6Up3wbGSroc+DNwW4rfBvxC0gKyFspQgIiYLeluYA6wFrggIta1fbXNzKygqkklIiYCE9Pyc5R4eisiVgOnN7H9FcAVlauhmZltDr9Rb2ZmuXFSMTOz3DipmJlZbpxUzMwsN04qZmaWGycVMzPLzZb0noqZ2dar2iNVR8lRrHLnloqZmeXGScXMzHLjpGJmZrlxUjEzs9w4qZiZWW6cVMzMLDdOKmZmlhsnFTMzy42TipmZ5cZJxczMctPmSUXSnpImSJorabakr6b4rpIekTQ//eya4pI0QtICSTMl9S/a17BUfr6kYU0d08zM2kY1WiprgW9ExP7AkcAFkg4ALgQejYi+wKPpO8AJQN/0GQ78BLIkBFwCHEE2DfElhURkZmbV0eZJJSIWRcTTaXklMBfoBQwBRqdio4FT0vIQYExkngS6SOoJHAc8EhHLIuJ14BHg+DY8FTMza6SqfSqS6oBDgSnA7hGxCLLEA/RIxXoBC4s2a0ixpuJmZlYlVUsqkjoD9wBfi4h/NFe0RCyaiZc61nBJ0yRNW7x48eZX1szMylKVpCKpA1lC+WVE/DqFX023tUg/X0vxBmDPos17Ay83E3+XiBgZEfURUd+9e/f8TsTMzDZSjae/BNwGzI2IHxetGg8UnuAaBtxXFD87PQV2JLAi3R57CBgkqWvqoB+UYmZmViXVmPnxQ8BngGclzUix7wBXA3dLOhd4CTg9rXsAOBFYALwJfBYgIpZJugx4KpW7NCKWtc0pmJlZKW2eVCJiMqX7QwAGligfwAVN7GsUMCq/2m3htpHpSM2sdvmNejMzy42TipmZ5cZJxczMcuOkYmZmuXFSMTOz3DipmJlZbpxUzMwsN04qZmaWGycVMzPLjZOKmZnlxknFzMxy46RiZma5cVIxM7PcOKmYmVlunFTMzCw3TipmZpYbJxUzM8uNk4qZmeWm5pOKpOMlzZO0QNKF1a6Pmdm2rKaTiqR2wM3ACcABwFmSDqhurczMtl01nVSAw4EFEfFcRLwFjAWGVLlOZmbbrPbVrkAr9QIWFn1vAI5oXEjScGB4+rpK0rw2qFvuBN2AJdWrgKp26Dz4+rWOr1/rbAXXb+9yCtV6Uil1leJdgYiRwMjKV6eyJE2LiPpq16NW+fq1jq9f62wr16/Wb381AHsWfe8NvFylupiZbfNqPak8BfSV1EfS9sBQYHyV62Rmts2q6dtfEbFW0peBh4B2wKiImF3lalVSzd/CqzJfv9bx9WudbeL6KeJdXRBmZmYtUuu3v8zMbAvipGJmZrlxUmlDkq6X9LWi7w9J+lnR9+skfUfSuDL29Z1K1XNLJ+m7kmZLmilphqR3vZvUzLaDt6XhfFpzrewdkj4uKSS9r4yyX5P0L21Rry2R+1TakKTTgdMj4gxJ25E9vfZWRByV1j8BfC0ippSxr1UR0Xkzj98uIta1pO5bCklHAT8GBkTEGkndgO0jYpOPkktqHxFrK17JLURrrlVbkCSyv0Hrq12XTZF0N9ATeDQifrCJsi8A9RFRvRcdq8gtlbb1R+CDaflAYBawUlJXSTsA+wOvS5oFIOkcSb+W9HtJ8yX9KMWvBjqlf3n+MsU+LWlqit2axkVD0ipJl0qaAhzVtqdbET2BJRGxBiAilkTEy5JekHRNugZTJe0HIOl2ST+WNAG4Jl3Tm4rWjZD0J0nPSTotxbeTdEv6F/79kh4orKsxzV2rbgCS6iVNTMs/kDRa0sOpzKmSfiTp2fQ72CGVe0HSlZKekDRNUv/U6v6bpPMLB5f0LUlPpVbSD1OsTtJcSbcAT7Pxe2ZbJEmdgQ8B55K9toCkAZLuLypzU/rd+g9gD2BC+p1D0qqicqdJuj0t3y7pJ5ImpN+/D0sala7P7UXbrJJ0haRnJD0pafc2OO0Wc1JpQ+lfiGsl7UWWXJ4ACn/s64GZwFuNNjsEOBM4CDhT0p4RcSHwz4g4JCI+JWn/VOZDEXEIsA74VNp+R2BWRBwREZMrfIpt4WFgT0l/TX/4P1y07h8RcThwE3BDUfw9wEcj4hsl9tcTOBo4Gbg6xU4F6siu+eep3WTc3LVqyr7ASWRj6N0BTIiIg4B/pnjBwtTCfhy4HTgNOBK4FEDSIKAv2fh8hwAfkHRM2va9wJiIODQiXmzlObaFU4DfR8RfgWWS+jdVMCJGkL2A/ZGI+EgZ++4KHAv8P+C3wPVk/+A8SNIhqcyOwJMR8X5gEnBei8+kDTiptL1Ca6WQVJ4o+v6nEuUfjYgVEbEamEPp8XcGAh8AnpI0I33fJ61bB9yT6xlUUUSsIjvX4cBi4C5J56TVdxb9LE4E/9vMbb/fRMT6iJgDFP4FeHTaZn1EvAJMyPMc2somrlVTHoyIt4Fnyd79+n2KP0uWaAvGF8WnRMTKiFgMrJbUBRiUPn8ma5G8jyzJALwYEU+24tTa2llkg9WSfp6V475/G1kfxLPAqxHxbLodOJt3rvdbQKFVNJ2N/ztscWr65cca9SeyBHIQ2e2vhcA3gH8Ao0qUX1O0vI7S/80EjI6Ii0qsW13r/SiNpfOZCEyU9CwwrLCquFjR8hvN7K74+qrRz5rXxLVayzv/oOzYaJPCrbL1kt6Odzpd17Px796aonjxNSyUE3BVRNxavHNJdTT/32OLImk3spZEP0lBlmiDLKkW/6O88XUsVvy7WPJ60/R1BCj+79DU34Athlsqbe+PZLdalkXEuohYBnQh+5f1E5uxn7cL97iBR4HTJPUAkLSrpLJGFK01kt4rqW9R6BCgcAvlzKKfm3MtG5sMfCL1rewODGjFvqqmmWv1AlkLBuATFTr8Q8DnUn8EknoVfj9rzGlkt+r2joi6iNgTeD6tO0DSDpJ2Ibs7ULAS2Kno+6uS9lf2cM7H26ba1bNFZ7yt1LNkQ2D/qlGsc0QsKfxPWIaRwExJT6d+lYuBh9Mv7tvABbzzx3Zr0hn473SLZS2wgOz2zsnADumBhO1o3S2Ke8j+SMwC/krW77WiNZWukqau1f7AbcoeS9/kk4YtEREPp76+J5QNub4K+DTZv7RryVm809dWcA/wSeBusn7Q+WS3+QpGAg9KWpT6VS4ku321kOx3arOe2qw1fqTYtgrK+TFOSZ0jYlW6/TGV7CGIV/LYt9nWzC0Vs9LuT//C3x64zAnFrDxuqZiZWW7cUW9mZrlxUjEzs9w4qZiZWW6cVMzMLDdOKmZmlpv/DyavDYTRCkGaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be59f6a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_files = ['./data/Chicago-2016-Summary.csv',\n",
    "              './data/NYC-2016-Summary.csv',\n",
    "              './data/Washington-2016-Summary.csv',]\n",
    "for data_file in data_files:\n",
    "    city = data_file.split('-')[0].split('/')[-1]\n",
    "    data = season_ridership_by_user_type(data_file)\n",
    "    subscriber = data[0]\n",
    "    customer = data[1]\n",
    "    plot_season_ridership(subscriber, customer,city)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference for bar plot**: https://www.youtube.com/watch?v=iedmZlFxjfA, visited on 26-10-2018\n",
    "**Reference for bar color**: https://www.color-hex.com/color/0000ff visited on 26-10-2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 6b :**\n",
    "\n",
    "Is the pattern of ridership different on **the weekends** versus **weekdays**? On what days are Subscribers most likely to use the system? What about Customers? Does the average duration of rides change depending on the day of the week?\n",
    "\n",
    "**6b answer :**\n",
    "\n",
    "1- The bars below show the number of trips on weekdays in the three cities. For the  subscribers, note that all days of the week is more than others days (Saturday and Sunday) in three cities, that indicating most of them work (employee) in fixed places and times.\n",
    "\n",
    "2- It is reasonable to expect that the number of trips by Customers in the weekends is more than others days in three cities, that indicating most of them are tourists. \n",
    "\n",
    "3- The bars of trips duration in the three cities is opposite of numbor of trips, it is longer in all the days of week. The duration bigger than 30 minutes in NYC and Washington because the limit of normal trip is 45 mins in Washington and 3 hours in NYC, that explains the graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def days_ridership_dur_by_user_type(filename):\n",
    "    \"\"\"\n",
    "    to calculate ridership difference on the weekends versus weekdays \n",
    "    and to know On what days are Subscribers versus Customers most likely to use the system? \n",
    "    Does the average duration of rides change depending on the day of the week?\n",
    "    \"\"\"\n",
    "    with open(filename, 'r') as days_f:\n",
    "        days = csv.DictReader(days_f)\n",
    "        subscriber_rides = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "        customer_rides = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "        subscriber_durations = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "        customer_durations = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "\n",
    "        for trip in days:\n",
    "            if trip['day_of_week'] == 'Saturday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Sat'] += 1\n",
    "                    subscriber_durations['Sat'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Sat'] += 1\n",
    "                    customer_durations['Sat'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Sunday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Sun'] += 1\n",
    "                    subscriber_durations['Sun'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Sun'] += 1\n",
    "                    customer_durations['Sun'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Monday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Mon'] += 1\n",
    "                    subscriber_durations['Mon'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Mon'] += 1\n",
    "                    customer_durations['Mon'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Tuesday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Tue'] += 1\n",
    "                    subscriber_durations['Tue'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Tue'] += 1\n",
    "                    customer_durations['Tue'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Wednesday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Wed'] += 1\n",
    "                    subscriber_durations['Wed'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Wed'] += 1\n",
    "                    customer_durations['Wed'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Thursday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Thu'] += 1\n",
    "                    subscriber_durations['Thu'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Thu'] += 1\n",
    "                    customer_durations['Thu'] += float(trip['duration'])\n",
    "            if trip['day_of_week'] == 'Friday':\n",
    "                if trip['user_type'] == 'Subscriber':\n",
    "                    subscriber_rides['Fri'] += 1\n",
    "                    subscriber_durations['Fri'] += float(trip['duration'])\n",
    "                else: \n",
    "                    customer_rides['Fri'] += 1\n",
    "                    customer_durations['Fri'] += float(trip['duration'])\n",
    "    \n",
    "    subscriber_dur_avg = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "    customer_dur_avg = {'Sat' : 0, 'Sun' : 0, 'Mon' : 0, 'Tue' : 0, 'Wed' : 0, 'Thu' : 0, 'Fri' : 0}\n",
    "    for key,value in subscriber_durations.items():\n",
    "        subscriber_dur_avg[key] = value/subscriber_rides[key]\n",
    "        \n",
    "    for key,value in customer_durations.items():\n",
    "        customer_dur_avg[key] = value/customer_rides[key]        \n",
    "            \n",
    "                    \n",
    "    return (subscriber_rides, customer_rides, subscriber_dur_avg, customer_dur_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dict_to_list(dic):\n",
    "    dictlist = []\n",
    "    for key, value in dic.items():\n",
    "        temp = [key,value]\n",
    "        dictlist.append(value)\n",
    "    return dictlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_days_of_week_trips(subscriber, customer, city):\n",
    "    '''\n",
    "    Plotting bar chart of 'days_ridership_dur_by_user_type' function using two \n",
    "    categorical variables ('User Type' and 'day of week')\n",
    "    '''\n",
    "    Days=['Sat','Sun','Mon','Tue','Wed','Thu','Fri']\n",
    "    ypos = np.arange(len(Days))\n",
    "    plt.gcf().clear()\n",
    "    plt.xticks(ypos, Days)\n",
    "    plt.ylabel('Number of trips')\n",
    "    plt.title('Days of Week Trips Number\\n{}'.format(city))\n",
    "    sub_list = dict_to_list(subscriber)\n",
    "    cus_list = dict_to_list(customer)\n",
    "    plt.bar(ypos-0.2,sub_list, width=0.4, label='Subscriber', color = '#000099')\n",
    "    plt.bar(ypos+0.2,cus_list, width=0.4, label='Customer', color = 'red')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_days_of_week_duration(subscriber, customer, city):\n",
    "    '''\n",
    "    Plotting bar chart of 'days_ridership_dur_by_user_type' function using two \n",
    "    categorical variables ('User Type' and 'day of week')\n",
    "    '''\n",
    "    Days=['Sat','Sun','Mon','Tue','Wed','Thu','Fri']\n",
    "    ypos = np.arange(len(Days))\n",
    "    plt.gcf().clear()\n",
    "    plt.xticks(ypos, Days)\n",
    "    plt.ylabel('Trips Duration (m)')\n",
    "    plt.title('Days of Week Trips Duration Avg\\n{}'.format(city))\n",
    "    sub_list = dict_to_list(subscriber)\n",
    "    cus_list = dict_to_list(customer)\n",
    "    plt.bar(ypos-0.2,sub_list, width=0.4, label='Subscriber', color = '#000099')\n",
    "    plt.bar(ypos+0.2,cus_list, width=0.4, label='Customer', color = 'red')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: Chicago\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be685a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69c123cf98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: NYC\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEXCAYAAABlI9noAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmYVOWZ9/HvT0AhQUUF8yKLoHZmRCWobCYOEjWAS0SNJphESYISE8020VGjcYvraxITx2VCRgQ0EQ2+RuJgkFERiQuLtggSQ7vSagTZAiJE4H7/OE9D0VR3F1jV1Q2/z3XV1VX3ec45dx2x7jrPeeo5igjMzMyKYadyJ2BmZtsPFxUzMysaFxUzMysaFxUzMysaFxUzMysaFxUzMysaFxXb7ilzl6RlkmaUOZepks4u0bZ/Kum/SrHtxiKppaSQ1K3cudi2cVGxj0XSG5I+lLRS0nJJT0s6V1JT+rd1JPAFoHNE9M1dkD7EVknqmxP7Wvpgqx37a2MlnHKqeWxIx7jm9dfyrRMRP4uIc4ucx7HpWPy6VvxZSV8v5r5s+9CU/se35uuLEbErsC9wA3ARcGd5U9rMvsAbEfFB7QURsQ54BjgqJzwA+Gue2LRSJlkrr7Y1D+AtsmNcE/td7faSWpYwnZXAtyR1KeE+iq7Ex8Tq4KJiRRMRKyJiIvAVYLikgwEknSDpBUn/kLRQ0pU160j6H0nfy92OpDmSTk7dVjdLWiRpRYofnG/fkvaRNFHSUklVks5J8RHAfwNHpG/5V+VZfRpZ0ajxb8CNeWLT0jZ3knSxpFclLZF0v6Q9c3Lpn87Ylkt6UdLAOnLumN7TBXkPaD0kXSPpPkn3SloJfD3FxqTlB6QzjHMkvZMeP6qV4/Ppv8l7km6qZ3dLgXuAy+vJZUzO6wMkRc7r6ZKuTmc3H0j6o6S9Uu7/kPScpK61NvtFSa9Lel/SDblnvpLOlvTX1J35SE2xy+k6+66kKrIvBtbYIsIPP7b5AbwBHJsn/hbwnfR8IHAI2ZeYnsB7wMlp2ZeB53LW+wywBNgZGAzMBtoBAg4EOtaRx5PA7UBroBewGDgmLfsGML2e93AU2QfnTkB74E3gEynPmtgGoGtq/0PgWaAzsAvwG+DetKxTyv/4tO4X0usOaflU4GygG/A3YOS2HGPgGuCfwBfTftqk2Ji0/AAggLvTe6k5rgPT8pnAGen5rkC/OvZ9bNp/J7IzlgNS/Fng6zm5jMlZ54Dso2Xj6+npve4H7EH2Yf8K8HmgJfB74LepbcuU9/+mtt2AKuAbaflpad1/SW2vBJ6qte6f07ptyv3/x4748JmKlco7wJ4AETE1Il6KiA0RMQe4l01dSw8BFZIq0uszgfsi4p/AR2QfeP8KKCLmR8S7tXeUvqkeCVwUEWsiopLs7OTMAnN9juyD9xCyM5LpEbEaeD0n9mZEvJXafxu4NCKqI2It2Qfbaam75evApIiYlN7vFGAWWZGp0YOsuFwREaMKzDGf6RHxp7SfD+toc1VErI6IF4GxwBkp/hHZcd8rIlZGxHP17Sgi3gZ+C+Q70yvEnRHxWkQsAyYDf4uIJyLrfvwDcGit9jdExLKIeAO4JSfvbwPXRcQrad1rgL6SOuWse11at65jYiXkomKl0ons2z+S+kl6QtJiSSuAc8m+/ZM+lO8n677ZiezD4+607HHgVuA24D1JoyTtlmdf+wBLI2JlTuzNlEODImINMIOsu2sA8FRaND0nlns9ZV/gwdS9tRyYD6wHPpWWnV6zLC0/EuiYs/7XgLeBCYXkV4+FW9nmTbJjBfBNsuL2iqQZko7fYs0tXQ+cWFcXZAPey3n+YZ7XbWu1ryvvfYHbco7t+2RnkZ3rWNcamYuKFZ2kPmQf6NNT6PfARKBLROwO/BdZd1aNsWQftMcAqyPimZoFEXFLRBwOHAR8Grgwzy7fAfaUtGtOrCvZB3ehaq6r/BubispTObHcorIQOC4i2uU8Wqdv8wuBu2st+2RE3JCz/pVkH4a/l9RiK3KsrZApxnMvrnclO1akb/rDgL2BXwAPSGpd784iFgP/CVxda9EHZGd6Nf5PAXk1JG/eZMd3RK3j26bWmZanXi8jFxUrGkm7SToRGA/cExEvpUW7kp1JrFE2TPerueulIrKB7MPt7pzt9UlnOa3IPrjWkJ0RUGv9hcDTwPWSWkvqCYwAthglVY9pZH38XYCXU2w62fWgXmxeVP4LuFbSvinPDpKGpmX3kF1kHiypRcpnoKTcb9IfAacDnwTuVmmHX/9UUhtJhwDDgftSzmdKah8RG4AVZB/EGwrY3s/JjklFTqwSOEpSF0ntgIuLkPd/SGqXLuB/vyZvsmN/qaQD0/toJ+m0IuzPisRFxYrhT2kE0kLgUuCXZN0rNb4LXJ3aXE7W3VXbOLLrF/fkxHYj68dfRtYFsoTsQy2fM8gu6r4DPEh2vWLKVryHp4HdyQYNBEBELCG74L8oIhbktP012ZnXo+k9PQv0S+ssBIYCP0nrLiQ7u9rs/7V0zehUsjOF0SUsLNOB14BHgetTlyJk13jmp/x/Dnwl5VSviFie2u+ZE/4z2TF/iawbcWIR8v4TWbF6IW17TNr/H8j+ff1B0j+AOWQDOqyJUPr/x6ysJJ1FNhLqyHLnsj2QdACwICLUYGOzIvKZipWdpE+Qnc18nJFQZtYEuKhYWUkaTNZN9B7ZBX0za8bc/WVmZkXjMxUzMysaFxUzMysaFxWzElJ2a4D3JH0yJ3a2pCclTZN0ea32w5VNVPmJ9LqvpEnpF+RL06/fv1l7P2ZNhYuKWem1BH5QKxZkP9D8d0kHQfYjSrLfgJwdEaslHQE8TjZZ5gHAXsB3gOMaK3GzreUL9WYlJOkNsl+B/wewX0QsV3bnx69HxEBJFwJfAj5LNgPAikg32pI0HXgxIs4rT/ZmW89nKmalN4tsVuJ89035Jdk8aBOAz5HmNkvdX0fw8SedNGtUvjOaWeO4HPiLat2WNyLWS/oWMJfsHjM1My3vQfalb4up/s2aMp+pmDWCiJgLPEyeyRYjYl56Oi8nvIxsgseOtdubNWUuKmaN5wrgHAq4z0u6SdgzZNdbzJoNFxWzRhIRVWRTuH+/wFX+A/iGpAsl7QUg6TOSxpcqR7OPy0XFrHFdTXYflQZFxNPA0enxmqSlZJNuTipdemYfj4cUm5lZ0fhMxczMisZFxczMisZFxczMisZFxczMimaH+0V9+/bto1u3buVOw8ys2Wjfvj2TJ0+eHBFDGmq7wxWVbt26MWvWrHKnYWbWrEhqX0g7d3+ZmVnRuKiYmVnRuKiYmVnR7HDXVPL56KOPqK6uZs2aNeVOZbvUunVrOnfuTKtWrcqdipmVmIsKUF1dza677kq3bt2QVO50tisRwZIlS6iurqZ79+7lTsfMSszdX8CaNWvYa6+9XFBKQBJ77bWXzwLNdhAuKokLSun42JrtOFxUzMysaHxNJQ9pVFG3FzGywTbXXnstv//972nRogU77bQTv/nNb+jXr1/etldeeSVt27blggsuKFqOs2bNYty4cdxyyy0l2b6Z7RhcVJqAZ555hocffpjnn3+eXXbZhffff59//vOfjbb/devW0bt3b3r37v2xtxURRAQ77eST4KasWF+cCvnCZDsW/5/fBLz77ru0b9+eXXbZBcjm2dlnn33o1q0b77//PpCdSQwcOHDjOi+++CJHH300FRUV/Pa3v924nQEDBtCrVy8OPvhgnnrqKQD+/Oc/c9hhh/GZz3yGY445BsjOdkaOHMmgQYM466yzmDp1KieeeGK92we46aab6NOnDz179uSKK64A4I033uDAAw/ku9/9LocddhgLFy4s3cGyHZY0qigPKy2fqTQBgwYN4uqrr+bTn/40xx57LF/5ylc46qij6l1nzpw5PPvss3zwwQcceuihnHDCCdx7770MHjyYSy+9lPXr17N69WoWL17MOeecw7Rp0+jevTtLly7duI3Zs2czffp02rRpw9SpUxvc/ty5c1mwYAEzZswgIjjppJOYNm0aXbt25ZVXXuGuu+7i9ttvL8UhMrNmwkWlCWjbti2zZ8/mqaee4oknnuArX/kKN9xwQ73rDB06lDZt2tCmTRs+//nPM2PGDPr06cO3vvUtPvroI04++WR69erF1KlTGTBgwMbfiOy5554bt3HSSSfRpk2bgrc/ffp0Hn30UQ499FAAVq1axYIFC+jatSv77rsv/fv3L9IRMbPmquTdX5JaSHpB0sPpdXdJz0laIOk+STun+C7pdVVa3i1nG5ek+CuSBufEh6RYlaSLS/1eSqlFixYMHDiQq666iltvvZUHHniAli1bsmHDBoAtfudRe5iuJAYMGMC0adPo1KkTZ555JuPGjSMi6hzS+8lPfrLOfPJtPyK45JJLqKyspLKykqqqKkaMGNHgtsxsx9EY11R+AMzPeX0jcHNEVADLgBEpPgJYFhEHADendkjqAQwDDgKGALenQtUCuA04DugBnJHaNjuvvPIKCxYs2Pi6srKSfffdl27dujF79mwAHnjggc3Weeihh1izZg1Llixh6tSp9OnThzfffJO9996bc845hxEjRvD8889zxBFH8OSTT/L6668DbNb9VZ982x88eDCjR49m1apVALz99tssWrSoGIfAzLYTJe3+ktQZOAG4Fvh3ZV9/jwa+mpqMBa4E7gCGpucAE4BbU/uhwPiIWAu8LqkK6JvaVUXEa2lf41Pblz9u3o09omXVqlV873vfY/ny5bRs2ZIDDjiAUaNGMX/+fEaMGMF11123xfDivn37csIJJ/DWW2/x05/+lH322YexY8dy00030apVK9q2bcu4cePo0KEDo0aN4tRTT2XDhg3svffeTJkypcGc8m1/n332Yf78+RxxxBFA1m13zz330KJFi5IcFzNrfhQRpdu4NAG4HtgVuAD4BvBsOhtBUhfgkYg4WNJcYEhEVKdlrwL9yArNsxFxT4rfCTySdjEkIs5O8TOBfhFxfp48RgIjAbp27Xr4m2++udny+fPnc+CBBxbxnVttPsZNS3McUtwcc96eSJodEQ3+7qBk3V+STgQWRcTs3HCeptHAsq2NbxmMGBURvSOid4cOHerJ2szMPo5Sdn99DjhJ0vFAa2A34FdAO0ktI2Id0Bl4J7WvBroA1ZJaArsDS3PiNXLXqStu1mj8Ddpsk5KdqUTEJRHROSK6kV1ofzwivgY8AZyWmg0HHkrPJ6bXpOWPR9Y3NxEYlkaHdQcqgBnATKAijSbbOe1jYqnej5mZNawcv1O5CBgv6RrgBeDOFL8TuDtdiF9KViSIiHmS7ie7AL8OOC8i1gNIOh+YDLQARkfEvEZ9J2ZmtplGKSoRMRWYmp6/xqbRW7lt1gCn17H+tWQjyGrHJwGTipiqmZl9DJ77y8zMisZFJR+puI8C/P3vf2fYsGHsv//+9OjRg+OPP56//e1vW5X2H//4R15++WP/TMfMbJu5qDQBEcEpp5zCwIEDefXVV3n55Ze57rrreO+997ZqO+UoKuvXr2/U/ZlZ0+ai0gQ88cQTtGrVinPPPXdjrFevXqxfv36z6ejPP/98xowZA8DFF19Mjx496NmzJxdccAFPP/00EydO5MILL6RXr168+uqrVFZW0r9/f3r27Mkpp5zCsmXLABg4cCA/+tGPGDBgAAceeCAzZ87k1FNPpaKigssuu2zj/u655x769u1Lr169+Pa3v72xgLRt25bLL7+cfv368cwzzzTCETKz5sJFpQmYO3cuhx9+eMHtly5dyoMPPsi8efOYM2cOl112GZ/97Gc56aSTuOmmm6isrGT//ffnrLPO4sYbb2TOnDkccsghXHXVVRu3sfPOOzNt2jTOPfdchg4dym233cbcuXMZM2YMS5YsYf78+dx333385S9/obKykhYtWvC73/0OgA8++ICDDz6Y5557jiOPPLLox8PMmi9Pfd8M7bbbbrRu3Zqzzz6bE044YbOzmRorVqxg+fLlG+/LMnz4cE4/fdPgupNOOgmAQw45hIMOOoiOHTsCsN9++7Fw4UKmT5/O7Nmz6dOnDwAffvghe++9N5DNqPylL32ppO/RzJonF5Um4KCDDmLChAlbxHOnvodN09+3bNmSGTNm8NhjjzF+/HhuvfVWHn/88a3aZ81dJnfaaaeNz2ter1u3johg+PDhXH/99Vus27p1a08iadaAHXWmBXd/NQFHH300a9eu3ey2vTNnzmT9+vW8/PLLrF27lhUrVvDYY48B2azGK1as4Pjjj+dXv/oVlZWVAOy6666sXLkSgN1335099thj4y2F77777gbvJpnrmGOOYcKECRuntl+6dCm1J+I0M6vNZyr5lHDm5nwk8eCDD/LDH/6QG264gdatW9OtWzd+9atf8eUvf5mePXtSUVGx8Y6LK1euZOjQoaxZs4aI4OabbwZg2LBhnHPOOdxyyy1MmDCBsWPHcu6557J69Wr2228/7rrrroJz6tGjB9dccw2DBg1iw4YNtGrVittuu4199923JMfAzLYPJZ36vinq3bt3zJo1a7OYp2Uvve35GDfHbg7nXHrNLd+GlH3qezMz2/G4qJiZWdG4qCQ7WjdgY/KxNdtxuKiQDZFdsmSJP/xKICJYsmQJrVu3LncqZtYIPPoL6Ny5M9XV1SxevLjcqWyXWrduTefOncudhpk1AhcVoFWrVnTv3r3caZiZNXslKyqSWgPTgF3SfiZExBWSxgBHAStS029ERKUkAb8GjgdWp/jzaVvDgZqZDq+JiLEpfjgwBmhDdrOuH4T7sJq17W0YptmOppRnKmuBoyNilaRWwHRJj6RlF0ZE7XlJjiO7/3wF0A+4A+gnaU/gCqA3EMBsSRMjYllqMxJ4lqyoDAEewczMyqJkF+ojsyq9bJUe9Z1FDAXGpfWeBdpJ6ggMBqZExNJUSKYAQ9Ky3SLimXR2Mg44uVTvx8zMGlbS0V+SWkiqBBaRFYbn0qJrJc2RdLOkmtkMOwELc1avTrH64tV54vnyGClplqRZvhhvZlY6JS0qEbE+InoBnYG+kg4GLgH+FegD7AlclJrnu+9ubEM8Xx6jIqJ3RPTu0KHDVr4LMzMrVKP8TiUilgNTgSER8W7q4loL3AX0Tc2qgS45q3UG3mkg3jlP3MzMyqRkRUVSB0nt0vM2wLHAX9O1ENJor5OBuWmVicBZyvQHVkTEu8BkYJCkPSTtAQwCJqdlKyX1T9s6C3ioVO/HzMwaVsrRXx2BsZJakBWv+yPiYUmPS+pA1n1VCdTcmH0S2XDiKrIhxd8EiIilkn4GzEztro6Ipen5d9g0pPgRPPLLzKysSlZUImIOcGie+NF1tA/gvDqWjQZG54nPAg7+eJmamVmxeO4vMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrmlLeTri1pBmSXpQ0T9JVKd5d0nOSFki6T9LOKb5Lel2VlnfL2dYlKf6KpME58SEpViXp4lK9FzMzK0wpz1TWAkdHxGeAXsCQdO/5G4GbI6ICWAaMSO1HAMsi4gDg5tQOST2AYcBBwBDgdkkt0m2KbwOOA3oAZ6S2ZmZWJiUrKpFZlV62So8AjgYmpPhY4OT0fGh6TVp+jCSl+PiIWBsRr5Pdw75velRFxGsR8U9gfGprZmZlUtJrKumMohJYBEwBXgWWR8S61KQa6JSedwIWAqTlK4C9cuO11qkrni+PkZJmSZq1ePHiYrw1MzPLo6RFJSLWR0QvoDPZmcWB+Zqlv6pj2dbG8+UxKiJ6R0TvDh06NJy4mZltk0YZ/RURy4GpQH+gnaSWaVFn4J30vBroApCW7w4szY3XWqeuuJmZlUkpR391kNQuPW8DHAvMB54ATkvNhgMPpecT02vS8scjIlJ8WBod1h2oAGYAM4GKNJpsZ7KL+RNL9X7MzKxhLRtqIOl04M8RsVLSZcBhwDUR8XwDq3YExqZRWjsB90fEw5JeBsZLugZ4Abgztb8TuFtSFdkZyjCAiJgn6X7gZWAdcF5ErE+5nQ9MBloAoyNi3ta8eTMzK64Giwrw04j4g6QjgcHAz4E7gH71rRQRc4BD88RfI7u+Uju+Bji9jm1dC1ybJz4JmFTAezAzs0ZQSPfX+vT3BOCOiHgI2Ll0KZmZWXNVSFF5W9JvgC8DkyTtUuB6Zma2gymkOHyZ7LrFkDSKa0/gwpJmZWZmzVKDRSUiVgNvAMdJ+h7QMSIeLXViZmbW/DRYVCRdTjZ9yl5Ae+CuNArMzMxsM4WM/joDODSNzkLSDcDzwDWlTMzMzJqfQq6pvAG0znm9C9kcXmZmZpsp5ExlLTBP0hSyubW+AEyXdAtARHy/hPmZmVkzUkhReTA9akwtTSpmZtbcNVhUImJsQ23MzMygnqIi6f6I+LKkl8gzpXxE9CxpZmZm1uzUd6byg/T3xMZIxMzMmr86i0pEvJtmGL4zIo5txJzMzKyZqveaSkSsl7Ra0u4RsaKxkmqqpFFF2U7EyKJsx8ysqSlk9Nca4KU0pPiDmqCHEpuZWW2FFJX/SY9cee8Fb2ZmO7ZCflHfLiLG5j6APRpaSVIXSU9Imi9pnqQfpPiVkt6WVJkex+esc4mkKkmvSBqcEx+SYlWSLs6Jd5f0nKQFku5LtxU2M7MyKaSoDM8T+0YB660DfhwRBwL9gfMk9UjLbo6IXukxCSAtGwYcBAwBbpfUIg0WuA04DugBnJGznRvTtiqAZcCIAvIyM7MSqe93KmcAXwW6S5qYs2hXYElDG46Id4F30/OVkuYDnepZZSgwPiLWAq+ne9XX3Ha4Kt2GGEnjgaFpe0enHCGbSflKslsdm5lZGdR3TeVpsqLQHvhFTnwlMGdrdiKpG9n96p8DPgecL+ksYBbZ2cwysoLzbM5q1WwqQgtrxfuRTcW/PCLW5Wlfe/8jgZEAXbt23ZrUzcxsK9TZ/RURb0bE1Ig4IiKezHk8n/NB3iBJbYEHgB9GxD/IziT2B3qRFa2agqV8aWxDPN97GRURvSOid4cOHQpN3czMtlIho7+2maRWZAXldxHx/wAi4r2c5b8FHk4vq4EuOat3Bt5Jz/PF3wfaSWqZilxue0v82xoza0yFXKjfJpIE3AnMj4hf5sQ75jQ7BZibnk8EhknaRVJ3oAKYAcwEKtJIr53JLuZPjIgAngBOS+sPBx4q1fsxM7OG1VlUJD2W/t64jdv+HHAmcHSt4cP/V9JLkuYAnwd+BBAR84D7gZeBPwPnRcT6dBZyPjAZmA/cn9oCXAT8e7qovxdZETMzszKpr/uro6SjgJPSiKvNrmFExPP1bTgiptdeJ5lUzzrXAtfmiU/Kt14aEda3dtzMzMqjvqJyOXAx2bWKX9ZaFmTDec3MzDaqb5biCcAEST+NiJ81Yk5mZtZMFXLnx59JOgkYkEJTI+Lh+tYxM7MdU4OjvyRdT3bDrpfT4wcpZmZmtplCfqdyAtArIjYASBoLvABcUsrEzMys+Sn0dyrtcp7vXopEzMys+SvkTOV64AVJT5ANER6Az1LMzCyPQi7U3ytpKtCHrKhcFBF/L3ViZmbW/BQ091eaxn5igw3NzGyHVrK5v8zMbMfjomJmZkVTb1GRtJOkufW1MTMzq1FvUUm/TXlRkm+XaGZmDSrkQn1HYJ6kGcAHNcGIOKlkWZmZWbNUSFG5quRZmJnZdqGQ36k8KWlfoCIi/lfSJ4AWpU/NzMyam0ImlDwHmAD8JoU6AX8sYL0ukp6QNF/SPEk/SPE9JU2RtCD93SPFJekWSVWS5kg6LGdbw1P7BZKG58QPT3eRrErr5rspmJmZNZJChhSfR3Zr4H8ARMQCYO8C1lsH/DgiDgT6A+dJ6kF246/HIqICeCy9BjiO7L70FcBI4A7IihBwBdCP7C6PV9QUotRmZM56QwrIy8zMSqSQorI2Iv5Z80JSS7I7P9YrIt6tueVwRKwku798J2AoMDY1GwucnJ4PBcZF5lmgnaSOwGBgSkQsjYhlwBRgSFq2W0Q8ExEBjMvZlpmZlUEhReVJST8B2kj6AvAH4E9bsxNJ3YBDgeeAT6VpX2qmf6k56+kELMxZrTrF6otX54nn2/9ISbMkzVq8ePHWpG5mZluhkKJyMbAYeAn4NjAJuKzQHUhqCzwA/DAi/lFf0zyx2Ib4lsGIURHROyJ6d+jQoaGUzcxsGxUy+mtDujHXc2Qf2q+k7qYGSWpFVlB+FxH/L4Xfk9QxIt5NXViLUrwa6JKzemfgnRQfWCs+NcU752lvZmZlUsjorxOAV4FbgFuBKknHFbCegDuB+RHxy5xFE4GaEVzDgYdy4melUWD9gRWpe2wyMEjSHukC/SBgclq2UlL/tK+zcrZlZmZlUMiPH38BfD4iqgAk7Q/8D/BIA+t9DjgTeElSZYr9BLgBuF/SCOAt4PS0bBJwPFAFrAa+CRARSyX9DJiZ2l0dEUvT8+8AY4A2KZ+GcjIzsxIqpKgsqikoyWts6rKqU0RMJ/91D4Bj8rQPsuHL+bY1GhidJz4LOLihXMzMrHHUWVQknZqezpM0Cbif7JrK6Ww6azAzM9uovjOVL+Y8fw84Kj1fDOyxZXMzM9vR1VlUIuKbjZmImZk1fw1eU5HUHfge0C23vae+NzOz2gq5UP9HsqHBfwI2lDYdMzNrzgopKmsi4paSZ2JmZs1eIUXl15KuAB4F1tYEayaLNDMzq1FIUTmE7EeMR7Op+yvSazMzs40KKSqnAPvlTn9vZmaWTyGzFL8ItCt1ImZm1vwVcqbyKeCvkmay+TUVDyk2M7PNFFJUrih5FmZmtl0o5H4qTzZGImZm1vwV8ov6lWy6o+LOQCvgg4jYrZSJmZlZ81PImcquua8lnQz0LVlGZmbWbBUy+mszEfFH/BsVMzPLo5DbCZ+a8zhN0g1s6g6rb73RkhZJmpsTu1LS25Iq0+P4nGWXSKqS9IqkwTnxISlWJeninHh3Sc9JWiDpPkk7b9U7NzOzoivkTOWLOY/BwEpgaAHrjQGG5InfHBG90mMSgKS7FBMbAAAMMUlEQVQewDDgoLTO7ZJaSGoB3AYcB/QAzkhtAW5M26oAlgEjCsjJzMxKqJBrKtt0X5WImCapW4HNhwLjI2It8LqkKjZdt6mKiNcAJI0HhkqaT9YF99XUZixwJXDHtuRqZmbFUd/thC+vZ72IiJ9t4z7Pl3QWMAv4cUQsAzoBz+a0qU4xgIW14v2AvYDlEbEuT/stSBoJjATo2rXrNqZtZmYNqa/764M8D8i6mS7axv3dAewP9ALeBX6R4srTNrYhnldEjIqI3hHRu0OHDluXsZmZFay+2wnXfOAjaVfgB8A3gfFsKgZbJSLey9nmb4GH08tqoEtO087AO+l5vvj7QDtJLdPZSm57MzMrk3ov1EvaU9I1wByyAnRYRFwUEYu2ZWeSOua8PAWoGRk2ERgmaZd0++IKYAYwE6hII712JruYPzEiAngCOC2tPxx4aFtyMjOz4qnvmspNwKnAKOCQiFi1NRuWdC8wEGgvqZpsDrGBknqRdVW9AXwbICLmSbofeBlYB5wXEevTds4HJgMtgNERMS/t4iJgfCp6L5Dd8tjMzMqovtFfPyablfgy4FJp42UMkV2or3ealog4I0+4zg/+iLgWuDZPfBIwKU/8NfzLfjOzJqW+aypb/Wt7MzPbsblwmJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0biomJlZ0ZSsqEgaLWmRpLk5sT0lTZG0IP3dI8Ul6RZJVZLmSDosZ53hqf0CScNz4odLeimtc4ty7iJmZmblUcozlTHAkFqxi4HHIqICeCy9BjiO7L70FcBI4A7IihDZbYj7kd3l8YqaQpTajMxZr/a+zMyskZWsqETENGBprfBQYGx6PhY4OSc+LjLPAu0kdQQGA1MiYmlELAOmAEPSst0i4pmICGBczrbMzKxMGvuayqci4l2A9HfvFO8ELMxpV51i9cWr88TzkjRS0ixJsxYvXvyx34SZmeXXVC7U57seEtsQzysiRkVE74jo3aFDh21M0czMGtLYReW91HVF+rsoxauBLjntOgPvNBDvnCduZmZl1NhFZSJQM4JrOPBQTvysNAqsP7AidY9NBgZJ2iNdoB8ETE7LVkrqn0Z9nZWzLTMzK5OWpdqwpHuBgUB7SdVko7huAO6XNAJ4Czg9NZ8EHA9UAauBbwJExFJJPwNmpnZXR0TNxf/vkI0wawM8kh5mZlZGJSsqEXFGHYuOydM2gPPq2M5oYHSe+Czg4I+To5mZFVdTuVBvZmbbARcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrGhcVMzMrmpL9+NHqUaz7iUWdc2iamZWFi4oVphiF0EXQbLvn7i8zMysaFxUzMysaFxUzMysaFxUzMysaFxUzMysaFxUzMyuashQVSW9IeklSpaRZKbanpCmSFqS/e6S4JN0iqUrSHEmH5WxneGq/QNLwuvZnZmaNo5xnKp+PiF4R0Tu9vhh4LCIqgMfSa4DjgIr0GAncAVkRIrtFcT+gL3BFTSEyM7PyaErdX0OBsen5WODknPi4yDwLtJPUERgMTImIpRGxDJgCDGnspM3MbJNyFZUAHpU0W9LIFPtURLwLkP7uneKdgIU561anWF3xLUgaKWmWpFmLFy8u4tswM7Nc5Zqm5XMR8Y6kvYEpkv5aT9t884NEPfEtgxGjgFEAvXv39lwhZmYlUpYzlYh4J/1dBDxIdk3kvdStRfq7KDWvBrrkrN4ZeKeeuJmZlUmjFxVJn5S0a81zYBAwF5gI1IzgGg48lJ5PBM5Ko8D6AytS99hkYJCkPdIF+kEpZmZmZVKO7q9PAQ8qm/W2JfD7iPizpJnA/ZJGAG8Bp6f2k4DjgSpgNfBNgIhYKulnwMzU7uqIWNp4b8PMzGpr9KISEa8Bn8kTXwIckycewHl1bGs0MLrYOZqZ2bbx/VRs++Qbodn2opn9W25Kv1MxM7NmzkXFzMyKxt1fZrbtmlnXDNA8c25GXFTMmgp/2Nl2wN1fZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNC4qZmZWNM2+qEgaIukVSVWSLi53PmZmO7JmXVQktQBuA44DegBnSOpR3qzMzHZczbqoAH2Bqoh4LSL+CYwHhpY5JzOzHVZzn/q+E7Aw53U10K92I0kjgZHp5SpJrzRCbnUStAfe//gbKtJU6YXsqhg5N7d8wTk3tCvnXHJNJN+C99/ci0q+o7TFzSQiYhQwqvTpFEbSrIjoXe48tkZzy7m55QvOubE0t5ybW77NvfurGuiS87oz8E6ZcjEz2+E196IyE6iQ1F3SzsAwYGKZczIz22E16+6viFgn6XxgMtACGB0R88qcViGaTFfcVmhuOTe3fME5N5bmlnOzylfh+1mbmVmRNPfuLzMza0JcVMzMrGhcVEpE0qWS5kmaI6lS0ha/n8lp+w1J+zRmfnlyKDjfcpMUku7Oed1S0mJJD5czr/pI2isd10pJf5f0ds7rncudXy5JN0v6Yc7ryZL+O+f1LyT9e4HbulLSBaXIM8++6jrGyyW93Bg5fFyS1ue8h0pJ3fK02UfShMbPrjDN+kJ9UyXpCOBE4LCIWCupPVDfB8c3gLmUaTj0NuRbbh8AB0tqExEfAl8A3i5zTvWKiCVAL8g+aIFVEfHzsiZVt6eB04FfSdqJ7Md3u+Us/yzww3wrllNdxzh9MDfZLxy1fBgRvepaKKllRLwDnNaIOW0Vn6mURkfg/YhYCxAR70fEO5IulzRT0lxJo5Q5DegN/C59M2nThPJ9IxUYJPWWNDU9v1LSaElTJb0m6ftlyPkR4IT0/Azg3poFkvaU9Md01vWspJ5NKO/NSDpAUmXO64slXZaeV6SzhNmSpkn6dCOl9ReywgFwENkXnpWS9pC0C3Ag8IKkC9O/5zmSrsp5D5emSV7/F/iXRsq5IS0k/TadjT9a8/9Z+rfQOz1vL+mNsmaZR+rJ+IOkPwGPSuomaW6586qLi0ppPAp0kfQ3SbdLOirFb42IPhFxMNAGODEiJgCzgK9FRK/0zbup5FuffwUGk82/doWkViXNcEvjgWGSWgM9gedyll0FvBARPYGfAONylpU7760xCvhuRBwOXALc2hg7Td+E10nqSlZcniE7vkeQfQGaAwwEKsiOYy/gcEkDJB1O9nuxQ4FTgT6NkXMBKoDbIuIgYDnwpTLnU5c2OV1fD+bEjwCGR8TR5UqsUO7+KoGIWJX+5/o34PPAfcqm5V8p6T+ATwB7AvOAP5Uv00w9+dbnf9KZzVpJi4BPkc1w0CgiYk7q1jgDmFRr8ZGkD42IeDz1te+elpU170JJagf0Bx7QpjmbGvP/15qzlc8CvySbZ++zwAqy7rFB6fFCat+W7IN7V+DBiFgNIKmp/Bj59YioOSOcDXQrYy71qav7a0pELG30bLaBi0qJRMR6YCowVdJLwLfJvlH3joiFqc+3dfky3FyefIcD69h0Nls717U5z9dTnn9LE4Gfk31r3isnXt+ccE0h71y5xxiy47yO7D28X1//eok9TVZEDiHr/loI/Bj4BzCa7JhfHxG/yV0pXeBvij9+q/3fvaabub5/403JB+VOoFDu/ioBSf8iqSIn1AuomRn5fUlt2fxC20qyb3hlUUe+bwJvAIenWFPsLhgNXB0RL9WKTwO+BiBpINmH8z8aObdC/R3YJ12vaE26ThQRy4B3JZ0CIGknSZ9pxLz+QjZ4Y2lErE/fktuRdcM8QzaLxbfSv2UkdZK0N9mxP0VSG0m7Al9sxJy3xRts+jfeZC9+Nyfl/pa2vWoL/GfqwlgHVJFNvb8ceInsH/LMnPZjgP+S9CFwRBmuq9SV74HAnZJ+wubXLJqEiKgGfp1n0ZXAXZLmAKvJzrqapIhYI+k6sn8PrwG5Q1+HAXeks9qdgXuAFxsptZfIRn39vlasbUS8T3bB+EDgmdQ9twr4ekQ8L+k+oJLsi8lTjZTvtvo5cL+kM4HHy53M9sDTtJiZWdG4+8vMzIrGRcXMzIrGRcXMzIrGRcXMzIrGRcXMzIrGRcXMzIrGRcXMzIrm/wPWyFxaydQ/4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be685c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69bed748d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City: Washington\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69c123c048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69be67b710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_files = ['./data/Chicago-2016-Summary.csv',\n",
    "              './data/NYC-2016-Summary.csv',\n",
    "              './data/Washington-2016-Summary.csv',]\n",
    "for data_file in data_files:\n",
    "    city = data_file.split('-')[0].split('/')[-1]\n",
    "    print('\\nCity: {}'.format(city))\n",
    "    data = days_ridership_dur_by_user_type(data_file)\n",
    "    sub_trips = data[0]\n",
    "    cust_trips = data[1]\n",
    "    sub_dur = data[2]\n",
    "    cust_dur = data[3]\n",
    "    plot_days_of_week_trips(sub_trips, cust_trips, city)\n",
    "    plot_days_of_week_duration(sub_dur, cust_dur, city)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "Congratulations on completing the project! This is only a sampling of the data analysis process: from generating questions, wrangling the data, and to exploring the data. Normally, at this point in the data analysis process, you might want to draw conclusions about the data by performing a statistical test or fitting the data to a model for making predictions. There are also a lot of potential analyses that could be performed on the data which are not possible with only the data provided. For example, detailed location data has not been investigated. Where are the most commonly used docks? What are the most common routes? As another example, weather has potential to have a large impact on daily ridership. How much is ridership impacted when there is rain or snow? Are subscribers or customers affected more by changes in weather?\n",
    "\n",
    "**Question 7**: Putting the bike share data aside, think of a topic or field of interest where you would like to be able to apply the techniques of data science. What would you like to be able to learn from your chosen subject?\n",
    "\n",
    "**Answer**: \n",
    "\n",
    "**I recently conducted a study on the gaps between expectations and perceptions as part of my MBA studies. My analysis was somewhat limited about the services in my library, but it showed conclusively the extent to which data science is capable to measure the clients satisfaction clearly. Data analysis techniques helped me to analyze the questionnaire and abstracting the results.**\n",
    "\n",
    "**I would like to delve into the data analysis to pursue it as a new field. It would be interesting to know where I would most likely find these jobs.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Bike_Share_Analysis.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}