HW1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff1cb5e9",
   "metadata": {},
   "source": [
    "# Foundations of Data Science\n",
    "## (Mini-)Homework 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85619e40",
   "metadata": {},
   "source": [
    "### Description\n",
    "In this homework you are supposed to get comfortable (or make sure that you are!) with the very basic python tools frequently used in data science. All you have to do in this homework is to load several types of datasets into Pandas, extract simple statistics from it, and simply visualize your insights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea676e1",
   "metadata": {},
   "source": [
    "### Name: Kimia Mohsenian\n",
    "### Student ID: 97110671"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1a58625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "858bc87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647c84a6",
   "metadata": {},
   "source": [
    "## Question 1. Another LEGO brick in the wall\n",
    "LEGO is a popular brand of toy building bricks. They are often sold in sets in order to build a specific object. Each set contains a number of parts in different shapes, sizes and colors. This database contains information on which parts are included in different LEGO sets. It was originally compiled to help people who owned some LEGO sets already figure out what other sets they could build with the pieces they had.\n",
    "\n",
    "This dataset contains the official LEGO colors, parts, inventories (i.e., sets of LEGO parts which assembled create an object in the LEGO world) and sets (i.e., sets of LEGO inventories which assembled create a LEGO ecosystem). The schema of the dataset can be shown in the following UML diagram: \n",
    "\n",
    "![lego-schema](lego-schema.png)\n",
    "\n",
    "In this task you have to apply the following Data Wrangling pipeline:\n",
    "* Load your data into `Pandas`\n",
    "* Explore it and clean its dirty parts\n",
    "* Use it to answer a set of queries\n",
    "\n",
    "Each of these subtasks are described in detail below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f67de25",
   "metadata": {},
   "source": [
    "### 1.1. Loading phase\n",
    "Load all the csv files into different `DataFrames`. Use meaningful names for your `DataFrames` (e.g., the respective filenames).\n",
    "\n",
    "*Hint: You can load files without first unzipping them (for `Pandas` version >= 0.18.1).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4cd7971d",
   "metadata": {},
   "outputs": [],
   "source": [
    "LegoPath = DATA_FOLDER + \"/lego/\"\n",
    "# loading csv files\n",
    "colors = pd.read_csv(LegoPath+\"colors.csv.zip\")\n",
    "inventories = pd.read_csv(LegoPath+\"inventories.csv.zip\")\n",
    "inventory_parts = pd.read_csv(LegoPath+\"inventory_parts.csv.zip\")\n",
    "inventory_sets = pd.read_csv(LegoPath+\"inventory_sets.csv.zip\")\n",
    "parts = pd.read_csv(LegoPath+\"parts.csv.zip\")\n",
    "sets = pd.read_csv(LegoPath+\"sets.csv.zip\")\n",
    "themes = pd.read_csv(LegoPath+\"themes.csv.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26cfd7b",
   "metadata": {},
   "source": [
    "### 1.2. Cleaning phase\n",
    "Explore the following columns from your dataset:\n",
    "\n",
    "* sets: year\n",
    "* inventory_parts: quantity\n",
    "\n",
    "What is the time range of the sets? \n",
    "What is the average quantity of the inventory parts? \n",
    "Do you see any inconsistencies? \n",
    "Provide code that detects and cleans such inconsistencies and validates the coherence of your dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0c92d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-1</td>\n",
       "      <td>Weetabix Castle</td>\n",
       "      <td>70s</td>\n",
       "      <td>414</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0011-2</td>\n",
       "      <td>Town Mini-Figures</td>\n",
       "      <td>1978</td>\n",
       "      <td>84</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0011-3</td>\n",
       "      <td>Castle 2 for 1 Bonus Offer</td>\n",
       "      <td>1987</td>\n",
       "      <td>199</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0012-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0013-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0014-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0015-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0016-1</td>\n",
       "      <td>Castle Mini Figures</td>\n",
       "      <td>19788</td>\n",
       "      <td>186</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00-2</td>\n",
       "      <td>Weetabix Promotional House 1</td>\n",
       "      <td>1976</td>\n",
       "      <td>413</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00-3</td>\n",
       "      <td>Weetabix Promotional House 2</td>\n",
       "      <td>1976</td>\n",
       "      <td>413</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00-4</td>\n",
       "      <td>Weetabix Promotional Windmill</td>\n",
       "      <td>1976</td>\n",
       "      <td>413</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           name   year  theme_id  num_parts\n",
       "0     00-1                Weetabix Castle    70s       414        471\n",
       "1   0011-2              Town Mini-Figures   1978        84         12\n",
       "2   0011-3     Castle 2 for 1 Bonus Offer   1987       199          2\n",
       "3   0012-1             Space Mini-Figures   1979       143         12\n",
       "4   0013-1             Space Mini-Figures   1979       143         12\n",
       "5   0014-1             Space Mini-Figures   1979       143         12\n",
       "6   0015-1             Space Mini-Figures   1979       143         18\n",
       "7   0016-1            Castle Mini Figures  19788       186         15\n",
       "8     00-2   Weetabix Promotional House 1   1976       413        147\n",
       "9     00-3   Weetabix Promotional House 2   1976       413        149\n",
       "10    00-4  Weetabix Promotional Windmill   1976       413        126"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7eada14f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80s\n",
      "-1962\n"
     ]
    }
   ],
   "source": [
    "year = sets['year']\n",
    "print(year.max())\n",
    "print(year.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "357f46aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# detecting inconsitent year values and their corresponding indexes\n",
    "bad_indexes = []\n",
    "for i in range(len(year)):\n",
    "    if year[i].isnumeric()==False:\n",
    "        bad_indexes.append(i)\n",
    "    else:\n",
    "        if int(year[i])<0 or int(year[i])>2022:\n",
    "            bad_indexes.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "776fee83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of total columns:  11673\n",
      "Number of inconsistent columns:  3085\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of total columns: \", len(year))\n",
    "print(\"Number of inconsistent columns: \", len(bad_indexes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbf6c1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0011-2</td>\n",
       "      <td>Town Mini-Figures</td>\n",
       "      <td>1978</td>\n",
       "      <td>84</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0011-3</td>\n",
       "      <td>Castle 2 for 1 Bonus Offer</td>\n",
       "      <td>1987</td>\n",
       "      <td>199</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0012-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0013-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0014-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11664</th>\n",
       "      <td>vwkit-1</td>\n",
       "      <td>Volkswagen Kit</td>\n",
       "      <td>1959</td>\n",
       "      <td>366</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11665</th>\n",
       "      <td>W098-1</td>\n",
       "      <td>Watch Set, Classic Blue</td>\n",
       "      <td>2006</td>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11666</th>\n",
       "      <td>W991526-1</td>\n",
       "      <td>Homeschool Introduction to Simple and Motorize...</td>\n",
       "      <td>2009</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11670</th>\n",
       "      <td>Wiesbaden-1</td>\n",
       "      <td>LEGO Store Grand Opening Exclusive Set, Wiesba...</td>\n",
       "      <td>2010</td>\n",
       "      <td>408</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11672</th>\n",
       "      <td>wwgp1-1</td>\n",
       "      <td>Wild West Limited Edition Gift Pack</td>\n",
       "      <td>1996</td>\n",
       "      <td>476</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8588 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                                               name  year  \\\n",
       "1           0011-2                                  Town Mini-Figures  1978   \n",
       "2           0011-3                         Castle 2 for 1 Bonus Offer  1987   \n",
       "3           0012-1                                 Space Mini-Figures  1979   \n",
       "4           0013-1                                 Space Mini-Figures  1979   \n",
       "5           0014-1                                 Space Mini-Figures  1979   \n",
       "...            ...                                                ...   ...   \n",
       "11664      vwkit-1                                     Volkswagen Kit  1959   \n",
       "11665       W098-1                            Watch Set, Classic Blue  2006   \n",
       "11666    W991526-1  Homeschool Introduction to Simple and Motorize...  2009   \n",
       "11670  Wiesbaden-1  LEGO Store Grand Opening Exclusive Set, Wiesba...  2010   \n",
       "11672      wwgp1-1                Wild West Limited Edition Gift Pack  1996   \n",
       "\n",
       "       theme_id  num_parts  \n",
       "1            84         12  \n",
       "2           199          2  \n",
       "3           143         12  \n",
       "4           143         12  \n",
       "5           143         12  \n",
       "...         ...        ...  \n",
       "11664       366         22  \n",
       "11665       501          0  \n",
       "11666       529          0  \n",
       "11670       408        146  \n",
       "11672       476          3  \n",
       "\n",
       "[8588 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping rows with inconsistencies\n",
    "modified_sets = sets.drop(bad_indexes)\n",
    "modified_sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09b47b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The range of year numbers is  1950 to  2017\n"
     ]
    }
   ],
   "source": [
    "# range of years\n",
    "new_years = modified_sets['year']\n",
    "print(\"The range of year numbers is \", np.min(new_years), \"to \", np.max(new_years))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de0f037",
   "metadata": {},
   "source": [
    " **Explaning the method:** \n",
    "I looped over all items of \"year\" and checked if it is numeric; if it wasn't, then I saved its index to `bad_indexes` list. If it was numeric but the value was not reasonable, meaning that the number was less than zero or greater than 2022, I added its index to `bad_index`. Finally, I removed inconsistencies which means dropping rows with bad indexes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ba002d",
   "metadata": {},
   "source": [
    "### 1.3. Querying phase\n",
    "Answer the following queries using the functionality of `Pandas`:\n",
    "\n",
    "* 1. List the ids of the inventories that belong to sets that contain cars. (*Hint: Find a smart way to distinguish which sets contain cars based on the sets' name*).\n",
    "* 2. Plot the distribution of part categories as a (horizontal) bar chart. Restrict yourself to the 20 largest part categories (in terms of the number of parts belonging to the category).\n",
    "* 3. Find the dominant color of each set. Then, plot using a (horizontal) bar chart, the number of sets per dominant color. Color each bar with the respective color that it represents.\n",
    "* 4. Create a scatter plot of the *luminance** of the sets vs their publishing year. Do you see a trend over the last years? How do you interpret it?\n",
    "\n",
    "\\*The luminance of a color is a [measure of brightness](https://en.wikipedia.org/wiki/Luminance) which, given its RGB representation, can be computed as follows:\n",
    "\n",
    "$luminance = \\sqrt{0.299*R^2 + 0.587*G^2 + 0.114*B^2}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f53f58",
   "metadata": {},
   "source": [
    "**Query 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3827e8cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 most frequent ids:  [42, 1, 47, 29, 37, 48, 16, 36, 3, 38, 45, 28, 2, 41, 24, 10, 4, 17, 27, 13]\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "counts of 20 most frequent ids:  [234, 250, 268, 270, 275, 278, 405, 409, 410, 428, 462, 742, 744, 1116, 1125, 1191, 1552, 1964, 2071, 8556]\n"
     ]
    },
    {
     "data": {
      "image/png": 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33HOPpk+fLj8/Py1ZskSbN2+u9dqSNGvWLA0cOFA+Pj7q3r27rrnmGo0ePVp/+MMftH79el1//fUKCgpSUVGRVq1apbi4ON17770KCQnRxIkTlZWVpbZt2+qWW27Rvn379Mgjj6h9+/a64IL/G7v64x//qJdeekmDBw/Wo48+qo4dO+q9997TggULdO+993r08wGnmz9/vq699lpdd911uvfee9WpUycdOXJEX3/9td555x1zbnZdvgcBnAev/kgkgCbp7bffNrp37274+/sbHTp0MGbOnGmuIHG6M1femDNnjpGYmGiEhYWZ7x0xYoSxe/dut/dNmzbNiIyMNC644AJDkvHxxx+b5xs8eHCtPZ1tlY+XX37ZmDBhgtGuXTvDbrcb1113nbF+/foa73/xxReNrl27Gq1atTK6detmvP766zVW+TAMw/jwww+NK664wrDb7YYk85pnrvJxyvPPP29+Vg6Hw/j1r39tbNu2za1m2LBhRlBQUI2eavtMa1NVVWXMmjXL6Ny5s+Hn52eEhYUZd955p1FYWFjr+eq6ykdQUJDxxRdfGElJSUZAQIAREhJi3HvvvcbRo0fdat955x3j8ssvN1q1amX84he/MP70pz8ZH3zwgduzM4wfV/m47LLLar3e7t27jf79+xvBwcGGpBqfe233PG/ePCM2Ntb8bBMSEox33nnHrFm9erWRkJBgBAYGGu3atTNGjhxpbNy4scYKGRUVFcbIkSONdu3aGTabrcZz/Pvf/27Ex8cbQUFBRkBAgHHJJZcYd911l9vXUXV1tTFjxgzjoosuMvz9/Y3u3bsb7777rnH55Zcbt9xyi1vve/bsMVJTU43Q0FDDz8/P6NKli/H4448bVVVVZs2plTwef/zxGvde2yofp/YPHz7c+MUvfmH4+fkZ7dq1MxITE40ZM2aYNXX9HgTgGZthnPb/QgEA0I+rTrzxxhs6evSot1tpsnbt2qVLL71U06dP14MPPujtdgA0IKZ8AABg0ebNm/Xaa68pMTFRrVu31o4dO8xpQyNGjPB2ewAaGIEaAACLgoKCtH79er3wwgs6fPiwHA6HkpKS9Nhjj9W6nB6A5oUpHwAAAIAFLJsHAAAAWECgBgAAACwgUAMAAAAW8EOJdVRdXa39+/crODi4Xn51LgAAABo3wzB05MgRRUZGuv2SpjMRqOto//79ioqK8nYbAAAA+JkVFhbqoosuOutxAnUdnfq1x4WFhTV+HTEAAACan7KyMkVFRZk58GwI1HV0appH69atCdQAAAAtyE9N9+WHEgEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYIGvtxsA0PA6TX2vTnW7Zw5u4E4AAGh+GKEGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABY4NVAffLkST388MOKjo5WQECALr74Yj366KOqrq42awzDUEZGhiIjIxUQEKCkpCRt27bN7TwVFRUaP368wsLCFBQUpJSUFO3bt8+tprS0VGlpaXI4HHI4HEpLS9Phw4d/jtsEAABAM+bVQD1r1iz97W9/U3Z2trZv367Zs2fr8ccf11NPPWXWzJ49W3PnzlV2drby8/PldDrVr18/HTlyxKxJT0/XsmXLtHTpUq1atUpHjx5VcnKyqqqqzJrU1FQVFBQoJydHOTk5KigoUFpa2s96vwAAAGh+bIZhGN66eHJysiIiIvTCCy+Y+377298qMDBQL7/8sgzDUGRkpNLT0/XAAw9I+nE0OiIiQrNmzdKYMWPkcrnUrl07vfzyy7r11lslSfv371dUVJTef/993XTTTdq+fbu6deumvLw8xcfHS5Ly8vKUkJCgL7/8Ul26dPnJXsvKyuRwOORyudS6desG+DSAhtNp6nt1qts9c3ADdwIAQNNR1/zn1RHqa6+9Vh999JG++uorSdLmzZu1atUqDRo0SJK0a9cuFRcXq3///uZ77Ha7evfurdWrV0uSNmzYoBMnTrjVREZGKjY21qxZs2aNHA6HGaYlqVevXnI4HGbNmSoqKlRWVua2AQAAAGfy9ebFH3jgAblcLl166aXy8fFRVVWVHnvsMd1+++2SpOLiYklSRESE2/siIiK0Z88es8bf319t27atUXPq/cXFxQoPD69x/fDwcLPmTFlZWXrkkUes3SAAAACaPa+OUL/++ut65ZVX9Oqrr2rjxo168cUX9de//lUvvviiW53NZnN7bRhGjX1nOrOmtvpznWfatGlyuVzmVlhYWNfbAgAAQAvi1RHqP/3pT5o6dapuu+02SVJcXJz27NmjrKwsDRs2TE6nU9KPI8zt27c331dSUmKOWjudTlVWVqq0tNRtlLqkpESJiYlmzYEDB2pc/+DBgzVGv0+x2+2y2+31c6MAAABotrw6Qn38+HFdcIF7Cz4+PuayedHR0XI6nVqxYoV5vLKyUrm5uWZY7tGjh/z8/NxqioqKtHXrVrMmISFBLpdL69atM2vWrl0rl8tl1gAAAADnw6sj1DfffLMee+wxdejQQZdddpk2bdqkuXPnavjw4ZJ+nKaRnp6uzMxMxcTEKCYmRpmZmQoMDFRqaqokyeFwaMSIEZo0aZJCQ0MVEhKiyZMnKy4uTn379pUkde3aVQMGDNCoUaO0cOFCSdLo0aOVnJxcpxU+AAAAgLPxaqB+6qmn9Oc//1ljx45VSUmJIiMjNWbMGP3P//yPWTNlyhSVl5dr7NixKi0tVXx8vJYvX67g4GCzZt68efL19dXQoUNVXl6uPn36aPHixfLx8TFrlixZogkTJpirgaSkpCg7O/vnu1kAAAA0S15dh7opYR1qNGWsQw0AgOeaxDrUAAAAQFNHoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWeDVQd+rUSTabrcY2btw4SZJhGMrIyFBkZKQCAgKUlJSkbdu2uZ2joqJC48ePV1hYmIKCgpSSkqJ9+/a51ZSWliotLU0Oh0MOh0NpaWk6fPjwz3WbAAAAaMa8Gqjz8/NVVFRkbitWrJAk/f73v5ckzZ49W3PnzlV2drby8/PldDrVr18/HTlyxDxHenq6li1bpqVLl2rVqlU6evSokpOTVVVVZdakpqaqoKBAOTk5ysnJUUFBgdLS0n7emwUAAECzZDMMw/B2E6ekp6fr3Xff1c6dOyVJkZGRSk9P1wMPPCDpx9HoiIgIzZo1S2PGjJHL5VK7du308ssv69Zbb5Uk7d+/X1FRUXr//fd10003afv27erWrZvy8vIUHx8vScrLy1NCQoK+/PJLdenSpU69lZWVyeFwyOVyqXXr1g1w90DD6TT1vTrV7Z45uIE7AQCg6ahr/ms0c6grKyv1yiuvaPjw4bLZbNq1a5eKi4vVv39/s8Zut6t3795avXq1JGnDhg06ceKEW01kZKRiY2PNmjVr1sjhcJhhWpJ69eolh8Nh1tSmoqJCZWVlbhsAAABwpkYTqN966y0dPnxYd999tySpuLhYkhQREeFWFxERYR4rLi6Wv7+/2rZte86a8PDwGtcLDw83a2qTlZVlzrl2OByKioo673sDAABA89VoAvULL7yggQMHKjIy0m2/zWZze20YRo19Zzqzprb6nzrPtGnT5HK5zK2wsLAutwEAAIAWplEE6j179ujDDz/UyJEjzX1Op1OSaowil5SUmKPWTqdTlZWVKi0tPWfNgQMHalzz4MGDNUa/T2e329W6dWu3DQAAADhTowjUixYtUnh4uAYP/r8fiIqOjpbT6TRX/pB+nGedm5urxMRESVKPHj3k5+fnVlNUVKStW7eaNQkJCXK5XFq3bp1Zs3btWrlcLrMGAAAAOF++3m6gurpaixYt0rBhw+Tr+3/t2Gw2paenKzMzUzExMYqJiVFmZqYCAwOVmpoqSXI4HBoxYoQmTZqk0NBQhYSEaPLkyYqLi1Pfvn0lSV27dtWAAQM0atQoLVy4UJI0evRoJScn13mFDwAAAOBsvB6oP/zwQ+3du1fDhw+vcWzKlCkqLy/X2LFjVVpaqvj4eC1fvlzBwcFmzbx58+Tr66uhQ4eqvLxcffr00eLFi+Xj42PWLFmyRBMmTDBXA0lJSVF2dnbD3xwAAACavUa1DnVjxjrUaMpYhxoAAM81uXWoAQAAgKaIQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFjg9UD97bff6s4771RoaKgCAwP1q1/9Shs2bDCPG4ahjIwMRUZGKiAgQElJSdq2bZvbOSoqKjR+/HiFhYUpKChIKSkp2rdvn1tNaWmp0tLS5HA45HA4lJaWpsOHD/8ctwgAAIBmzKuBurS0VNdcc438/Pz0wQcf6D//+Y/mzJmjNm3amDWzZ8/W3LlzlZ2drfz8fDmdTvXr109Hjhwxa9LT07Vs2TItXbpUq1at0tGjR5WcnKyqqiqzJjU1VQUFBcrJyVFOTo4KCgqUlpb2c94uAAAAmiGbYRiGty4+depUff755/rss89qPW4YhiIjI5Wenq4HHnhA0o+j0REREZo1a5bGjBkjl8uldu3a6eWXX9att94qSdq/f7+ioqL0/vvv66abbtL27dvVrVs35eXlKT4+XpKUl5enhIQEffnll+rSpctP9lpWViaHwyGXy6XWrVvX0ycA/Dw6TX2vTnW7Zw5u4E4AAGg66pr/vDpC/fbbb6tnz576/e9/r/DwcF1xxRV67rnnzOO7du1ScXGx+vfvb+6z2+3q3bu3Vq9eLUnasGGDTpw44VYTGRmp2NhYs2bNmjVyOBxmmJakXr16yeFwmDVnqqioUFlZmdsGAAAAnMmrgfqbb77RM888o5iYGP373//WPffcowkTJuill16SJBUXF0uSIiIi3N4XERFhHisuLpa/v7/atm17zprw8PAa1w8PDzdrzpSVlWXOt3Y4HIqKirJ2swAAAGiWvBqoq6urdeWVVyozM1NXXHGFxowZo1GjRumZZ55xq7PZbG6vDcOose9MZ9bUVn+u80ybNk0ul8vcCgsL63pbAAAAaEG8Gqjbt2+vbt26ue3r2rWr9u7dK0lyOp2SVGMUuaSkxBy1djqdqqysVGlp6TlrDhw4UOP6Bw8erDH6fYrdblfr1q3dNgAAAOBMXg3U11xzjXbs2OG276uvvlLHjh0lSdHR0XI6nVqxYoV5vLKyUrm5uUpMTJQk9ejRQ35+fm41RUVF2rp1q1mTkJAgl8uldevWmTVr166Vy+UyawAAAIDz4evNi//xj39UYmKiMjMzNXToUK1bt07PPvusnn32WUk/TtNIT09XZmamYmJiFBMTo8zMTAUGBio1NVWS5HA4NGLECE2aNEmhoaEKCQnR5MmTFRcXp759+0r6cdR7wIABGjVqlBYuXChJGj16tJKTk+u0wgcAAABwNl4N1FdddZWWLVumadOm6dFHH1V0dLSeeOIJ3XHHHWbNlClTVF5errFjx6q0tFTx8fFavny5goODzZp58+bJ19dXQ4cOVXl5ufr06aPFixfLx8fHrFmyZIkmTJhgrgaSkpKi7Ozsn+9mAQAA0Cx5dR3qpoR1qNGUsQ41AACeaxLrUAMAAABNHYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALLAcqMvKyvTWW29p+/bt9dEPAAAA0KR4HKiHDh2q7OxsSVJ5ebl69uypoUOHqnv37vrnP/9Z7w0CAAAAjZnHgfrTTz/VddddJ0latmyZDMPQ4cOH9eSTT2rGjBn13iAAAADQmHkcqF0ul0JCQiRJOTk5+u1vf6vAwEANHjxYO3furPcGAQAAgMbM40AdFRWlNWvW6NixY8rJyVH//v0lSaWlpWrVqlW9NwgAAAA0Zr6eviE9PV133HGHLrzwQnXo0EFJSUmSfpwKEhcXV9/9AQAAAI2ax4F67Nixuvrqq1VYWKh+/frpggt+HOS++OKLmUMNAACAFue8ls3r2bOnBg8erG+//VYnT56UJA0ePFjXXHONR+fJyMiQzWZz25xOp3ncMAxlZGQoMjJSAQEBSkpK0rZt29zOUVFRofHjxyssLExBQUFKSUnRvn373GpKS0uVlpYmh8Mhh8OhtLQ0HT58+HxuHQAAAHDjcaA+fvy4RowYocDAQF122WXau3evJGnChAmaOXOmxw1cdtllKioqMrctW7aYx2bPnq25c+cqOztb+fn5cjqd6tevn44cOWLWpKena9myZVq6dKlWrVqlo0ePKjk5WVVVVWZNamqqCgoKlJOTo5ycHBUUFCgtLc3jXgEAAIAzeRyop02bps2bN+uTTz5x+yHEvn376vXXX/e4AV9fXzmdTnNr166dpB9Hp5944gk99NBDGjJkiGJjY/Xiiy/q+PHjevXVVyX9uOLICy+8oDlz5qhv37664oor9Morr2jLli368MMPJUnbt29XTk6Onn/+eSUkJCghIUHPPfec3n33Xe3YscPjfgEAAIDTeRyo33rrLWVnZ+vaa6+VzWYz93fr1k3//e9/PW5g586dioyMVHR0tG677TZ98803kqRdu3apuLjYXEVEkux2u3r37q3Vq1dLkjZs2KATJ0641URGRio2NtasWbNmjRwOh+Lj482aXr16yeFwmDW1qaioUFlZmdsGAAAAnMnjQH3w4EGFh4fX2H/s2DG3gF0X8fHxeumll/Tvf/9bzz33nIqLi5WYmKhDhw6puLhYkhQREeH2noiICPNYcXGx/P391bZt23PW1NZveHi4WVObrKwsc861w+FQVFSUR/cGAACAlsHjQH3VVVfpvffeM1+fCtHPPfecEhISPDrXwIED9dvf/lZxcXHq27eved4XX3yxxvlPMQzjJ4P7mTW11f/UeaZNmyaXy2VuhYWFdbonAAAAtCweL5uXlZWlAQMG6D//+Y9Onjyp+fPna9u2bVqzZo1yc3MtNRMUFKS4uDjt3LlTv/nNbyT9OMLcvn17s6akpMQctXY6naqsrFRpaanbKHVJSYkSExPNmgMHDtS41sGDB2uMfp/ObrfLbrdbuh8AAAA0fx6PUCcmJurzzz/X8ePHdckll2j58uWKiIjQmjVr1KNHD0vNVFRUaPv27Wrfvr2io6PldDq1YsUK83hlZaVyc3PNsNyjRw/5+fm51RQVFWnr1q1mTUJCglwul9atW2fWrF27Vi6Xy6wBAAAAzpfHI9SSFBcX5zYt43xNnjxZN998szp06KCSkhLNmDFDZWVlGjZsmGw2m9LT05WZmamYmBjFxMQoMzNTgYGBSk1NlSQ5HA6NGDFCkyZNUmhoqEJCQjR58mRzCokkde3aVQMGDNCoUaO0cOFCSdLo0aOVnJysLl26WL4HAAAAtGx1CtRlZWVq3bq1+d/ncqquLvbt26fbb79d3333ndq1a6devXopLy9PHTt2lCRNmTJF5eXlGjt2rEpLSxUfH6/ly5crODjYPMe8efPk6+uroUOHqry8XH369NHixYvl4+Nj1ixZskQTJkwwVwNJSUlRdnZ2nfsEAAAAzsZmGIbxU0U+Pj4qKipSeHi4LrjggnP+kN/pv1ClOSkrK5PD4ZDL5fLoHw1AY9Bp6ns/XSRp98zBDdwJAABNR13zX51GqFeuXKmQkBBJ0scff1w/HQIAAADNQJ0Cde/evWv9bwAAAKCl83iVj0WLFukf//hHjf3/+Mc/6uUHFQEAAICmxONAPXPmTIWFhdXYHx4erszMzHppCgAAAGgqPA7Ue/bsUXR0dI39HTt21N69e+ulKQAAAKCp8DhQh4eH64svvqixf/PmzQoNDa2XpgAAAICmwuNAfdttt2nChAn6+OOPVVVVpaqqKq1cuVL333+/brvttoboEQAAAGi0PP5NiTNmzNCePXvUp08f+fr++Pbq6mrdddddzKEGAABAi+NxoPb399frr7+uv/zlL9q8ebMCAgIUFxdn/nZDAAAAoCXxOFCf0rlzZ3Xu3Lk+ewEAAACaHI8DdVVVlRYvXqyPPvpIJSUlqq6udju+cuXKemsOAAAAaOw8DtT333+/Fi9erMGDBys2NlY2m60h+gIAAACaBI8D9dKlS/W///u/GjRoUEP0AwAAADQpHi+b5+/vr1/+8pcN0QsAAADQ5HgcqCdNmqT58+fLMIyG6AcAAABoUjye8rFq1Sp9/PHH+uCDD3TZZZfJz8/P7fibb75Zb80BAAAAjZ3HgbpNmza65ZZbGqIXAAAAoMnxOFAvWrSoIfoAAAAAmiSP51BL0smTJ/Xhhx9q4cKFOnLkiCRp//79Onr0aL02BwAAADR2Ho9Q79mzRwMGDNDevXtVUVGhfv36KTg4WLNnz9YPP/ygv/3tbw3RJwAAANAoeTxCff/996tnz54qLS1VQECAuf+WW27RRx99VK/NAQAAAI3dea3y8fnnn8vf399tf8eOHfXtt9/WW2MAAABAU+DxCHV1dbWqqqpq7N+3b5+Cg4PrpSkAAACgqfA4UPfr109PPPGE+dpms+no0aOaPn06v44cAAAALY7HUz7mzZunG264Qd26ddMPP/yg1NRU7dy5U2FhYXrttdcaokcAAACg0fI4UEdGRqqgoECvvfaaNm7cqOrqao0YMUJ33HGH2w8pAgAAAC2Bx4FakgICAjR8+HANHz68vvsBAAAAmhSPA/VLL710zuN33XXXeTcDAAAANDUeB+r777/f7fWJEyd0/Phx+fv7KzAwkEANAACAFsXjVT5KS0vdtqNHj2rHjh269tpr+aFEAAAAtDgeB+raxMTEaObMmTVGrwEAAIDmrl4CtST5+Pho//799XU6AAAAoEnweA7122+/7fbaMAwVFRUpOztb11xzTb01BgAAADQFHgfq3/zmN26vbTab2rVrpxtvvFFz5sypr74AAACAJsHjQF1dXd0QfQAAAABNUr3NoQYAAABaIo9HqCdOnFjn2rlz53p6egAAAKBJ8ThQb9q0SRs3btTJkyfVpUsXSdJXX30lHx8fXXnllWadzWarvy4BAACARsrjQH3zzTcrODhYL774otq2bSvpx1/28oc//EHXXXedJk2aVO9NAgAAAI2Vx3Oo58yZo6ysLDNMS1Lbtm01Y8YMVvkAAABAi+NxoC4rK9OBAwdq7C8pKdGRI0fqpSkAAACgqfA4UN9yyy36wx/+oDfeeEP79u3Tvn379MYbb2jEiBEaMmRIQ/QIAAAANFoeB+q//e1vGjx4sO6880517NhRHTt21B133KGBAwdqwYIF591IVlaWbDab0tPTzX2GYSgjI0ORkZEKCAhQUlKStm3b5va+iooKjR8/XmFhYQoKClJKSor27dvnVlNaWqq0tDQ5HA45HA6lpaXp8OHD590rAAAAcIrHgTowMFALFizQoUOHzBU/vv/+ey1YsEBBQUHn1UR+fr6effZZde/e3W3/7NmzNXfuXGVnZys/P19Op1P9+vVzm1qSnp6uZcuWaenSpVq1apWOHj2q5ORkVVVVmTWpqakqKChQTk6OcnJyVFBQoLS0tPPqFQAAADjdef9il6KiIhUVFalz584KCgqSYRjndZ6jR4/qjjvu0HPPPef2g46GYeiJJ57QQw89pCFDhig2NlYvvviijh8/rldffVWS5HK59MILL2jOnDnq27evrrjiCr3yyivasmWLPvzwQ0nS9u3blZOTo+eff14JCQlKSEjQc889p3fffVc7duw439sHAAAAJJ1HoD506JD69Omjzp07a9CgQSoqKpIkjRw58ryWzBs3bpwGDx6svn37uu3ftWuXiouL1b9/f3Of3W5X7969tXr1aknShg0bdOLECbeayMhIxcbGmjVr1qyRw+FQfHy8WdOrVy85HA6zpjYVFRUqKytz2wAAAIAzeRyo//jHP8rPz0979+5VYGCguf/WW29VTk6OR+daunSpNm7cqKysrBrHiouLJUkRERFu+yMiIsxjxcXF8vf3dxvZrq0mPDy8xvnDw8PNmtpkZWWZc64dDoeioqI8ujcAAAC0DB4H6uXLl2vWrFm66KKL3PbHxMRoz549dT5PYWGh7r//fr3yyitq1arVWevO/I2LhmH85G9hPLOmtvqfOs+0adPkcrnMrbCw8JzXBAAAQMvkcaA+duyY28j0Kd99953sdnudz7NhwwaVlJSoR48e8vX1la+vr3Jzc/Xkk0/K19fXHJk+cxS5pKTEPOZ0OlVZWanS0tJz1tS2bvbBgwdrjH6fzm63q3Xr1m4bAAAAcCaPA/X111+vl156yXxts9lUXV2txx9/XDfccEOdz9OnTx9t2bJFBQUF5tazZ0/dcccdKigo0MUXXyyn06kVK1aY76msrFRubq4SExMlST169JCfn59bTVFRkbZu3WrWJCQkyOVyad26dWbN2rVr5XK5zBoAAADgfPl6+obHH39cSUlJWr9+vSorKzVlyhRt27ZN33//vT7//PM6nyc4OFixsbFu+4KCghQaGmruT09PV2ZmpmJiYhQTE6PMzEwFBgYqNTVVkuRwODRixAhNmjRJoaGhCgkJ0eTJkxUXF2f+kGPXrl01YMAAjRo1SgsXLpQkjR49WsnJyerSpYuntw8AAAC48ThQd+vWTV988YWeeeYZ+fj46NixYxoyZIjGjRun9u3b12tzU6ZMUXl5ucaOHavS0lLFx8dr+fLlCg4ONmvmzZsnX19fDR06VOXl5erTp48WL14sHx8fs2bJkiWaMGGCuRpISkqKsrOz67VXAAAAtEw2w4MFpE8tUbdw4UJ17ty5IftqdMrKyuRwOORyuZhPjSan09T36lS3e+bgBu4EAICmo675z6M51H5+ftq6detPrrIBAAAAtBQe/1DiXXfdpRdeeKEhegEAAACaHI/nUFdWVur555/XihUr1LNnTwUFBbkdnzt3br01BwAAADR2dQrUX3zxhWJjY3XBBRdo69atuvLKKyVJX331lVsdU0EAAADQ0tQpUF9xxRUqKipSeHi49uzZo/z8fIWGhjZ0bwAAAECjV6c51G3atNGuXbskSbt371Z1dXWDNgUAAAA0FXUaof7tb3+r3r17q3379rLZbOrZs6fbOs+n++abb+q1QQAAAKAxq1OgfvbZZzVkyBB9/fXXmjBhgkaNGuX2y1UAAACAlqrOq3wMGDBAkrRhwwbdf//9BGoAAABA57Fs3qJFixqiDwAAAKBJ8vgXuwAAAAD4PwRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAVeDdTPPPOMunfvrtatW6t169ZKSEjQBx98YB43DEMZGRmKjIxUQECAkpKStG3bNrdzVFRUaPz48QoLC1NQUJBSUlK0b98+t5rS0lKlpaXJ4XDI4XAoLS1Nhw8f/jluEQAAAM2cVwP1RRddpJkzZ2r9+vVav369brzxRv361782Q/Ps2bM1d+5cZWdnKz8/X06nU/369dORI0fMc6Snp2vZsmVaunSpVq1apaNHjyo5OVlVVVVmTWpqqgoKCpSTk6OcnBwVFBQoLS3tZ79fAAAAND82wzAMbzdxupCQED3++OMaPny4IiMjlZ6ergceeEDSj6PRERERmjVrlsaMGSOXy6V27drp5Zdf1q233ipJ2r9/v6KiovT+++/rpptu0vbt29WtWzfl5eUpPj5ekpSXl6eEhAR9+eWX6tKlS619VFRUqKKiwnxdVlamqKgouVwutW7duoE/BaB+dZr6Xp3qds8c3MCdAADQdJSVlcnhcPxk/ms0c6irqqq0dOlSHTt2TAkJCdq1a5eKi4vVv39/s8Zut6t3795avXq1JGnDhg06ceKEW01kZKRiY2PNmjVr1sjhcJhhWpJ69eolh8Nh1tQmKyvLnCLicDgUFRVV37cMAACAZsDrgXrLli268MILZbfbdc8992jZsmXq1q2biouLJUkRERFu9REREeax4uJi+fv7q23btuesCQ8Pr3Hd8PBws6Y206ZNk8vlMrfCwkJL9wkAAIDmydfbDXTp0kUFBQU6fPiw/vnPf2rYsGHKzc01j9tsNrd6wzBq7DvTmTW11f/Ueex2u+x2e11vAwAAAC2U10eo/f399ctf/lI9e/ZUVlaWLr/8cs2fP19Op1OSaowil5SUmKPWTqdTlZWVKi0tPWfNgQMHalz34MGDNUa/AQAAAE95PVCfyTAMVVRUKDo6Wk6nUytWrDCPVVZWKjc3V4mJiZKkHj16yM/Pz62mqKhIW7duNWsSEhLkcrm0bt06s2bt2rVyuVxmDQAAAHC+vDrl48EHH9TAgQMVFRWlI0eOaOnSpfrkk0+Uk5Mjm82m9PR0ZWZmKiYmRjExMcrMzFRgYKBSU1MlSQ6HQyNGjNCkSZMUGhqqkJAQTZ48WXFxcerbt68kqWvXrhowYIBGjRqlhQsXSpJGjx6t5OTks67wAQAAANSVVwP1gQMHlJaWpqKiIjkcDnXv3l05OTnq16+fJGnKlCkqLy/X2LFjVVpaqvj4eC1fvlzBwcHmOebNmydfX18NHTpU5eXl6tOnjxYvXiwfHx+zZsmSJZowYYK5GkhKSoqys7N/3psFAABAs9To1qFurOq6DiHQGLEONQAAnmty61ADAAAATRGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsMCrgTorK0tXXXWVgoODFR4ert/85jfasWOHW41hGMrIyFBkZKQCAgKUlJSkbdu2udVUVFRo/PjxCgsLU1BQkFJSUrRv3z63mtLSUqWlpcnhcMjhcCgtLU2HDx9u6FsEANRRp6nv1WkDgMbGq4E6NzdX48aNU15enlasWKGTJ0+qf//+OnbsmFkze/ZszZ07V9nZ2crPz5fT6VS/fv105MgRsyY9PV3Lli3T0qVLtWrVKh09elTJycmqqqoya1JTU1VQUKCcnBzl5OSooKBAaWlpP+v9AgAAoPmxGYZheLuJUw4ePKjw8HDl5ubq+uuvl2EYioyMVHp6uh544AFJP45GR0REaNasWRozZoxcLpfatWunl19+Wbfeeqskaf/+/YqKitL777+vm266Sdu3b1e3bt2Ul5en+Ph4SVJeXp4SEhL05ZdfqkuXLj/ZW1lZmRwOh1wul1q3bt1wHwLQAOo6qrd75uAG7gQ4O75OATQ2dc1/jWoOtcvlkiSFhIRIknbt2qXi4mL179/frLHb7erdu7dWr14tSdqwYYNOnDjhVhMZGanY2FizZs2aNXI4HGaYlqRevXrJ4XCYNWeqqKhQWVmZ2wYAAACcqdEEasMwNHHiRF177bWKjY2VJBUXF0uSIiIi3GojIiLMY8XFxfL391fbtm3PWRMeHl7jmuHh4WbNmbKyssz51g6HQ1FRUdZuEAAAAM1SownU9913n7744gu99tprNY7ZbDa314Zh1Nh3pjNraqs/13mmTZsml8tlboWFhXW5DQAAALQwvt5uQJLGjx+vt99+W59++qkuuugic7/T6ZT04whz+/btzf0lJSXmqLXT6VRlZaVKS0vdRqlLSkqUmJho1hw4cKDGdQ8ePFhj9PsUu90uu91u/eYA1Iu6zK9lbi0AwBu8OkJtGIbuu+8+vfnmm1q5cqWio6PdjkdHR8vpdGrFihXmvsrKSuXm5pphuUePHvLz83OrKSoq0tatW82ahIQEuVwurVu3zqxZu3atXC6XWQMAAACcD6+OUI8bN06vvvqq/vWvfyk4ONicz+xwOBQQECCbzab09HRlZmYqJiZGMTExyszMVGBgoFJTU83aESNGaNKkSQoNDVVISIgmT56suLg49e3bV5LUtWtXDRgwQKNGjdLChQslSaNHj1ZycnKdVvgAAAAAzsargfqZZ56RJCUlJbntX7Roke6++25J0pQpU1ReXq6xY8eqtLRU8fHxWr58uYKDg836efPmydfXV0OHDlV5ebn69OmjxYsXy8fHx6xZsmSJJkyYYK4GkpKSouzs7Ia9QQAAADR7jWod6saMdajRlDWH9X2ZQ938NYevUwDNS5NchxoAAABoagjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALfL3dAJqWTlPfq1Pd7pmDG7gTAACAxoERagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAEAAAALCNQAAACABQRqAAAAwAICNQAAAGCBr7cbAJqbTlPfq1Pd7pmDG7gTAADwc2CEGgAAALCAQA0AAABYQKAGAAAALGAONQC0UMz3B85PXb53+L5pWRihBgAAACzwaqD+9NNPdfPNNysyMlI2m01vvfWW23HDMJSRkaHIyEgFBAQoKSlJ27Ztc6upqKjQ+PHjFRYWpqCgIKWkpGjfvn1uNaWlpUpLS5PD4ZDD4VBaWpoOHz7cwHcHAACAlsCrgfrYsWO6/PLLlZ2dXevx2bNna+7cucrOzlZ+fr6cTqf69eunI0eOmDXp6elatmyZli5dqlWrVuno0aNKTk5WVVWVWZOamqqCggLl5OQoJydHBQUFSktLa/D7AwAAQPPn1TnUAwcO1MCBA2s9ZhiGnnjiCT300EMaMmSIJOnFF19URESEXn31VY0ZM0Yul0svvPCCXn75ZfXt21eS9MorrygqKkoffvihbrrpJm3fvl05OTnKy8tTfHy8JOm5555TQkKCduzYoS5duvw8NwsAAIBmqdHOod61a5eKi4vVv39/c5/dblfv3r21evVqSdKGDRt04sQJt5rIyEjFxsaaNWvWrJHD4TDDtCT16tVLDofDrKlNRUWFysrK3DYAAADgTI02UBcXF0uSIiIi3PZHRESYx4qLi+Xv76+2bduesyY8PLzG+cPDw82a2mRlZZlzrh0Oh6KioizdDwAAAJqnRhuoT7HZbG6vDcOose9MZ9bUVv9T55k2bZpcLpe5FRYWetg5AAAAWoJGG6idTqck1RhFLikpMUetnU6nKisrVVpaes6aAwcO1Dj/wYMHa4x+n85ut6t169ZuGwAAAHCmRhuoo6Oj5XQ6tWLFCnNfZWWlcnNzlZiYKEnq0aOH/Pz83GqKioq0detWsyYhIUEul0vr1q0za9auXSuXy2XWAAAAAOfLq6t8HD16VF9//bX5eteuXSooKFBISIg6dOig9PR0ZWZmKiYmRjExMcrMzFRgYKBSU1MlSQ6HQyNGjNCkSZMUGhqqkJAQTZ48WXFxceaqH127dtWAAQM0atQoLVy4UJI0evRoJScns8IHAAAALPNqoF6/fr1uuOEG8/XEiRMlScOGDdPixYs1ZcoUlZeXa+zYsSotLVV8fLyWL1+u4OBg8z3z5s2Tr6+vhg4dqvLycvXp00eLFy+Wj4+PWbNkyRJNmDDBXA0kJSXlrGtfAwAAAJ7waqBOSkqSYRhnPW6z2ZSRkaGMjIyz1rRq1UpPPfWUnnrqqbPWhISE6JVXXrHSKgAAAFCrRjuHGgAAAGgKCNQAAACABQRqAAAAwAICNQAAAGABgRoAAACwgEANAAAAWECgBgAAACzw6jrUqH+dpr5Xp7rdMwc3cCcA0PDq8mcef94BaGiMUAMAAAAWEKgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALDA19sNAAAA4OfTaep7darbPXNwA3fSfDBCDQAAAFjACDUAAGi0GE1FU0CgbuH4gwrewNcd0Hzw/Qww5QMAAACwhEANAAAAWECgBgAAACwgUAMAAAAWEKgBAAAACwjUAAAAgAUsmwcAaDHqssQby7s1LJbZaxk8fc5N/euCQN3INfUvMAAAgOaOQA0AANCAGBxr/phDDQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCgRQXqBQsWKDo6Wq1atVKPHj302WefebslAAAANHEtJlC//vrrSk9P10MPPaRNmzbpuuuu08CBA7V3715vtwYAAIAmrMUE6rlz52rEiBEaOXKkunbtqieeeEJRUVF65plnvN0aAAAAmjBfbzfwc6isrNSGDRs0depUt/39+/fX6tWra31PRUWFKioqzNcul0uSVFZW1nCN1qK64nid6k711djqW6LG+Bk1tp7Op5+6vKclf92dj5b4dcHXUf1riX/vNMTX3ZnvaUg/Rz/N4Tmffj3DMM5daLQA3377rSHJ+Pzzz932P/bYY0bnzp1rfc/06dMNSWxsbGxsbGxsbC18KywsPGfWbBEj1KfYbDa314Zh1Nh3yrRp0zRx4kTzdXV1tb7//nuFhoae9T3no6ysTFFRUSosLFTr1q3r7bxoPHjGLQPPuWXgOTd/POOWoa7P2TAMHTlyRJGRkec8X4sI1GFhYfLx8VFxcbHb/pKSEkVERNT6HrvdLrvd7ravTZs2DdWiWrduzTduM8czbhl4zi0Dz7n54xm3DHV5zg6H4yfP0yJ+KNHf3189evTQihUr3PavWLFCiYmJXuoKAAAAzUGLGKGWpIkTJyotLU09e/ZUQkKCnn32We3du1f33HOPt1sDAABAE9ZiAvWtt96qQ4cO6dFHH1VRUZFiY2P1/vvvq2PHjl7ty263a/r06TWml6D54Bm3DDznloHn3PzxjFuG+n7ONsP4qXVAAAAAAJxNi5hDDQAAADQUAjUAAABgAYEaAAAAsIBADQAAAFhAoPaiBQsWKDo6Wq1atVKPHj302WefebslWPDpp5/q5ptvVmRkpGw2m9566y2344ZhKCMjQ5GRkQoICFBSUpK2bdvmnWZxXrKysnTVVVcpODhY4eHh+s1vfqMdO3a41fCcm75nnnlG3bt3N3/hQ0JCgj744APzOM+4+cnKypLNZlN6erq5j+fc9GVkZMhms7ltTqfTPF6fz5hA7SWvv/660tPT9dBDD2nTpk267rrrNHDgQO3du9fbreE8HTt2TJdffrmys7NrPT579mzNnTtX2dnZys/Pl9PpVL9+/XTkyJGfuVOcr9zcXI0bN055eXlasWKFTp48qf79++vYsWNmDc+56bvooos0c+ZMrV+/XuvXr9eNN96oX//61+ZftDzj5iU/P1/PPvusunfv7raf59w8XHbZZSoqKjK3LVu2mMfq9Rkb8Iqrr77auOeee9z2XXrppcbUqVO91BHqkyRj2bJl5uvq6mrD6XQaM2fONPf98MMPhsPhMP72t795oUPUh5KSEkOSkZubaxgGz7k5a9u2rfH888/zjJuZI0eOGDExMcaKFSuM3r17G/fff79hGHwvNxfTp083Lr/88lqP1fczZoTaCyorK7Vhwwb179/fbX///v21evVqL3WFhrRr1y4VFxe7PXO73a7evXvzzJswl8slSQoJCZHEc26OqqqqtHTpUh07dkwJCQk842Zm3LhxGjx4sPr27eu2n+fcfOzcuVORkZGKjo7Wbbfdpm+++UZS/T/jFvObEhuT7777TlVVVYqIiHDbHxERoeLiYi91hYZ06rnW9sz37NnjjZZgkWEYmjhxoq699lrFxsZK4jk3J1u2bFFCQoJ++OEHXXjhhVq2bJm6detm/kXLM276li5dqo0bNyo/P7/GMb6Xm4f4+Hi99NJL6ty5sw4cOKAZM2YoMTFR27Ztq/dnTKD2IpvN5vbaMIwa+9C88Mybj/vuu09ffPGFVq1aVeMYz7np69KliwoKCnT48GH985//1LBhw5Sbm2se5xk3bYWFhbr//vu1fPlytWrV6qx1POembeDAgeZ/x8XFKSEhQZdccolefPFF9erVS1L9PWOmfHhBWFiYfHx8aoxGl5SU1PiXEpqHUz9VzDNvHsaPH6+3335bH3/8sS666CJzP8+5+fD399cvf/lL9ezZU1lZWbr88ss1f/58nnEzsWHDBpWUlKhHjx7y9fWVr6+vcnNz9eSTT8rX19d8ljzn5iUoKEhxcXHauXNnvX8vE6i9wN/fXz169NCKFSvc9q9YsUKJiYle6goNKTo6Wk6n0+2ZV1ZWKjc3l2fehBiGofvuu09vvvmmVq5cqejoaLfjPOfmyzAMVVRU8IybiT59+mjLli0qKCgwt549e+qOO+5QQUGBLr74Yp5zM1RRUaHt27erffv29f69zJQPL5k4caLS0tLUs2dPJSQk6Nlnn9XevXt1zz33eLs1nKejR4/q66+/Nl/v2rVLBQUFCgkJUYcOHZSenq7MzEzFxMQoJiZGmZmZCgwMVGpqqhe7hifGjRunV199Vf/6178UHBxsjmw4HA4FBASY69jynJu2Bx98UAMHDlRUVJSOHDmipUuX6pNPPlFOTg7PuJkIDg42f/bhlKCgIIWGhpr7ec5N3+TJk3XzzTerQ4cOKikp0YwZM1RWVqZhw4bV//eyx+uCoN48/fTTRseOHQ1/f3/jyiuvNJfeQtP08ccfG5JqbMOGDTMM48cleqZPn244nU7Dbrcb119/vbFlyxbvNg2P1PZ8JRmLFi0ya3jOTd/w4cPNP5vbtWtn9OnTx1i+fLl5nGfcPJ2+bJ5h8Jybg1tvvdVo37694efnZ0RGRhpDhgwxtm3bZh6vz2dsMwzDqKd/CAAAAAAtDnOoAQAAAAsI1AAAAIAFBGoAAADAAgI1AAAAYAGBGgAAALCAQA0AAABYQKAGAAAALCBQAwAAABYQqAGghUtKSlJ6evpZj3fq1ElPPPHEz9YPADQ1vt5uAADgXW+++ab8/Py83QYANFkEagBo4UJCQrzdAgA0aUz5AIAW7vQpHyUlJbr55psVEBCg6OhoLVmypEZ9RkaGOnToILvdrsjISE2YMOFn7hgAGhdGqAEAprvvvluFhYVauXKl/P39NWHCBJWUlJjH33jjDc2bN09Lly7VZZddpuLiYm3evNmLHQOA9xGoAQCSpK+++koffPCB8vLyFB8fL0l64YUX1LVrV7Nm7969cjqd6tu3r/z8/NShQwddffXV3moZABoFpnwAACRJ27dvl6+vr3r27Gnuu/TSS9WmTRvz9e9//3uVl5fr4osv1qhRo7Rs2TKdPHnSC90CQONBoAYASJIMw5Ak2Wy2s9ZERUVpx44devrppxUQEKCxY8fq+uuv14kTJ36uNgGg0SFQAwAkSV27dtXJkye1fv16c9+OHTt0+PBht7qAgAClpKToySef1CeffKI1a9Zoy5YtP3O3ANB4MIcaACBJ6tKliwYMGKBRo0bp2Wefla+vr9LT0xUQEGDWLF68WFVVVYqPj1dgYKBefvllBQQEqGPHjl7sHAC8ixFqAIBp0aJFioqKUu/evTVkyBCNHj1a4eHh5vE2bdroueee0zXXXKPu3bvro48+0jvvvKPQ0FAvdg0A3mUzTk2aAwAAAOAxRqgBAAAACwjUAAAAgAUEagAAAMACAjUAAABgAYEaAAAAsIBADQAAAFhAoAYAAAAsIFADAAAAFhCoAQAAAAsI1AAAAIAFBGoAAADAgv8HRftYn1rnVSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "part_cat_ids = parts['part_cat_id']\n",
    "part_cat_ids = list(part_cat_ids)\n",
    "max_ = max(part_cat_ids)\n",
    "min_ = min(part_cat_ids)\n",
    "# counting frequencies of each id\n",
    "counts = []\n",
    "for i in range(min_, max_+1):\n",
    "    counts.append(part_cat_ids.count(i))\n",
    "\n",
    "# finding indexes of 20 largest part categories\n",
    "max_indexes = sorted(range(len(counts)), key=lambda x: counts[x])\n",
    "max_indexes = sorted(range(len(counts)), key=lambda x: counts[x])[-20:]\n",
    "\n",
    "max_ids = [idx+1 for idx in max_indexes]\n",
    "max_ids_counts = []\n",
    "for index in max_indexes:\n",
    "    max_ids_counts.append(counts[index])\n",
    "\n",
    "print(\"20 most frequent ids: \", max_ids)\n",
    "print(\"----------------------------------------------------------------------------------------------------------\")\n",
    "print(\"counts of 20 most frequent ids: \", max_ids_counts)\n",
    "\n",
    "# plotting the distribution\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(max_ids, max_ids_counts)\n",
    "ax.set_xlabel('ids')\n",
    "ax.set_ylabel('frequencies')\n",
    "ax.set_title(\"distribution of part categories \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9229fcec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>rgb</th>\n",
       "      <th>is_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0033B2</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>05131D</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0055BF</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Green</td>\n",
       "      <td>237841</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Dark Turquoise</td>\n",
       "      <td>008F9B</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1004</td>\n",
       "      <td>Trans Flame Yellowish Orange</td>\n",
       "      <td>FCB76D</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1005</td>\n",
       "      <td>Trans Fire Yellow</td>\n",
       "      <td>FBE890</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1006</td>\n",
       "      <td>Trans Light Royal Blue</td>\n",
       "      <td>B4D4F7</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1007</td>\n",
       "      <td>Reddish Lilac</td>\n",
       "      <td>8E5597</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>9999</td>\n",
       "      <td>[No Color]</td>\n",
       "      <td>05131D</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                          name     rgb is_trans\n",
       "0      -1                       Unknown  0033B2        f\n",
       "1       0                         Black  05131D        f\n",
       "2       1                          Blue  0055BF        f\n",
       "3       2                         Green  237841        f\n",
       "4       3                Dark Turquoise  008F9B        f\n",
       "..    ...                           ...     ...      ...\n",
       "130  1004  Trans Flame Yellowish Orange  FCB76D        t\n",
       "131  1005             Trans Fire Yellow  FBE890        t\n",
       "132  1006        Trans Light Royal Blue  B4D4F7        t\n",
       "133  1007                 Reddish Lilac  8E5597        f\n",
       "134  9999                    [No Color]  05131D        f\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query 3\n",
    "colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4415b90b",
   "metadata": {},
   "source": [
    "## Question 2. Drop the bike\n",
    "\n",
    "*Los Angeles Metro* has been sharing publicly [anonymized *Metro Bike Share* trip data](https://bikeshare.metro.net/about/data/) under the [Open Database License (ODbL)](http://opendatacommons.org/licenses/odbl/1.0/).\n",
    "\n",
    "In this question you will again perform data wrangling and interpretation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0101a922",
   "metadata": {},
   "source": [
    "### 2.1. Loading phase\n",
    "Load the json file into a `DataFrame`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5158d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Duration</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Ending Station ID</th>\n",
       "      <th>Ending Station Latitude</th>\n",
       "      <th>Ending Station Longitude</th>\n",
       "      <th>Passholder Type</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Starting Station Latitude</th>\n",
       "      <th>Starting Station Longitude</th>\n",
       "      <th>Trip ID</th>\n",
       "      <th>Trip Route Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6281.0</td>\n",
       "      <td>180</td>\n",
       "      <td>2016-07-07T04:20:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>Monthly Pass</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-07-07T04:17:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>1912818</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6281.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2016-07-07T06:33:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>Monthly Pass</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-07-07T06:00:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>1919661</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5861.0</td>\n",
       "      <td>300</td>\n",
       "      <td>2016-07-07T10:37:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>Flex Pass</td>\n",
       "      <td>365.0</td>\n",
       "      <td>2016-07-07T10:32:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>1933383</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5861.0</td>\n",
       "      <td>10860</td>\n",
       "      <td>2016-07-07T13:38:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>Flex Pass</td>\n",
       "      <td>365.0</td>\n",
       "      <td>2016-07-07T10:37:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>1944197</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6674.0</td>\n",
       "      <td>420</td>\n",
       "      <td>2016-07-07T12:58:00</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>34.049889</td>\n",
       "      <td>-118.255880</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-07-07T12:51:00</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>34.049889</td>\n",
       "      <td>-118.255880</td>\n",
       "      <td>1940317</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132422</th>\n",
       "      <td>5978.0</td>\n",
       "      <td>540</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>34.048401</td>\n",
       "      <td>-118.260948</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-03-31T23:43:00</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>34.044159</td>\n",
       "      <td>-118.251579</td>\n",
       "      <td>23660993</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132423</th>\n",
       "      <td>6478.0</td>\n",
       "      <td>480</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>34.048401</td>\n",
       "      <td>-118.260948</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-03-31T23:44:00</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>34.044159</td>\n",
       "      <td>-118.251579</td>\n",
       "      <td>23660992</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132424</th>\n",
       "      <td>6183.0</td>\n",
       "      <td>480</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>34.039219</td>\n",
       "      <td>-118.236488</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-03-31T23:44:00</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>34.046520</td>\n",
       "      <td>-118.237411</td>\n",
       "      <td>23660991</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132425</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>420</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>34.039219</td>\n",
       "      <td>-118.236488</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-03-31T23:45:00</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>34.046520</td>\n",
       "      <td>-118.237411</td>\n",
       "      <td>23660990</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132426</th>\n",
       "      <td>6207.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>2017-04-01T00:06:00</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>34.048851</td>\n",
       "      <td>-118.246422</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-03-31T23:45:00</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>34.064281</td>\n",
       "      <td>-118.238937</td>\n",
       "      <td>23664826</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132427 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Bike ID  Duration             End Time  Ending Station ID  \\\n",
       "0        6281.0       180  2016-07-07T04:20:00             3014.0   \n",
       "1        6281.0      1980  2016-07-07T06:33:00             3014.0   \n",
       "2        5861.0       300  2016-07-07T10:37:00             3016.0   \n",
       "3        5861.0     10860  2016-07-07T13:38:00             3016.0   \n",
       "4        6674.0       420  2016-07-07T12:58:00             3032.0   \n",
       "...         ...       ...                  ...                ...   \n",
       "132422   5978.0       540  2017-03-31T23:52:00             3035.0   \n",
       "132423   6478.0       480  2017-03-31T23:52:00             3035.0   \n",
       "132424   6183.0       480  2017-03-31T23:52:00             3054.0   \n",
       "132425   5900.0       420  2017-03-31T23:52:00             3054.0   \n",
       "132426   6207.0      1260  2017-04-01T00:06:00             3029.0   \n",
       "\n",
       "        Ending Station Latitude  Ending Station Longitude Passholder Type  \\\n",
       "0                     34.056610               -118.237210    Monthly Pass   \n",
       "1                     34.056610               -118.237210    Monthly Pass   \n",
       "2                     34.052898               -118.241560       Flex Pass   \n",
       "3                     34.052898               -118.241560       Flex Pass   \n",
       "4                     34.049889               -118.255880         Walk-up   \n",
       "...                         ...                       ...             ...   \n",
       "132422                34.048401               -118.260948         Walk-up   \n",
       "132423                34.048401               -118.260948         Walk-up   \n",
       "132424                34.039219               -118.236488         Walk-up   \n",
       "132425                34.039219               -118.236488         Walk-up   \n",
       "132426                34.048851               -118.246422         Walk-up   \n",
       "\n",
       "        Plan Duration           Start Time  Starting Station ID  \\\n",
       "0                30.0  2016-07-07T04:17:00               3014.0   \n",
       "1                30.0  2016-07-07T06:00:00               3014.0   \n",
       "2               365.0  2016-07-07T10:32:00               3016.0   \n",
       "3               365.0  2016-07-07T10:37:00               3016.0   \n",
       "4                 0.0  2016-07-07T12:51:00               3032.0   \n",
       "...               ...                  ...                  ...   \n",
       "132422            0.0  2017-03-31T23:43:00               3055.0   \n",
       "132423            0.0  2017-03-31T23:44:00               3055.0   \n",
       "132424            0.0  2017-03-31T23:44:00               3082.0   \n",
       "132425            0.0  2017-03-31T23:45:00               3082.0   \n",
       "132426            0.0  2017-03-31T23:45:00               3078.0   \n",
       "\n",
       "        Starting Station Latitude  Starting Station Longitude   Trip ID  \\\n",
       "0                       34.056610                 -118.237210   1912818   \n",
       "1                       34.056610                 -118.237210   1919661   \n",
       "2                       34.052898                 -118.241560   1933383   \n",
       "3                       34.052898                 -118.241560   1944197   \n",
       "4                       34.049889                 -118.255880   1940317   \n",
       "...                           ...                         ...       ...   \n",
       "132422                  34.044159                 -118.251579  23660993   \n",
       "132423                  34.044159                 -118.251579  23660992   \n",
       "132424                  34.046520                 -118.237411  23660991   \n",
       "132425                  34.046520                 -118.237411  23660990   \n",
       "132426                  34.064281                 -118.238937  23664826   \n",
       "\n",
       "       Trip Route Category  \n",
       "0               Round Trip  \n",
       "1               Round Trip  \n",
       "2               Round Trip  \n",
       "3               Round Trip  \n",
       "4               Round Trip  \n",
       "...                    ...  \n",
       "132422             One Way  \n",
       "132423             One Way  \n",
       "132424             One Way  \n",
       "132425             One Way  \n",
       "132426             One Way  \n",
       "\n",
       "[132427 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BikeFile = DATA_FOLDER + \"/bikes/metro-bike-share-trip-data.json\"\n",
    "CovidFile = DATA_FOLDER + \"/covid/\"\n",
    "with open(BikeFile, 'r') as json_file:\n",
    "    BikeData = json.load(json_file)\n",
    "\n",
    "# converting to dataframe\n",
    "BikeData_df = pd.DataFrame(BikeData)\n",
    "BikeData_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c637c0b",
   "metadata": {},
   "source": [
    "### 2.2. Cleaning phase\n",
    "Describe the type and the value range of each attribute. Indicate and transform the attributes that are `Categorical`. Are there redundant columns in the dataset (i.e., are there columns whose value depends only on the value of another column)? What are the possible pitfalls of having such columns? Reduce *data redundancy* by extracting such columns to separate `DataFrames`. Which of the two formats (the initial one or the one with reduced data redundancy) is more susceptible to inconsistencies? At the end print for each `Dataframe` the *type of each column* and it's *shape*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e23e9f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "Bike_ID = BikeData_df['Bike ID']\n",
    "Duration = BikeData_df['Duration']\n",
    "End_Time = BikeData_df['End Time']\n",
    "Ending_Station_ID = BikeData_df['Ending Station ID']\n",
    "Ending_Station_Latitude = BikeData_df['Ending Station Latitude']\n",
    "Passholder_Type = BikeData_df['Passholder Type']\n",
    "Plan_Duration = BikeData_df['Plan Duration']\n",
    "Start_Time = BikeData_df['Start Time']\n",
    "Starting_Station_ID = BikeData_df['Starting Station ID']\n",
    "Starting_Station_Latitude = BikeData_df['Starting Station Latitude']\n",
    "Starting_Station_Longitude = BikeData_df['Starting Station Longitude']\n",
    "Trip_ID = BikeData_df['Trip ID']\n",
    "Trip_Route_Category = BikeData_df['Trip Route Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ca35cff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of Bike ID:  <class 'numpy.float64'> - Range of Bike ID:  1349.0  to  6728.0\n",
      "Type of Duration:  <class 'numpy.int64'> - Range of Duration:  60  to  86400\n",
      "Type of End Time:  <class 'str'>\n",
      "Type of Ending Station ID:  <class 'numpy.float64'> - Range of Ending Station ID:  3000.0  to  4108.0\n",
      "Type of Ending Station Latitude:  <class 'numpy.float64'> - Range of Ending Station Latitude:  0.0  to  34.0642815\n",
      "Type of Passholder Type:  <class 'str'>\n",
      "Type of Plan Duration:  <class 'numpy.float64'> - Range of Plan Duration:  0.0  to  365.0\n",
      "Type of Start Time:  <class 'str'>\n",
      "Type of Starting Station ID:  <class 'numpy.float64'> - Range of Starting Station ID:  3000.0  to  4108.0\n",
      "Type of Starting Station Latitude:  <class 'numpy.float64'> - Range of Starting Station Latitude:  0.0  to  34.0642815\n",
      "Type of Starting Station Longitude:  <class 'numpy.float64'> - Range of starting Station Longitude:  -118.472832  to  0.0\n",
      "Type of Trip ID:  <class 'numpy.int64'> - Range of Trip ID:  1912818  to  23794218\n",
      "Type of Trip Route Category:  <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# describing the type and value range of attributes\n",
    "print(\"Type of Bike ID: \", type(Bike_ID[0]), \"- Range of Bike ID: \", min(list(Bike_ID)), \" to \", max(list(Bike_ID)))\n",
    "print(\"Type of Duration: \", type(Duration[0]), \"- Range of Duration: \", min(list(Duration)), \" to \", max(list(Duration)))\n",
    "print(\"Type of End Time: \", type(End_Time[0]))\n",
    "print(\"Type of Ending Station ID: \", type(Ending_Station_ID[0]), \"- Range of Ending Station ID: \", min(list(Ending_Station_ID)), \" to \", max(list(Ending_Station_ID)))\n",
    "print(\"Type of Ending Station Latitude: \", type(Ending_Station_Latitude[0]), \"- Range of Ending Station Latitude: \", min(list(Ending_Station_Latitude)), \" to \", max(list(Ending_Station_Latitude)))\n",
    "print(\"Type of Passholder Type: \", type(Passholder_Type[0]))\n",
    "print(\"Type of Plan Duration: \", type(Plan_Duration[0]), \"- Range of Plan Duration: \", min(list(Plan_Duration)), \" to \", max(list(Plan_Duration)))\n",
    "print(\"Type of Start Time: \", type(Start_Time[0]))\n",
    "print(\"Type of Starting Station ID: \", type(Starting_Station_ID[0]), \"- Range of Starting Station ID: \", min(list(Starting_Station_ID)), \" to \", max(list(Starting_Station_ID)))\n",
    "print(\"Type of Starting Station Latitude: \", type(Starting_Station_Latitude[0]), \"- Range of Starting Station Latitude: \", min(list(Starting_Station_Latitude)), \" to \", max(list(Starting_Station_Latitude)))\n",
    "print(\"Type of Starting Station Longitude: \", type(Starting_Station_Longitude[0]), \"- Range of starting Station Longitude: \", min((list(Starting_Station_Longitude))), \" to \", max(list(Starting_Station_Longitude)))\n",
    "print(\"Type of Trip ID: \", type(Trip_ID[0]), \"- Range of Trip ID: \", min(list(Trip_ID)), \" to \", max(list(Trip_ID)))\n",
    "print(\"Type of Trip Route Category: \", type(Trip_Route_Category[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf1343d",
   "metadata": {},
   "source": [
    "**Categorical attributes:** From the above results, we can see that Passholder Type and Trip Route Category are categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af956a4",
   "metadata": {},
   "source": [
    "Transforming categorical attributes to numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "812fdb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passholder_Type\n",
    "label_encoder1 = LabelEncoder()\n",
    "Passholder_Type_Encoded = label_encoder1.fit_transform(Passholder_Type)\n",
    "# Trip Route Category\n",
    "label_encoder2 = LabelEncoder()\n",
    "Trip_Route_Category_Encoded = label_encoder2.fit_transform(Trip_Route_Category)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1920f63d",
   "metadata": {},
   "source": [
    "#### Redundant columns:\n",
    "\n",
    "**Start time:** as we have duration and end time, star time can be easily calculated, so this column is redundant.\n",
    "\n",
    "**Trip Route Category**: If the latitudes and longtitudes of starting and ending destination are the same, then the trip is round-trip, otherwise it is one-way. So this column is redundant.\n",
    "\n",
    "**Pass-holder type**: This column is redundant because it can be easily figured out using plan duration\tentries. If plan duration is 30, then pass-holder type is monthly pass, if plan duration is 365, then pass-holder type is flex pass, if plan duration is 0, then pass-holder type is walk-up. So this column is redundant.\n",
    "\n",
    "Having redundant columns makes our dataset huge, which is not convenient. Also, it increases potential of data inconsistencies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5a5b5c",
   "metadata": {},
   "source": [
    "Removing data redundancy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "855520a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Duration</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Ending Station ID</th>\n",
       "      <th>Ending Station Latitude</th>\n",
       "      <th>Ending Station Longitude</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Starting Station Latitude</th>\n",
       "      <th>Starting Station Longitude</th>\n",
       "      <th>Trip ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6281.0</td>\n",
       "      <td>180</td>\n",
       "      <td>2016-07-07T04:20:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>1912818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6281.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2016-07-07T06:33:00</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>34.056610</td>\n",
       "      <td>-118.237210</td>\n",
       "      <td>1919661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5861.0</td>\n",
       "      <td>300</td>\n",
       "      <td>2016-07-07T10:37:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>365.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>1933383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5861.0</td>\n",
       "      <td>10860</td>\n",
       "      <td>2016-07-07T13:38:00</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>365.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>34.052898</td>\n",
       "      <td>-118.241560</td>\n",
       "      <td>1944197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6674.0</td>\n",
       "      <td>420</td>\n",
       "      <td>2016-07-07T12:58:00</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>34.049889</td>\n",
       "      <td>-118.255880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>34.049889</td>\n",
       "      <td>-118.255880</td>\n",
       "      <td>1940317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132422</th>\n",
       "      <td>5978.0</td>\n",
       "      <td>540</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>34.048401</td>\n",
       "      <td>-118.260948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>34.044159</td>\n",
       "      <td>-118.251579</td>\n",
       "      <td>23660993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132423</th>\n",
       "      <td>6478.0</td>\n",
       "      <td>480</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>34.048401</td>\n",
       "      <td>-118.260948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>34.044159</td>\n",
       "      <td>-118.251579</td>\n",
       "      <td>23660992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132424</th>\n",
       "      <td>6183.0</td>\n",
       "      <td>480</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>34.039219</td>\n",
       "      <td>-118.236488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>34.046520</td>\n",
       "      <td>-118.237411</td>\n",
       "      <td>23660991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132425</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>420</td>\n",
       "      <td>2017-03-31T23:52:00</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>34.039219</td>\n",
       "      <td>-118.236488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>34.046520</td>\n",
       "      <td>-118.237411</td>\n",
       "      <td>23660990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132426</th>\n",
       "      <td>6207.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>2017-04-01T00:06:00</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>34.048851</td>\n",
       "      <td>-118.246422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>34.064281</td>\n",
       "      <td>-118.238937</td>\n",
       "      <td>23664826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132427 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Bike ID  Duration             End Time  Ending Station ID  \\\n",
       "0        6281.0       180  2016-07-07T04:20:00             3014.0   \n",
       "1        6281.0      1980  2016-07-07T06:33:00             3014.0   \n",
       "2        5861.0       300  2016-07-07T10:37:00             3016.0   \n",
       "3        5861.0     10860  2016-07-07T13:38:00             3016.0   \n",
       "4        6674.0       420  2016-07-07T12:58:00             3032.0   \n",
       "...         ...       ...                  ...                ...   \n",
       "132422   5978.0       540  2017-03-31T23:52:00             3035.0   \n",
       "132423   6478.0       480  2017-03-31T23:52:00             3035.0   \n",
       "132424   6183.0       480  2017-03-31T23:52:00             3054.0   \n",
       "132425   5900.0       420  2017-03-31T23:52:00             3054.0   \n",
       "132426   6207.0      1260  2017-04-01T00:06:00             3029.0   \n",
       "\n",
       "        Ending Station Latitude  Ending Station Longitude  Plan Duration  \\\n",
       "0                     34.056610               -118.237210           30.0   \n",
       "1                     34.056610               -118.237210           30.0   \n",
       "2                     34.052898               -118.241560          365.0   \n",
       "3                     34.052898               -118.241560          365.0   \n",
       "4                     34.049889               -118.255880            0.0   \n",
       "...                         ...                       ...            ...   \n",
       "132422                34.048401               -118.260948            0.0   \n",
       "132423                34.048401               -118.260948            0.0   \n",
       "132424                34.039219               -118.236488            0.0   \n",
       "132425                34.039219               -118.236488            0.0   \n",
       "132426                34.048851               -118.246422            0.0   \n",
       "\n",
       "        Starting Station ID  Starting Station Latitude  \\\n",
       "0                    3014.0                  34.056610   \n",
       "1                    3014.0                  34.056610   \n",
       "2                    3016.0                  34.052898   \n",
       "3                    3016.0                  34.052898   \n",
       "4                    3032.0                  34.049889   \n",
       "...                     ...                        ...   \n",
       "132422               3055.0                  34.044159   \n",
       "132423               3055.0                  34.044159   \n",
       "132424               3082.0                  34.046520   \n",
       "132425               3082.0                  34.046520   \n",
       "132426               3078.0                  34.064281   \n",
       "\n",
       "        Starting Station Longitude   Trip ID  \n",
       "0                      -118.237210   1912818  \n",
       "1                      -118.237210   1919661  \n",
       "2                      -118.241560   1933383  \n",
       "3                      -118.241560   1944197  \n",
       "4                      -118.255880   1940317  \n",
       "...                            ...       ...  \n",
       "132422                 -118.251579  23660993  \n",
       "132423                 -118.251579  23660992  \n",
       "132424                 -118.237411  23660991  \n",
       "132425                 -118.237411  23660990  \n",
       "132426                 -118.238937  23664826  \n",
       "\n",
       "[132427 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing redundant columns from the data frame\n",
    "BikeData_df_new = BikeData_df.drop(['Passholder Type', 'Start Time', 'Trip Route Category'], axis=1)\n",
    "BikeData_df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddee2579",
   "metadata": {},
   "source": [
    "This new `DataFrame` (shown above) is more susceptible to inconsistencies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f1d38c",
   "metadata": {},
   "source": [
    "**Type of each column** and **shapes** of initial and modified `DataFrames`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e85c3ca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the initial DataFrame:  (132427, 14)\n",
      "Shape of the modified DataFrame:  (132427, 11)\n"
     ]
    }
   ],
   "source": [
    "# shape\n",
    "print(\"Shape of the initial DataFrame: \", np.shape(BikeData_df))\n",
    "print(\"Shape of the modified DataFrame: \", np.shape(BikeData_df_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd1302c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column types of the initial DataFrame:\n",
      "Bike ID:  <class 'numpy.float64'>\n",
      "Duration:  <class 'numpy.int64'>\n",
      "End Time:  <class 'str'>\n",
      "Ending Station ID:  <class 'numpy.float64'>\n",
      "Ending Station Latitude:  <class 'numpy.float64'>\n",
      "Passholder Type:  <class 'str'>\n",
      "Plan Duration:  <class 'numpy.float64'>\n",
      "Start Time:  <class 'str'>\n",
      "Starting Station ID:  <class 'numpy.float64'>\n",
      "Starting Station Latitude:  <class 'numpy.float64'>\n",
      "Starting Station Longitude:  <class 'numpy.float64'>\n",
      "Trip ID:  <class 'numpy.int64'>\n",
      "Trip Route Category:  <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# column types of initial DataFrame\n",
    "print(\"Column types of the initial DataFrame:\")\n",
    "print(\"Bike ID: \", type(Bike_ID[0]))\n",
    "print(\"Duration: \", type(Duration[0]))\n",
    "print(\"End Time: \", type(End_Time[0]))\n",
    "print(\"Ending Station ID: \", type(Ending_Station_ID[0]))\n",
    "print(\"Ending Station Latitude: \", type(Ending_Station_Latitude[0]))\n",
    "print(\"Passholder Type: \", type(Passholder_Type[0]))\n",
    "print(\"Plan Duration: \", type(Plan_Duration[0]))\n",
    "print(\"Start Time: \", type(Start_Time[0]))\n",
    "print(\"Starting Station ID: \", type(Starting_Station_ID[0]))\n",
    "print(\"Starting Station Latitude: \", type(Starting_Station_Latitude[0]))\n",
    "print(\"Starting Station Longitude: \", type(Starting_Station_Longitude[0]))\n",
    "print(\"Trip ID: \", type(Trip_ID[0]))\n",
    "print(\"Trip Route Category: \", type(Trip_Route_Category[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3fb68a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column types of the modified DataFrame:\n",
      "Bike ID:  <class 'numpy.float64'>\n",
      "Duration:  <class 'numpy.int64'>\n",
      "End Time:  <class 'str'>\n",
      "Ending Station ID:  <class 'numpy.float64'>\n",
      "Ending Station Latitude:  <class 'numpy.float64'>\n",
      "Plan Duration:  <class 'numpy.float64'>\n",
      "Starting Station ID:  <class 'numpy.float64'>\n",
      "Starting Station Latitude:  <class 'numpy.float64'>\n",
      "Starting Station Longitude:  <class 'numpy.float64'>\n",
      "Trip ID:  <class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "# column types of modified DataFrame\n",
    "print(\"Column types of the modified DataFrame:\")\n",
    "print(\"Bike ID: \", type(Bike_ID[0]))\n",
    "print(\"Duration: \", type(Duration[0]))\n",
    "print(\"End Time: \", type(End_Time[0]))\n",
    "print(\"Ending Station ID: \", type(Ending_Station_ID[0]))\n",
    "print(\"Ending Station Latitude: \", type(Ending_Station_Latitude[0]))\n",
    "print(\"Plan Duration: \", type(Plan_Duration[0]))\n",
    "print(\"Starting Station ID: \", type(Starting_Station_ID[0]))\n",
    "print(\"Starting Station Latitude: \", type(Starting_Station_Latitude[0]))\n",
    "print(\"Starting Station Longitude: \", type(Starting_Station_Longitude[0]))\n",
    "print(\"Trip ID: \", type(Trip_ID[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33195a3",
   "metadata": {},
   "source": [
    "### 2.3. Querying phase\n",
    "Answer the following queries using the functionality of `Pandas`.\n",
    "\n",
    "1. * Plot the *distribution* of the number of outgoing trips from each station in a histogram with 20 bins (Hint: each bin describes a range of counts, not stations).\n",
    "2. * Plot histograms for the *duration* and *trip starting hour in the day* attributes. For both the *duration*  and the *trip starting hour* use *discrete 1-hour intervals*. What do you observe in each plot? What are some popular values in the *duration* plot? Explain the local maxima and the trends you observe on the *trip starting hour* plot based on human behavior.\n",
    "3. * For each *trip route category*, calculate the proportion of trips by *passholder type* and present your results in *a stacked bar chart with normalized height*.\n",
    "4. * Considering only trips that begin in the morning hours (before noon), plot in *a single bar chart* the proportion of trips by *passholder type* and *trip route category*. Explain any outliers you observe.\n",
    "5. * Separate the hours of the day into two intervals that have (approximately) the same number of bikes leaving the stations. For each of the two intervals calculate the proportion of trips by *passholder type* and 6. *trip route category*. Present your results in a `DataFrame` which has a unique, non-composite index. Does the proportion of trips depend on whether it is the first or second hour interval? Would the company have any significant benefit by creating a more complex paying scheme where monthly pass users would pay less in the first interval and (equally) more on the second one? Assume that the number of trips per interval will not change if the scheme changes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a958c1d0",
   "metadata": {},
   "source": [
    "**Query 1:**\n",
    "\n",
    "For this part, I calculated frequency of each station ID, which is number of outgoing trips from each station using the `Starting_Station_ID` column. So I looped over items of this column, and counted how many times they were repeated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "423bafa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Starting_Station_ID_list = list(Starting_Station_ID)\n",
    "# calculating number of frequencies for each station ID, which is number of outgoing trips for each station\n",
    "vals = []\n",
    "counts = []\n",
    "for item in Starting_Station_ID:\n",
    "    if item not in vals:\n",
    "        vals.append(item)\n",
    "        counts.append(Starting_Station_ID_list.count(item))\n",
    "        \n",
    "# plot histogram of the first 20 vals\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(vals[0:20], counts[0:20])\n",
    "ax.set_xlabel('Station ID')\n",
    "ax.set_ylabel('Number of outgoing trips')\n",
    "ax.set_title(\"distribution of outgoing trips from each station\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4c8ea7",
   "metadata": {},
   "source": [
    "**Query 2:**\n",
    "\n",
    "For this part, at first I plot the histogram of *duration*.\n",
    "\n",
    "Then for plotting histogram of trip starting hour in the day, I need to extract hour from the `Start time` column. I do this by splitting the corresponding strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f401a067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# converting duration scale to hour\n",
    "Duration_h = [item/60 for item in Duration]\n",
    "# histogram\n",
    "plt.hist(Duration_h)\n",
    "plt.title(\"Histogram of Duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dbd0aa",
   "metadata": {},
   "source": [
    "From the above plot, we can see that popular duration is around 0 to 180 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9c19ca9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extracting starting hour in days\n",
    "times = [item.split(\"T\")[1] for item in Start_Time]  # time\n",
    "hours = [item.split(\":\")[0] for item in times]       # hour\n",
    "hours_int = [int(item) for item in hours]            # hour string converted to int\n",
    "# histogram\n",
    "plt.hist(hours_int)\n",
    "plt.title(\"Histogram of starting hours in days\")\n",
    "plt.xlabel(\"starting hours\")\n",
    "plt.ylabel(\"frequencies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ada5350",
   "metadata": {},
   "source": [
    "From the above plot, we can see that starting hour has a peak around 3 p.m."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b77854",
   "metadata": {},
   "source": [
    "**Query 3**\n",
    "\n",
    "In this part, I loop over all Trip_Route_Category items and calculate portion of Passholder_Type for each route category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8a7a144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Trip_Route_Category = BikeData_df['Trip Route Category']\n",
    "Passholder_Type = BikeData_df['Passholder Type']\n",
    "\n",
    "round_monthly = 0\n",
    "round_flex = 0\n",
    "round_walk = 0\n",
    "\n",
    "one_monthly = 0\n",
    "one_flex = 0\n",
    "one_walk = 0\n",
    "\n",
    "for i in range(len(Trip_Route_Category)):\n",
    "    if Trip_Route_Category[i] == \"Round Trip\":\n",
    "        if Passholder_Type[i] == \"Monthly Pass\":\n",
    "            round_monthly = round_monthly + 1\n",
    "        if Passholder_Type[i] == \"Flex Pass\":\n",
    "            round_flex = round_flex +  1\n",
    "        else:\n",
    "            round_walk = round_walk + 1\n",
    "    if Trip_Route_Category[i] == \"One Way\":\n",
    "        if Passholder_Type[i] == \"Monthly Pass\":\n",
    "            one_monthly = one_monthly + 1\n",
    "        if Passholder_Type[i] == \"Flex Pass\":\n",
    "            one_flex = one_flex + 1\n",
    "        else:\n",
    "            one_walk = one_walk + 1   \n",
    "            \n",
    "# calculating portions\n",
    "round_monthly_portion = round_monthly/(round_monthly+round_flex+round_walk) \n",
    "round_flex_portion = round_flex/(round_monthly+round_flex+round_walk)\n",
    "round_walk_portion = round_walk/(round_monthly+round_flex+round_walk)\n",
    "\n",
    "one_monthly_portion = one_monthly/(one_monthly+one_flex+one_walk)\n",
    "one_flex_portion = one_flex/(one_monthly+one_flex+one_walk)\n",
    "one_walk_portion = one_walk/(one_monthly+one_flex+one_walk)\n",
    "\n",
    "width = 0.65\n",
    "\n",
    "# plotting the chart bar\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(['Monthly Pass', 'Flex Pass', 'Walk-up pass'], [round_monthly_portion, round_flex_portion, round_walk_portion], width, label='Round Trip')\n",
    "ax.bar(['Monthly Pass', 'Flex Pass', 'Walk-up pass'], [one_monthly_portion, one_flex_portion, one_walk_portion], width, bottom=[round_monthly_portion, round_flex_portion, round_walk_portion], label='One Way')\n",
    "ax.set_xlabel(\"Route Trip Category\")\n",
    "ax.set_ylabel(\"Portions\")\n",
    "ax.set_title(\"Portion of Trips\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6685f9",
   "metadata": {},
   "source": [
    "**Query 4**\n",
    "\n",
    "I use the extracted starting hours of days from the previous parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4964b897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extracting indexes of trips with starting hours in the morning\n",
    "morning_indexes = []\n",
    "for i in range(len(hours_int)):\n",
    "    if hours_int[i]<12:\n",
    "        morning_indexes.append(i)\n",
    "\n",
    "Trip_Route_Category = list(BikeData_df['Trip Route Category'])\n",
    "Passholder_Type = list(BikeData_df['Passholder Type'])\n",
    "\n",
    "Trip_Route_Category_morning = [Trip_Route_Category[index] for index in morning_indexes]\n",
    "Passholder_Type_morning = [Passholder_Type[index] for index in morning_indexes]\n",
    "\n",
    "round_monthly_morning = 0\n",
    "round_flex_morning = 0\n",
    "round_walk_morning = 0\n",
    "\n",
    "one_monthly_morning = 0\n",
    "one_flex_morning = 0\n",
    "one_walk_morning = 0\n",
    "\n",
    "for i in range(len(Trip_Route_Category_morning)):\n",
    "    if Trip_Route_Category_morning[i] == \"Round Trip\":\n",
    "        if Passholder_Type_morning[i] == \"Monthly Pass\":\n",
    "            round_monthly_morning = round_monthly_morning + 1\n",
    "        if Passholder_Type_morning[i] == \"Flex Pass\":\n",
    "            round_flex_morning = round_flex_morning +  1\n",
    "        else:\n",
    "            round_walk_morning = round_walk_morning + 1\n",
    "    if Trip_Route_Category_morning[i] == \"One Way\":\n",
    "        if Passholder_Type_morning[i] == \"Monthly Pass\":\n",
    "            one_monthly_morning = one_monthly_morning + 1\n",
    "        if Passholder_Type_morning[i] == \"Flex Pass\":\n",
    "            one_flex_morning = one_flex_morning + 1\n",
    "        else:\n",
    "            one_walk_morning = one_walk_morning + 1   \n",
    "            \n",
    "# calculating portions\n",
    "round_monthly_morning_portion = round_monthly_morning/(round_monthly_morning+round_flex_morning+round_walk_morning) \n",
    "round_flex_morning_portion = round_flex_morning/(one_monthly_morning+one_flex_morning+round_walk_morning)\n",
    "round_walk_morning_portion = round_walk_morning/(round_monthly_morning+round_flex_morning+round_walk_morning)\n",
    "\n",
    "one_monthly_morning_portion = one_monthly_morning/(one_monthly_morning+one_flex_morning+one_walk_morning)\n",
    "one_flex_morning_portion = one_flex_morning/(one_monthly_morning+one_flex_morning+one_walk_morning)\n",
    "one_walk_morning_portion = one_walk_morning/(one_monthly_morning+one_flex_morning+one_walk_morning)\n",
    "\n",
    "width = 0.65\n",
    "\n",
    "# plotting the chart bar\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(['Monthly Pass', 'Flex Pass', 'Walk-up pass'], [round_monthly_morning_portion, round_flex_morning_portion, round_walk_morning_portion], width, label='Round Trip')\n",
    "ax.bar(['Monthly Pass', 'Flex Pass', 'Walk-up pass'], [one_monthly_morning_portion, one_flex_morning_portion, one_walk_morning_portion], width, bottom=[round_monthly_portion, round_flex_portion, round_walk_portion], label='One Way')\n",
    "ax.set_xlabel(\"Route Trip Category\")\n",
    "ax.set_ylabel(\"Portions\")\n",
    "ax.set_title(\"Portion of Trips/Starting in Morning\")\n",
    "ax.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9269b573",
   "metadata": {},
   "source": [
    "## Question 3. Class War in Titanic\n",
    "\n",
    "Use pandas to import the data file `Data/titanic.xls`. It contains data on all the passengers that travelled on the Titanic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c95a59",
   "metadata": {},
   "source": [
    "[Run the following cell for a brief description about the dataset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e947607",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr><h2>Data frame:titanic3</h2>1309 observations and 14 variables, maximum # NAs:1188<hr>\n",
       "<TABLE BORDER>\n",
       "<tr><td>Name</td><td>Labels</td><td>Units</td><td>Levels</td><td>Storage</td><td>NAs</td></tr>\n",
       "<tr><td>pclass</td><td></td><td></td><td><a href=\"#pclass\"><div align=right>  3</div></a></td><td>integer</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>survived</td><td>Survived</td><td></td><td><div align=right></div></td><td>double</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>name</td><td>Name</td><td></td><td><div align=right></div></td><td>character</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>sex</td><td></td><td></td><td><a href=\"#sex\"><div align=right>  2</div></a></td><td>integer</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>age</td><td>Age</td><td>Year</td><td><div align=right></div></td><td>double</td><td><div align=right> 263</div></td></tr>\n",
       "<tr><td>sibsp</td><td>Number of Siblings/Spouses Aboard</td><td></td><td><div align=right></div></td><td>double</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>parch</td><td>Number of Parents/Children Aboard</td><td></td><td><div align=right></div></td><td>double</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>ticket</td><td>Ticket Number</td><td></td><td><div align=right></div></td><td>character</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>fare</td><td>Passenger Fare</td><td>British Pound (\\243)</td><td><div align=right></div></td><td>double</td><td><div align=right>   1</div></td></tr>\n",
       "<tr><td>cabin</td><td></td><td></td><td><a href=\"#cabin\"><div align=right>187</div></a></td><td>integer</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>embarked</td><td></td><td></td><td><a href=\"#embarked\"><div align=right>  3</div></a></td><td>integer</td><td><div align=right>   2</div></td></tr>\n",
       "<tr><td>boat</td><td></td><td></td><td><a href=\"#boat\"><div align=right> 28</div></a></td><td>integer</td><td><div align=right>   0</div></td></tr>\n",
       "<tr><td>body</td><td>Body Identification Number</td><td></td><td><div align=right></div></td><td>double</td><td><div align=right>1188</div></td></tr>\n",
       "<tr><td>home.dest</td><td>Home/Destination</td><td></td><td><div align=right></div></td><td>character</td><td><div align=right>   0</div></td></tr>\n",
       "</TABLE>\n",
       "<hr>\n",
       "<TABLE BORDER>\n",
       "<tr><td>Variable</td><td>Levels</td></tr>\n",
       "<tr><td><a name=\"pclass\">pclass</a></td><td>1st</td></tr>\n",
       "<tr><td></td><td>2nd</td></tr>\n",
       "<tr><td></td><td>3rd</td></tr>\n",
       "<tr><td><a name=\"sex\">sex</a></td><td>female</td></tr>\n",
       "<tr><td></td><td>male</td></tr>\n",
       "<tr><td><a name=\"cabin\">cabin</a></td><td></td></tr>\n",
       "<tr><td></td><td>A10</td></tr>\n",
       "<tr><td></td><td>A11</td></tr>\n",
       "<tr><td></td><td>A14</td></tr>\n",
       "<tr><td></td><td>A16</td></tr>\n",
       "<tr><td></td><td>A18</td></tr>\n",
       "<tr><td></td><td>A19</td></tr>\n",
       "<tr><td></td><td>A20</td></tr>\n",
       "<tr><td></td><td>A21</td></tr>\n",
       "<tr><td></td><td>A23</td></tr>\n",
       "<tr><td></td><td>A24</td></tr>\n",
       "<tr><td></td><td>A26</td></tr>\n",
       "<tr><td></td><td>A29</td></tr>\n",
       "<tr><td></td><td>A31</td></tr>\n",
       "<tr><td></td><td>A32</td></tr>\n",
       "<tr><td></td><td>A34</td></tr>\n",
       "<tr><td></td><td>A36</td></tr>\n",
       "<tr><td></td><td>A5</td></tr>\n",
       "<tr><td></td><td>A6</td></tr>\n",
       "<tr><td></td><td>A7</td></tr>\n",
       "<tr><td></td><td>A9</td></tr>\n",
       "<tr><td></td><td>B10</td></tr>\n",
       "<tr><td></td><td>B101</td></tr>\n",
       "<tr><td></td><td>B102</td></tr>\n",
       "<tr><td></td><td>B11</td></tr>\n",
       "<tr><td></td><td>B18</td></tr>\n",
       "<tr><td></td><td>B19</td></tr>\n",
       "<tr><td></td><td>B20</td></tr>\n",
       "<tr><td></td><td>B22</td></tr>\n",
       "<tr><td></td><td>B24</td></tr>\n",
       "<tr><td></td><td>B26</td></tr>\n",
       "<tr><td></td><td>B28</td></tr>\n",
       "<tr><td></td><td>B3</td></tr>\n",
       "<tr><td></td><td>B30</td></tr>\n",
       "<tr><td></td><td>B35</td></tr>\n",
       "<tr><td></td><td>B36</td></tr>\n",
       "<tr><td></td><td>B37</td></tr>\n",
       "<tr><td></td><td>B38</td></tr>\n",
       "<tr><td></td><td>B39</td></tr>\n",
       "<tr><td></td><td>B4</td></tr>\n",
       "<tr><td></td><td>B41</td></tr>\n",
       "<tr><td></td><td>B42</td></tr>\n",
       "<tr><td></td><td>B45</td></tr>\n",
       "<tr><td></td><td>B49</td></tr>\n",
       "<tr><td></td><td>B5</td></tr>\n",
       "<tr><td></td><td>B50</td></tr>\n",
       "<tr><td></td><td>B51 B53 B55</td></tr>\n",
       "<tr><td></td><td>B52 B54 B56</td></tr>\n",
       "<tr><td></td><td>B57 B59 B63 B66</td></tr>\n",
       "<tr><td></td><td>B58 B60</td></tr>\n",
       "<tr><td></td><td>B61</td></tr>\n",
       "<tr><td></td><td>B69</td></tr>\n",
       "<tr><td></td><td>B71</td></tr>\n",
       "<tr><td></td><td>B73</td></tr>\n",
       "<tr><td></td><td>B77</td></tr>\n",
       "<tr><td></td><td>B78</td></tr>\n",
       "<tr><td></td><td>B79</td></tr>\n",
       "<tr><td></td><td>B80</td></tr>\n",
       "<tr><td></td><td>B82 B84</td></tr>\n",
       "<tr><td></td><td>B86</td></tr>\n",
       "<tr><td></td><td>B94</td></tr>\n",
       "<tr><td></td><td>B96 B98</td></tr>\n",
       "<tr><td></td><td>C101</td></tr>\n",
       "<tr><td></td><td>C103</td></tr>\n",
       "<tr><td></td><td>C104</td></tr>\n",
       "<tr><td></td><td>C105</td></tr>\n",
       "<tr><td></td><td>C106</td></tr>\n",
       "<tr><td></td><td>C110</td></tr>\n",
       "<tr><td></td><td>C111</td></tr>\n",
       "<tr><td></td><td>C116</td></tr>\n",
       "<tr><td></td><td>C118</td></tr>\n",
       "<tr><td></td><td>C123</td></tr>\n",
       "<tr><td></td><td>C124</td></tr>\n",
       "<tr><td></td><td>C125</td></tr>\n",
       "<tr><td></td><td>C126</td></tr>\n",
       "<tr><td></td><td>C128</td></tr>\n",
       "<tr><td></td><td>C130</td></tr>\n",
       "<tr><td></td><td>C132</td></tr>\n",
       "<tr><td></td><td>C148</td></tr>\n",
       "<tr><td></td><td>C2</td></tr>\n",
       "<tr><td></td><td>C22 C26</td></tr>\n",
       "<tr><td></td><td>C23 C25 C27</td></tr>\n",
       "<tr><td></td><td>C28</td></tr>\n",
       "<tr><td></td><td>C30</td></tr>\n",
       "<tr><td></td><td>C31</td></tr>\n",
       "<tr><td></td><td>C32</td></tr>\n",
       "<tr><td></td><td>C39</td></tr>\n",
       "<tr><td></td><td>C45</td></tr>\n",
       "<tr><td></td><td>C46</td></tr>\n",
       "<tr><td></td><td>C47</td></tr>\n",
       "<tr><td></td><td>C49</td></tr>\n",
       "<tr><td></td><td>C50</td></tr>\n",
       "<tr><td></td><td>C51</td></tr>\n",
       "<tr><td></td><td>C52</td></tr>\n",
       "<tr><td></td><td>C53</td></tr>\n",
       "<tr><td></td><td>C54</td></tr>\n",
       "<tr><td></td><td>C55 C57</td></tr>\n",
       "<tr><td></td><td>C6</td></tr>\n",
       "<tr><td></td><td>C62 C64</td></tr>\n",
       "<tr><td></td><td>C65</td></tr>\n",
       "<tr><td></td><td>C68</td></tr>\n",
       "<tr><td></td><td>C7</td></tr>\n",
       "<tr><td></td><td>C70</td></tr>\n",
       "<tr><td></td><td>C78</td></tr>\n",
       "<tr><td></td><td>C80</td></tr>\n",
       "<tr><td></td><td>C82</td></tr>\n",
       "<tr><td></td><td>C83</td></tr>\n",
       "<tr><td></td><td>C85</td></tr>\n",
       "<tr><td></td><td>C86</td></tr>\n",
       "<tr><td></td><td>C87</td></tr>\n",
       "<tr><td></td><td>C89</td></tr>\n",
       "<tr><td></td><td>C90</td></tr>\n",
       "<tr><td></td><td>C91</td></tr>\n",
       "<tr><td></td><td>C92</td></tr>\n",
       "<tr><td></td><td>C93</td></tr>\n",
       "<tr><td></td><td>C95</td></tr>\n",
       "<tr><td></td><td>C97</td></tr>\n",
       "<tr><td></td><td>C99</td></tr>\n",
       "<tr><td></td><td>D</td></tr>\n",
       "<tr><td></td><td>D10 D12</td></tr>\n",
       "<tr><td></td><td>D11</td></tr>\n",
       "<tr><td></td><td>D15</td></tr>\n",
       "<tr><td></td><td>D17</td></tr>\n",
       "<tr><td></td><td>D19</td></tr>\n",
       "<tr><td></td><td>D20</td></tr>\n",
       "<tr><td></td><td>D21</td></tr>\n",
       "<tr><td></td><td>D22</td></tr>\n",
       "<tr><td></td><td>D26</td></tr>\n",
       "<tr><td></td><td>D28</td></tr>\n",
       "<tr><td></td><td>D30</td></tr>\n",
       "<tr><td></td><td>D33</td></tr>\n",
       "<tr><td></td><td>D34</td></tr>\n",
       "<tr><td></td><td>D35</td></tr>\n",
       "<tr><td></td><td>D36</td></tr>\n",
       "<tr><td></td><td>D37</td></tr>\n",
       "<tr><td></td><td>D38</td></tr>\n",
       "<tr><td></td><td>D40</td></tr>\n",
       "<tr><td></td><td>D43</td></tr>\n",
       "<tr><td></td><td>D45</td></tr>\n",
       "<tr><td></td><td>D46</td></tr>\n",
       "<tr><td></td><td>D47</td></tr>\n",
       "<tr><td></td><td>D48</td></tr>\n",
       "<tr><td></td><td>D49</td></tr>\n",
       "<tr><td></td><td>D50</td></tr>\n",
       "<tr><td></td><td>D56</td></tr>\n",
       "<tr><td></td><td>D6</td></tr>\n",
       "<tr><td></td><td>D7</td></tr>\n",
       "<tr><td></td><td>D9</td></tr>\n",
       "<tr><td></td><td>E10</td></tr>\n",
       "<tr><td></td><td>E101</td></tr>\n",
       "<tr><td></td><td>E12</td></tr>\n",
       "<tr><td></td><td>E121</td></tr>\n",
       "<tr><td></td><td>E17</td></tr>\n",
       "<tr><td></td><td>E24</td></tr>\n",
       "<tr><td></td><td>E25</td></tr>\n",
       "<tr><td></td><td>E31</td></tr>\n",
       "<tr><td></td><td>E33</td></tr>\n",
       "<tr><td></td><td>E34</td></tr>\n",
       "<tr><td></td><td>E36</td></tr>\n",
       "<tr><td></td><td>E38</td></tr>\n",
       "<tr><td></td><td>E39 E41</td></tr>\n",
       "<tr><td></td><td>E40</td></tr>\n",
       "<tr><td></td><td>E44</td></tr>\n",
       "<tr><td></td><td>E45</td></tr>\n",
       "<tr><td></td><td>E46</td></tr>\n",
       "<tr><td></td><td>E49</td></tr>\n",
       "<tr><td></td><td>E50</td></tr>\n",
       "<tr><td></td><td>E52</td></tr>\n",
       "<tr><td></td><td>E58</td></tr>\n",
       "<tr><td></td><td>E60</td></tr>\n",
       "<tr><td></td><td>E63</td></tr>\n",
       "<tr><td></td><td>E67</td></tr>\n",
       "<tr><td></td><td>E68</td></tr>\n",
       "<tr><td></td><td>E77</td></tr>\n",
       "<tr><td></td><td>E8</td></tr>\n",
       "<tr><td></td><td>F</td></tr>\n",
       "<tr><td></td><td>F E46</td></tr>\n",
       "<tr><td></td><td>F E57</td></tr>\n",
       "<tr><td></td><td>F E69</td></tr>\n",
       "<tr><td></td><td>F G63</td></tr>\n",
       "<tr><td></td><td>F G73</td></tr>\n",
       "<tr><td></td><td>F2</td></tr>\n",
       "<tr><td></td><td>F33</td></tr>\n",
       "<tr><td></td><td>F38</td></tr>\n",
       "<tr><td></td><td>F4</td></tr>\n",
       "<tr><td></td><td>G6</td></tr>\n",
       "<tr><td></td><td>T</td></tr>\n",
       "<tr><td><a name=\"embarked\">embarked</a></td><td>Cherbourg</td></tr>\n",
       "<tr><td></td><td>Queenstown</td></tr>\n",
       "<tr><td></td><td>Southampton</td></tr>\n",
       "<tr><td><a name=\"boat\">boat</a></td><td></td></tr>\n",
       "<tr><td></td><td>1</td></tr>\n",
       "<tr><td></td><td>10</td></tr>\n",
       "<tr><td></td><td>11</td></tr>\n",
       "<tr><td></td><td>12</td></tr>\n",
       "<tr><td></td><td>13</td></tr>\n",
       "<tr><td></td><td>13 15</td></tr>\n",
       "<tr><td></td><td>13 15 B</td></tr>\n",
       "<tr><td></td><td>14</td></tr>\n",
       "<tr><td></td><td>15</td></tr>\n",
       "<tr><td></td><td>15 16</td></tr>\n",
       "<tr><td></td><td>16</td></tr>\n",
       "<tr><td></td><td>2</td></tr>\n",
       "<tr><td></td><td>3</td></tr>\n",
       "<tr><td></td><td>4</td></tr>\n",
       "<tr><td></td><td>5</td></tr>\n",
       "<tr><td></td><td>5 7</td></tr>\n",
       "<tr><td></td><td>5 9</td></tr>\n",
       "<tr><td></td><td>6</td></tr>\n",
       "<tr><td></td><td>7</td></tr>\n",
       "<tr><td></td><td>8</td></tr>\n",
       "<tr><td></td><td>8 10</td></tr>\n",
       "<tr><td></td><td>9</td></tr>\n",
       "<tr><td></td><td>A</td></tr>\n",
       "<tr><td></td><td>B</td></tr>\n",
       "<tr><td></td><td>C</td></tr>\n",
       "<tr><td></td><td>C D</td></tr>\n",
       "<tr><td></td><td>D</td></tr>\n",
       "</TABLE>\n",
       "<hr>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(filename=DATA_FOLDER+'/titanic.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60270053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in /Users/kimiamohsenian/miniconda3/envs/data_course/lib/python3.10/site-packages (2.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e2012ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the dataset\n",
    "titanic_df = pd.read_excel(r'data/titanic.xls')\n",
    "titanic_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb7c7b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>female</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                       name     sex   age  sibsp  parch  \\\n",
       "1304       3         0       Zabour, Miss. Hileni  female  14.5      1      0   \n",
       "1305       3         0      Zabour, Miss. Thamine  female   NaN      1      0   \n",
       "1306       3         0  Zakarian, Mr. Mapriededer    male  26.5      0      0   \n",
       "1307       3         0        Zakarian, Mr. Ortin    male  27.0      0      0   \n",
       "1308       3         0         Zimmerman, Mr. Leo    male  29.0      0      0   \n",
       "\n",
       "      ticket     fare cabin embarked boat   body home.dest  \n",
       "1304    2665  14.4542   NaN        C  NaN  328.0       NaN  \n",
       "1305    2665  14.4542   NaN        C  NaN    NaN       NaN  \n",
       "1306    2656   7.2250   NaN        C  NaN  304.0       NaN  \n",
       "1307    2670   7.2250   NaN        C  NaN    NaN       NaN  \n",
       "1308  315082   7.8750   NaN        S  NaN    NaN       NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1ff203",
   "metadata": {},
   "source": [
    "For each of the following questions state clearly your assumptions and discuss your findings:\n",
    "1. Describe the *type* and the *value range* of each attribute. Indicate and transform the attributes that can be `Categorical`. \n",
    "2. Plot histograms for the *travel class*, *embarkation port*, *sex* and *age* attributes. For the latter one, use *discrete decade intervals*. \n",
    "3. Calculate the proportion of passengers by *cabin floor*. Present your results in a *pie chart*.\n",
    "4. For each *travel class*, calculate the proportion of the passengers that survived. Present your results in *pie charts*.\n",
    "5. Calculate the proportion of the passengers that survived by *travel class* and *sex*. Present your results in *a single histogram*.\n",
    "6. Create 2 equally populated *age categories* and calculate survival proportions by *age category*, *travel class* and *sex*. Present your results in a `DataFrame` with unique index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6272c3da",
   "metadata": {},
   "source": [
    "**Query 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6290fbce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass         int64\n",
       "survived       int64\n",
       "name          object\n",
       "sex           object\n",
       "age          float64\n",
       "sibsp          int64\n",
       "parch          int64\n",
       "ticket        object\n",
       "fare         float64\n",
       "cabin         object\n",
       "embarked      object\n",
       "boat          object\n",
       "body         float64\n",
       "home.dest     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing the type of each attribute\n",
    "titanic_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "588938b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.381971</td>\n",
       "      <td>29.881135</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>160.809917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.486055</td>\n",
       "      <td>14.413500</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>97.696922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>328.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass     survived          age        sibsp        parch  \\\n",
       "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
       "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
       "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
       "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
       "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
       "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
       "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
       "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
       "\n",
       "              fare        body  \n",
       "count  1308.000000  121.000000  \n",
       "mean     33.295479  160.809917  \n",
       "std      51.758668   97.696922  \n",
       "min       0.000000    1.000000  \n",
       "25%       7.895800   72.000000  \n",
       "50%      14.454200  155.000000  \n",
       "75%      31.275000  256.000000  \n",
       "max     512.329200  328.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing the value range of each attribute\n",
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13164730",
   "metadata": {},
   "source": [
    "From the min and max rows for each attribute, the range is obvious."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e09632",
   "metadata": {},
   "source": [
    "**Query 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "662d2028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting histograms\n",
    "pclass = titanic_df['pclass']\n",
    "embarked = list(titanic_df['embarked'])\n",
    "sex = titanic_df['sex']\n",
    "age = titanic_df['age']\n",
    "\n",
    "fig, ax = plt.subplots(1, 4, figsize=(16, 4))\n",
    "ax[0].hist(pclass)\n",
    "ax[0].set_title(\"Histogram of pclass\")\n",
    "ax[1].hist(embarked)\n",
    "ax[1].set_title(\"Histogram of embarked\")\n",
    "ax[2].hist(sex)\n",
    "ax[2].set_title(\"Histogram of sex\")\n",
    "ax[3].hist(age)\n",
    "ax[3].set_title(\"Histogram of age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9246ecf2",
   "metadata": {},
   "source": [
    "**Query 3**\n",
    "\n",
    "For this part, I extracted all cabin floor names and counted passengers for each cabin floor. I removed NaNs from the list of cabin floors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71656be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cabin = list(titanic_df['cabin'])\n",
    "# removing nan from the cabin list\n",
    "cabin_new = [item for item in cabin if str(item)!='nan']\n",
    "cabin_unique = list(set(cabin_new))  # unique names of cabin floors\n",
    "cabin_counts = [cabin_new.count(item) for item in cabin_unique]\n",
    "cabin_portions = [item/len(cabin_new) for item in cabin_counts]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(cabin_portions, labels=cabin_unique, \n",
    "        shadow=True, startangle=90, normalize=True, radius=10, rotatelabels=True)\n",
    "ax1.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ed3ff9",
   "metadata": {},
   "source": [
    "**Query 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dcf21a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass = titanic_df['pclass']\n",
    "survived = titanic_df['survived']\n",
    "# crearing counters\n",
    "survived_1 = 0\n",
    "survived_2 = 0\n",
    "survived_3 = 0\n",
    "\n",
    "for i in range(len(pclass)):\n",
    "    if pclass[i]==1:\n",
    "        survived_1 = survived_1 + survived[i]\n",
    "    if pclass[i]==2:\n",
    "        survived_2 = survived_2 + survived[i]\n",
    "    if pclass[i]==3:\n",
    "        survived_3 = survived_3 + survived[i]\n",
    "        \n",
    "# calculating the proportions\n",
    "survived_1_portion = survived_1/(survived_1+survived_2+survived_3)\n",
    "survived_2_portion = survived_2/(survived_1+survived_2+survived_3)\n",
    "survived_3_portion = survived_3/(survived_1+survived_2+survived_3)\n",
    "\n",
    "plt.pie([survived_1_portion, survived_2_portion, survived_3_portion], labels = ['class 1', 'class 2', 'class 3'])\n",
    "plt.title(\"Number of Survivals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16204bd",
   "metadata": {},
   "source": [
    "**Query 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20857f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass = titanic_df['pclass']\n",
    "sex = titanic_df['sex']\n",
    "survived = titanic_df['survived']\n",
    "\n",
    "female_1 = 0\n",
    "female_2 = 0\n",
    "female_3 = 0\n",
    "\n",
    "male_1 = 0\n",
    "male_2 = 0\n",
    "male_3 = 3\n",
    "\n",
    "for i in range(len(survived)):\n",
    "    if pclass[i]==1 and sex[i]=='female':\n",
    "        female_1 = female_1 + 1\n",
    "    if pclass[i]==1 and sex[i]=='male':\n",
    "        male_1 = male_1 + 1\n",
    "    if pclass[i]==2 and sex[i]=='female':\n",
    "        female_2 = female_2 + 1\n",
    "    if pclass[i]==2 and sex[i]=='male':\n",
    "        male_2 = male_2 + 1\n",
    "    if pclass[i]==3 and sex[i]=='female':\n",
    "        female_3 = female_3 + 1\n",
    "    if pclass[i]==3 and sex[i]=='male':\n",
    "        male_3 = male_3 + 1\n",
    "        \n",
    "#calculating the porportions\n",
    "female_1_por = female_1/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "female_2_por = female_2/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "female_3_por = female_3/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "male_1_por = male_1/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "male_2_por = male_2/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "male_3_por = male_3/(female_1+female_2+female_3+male_1+male_2+male_3)\n",
    "\n",
    "# plotting the chart bar\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(['f/1', 'f2', 'f/3', 'm/1', 'm/2', 'm/3'], [female_1_por, female_2_por, female_3_por, male_1_por, male_2_por, male_3_por], width=0.5)\n",
    "plt.title(\"Survival proportions by sex and trip class\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266c04f3",
   "metadata": {},
   "source": [
    "In the above plot, f/1 stands for female in the first class; f/2 stands for female in the second class, f/3 stands for female in the third class, m/1 stands for male in the first class, m/2 stands for male in the second class and m/3 stands for male in the third class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a5f806",
   "metadata": {},
   "source": [
    "## Question 4. Covid in the US\n",
    "In this question, you are required to reproduce the graphs shown in a real journalistic article. After all, reproducibility is an important part of professional journalism as well as data science. Also, a great deal of a data scientist's work is to communicate data in a way that anyone with any level of expertise in the field would understand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fa5b55",
   "metadata": {},
   "source": [
    "You should reproduce the 4 graphs shown in <a href=\"https://projects.fivethirtyeight.com/coronavirus-polls/\"> this article</a>. The data is openly accessible through <a href=\"https://github.com/fivethirtyeight/covid-19-polls/\"> FiveThirtyEight's github</a>. Nevertheless, both the data and the article are available in `data` folder in case of unstable internet connections!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f732f3d",
   "metadata": {},
   "source": [
    "* Note: Your graphs don't have to be as interactive or detailed as the ones on the website. Only showing the averages, some (not all) data points with light color, legends, titles, x values, and y values are necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97a89d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d55a8f",
   "metadata": {},
   "source": [
    "__\\* Briefly explain your approach and assumptions here \\*__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c205e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}