{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A6iFUUQLNDlE"
   },
   "source": [
    "<table class=\"m01-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/yy/dviz-course/blob/master/docs/m08-histogram/lab08.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://yyahn.com/dviz-course/m08-histogram/lab08/\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on Github</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://raw.githubusercontent.com/yy/dviz-course/master/docs/m08-histogram/lab08.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View raw on Github</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rHEGnwRZTG6O"
   },
   "source": [
    "# Module 8: Histogram and CDF\n",
    "\n",
    "A deep dive into Histogram and boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-06-14T19:55:00.229Z",
     "iopub.status.busy": "2020-06-14T19:55:00.202Z",
     "iopub.status.idle": "2020-06-14T19:55:00.311Z",
     "shell.execute_reply": "2020-06-14T19:55:00.333Z"
    },
    "executionInfo": {
     "elapsed": 184,
     "status": "ok",
     "timestamp": 1687818245973,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "S4vGQ3FkTG6R"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import altair as alt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N6blkVDDTG6T"
   },
   "source": [
    "## The tricky histogram with pre-counted data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95_k7X_-TG6T"
   },
   "source": [
    "Let's revisit the table from the class\n",
    "\n",
    "| Hours | Frequency |\n",
    "|-------|-----------|\n",
    "| 0-1   | 4,300     |\n",
    "| 1-3   | 6,900     |\n",
    "| 3-5   | 4,900     |\n",
    "| 5-10  | 2,000     |\n",
    "| 10-24 | 2,100     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CeO69PpmTG6U"
   },
   "source": [
    "You can draw a histogram by just providing bins and counts instead of a list of numbers. So, let's try that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-06-14T19:55:09.164Z",
     "iopub.status.busy": "2020-06-14T19:55:09.141Z",
     "iopub.status.idle": "2020-06-14T19:55:09.196Z",
     "shell.execute_reply": "2020-06-14T19:55:09.215Z"
    },
    "executionInfo": {
     "elapsed": 154,
     "status": "ok",
     "timestamp": 1687818249521,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "FZPPq6inTG6U"
   },
   "outputs": [],
   "source": [
    "bins = [0, 1, 3, 5, 10, 24]\n",
    "data = {0.5: 4300, 2: 6900, 4: 4900, 7: 2000, 15: 2100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:55:11.108Z",
     "iopub.status.busy": "2020-06-14T19:55:11.089Z",
     "iopub.status.idle": "2020-06-14T19:55:11.146Z",
     "shell.execute_reply": "2020-06-14T19:55:11.165Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1687818250050,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "0kJUBT1hTG6U",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "cf1ab5f7-e38b-4ddd-d37c-c993c7108c6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0.5, 2, 4, 7, 15])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dLAjvNswTG6V"
   },
   "source": [
    "**Q: Draw histogram using this data.** Useful query: [Google search: matplotlib histogram pre-counted](https://www.google.com/search?client=safari&rls=en&q=matplotlib+histogram+already+counted&ie=UTF-8&oe=UTF-8#q=matplotlib+histogram+pre-counted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:55:50.412Z",
     "iopub.status.busy": "2020-06-14T19:55:50.391Z",
     "iopub.status.idle": "2020-06-14T19:55:50.511Z",
     "shell.execute_reply": "2020-06-14T19:55:50.533Z"
    },
    "executionInfo": {
     "elapsed": 624,
     "status": "ok",
     "timestamp": 1687818251274,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "wo4Z0fgRTG6V",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "008af409-d043-4577-8c87-e513ddc1ab49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jdeSwaifTG6W"
   },
   "source": [
    "As you can see, the **default histogram does not normalize with binwidth and simply shows the counts**! This can be very misleading if you are working with variable bin width (e.g. logarithmic bins). So please be mindful about histograms when you work with variable bins.\n",
    "\n",
    "**Q: You can fix this by using the `density` option.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:55:58.905Z",
     "iopub.status.busy": "2020-06-14T19:55:58.882Z",
     "iopub.status.idle": "2020-06-14T19:55:58.991Z",
     "shell.execute_reply": "2020-06-14T19:55:59.009Z"
    },
    "executionInfo": {
     "elapsed": 610,
     "status": "ok",
     "timestamp": 1687818252370,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "4ucfqdvvTG6W",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "625b307a-c444-4fb5-f305-c04e22ae41b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4roBIweOTG6W"
   },
   "source": [
    "## Let's use an actual dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:24.048Z",
     "iopub.status.busy": "2020-06-14T19:56:24.027Z",
     "iopub.status.idle": "2020-06-14T19:56:24.081Z",
     "shell.execute_reply": "2020-06-14T19:56:24.100Z"
    },
    "executionInfo": {
     "elapsed": 151,
     "status": "ok",
     "timestamp": 1687818255570,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "xPTrYs55TG6X"
   },
   "outputs": [],
   "source": [
    "import vega_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:25.250Z",
     "iopub.status.busy": "2020-06-14T19:56:25.234Z",
     "iopub.status.idle": "2020-06-14T19:56:25.670Z",
     "shell.execute_reply": "2020-06-14T19:56:25.727Z"
    },
    "executionInfo": {
     "elapsed": 1381,
     "status": "ok",
     "timestamp": 1687818257413,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "a244eCSOTG6X",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "57724aad-7da7-436c-f146-84e94932e933"
   },
   "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>Title</th>\n",
       "      <th>US_Gross</th>\n",
       "      <th>Worldwide_Gross</th>\n",
       "      <th>US_DVD_Sales</th>\n",
       "      <th>Production_Budget</th>\n",
       "      <th>Release_Date</th>\n",
       "      <th>MPAA_Rating</th>\n",
       "      <th>Running_Time_min</th>\n",
       "      <th>Distributor</th>\n",
       "      <th>Source</th>\n",
       "      <th>Major_Genre</th>\n",
       "      <th>Creative_Type</th>\n",
       "      <th>Director</th>\n",
       "      <th>Rotten_Tomatoes_Rating</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>IMDB_Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Land Girls</td>\n",
       "      <td>146083.0</td>\n",
       "      <td>146083.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Jun 12 1998</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gramercy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>First Love, Last Rites</td>\n",
       "      <td>10876.0</td>\n",
       "      <td>10876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Aug 07 1998</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Strand</td>\n",
       "      <td>None</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I Married a Strange Person</td>\n",
       "      <td>203134.0</td>\n",
       "      <td>203134.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Aug 28 1998</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lionsgate</td>\n",
       "      <td>None</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Let's Talk About Sex</td>\n",
       "      <td>373615.0</td>\n",
       "      <td>373615.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Sep 11 1998</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fine Line</td>\n",
       "      <td>None</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Slam</td>\n",
       "      <td>1009819.0</td>\n",
       "      <td>1087521.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>Oct 09 1998</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trimark</td>\n",
       "      <td>Original Screenplay</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Contemporary Fiction</td>\n",
       "      <td>None</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Title   US_Gross  Worldwide_Gross  US_DVD_Sales  \\\n",
       "0              The Land Girls   146083.0         146083.0           NaN   \n",
       "1      First Love, Last Rites    10876.0          10876.0           NaN   \n",
       "2  I Married a Strange Person   203134.0         203134.0           NaN   \n",
       "3        Let's Talk About Sex   373615.0         373615.0           NaN   \n",
       "4                        Slam  1009819.0        1087521.0           NaN   \n",
       "\n",
       "   Production_Budget Release_Date MPAA_Rating  Running_Time_min Distributor  \\\n",
       "0          8000000.0  Jun 12 1998           R               NaN    Gramercy   \n",
       "1           300000.0  Aug 07 1998           R               NaN      Strand   \n",
       "2           250000.0  Aug 28 1998        None               NaN   Lionsgate   \n",
       "3           300000.0  Sep 11 1998        None               NaN   Fine Line   \n",
       "4          1000000.0  Oct 09 1998           R               NaN     Trimark   \n",
       "\n",
       "                Source Major_Genre         Creative_Type Director  \\\n",
       "0                 None        None                  None     None   \n",
       "1                 None       Drama                  None     None   \n",
       "2                 None      Comedy                  None     None   \n",
       "3                 None      Comedy                  None     None   \n",
       "4  Original Screenplay       Drama  Contemporary Fiction     None   \n",
       "\n",
       "   Rotten_Tomatoes_Rating  IMDB_Rating  IMDB_Votes  \n",
       "0                     NaN          6.1      1071.0  \n",
       "1                     NaN          6.9       207.0  \n",
       "2                     NaN          6.8       865.0  \n",
       "3                    13.0          NaN         NaN  \n",
       "4                    62.0          3.4       165.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = vega_datasets.data.movies()\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EuyhowKmTG6X"
   },
   "source": [
    "Let's plot the histogram of IMDB ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:30.176Z",
     "iopub.status.busy": "2020-06-14T19:56:30.152Z",
     "iopub.status.idle": "2020-06-14T19:56:30.285Z",
     "shell.execute_reply": "2020-06-14T19:56:30.306Z"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1687818258217,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "lCzR16y5TG6X",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "35899e50-84a1-4384-baf8-0c7a177b225f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9.,  39.,  76., 133., 293., 599., 784., 684., 323.,  48.]),\n",
       " array([1.4 , 2.18, 2.96, 3.74, 4.52, 5.3 , 6.08, 6.86, 7.64, 8.42, 9.2 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies.IMDB_Rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "az_zAN5wTG6Y"
   },
   "source": [
    "Did you get an error or a warning? What's going on?\n",
    "\n",
    "The problem is that the column contains `NaN` (Not a Number) values, which represent missing data points. The following command check whether each value is a `NaN` and returns the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:48.429Z",
     "iopub.status.busy": "2020-06-14T19:56:48.411Z",
     "iopub.status.idle": "2020-06-14T19:56:48.468Z",
     "shell.execute_reply": "2020-06-14T19:56:48.486Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1687818258701,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "x3jwldMSTG6Y",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "bca94b9a-d041-435a-fa39-dcff6d0cef18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3        True\n",
       "4       False\n",
       "        ...  \n",
       "3196    False\n",
       "3197     True\n",
       "3198    False\n",
       "3199    False\n",
       "3200    False\n",
       "Name: IMDB_Rating, Length: 3201, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.IMDB_Rating.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OxBEfsovTG6Y"
   },
   "source": [
    "As you can see there are a bunch of missing rows. You can count them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:51.688Z",
     "iopub.status.busy": "2020-06-14T19:56:51.671Z",
     "iopub.status.idle": "2020-06-14T19:56:51.722Z",
     "shell.execute_reply": "2020-06-14T19:56:51.739Z"
    },
    "executionInfo": {
     "elapsed": 148,
     "status": "ok",
     "timestamp": 1687818259537,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "3FfJ3a34TG6Y",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "1b2524c6-6262-4cc2-b5c6-d16dafeb7c32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(movies.IMDB_Rating.isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i1BRz3o5TG6Z"
   },
   "source": [
    "or drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:52.894Z",
     "iopub.status.busy": "2020-06-14T19:56:52.876Z",
     "iopub.status.idle": "2020-06-14T19:56:52.932Z",
     "shell.execute_reply": "2020-06-14T19:56:52.950Z"
    },
    "executionInfo": {
     "elapsed": 173,
     "status": "ok",
     "timestamp": 1687818261726,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "vwa6CfbsTG6Z",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "f32f7a7b-52f0-496f-95be-427c1968999c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2988"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings_nan_dropped = movies.IMDB_Rating.dropna()\n",
    "len(IMDB_ratings_nan_dropped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:54.792Z",
     "iopub.status.busy": "2020-06-14T19:56:54.775Z",
     "iopub.status.idle": "2020-06-14T19:56:54.826Z",
     "shell.execute_reply": "2020-06-14T19:56:54.843Z"
    },
    "executionInfo": {
     "elapsed": 156,
     "status": "ok",
     "timestamp": 1687818262363,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "vVZ3sm-ITG6Z",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "2ad2aa9f-5d76-4e3e-ae66-cab435e19eb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3201"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "213 + 2988"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v_MDtrt_TG6Z"
   },
   "source": [
    "The `dropna` can be applied to the dataframe too.\n",
    "\n",
    "**Q: drop rows from `movies` dataframe where either `IMDB_Rating` or `IMDB_Votes` is `NaN`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:55.835Z",
     "iopub.status.busy": "2020-06-14T19:56:55.817Z",
     "iopub.status.idle": "2020-06-14T19:56:55.861Z",
     "shell.execute_reply": "2020-06-14T19:56:55.878Z"
    },
    "executionInfo": {
     "elapsed": 150,
     "status": "ok",
     "timestamp": 1687818264693,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "eUxZKFWkTG6Z"
   },
   "outputs": [],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:56:56.768Z",
     "iopub.status.busy": "2020-06-14T19:56:56.751Z",
     "iopub.status.idle": "2020-06-14T19:56:56.803Z",
     "shell.execute_reply": "2020-06-14T19:56:56.819Z"
    },
    "executionInfo": {
     "elapsed": 145,
     "status": "ok",
     "timestamp": 1687818265230,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "c6jSjbxhTG6a",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "69d58a1c-61d4-4721-c1bd-3e9b568d9acf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    }
   ],
   "source": [
    "# Both should be zero.\n",
    "print(sum(movies.IMDB_Rating.isna()), sum(movies.IMDB_Votes.isna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ZeoyhBJTG6a"
   },
   "source": [
    "How does `matplotlib` decides the bins? Actually `matplotlib`'s `hist` function uses `numpy`'s `histogram` function under the hood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fOlXNjFyTG6a"
   },
   "source": [
    "**Q: Plot the histogram of movie ratings (`IMDB_Rating`) using the `plt.hist()` function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:00.883Z",
     "iopub.status.busy": "2020-06-14T19:57:00.865Z",
     "iopub.status.idle": "2020-06-14T19:57:00.973Z",
     "shell.execute_reply": "2020-06-14T19:57:01.058Z"
    },
    "executionInfo": {
     "elapsed": 1678,
     "status": "ok",
     "timestamp": 1687818275930,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "rn1cO2ByTG6a",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "2cd84d13-3fec-4ea2-9948-c299038ba14f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0aXrkkUcUEhJyyntWV1erurras+92uxtncAAAwHSmr9nZvXu34uPjddlllykzM1MlJSWSpK1bt6q2tlapqamevsnJyUpMTFRxcbEkqbi4WF26dFFMTIynT1pamtxut3bs2HHae+bm5srhcHi2hISERhodAAAwm6lhp2fPniooKNCqVau0cOFCfffdd/rFL36ho0ePyuVyKSQkRJGRkV7nxMTEyOVySZJcLpdX0Dlx/MSx08nJyVFlZaVn27dvn38HBgAAmgxTH2MNGjTI889du3ZVz5491a5dO73xxhsKCwtrtPuGhoYqNDS00a4PAACaDtMfY/2vyMhIXXnlldqzZ49iY2NVU1OjI0eOePUpLy/3rPGJjY096e2sE/unWgcEAAAuPU0q7FRVVembb75RXFycunfvruDgYBUVFXmO79y5UyUlJXI6nZIkp9Op7du3q6KiwtOnsLBQdrtdKSkpF7x+AADQ9Jj6GGvKlCm68cYb1a5dO5WWlurhhx9WYGCgbr/9djkcDo0ZM0aTJ09WVFSU7Ha77rnnHjmdTvXq1UuSNGDAAKWkpOiOO+7Q3Llz5XK59OCDDyo7O5vHVAAAQJLJYWf//v26/fbb9a9//Utt2rRR37599dFHH6lNmzaSpKeeekoBAQHKyMhQdXW10tLStGDBAs/5gYGBWrFihe6++245nU6Fh4crKytLM2fONGtIAACgibEZhmGYXYTZ3G63HA6HKisrZbfbzS4HgJ+1n/6e2SWgido7J93sEnAezvbvt09rdr799lufCwMAALiQfAo7V1xxhfr166dXXnlFx48f93dNAAAAfuNT2Pnkk0/UtWtXTZ48WbGxsfrd736njz/+2N+1AQAAnDefws4111yjZ555RqWlpXrppZdUVlamvn37qnPnzpo3b54OHjzo7zoBAAB8cl7f2QkKCtKwYcO0fPlyPf7449qzZ4+mTJmihIQEjRw5UmVlZf6qEwAAwCfnFXa2bNmi3//+94qLi9O8efM0ZcoUffPNNyosLFRpaaluuukmf9UJAADgE5++szNv3jzl5+dr586dGjx4sF5++WUNHjxYAQH/yU5JSUkqKChQ+/bt/VkrAADAOfMp7CxcuFCjR4/WnXfeqbi4uFP2iY6O1uLFi8+rOAAAgPPlU9jZvXv3GfuEhIQoKyvLl8sDAAD4jU9rdvLz87V8+fKT2pcvX64lS5acd1EAAAD+4lPYyc3NVevWrU9qj46O1uzZs8+7KAAAAH/xKeyUlJQoKSnppPZ27dqppKTkvIsCAADwF5/CTnR0tD7//POT2rdt26ZWrVqdd1EAAAD+4lPYuf3223Xvvfdq3bp1qq+vV319vdauXasJEyZo+PDh/q4RAADAZz69jTVr1izt3btX/fv3V1DQfy7R0NCgkSNHsmYHAAA0KT6FnZCQEL3++uuaNWuWtm3bprCwMHXp0kXt2rXzd30AAADnxaewc8KVV16pK6+80l+1AAAA+J1PYae+vl4FBQUqKipSRUWFGhoavI6vXbvWL8UBAACcL5/CzoQJE1RQUKD09HR17txZNpvN33UBAAD4hU9hZ9myZXrjjTc0ePBgf9cDAADgVz69eh4SEqIrrrjC37UAAAD4nU9h57777tMzzzwjwzD8XQ8AAIBf+fQY64MPPtC6deu0cuVKderUScHBwV7H33zzTb8UBwAAcL58CjuRkZG6+eab/V0LAACA3/kUdvLz8/1dBwAAQKPwac2OJNXV1WnNmjV64YUXdPToUUlSaWmpqqqq/FYcAADA+fJpZuf777/XwIEDVVJSourqat1www2KiIjQ448/rurqauXl5fm7TgAAAJ/4NLMzYcIE9ejRQ4cPH1ZYWJin/eabb1ZRUZHfigMAADhfPs3s/OMf/9DGjRsVEhLi1d6+fXsdOHDAL4UBAAD4g08zOw0NDaqvrz+pff/+/YqIiDjvogAAAPzFp7AzYMAAPf300559m82mqqoqPfzww/yEBAAAaFJ8eoz15JNPKi0tTSkpKTp+/Lh+85vfaPfu3WrdurVee+01f9cIAADgM5/CTtu2bbVt2zYtW7ZMn3/+uaqqqjRmzBhlZmZ6LVgGAAAwm09hR5KCgoI0YsQIf9YCAADgdz6t2Xn55Zd/cvPFnDlzZLPZNHHiRE/b8ePHlZ2drVatWqlFixbKyMhQeXm513klJSVKT09X8+bNFR0dralTp6qurs6nGgAAgPX4NLMzYcIEr/3a2lr98MMPCgkJUfPmzTVy5Mhzut7mzZv1wgsvqGvXrl7tkyZN0nvvvafly5fL4XBo/PjxGjZsmD788ENJUn19vdLT0xUbG6uNGzeqrKxMI0eOVHBwsGbPnu3L0AAAgMX4NLNz+PBhr62qqko7d+5U3759z3mBclVVlTIzM/Xiiy+qZcuWnvbKykotXrxY8+bN0/XXX6/u3bsrPz9fGzdu1EcffSRJev/99/Xll1/qlVde0TXXXKNBgwZp1qxZev7551VTU3Pae1ZXV8vtdnttAADAmnz+bawf69Chg+bMmXPSrM+ZZGdnKz09XampqV7tW7duVW1trVd7cnKyEhMTVVxcLEkqLi5Wly5dFBMT4+mTlpYmt9utHTt2nPaeubm5cjgcni0hIeGcagYAABcPv4Ud6T+LlktLS8+6/7Jly/TJJ58oNzf3pGMul0shISGKjIz0ao+JiZHL5fL0+d+gc+L4iWOnk5OTo8rKSs+2b9++s64ZAABcXHxas/O3v/3Na98wDJWVlem5555Tnz59zuoa+/bt04QJE1RYWKhmzZr5UobPQkNDFRoaekHvCQAAzOFT2Bk6dKjXvs1mU5s2bXT99dfrySefPKtrbN26VRUVFerWrZunrb6+Xhs2bNBzzz2n1atXq6amRkeOHPGa3SkvL1dsbKwkKTY2Vh9//LHXdU+8rXWiDwAAuLT5FHYaGhrO+8b9+/fX9u3bvdpGjRql5ORkTZs2TQkJCQoODlZRUZEyMjIkSTt37lRJSYmcTqckyel06rHHHlNFRYWio6MlSYWFhbLb7UpJSTnvGgEAwMXP548Knq+IiAh17tzZqy08PFytWrXytI8ZM0aTJ09WVFSU7Ha77rnnHjmdTvXq1UvSf36jKyUlRXfccYfmzp0rl8ulBx98UNnZ2TymAgAAknwMO5MnTz7rvvPmzfPlFpKkp556SgEBAcrIyFB1dbXS0tK0YMECz/HAwECtWLFCd999t5xOp8LDw5WVlaWZM2f6fE8AAGAtNsMwjHM9qV+/fvr0009VW1urq666SpK0a9cuBQYGeq3BsdlsWrt2rf+qbSRut1sOh0OVlZWy2+1mlwPAz9pPf8/sEtBE7Z2TbnYJOA9n+/fbp5mdG2+8UREREVqyZInnQ4CHDx/WqFGj9Itf/EL33Xefb1UDAAD4mU/f2XnyySeVm5vr9cXjli1b6tFHHz3rt7EAAAAuBJ/Cjtvt1sGDB09qP3jwoI4ePXreRQEAAPiLT2Hn5ptv1qhRo/Tmm29q//792r9/v/76179qzJgxGjZsmL9rBAAA8JlPa3by8vI0ZcoU/eY3v1Ftbe1/LhQUpDFjxuiJJ57wa4EAAADnw6ew07x5cy1YsEBPPPGEvvnmG0nS5ZdfrvDwcL8WBwAAcL7O64dAy8rKVFZWpg4dOig8PFw+vMUOAADQqHwKO//617/Uv39/XXnllRo8eLDKysok/eeLx7x2DgAAmhKfws6kSZMUHByskpISNW/e3NN+2223adWqVX4rDgAA4Hz5tGbn/fff1+rVq9W2bVuv9g4dOuj777/3S2EAAAD+4NPMzrFjx7xmdE44dOgQP8AJAACaFJ/Czi9+8Qu9/PLLnn2bzaaGhgbNnTtX/fr181txAAAA58unx1hz585V//79tWXLFtXU1Oj+++/Xjh07dOjQIX344Yf+rhEAAMBnPs3sdO7cWbt27VLfvn1100036dixYxo2bJg+/fRTXX755f6uEQAAwGfnPLNTW1urgQMHKi8vT3/4wx8aoyYAAAC/OeeZneDgYH3++eeNUQsAAIDf+fQYa8SIEVq8eLG/awEAAPA7nxYo19XV6aWXXtKaNWvUvXv3k34Ta968eX4pDgAA4HydU9j59ttv1b59e33xxRfq1q2bJGnXrl1efWw2m/+qAwAAOE/nFHY6dOigsrIyrVu3TtJ/fh5i/vz5iomJaZTiAAAAztc5rdn58a+ar1y5UseOHfNrQQAAAP7k0wLlE34cfgAAAJqacwo7NpvtpDU5rNEBAABN2Tmt2TEMQ3feeafnxz6PHz+uu+6666S3sd58803/VQgAAHAezinsZGVlee2PGDHCr8UAAAD42zmFnfz8/MaqAwAAoFGc1wJlAACAps6nLygDAGAF7ae/Z3YJ52zvnHSzS7joMLMDAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAszdSws3DhQnXt2lV2u112u11Op1MrV670HD9+/Liys7PVqlUrtWjRQhkZGSovL/e6RklJidLT09W8eXNFR0dr6tSpqquru9BDAQAATZSpYadt27aaM2eOtm7dqi1btuj666/XTTfdpB07dkiSJk2apHfffVfLly/X+vXrVVpaqmHDhnnOr6+vV3p6umpqarRx40YtWbJEBQUFmjFjhllDAgAATYzNaGI/XR4VFaUnnnhCt9xyi9q0aaOlS5fqlltukSR9/fXX6tixo4qLi9WrVy+tXLlSQ4YMUWlpqWJiYiRJeXl5mjZtmg4ePKiQkJCzuqfb7ZbD4VBlZaXsdnujjQ2AOS7Gb6kAp8N3dv7rbP9+N5k1O/X19Vq2bJmOHTsmp9OprVu3qra2VqmpqZ4+ycnJSkxMVHFxsSSpuLhYXbp08QQdSUpLS5Pb7fbMDp1KdXW13G631wYAAKzJ9LCzfft2tWjRQqGhobrrrrv01ltvKSUlRS6XSyEhIYqMjPTqHxMTI5fLJUlyuVxeQefE8RPHTic3N1cOh8OzJSQk+HdQAACgyTA97Fx11VX67LPPtGnTJt19993KysrSl19+2aj3zMnJUWVlpWfbt29fo94PAACYx/TfxgoJCdEVV1whSerevbs2b96sZ555Rrfddptqamp05MgRr9md8vJyxcbGSpJiY2P18ccfe13vxNtaJ/qcSmhoqEJDQ/08EgAA0BSZHnZ+rKGhQdXV1erevbuCg4NVVFSkjIwMSdLOnTtVUlIip9MpSXI6nXrsscdUUVGh6OhoSVJhYaHsdrtSUlJMGwNgZSz2BXCxMTXs5OTkaNCgQUpMTNTRo0e1dOlS/f3vf9fq1avlcDg0ZswYTZ48WVFRUbLb7brnnnvkdDrVq1cvSdKAAQOUkpKiO+64Q3PnzpXL5dKDDz6o7OxsZm4AAIAkk8NORUWFRo4cqbKyMjkcDnXt2lWrV6/WDTfcIEl66qmnFBAQoIyMDFVXVystLU0LFizwnB8YGKgVK1bo7rvvltPpVHh4uLKysjRz5kyzhgQAAJqYJvedHTPwnR3g7PEYCzAX39n5r4vuOzsAAACNgbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAszdSwk5ubq+uuu04RERGKjo7W0KFDtXPnTq8+x48fV3Z2tlq1aqUWLVooIyND5eXlXn1KSkqUnp6u5s2bKzo6WlOnTlVdXd2FHAoAAGiiTA0769evV3Z2tj766CMVFhaqtrZWAwYM0LFjxzx9Jk2apHfffVfLly/X+vXrVVpaqmHDhnmO19fXKz09XTU1Ndq4caOWLFmigoICzZgxw4whAQCAJsZmGIZhdhEnHDx4UNHR0Vq/fr1++ctfqrKyUm3atNHSpUt1yy23SJK+/vprdezYUcXFxerVq5dWrlypIUOGqLS0VDExMZKkvLw8TZs2TQcPHlRISMgZ7+t2u+VwOFRZWSm73d6oYwQudu2nv2d2CcAlbe+cdLNLaDLO9u93k1qzU1lZKUmKioqSJG3dulW1tbVKTU319ElOTlZiYqKKi4slScXFxerSpYsn6EhSWlqa3G63duzYccr7VFdXy+12e20AAMCamkzYaWho0MSJE9WnTx917txZkuRyuRQSEqLIyEivvjExMXK5XJ4+/xt0Thw/cexUcnNz5XA4PFtCQoKfRwMAAJqKJhN2srOz9cUXX2jZsmWNfq+cnBxVVlZ6tn379jX6PQEAgDmCzC5AksaPH68VK1Zow4YNatu2rac9NjZWNTU1OnLkiNfsTnl5uWJjYz19Pv74Y6/rnXhb60SfHwsNDVVoaKifRwEAAJoiU2d2DMPQ+PHj9dZbb2nt2rVKSkryOt69e3cFBwerqKjI07Zz506VlJTI6XRKkpxOp7Zv366KigpPn8LCQtntdqWkpFyYgQAAgCbL1Jmd7OxsLV26VO+8844iIiI8a2wcDofCwsLkcDg0ZswYTZ48WVFRUbLb7brnnnvkdDrVq1cvSdKAAQOUkpKiO+64Q3PnzpXL5dKDDz6o7OxsZm8AAIC5YWfhwoWSpP/7v//zas/Pz9edd94pSXrqqacUEBCgjIwMVVdXKy0tTQsWLPD0DQwM1IoVK3T33XfL6XQqPDxcWVlZmjlz5oUaBgAAaMKa1Hd2zMJ3doCzx3d2AHPxnZ3/uii/swMAAOBvhB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBpQWYXAFyq2k9/z+wSAOCSwMwOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNFPDzoYNG3TjjTcqPj5eNptNb7/9ttdxwzA0Y8YMxcXFKSwsTKmpqdq9e7dXn0OHDikzM1N2u12RkZEaM2aMqqqqLuAoAABAU2Zq2Dl27JiuvvpqPf/886c8PnfuXM2fP195eXnatGmTwsPDlZaWpuPHj3v6ZGZmaseOHSosLNSKFSu0YcMGjRs37kINAQAANHGm/hDooEGDNGjQoFMeMwxDTz/9tB588EHddNNNkqSXX35ZMTExevvttzV8+HB99dVXWrVqlTZv3qwePXpIkp599lkNHjxYf/rTnxQfH3/BxgIAAJqmJrtm57vvvpPL5VJqaqqnzeFwqGfPniouLpYkFRcXKzIy0hN0JCk1NVUBAQHatGnTaa9dXV0tt9vttQEAAGtqsmHH5XJJkmJiYrzaY2JiPMdcLpeio6O9jgcFBSkqKsrT51Ryc3PlcDg8W0JCgp+rBwAATUWTDTuNKScnR5WVlZ5t3759ZpcEAAAaSZMNO7GxsZKk8vJyr/by8nLPsdjYWFVUVHgdr6ur06FDhzx9TiU0NFR2u91rAwAA1tRkw05SUpJiY2NVVFTkaXO73dq0aZOcTqckyel06siRI9q6daunz9q1a9XQ0KCePXte8JoBAEDTY+rbWFVVVdqzZ49n/7vvvtNnn32mqKgoJSYmauLEiXr00UfVoUMHJSUl6aGHHlJ8fLyGDh0qSerYsaMGDhyosWPHKi8vT7W1tRo/fryGDx/Om1gAAECSyWFny5Yt6tevn2d/8uTJkqSsrCwVFBTo/vvv17FjxzRu3DgdOXJEffv21apVq9SsWTPPOa+++qrGjx+v/v37KyAgQBkZGZo/f/4FHwsAAGiabIZhGGYXYTa32y2Hw6HKykrW7+CCaT/9PbNLAHAR2jsn3ewSmoyz/fvdZNfsAAAA+ANhBwAAWBphBwAAWBphBwAAWJqpb2MB/sJiXwDA6TCzAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI3fxgIA4CJyMf4W4N456aben5kdAABgaYQdAABgaYQdAABgaazZwUkuxufBAACcDjM7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0izzq+fPP/+8nnjiCblcLl199dV69tln9fOf/9zssvgFcQAATGaJmZ3XX39dkydP1sMPP6xPPvlEV199tdLS0lRRUWF2aQAAwGSWCDvz5s3T2LFjNWrUKKWkpCgvL0/NmzfXSy+9ZHZpAADAZBf9Y6yamhpt3bpVOTk5nraAgAClpqaquLj4lOdUV1erurras19ZWSlJcrvdfq+vofoHv18TAICLSWP8ff3f6xqG8ZP9Lvqw889//lP19fWKiYnxao+JidHXX399ynNyc3P1xz/+8aT2hISERqkRAIBLmePpxr3+0aNH5XA4Tnv8og87vsjJydHkyZM9+w0NDTp06JBatWolm81mYmWNx+12KyEhQfv27ZPdbje7nEbDOK3jUhijxDithnFeWIZh6OjRo4qPj//Jfhd92GndurUCAwNVXl7u1V5eXq7Y2NhTnhMaGqrQ0FCvtsjIyMYqsUmx2+2W/j/gCYzTOi6FMUqM02oY54XzUzM6J1z0C5RDQkLUvXt3FRUVedoaGhpUVFQkp9NpYmUAAKApuOhndiRp8uTJysrKUo8ePfTzn/9cTz/9tI4dO6ZRo0aZXRoAADCZJcLObbfdpoMHD2rGjBlyuVy65pprtGrVqpMWLV/KQkND9fDDD5/0+M5qGKd1XApjlBin1TDOpslmnOl9LQAAgIvYRb9mBwAA4KcQdgAAgKURdgAAgKURdgAAgKURdiwuNzdX1113nSIiIhQdHa2hQ4dq586dZpflVwsXLlTXrl09H7dyOp1auXKl2WU1ujlz5shms2nixIlml+JXjzzyiGw2m9eWnJxsdlmN4sCBAxoxYoRatWqlsLAwdenSRVu2bDG7LL9q3779Sf972mw2ZWdnm12a39TX1+uhhx5SUlKSwsLCdPnll2vWrFln/L2mi9HRo0c1ceJEtWvXTmFhYerdu7c2b95sdllnZIlXz3F669evV3Z2tq677jrV1dXpgQce0IABA/Tll18qPDzc7PL8om3btpozZ446dOggwzC0ZMkS3XTTTfr000/VqVMns8trFJs3b9YLL7ygrl27ml1Ko+jUqZPWrFnj2Q8Kst5/qg4fPqw+ffqoX79+Wrlypdq0aaPdu3erZcuWZpfmV5s3b1Z9fb1n/4svvtANN9ygW2+91cSq/Ovxxx/XwoULtWTJEnXq1ElbtmzRqFGj5HA4dO+995pdnl/99re/1RdffKG//OUvio+P1yuvvKLU1FR9+eWX+tnPfmZ2eadn4JJSUVFhSDLWr19vdimNqmXLlsaf//xns8toFEePHjU6dOhgFBYWGr/61a+MCRMmmF2SXz388MPG1VdfbXYZjW7atGlG3759zS7jgpswYYJx+eWXGw0NDWaX4jfp6enG6NGjvdqGDRtmZGZmmlRR4/jhhx+MwMBAY8WKFV7t3bp1M/7whz+YVNXZ4THWJaayslKSFBUVZXIljaO+vl7Lli3TsWPHLPtzIdnZ2UpPT1dqaqrZpTSa3bt3Kz4+XpdddpkyMzNVUlJidkl+97e//U09evTQrbfequjoaF177bV68cUXzS6rUdXU1OiVV17R6NGjLfWjy71791ZRUZF27dolSdq2bZs++OADDRo0yOTK/Kuurk719fVq1qyZV3tYWJg++OADk6o6S2anLVw49fX1Rnp6utGnTx+zS/G7zz//3AgPDzcCAwMNh8NhvPfee2aX1Chee+01o3Pnzsa///1vwzAMS87s/L//9/+MN954w9i2bZuxatUqw+l0GomJiYbb7Ta7NL8KDQ01QkNDjZycHOOTTz4xXnjhBaNZs2ZGQUGB2aU1mtdff90IDAw0Dhw4YHYpflVfX29MmzbNsNlsRlBQkGGz2YzZs2ebXVajcDqdxq9+9SvjwIEDRl1dnfGXv/zFCAgIMK688kqzS/tJhJ1LyF133WW0a9fO2Ldvn9ml+F11dbWxe/duY8uWLcb06dON1q1bGzt27DC7LL8qKSkxoqOjjW3btnnarBh2fuzw4cOG3W633GPJ4OBgw+l0erXdc889Rq9evUyqqPENGDDAGDJkiNll+N1rr71mtG3b1njttdeMzz//3Hj55ZeNqKgoSwbXPXv2GL/85S8NSUZgYKBx3XXXGZmZmUZycrLZpf0kws4lIjs722jbtq3x7bffml3KBdG/f39j3LhxZpfhV2+99ZbnPzAnNkmGzWYzAgMDjbq6OrNLbDQ9evQwpk+fbnYZfpWYmGiMGTPGq23BggVGfHy8SRU1rr179xoBAQHG22+/bXYpfte2bVvjueee82qbNWuWcdVVV5lUUeOrqqoySktLDcMwjF//+tfG4MGDTa7op7Fmx+IMw9D48eP11ltvae3atUpKSjK7pAuioaFB1dXVZpfhV/3799f27dv12WefebYePXooMzNTn332mQIDA80usVFUVVXpm2++UVxcnNml+FWfPn1O+gzErl271K5dO5Mqalz5+fmKjo5Wenq62aX43Q8//KCAAO8/p4GBgWpoaDCposYXHh6uuLg4HT58WKtXr9ZNN91kdkk/yXrvc8JLdna2li5dqnfeeUcRERFyuVySJIfDobCwMJOr84+cnBwNGjRIiYmJOnr0qJYuXaq///3vWr16tdml+VVERIQ6d+7s1RYeHq5WrVqd1H4xmzJlim688Ua1a9dOpaWlevjhhxUYGKjbb7/d7NL8atKkSerdu7dmz56tX//61/r444+1aNEiLVq0yOzS/K6hoUH5+fnKysqy5GcEbrzxRj322GNKTExUp06d9Omnn2revHkaPXq02aX53erVq2UYhq666irt2bNHU6dOVXJyskaNGmV2aT/N7KklNC5Jp9zy8/PNLs1vRo8ebbRr184ICQkx2rRpY/Tv3994//33zS7rgrDimp3bbrvNiIuLM0JCQoyf/exnxm233Wbs2bPH7LIaxbvvvmt07tzZCA0NNZKTk41FixaZXVKjWL16tSHJ2Llzp9mlNAq3221MmDDBSExMNJo1a2Zcdtllxh/+8Aejurra7NL87vXXXzcuu+wyIyQkxIiNjTWys7ONI0eOmF3WGdkMw4KfeAQAAPj/sWYHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAHz0yCOP6JprrjG7DABnQNgB4LM777xTQ4cO9dq32Wy66667TuqbnZ0tm82mO++886T+NptNwcHBiomJ0Q033KCXXnrppB9RbN++vadvYGCg4uPjNWbMGB0+fPgna/zf85o3b64uXbroz3/+8zmP1Waz6e233/ZqmzJlioqKis75WgAuLMIOAL9KSEjQsmXL9O9//9vTdvz4cS1dulSJiYkn9R84cKDKysq0d+9erVy5Uv369dOECRM0ZMgQ1dXVefWdOXOmysrKVFJSoldffVUbNmzQvffee8aaTpz3xRdfaMSIERo7dqxWrlx53mNt0aKFWrVqdd7XAdC4CDsA/Kpbt25KSEjQm2++6Wl78803lZiYqGuvvfak/qGhoYqNjdXPfvYzdevWTQ888IDeeecdrVy5UgUFBV59IyIiPH379eunrKwsffLJJ2es6cR5l112maZNm6aoqCgVFhZ6jm/evFk33HCDWrduLYfDoV/96lde123fvr0k6eabb5bNZvPs//gx1omZrj/96U+Ki4tTq1atlJ2drdraWk+fsrIypaenKywsTElJSVq6dKnat2+vp59++ozjAOAbwg4Avxs9erTy8/M9+y+99JJGjRp11udff/31uvrqq70C048dOHBA7777rnr27HnW121oaNBf//pXHT58WCEhIZ72o0ePKisrSx988IE++ugjdejQQYMHD9bRo0cl/ScMSVJ+fr7Kyso8+6eybt06ffPNN1q3bp2WLFmigoICr9A2cuRIlZaW6u9//7v++te/atGiRaqoqDjrMQA4d4QdAH43YsQIffDBB/r+++/1/fff68MPP9SIESPO6RrJycnau3evV9u0adPUokULhYWFqW3btrLZbJo3b94Zr3XivNDQUN1yyy1q2bKlfvvb33qOX3/99RoxYoSSk5PVsWNHLVq0SD/88IPWr18vSWrTpo0kKTIyUrGxsZ79U2nZsqWee+45JScna8iQIUpPT/es6/n666+1Zs0avfjii+rZs6e6deumP//5z16P/AD4H2EHgN+1adNG6enpKigoUH5+vtLT09W6detzuoZhGLLZbF5tU6dO1WeffabPP//cEyDS09NVX1//k9c6cd7atWvVs2dPPfXUU7riiis8x8vLyzV27Fh16NBBDodDdrtdVVVVKikpOaeaJalTp04KDAz07MfFxXlmbnbu3KmgoCB169bNc/yKK65Qy5Ytz/k+AM5ekNkFALCm0aNHa/z48ZKk559//pzP/+qrr5SUlOTV1rp1a09I6dChg55++mk5nU6tW7dOqampp73WifOuuOIKLV++XF26dFGPHj2UkpIiScrKytK//vUvPfPMM2rXrp1CQ0PldDpVU1NzznUHBwd77dtstpPeLANwYTGzA6BRDBw4UDU1NaqtrVVaWto5nbt27Vpt375dGRkZP9nvxAzKuTwGSkhI0G233aacnBxP24cffqh7771XgwcPVqdOnRQaGqp//vOfXucFBwefcQbpTK666irV1dXp008/9bTt2bPnjK/PAzg/zOwAaBSBgYH66quvPP98OtXV1XK5XKqvr1d5eblWrVql3NxcDRkyRCNHjvTqe/ToUblcLhmGoX379un+++9XmzZt1Lt373OqbcKECercubO2bNmiHj16qEOHDvrLX/6iHj16yO12a+rUqQoLC/M6p3379ioqKlKfPn0UGhrq06On5ORkpaamaty4cVq4cKGCg4N13333KSws7KRHdgD8h5kdAI3GbrfLbrf/ZJ9Vq1YpLi5O7du318CBA7Vu3TrNnz9f77zzzkkhacaMGYqLi1N8fLyGDBmi8PBwvf/+++f8rZuUlBQNGDBAM2bMkCQtXrxYhw8fVrdu3XTHHXfo3nvvVXR0tNc5Tz75pAoLC5WQkHDKV+jP1ssvv6yYmBj98pe/1M0336yxY8cqIiJCzZo18/maAH6azTAMw+wiAOBStX//fiUkJGjNmjXq37+/2eUAlkTYAYALaO3ataqqqlKXLl1UVlam+++/XwcOHNCuXbtOWtwMwD9YswMAF1Btba0eeOABffvtt4qIiFDv3r316quvEnSARsTMDgAAsDQWKAMAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEv7/wCYoDQjdzK9wwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3BelR31kTG6a"
   },
   "source": [
    "Have you noticed that this function returns three objects? Take a look at the documentation [here](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) to figure out what they are.\n",
    "\n",
    "To get the returned three objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:04.322Z",
     "iopub.status.busy": "2020-06-14T19:57:04.229Z",
     "iopub.status.idle": "2020-06-14T19:57:05.023Z",
     "shell.execute_reply": "2020-06-14T19:57:05.046Z"
    },
    "executionInfo": {
     "elapsed": 1035,
     "status": "ok",
     "timestamp": 1687818276961,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "qrmt7ma_TG6a",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "139797bc-c5b3-4b84-9f47-523354786db2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  9.  39.  76. 133. 293. 599. 784. 684. 323.  48.]\n",
      "[1.4  2.18 2.96 3.74 4.52 5.3  6.08 6.86 7.64 8.42 9.2 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_raw, bins_raw, patches = plt.hist(movies.IMDB_Rating)\n",
    "print(n_raw)\n",
    "print(bins_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1SUgGDBwTG6b"
   },
   "source": [
    "Here, `n_raw` contains the values of histograms, i.e., the number of movies in each of the 10 bins. Thus, the sum of the elements in `n_raw` should be equal to the total number of movies.\n",
    "\n",
    "**Q: Test whether the sum of values in `n_raw` is equal to the number of movies in the `movies` dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:07.793Z",
     "iopub.status.busy": "2020-06-14T19:57:07.778Z",
     "iopub.status.idle": "2020-06-14T19:57:07.833Z",
     "shell.execute_reply": "2020-06-14T19:57:07.849Z"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1687818276962,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "iCGZ-jN2TG6b",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "3fabb0e6-1b39-4bbc-ebd3-ddf2009c3851"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2988.0\n",
      "2988\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c9gIcp06TG6b"
   },
   "source": [
    "The second returned object (`bins_raw`) is a list containing the edges of the 10 bins: the first bin is \\[1.4, 2.18\\], the second \\[2.18, 2.96\\], and so on. What's the width of the bins?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:10.040Z",
     "iopub.status.busy": "2020-06-14T19:57:10.025Z",
     "iopub.status.idle": "2020-06-14T19:57:10.081Z",
     "shell.execute_reply": "2020-06-14T19:57:10.097Z"
    },
    "executionInfo": {
     "elapsed": 151,
     "status": "ok",
     "timestamp": 1687818311779,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "im5eQCroTG6b",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "ca756ea4-bc06-404e-c650-0c2675d17369"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(bins_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C3TsmP_RTG6b"
   },
   "source": [
    "The width is same as the maximum value minus minimum value, divided by 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:11.016Z",
     "iopub.status.busy": "2020-06-14T19:57:10.997Z",
     "iopub.status.idle": "2020-06-14T19:57:11.052Z",
     "shell.execute_reply": "2020-06-14T19:57:11.068Z"
    },
    "executionInfo": {
     "elapsed": 223,
     "status": "ok",
     "timestamp": 1687818312845,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "GxrmxezwTG6b",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "e4ef2cd2-6171-4b28-d669-61ca6ed9d8b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4 9.2\n",
      "0.7799999999999999\n"
     ]
    }
   ],
   "source": [
    "min_rating = min(movies.IMDB_Rating)\n",
    "max_rating = max(movies.IMDB_Rating)\n",
    "print(min_rating, max_rating)\n",
    "print( (max_rating-min_rating) / 10 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JGxvd4CGTG6c"
   },
   "source": [
    "Now, let's plot a normalized (density) histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:12.070Z",
     "iopub.status.busy": "2020-06-14T19:57:12.053Z",
     "iopub.status.idle": "2020-06-14T19:57:12.176Z",
     "shell.execute_reply": "2020-06-14T19:57:12.239Z"
    },
    "executionInfo": {
     "elapsed": 432,
     "status": "ok",
     "timestamp": 1687818319541,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "OIrthY8ATG6c",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "92cb8ce3-30af-45ce-ac89-3a276607c7b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0038616  0.0167336  0.03260907 0.05706587 0.12571654 0.25701095\n",
      " 0.33638829 0.29348162 0.13858854 0.0205952 ]\n",
      "[1.4  2.18 2.96 3.74 4.52 5.3  6.08 6.86 7.64 8.42 9.2 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(movies.IMDB_Rating, density=True)\n",
    "print(n)\n",
    "print(bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-qKKEOXMTG6c"
   },
   "source": [
    "The ten bins do not change. But now `n` represents the density of the data inside each bin. In other words, the sum of the area of each bar will equal to 1.\n",
    "\n",
    "**Q: Can you verify this?**\n",
    "\n",
    "Hint: the area of each bar is calculated as height * width. You may get something like 0.99999999999999978 instead of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:13.305Z",
     "iopub.status.busy": "2020-06-14T19:57:13.290Z",
     "iopub.status.idle": "2020-06-14T19:57:13.338Z",
     "shell.execute_reply": "2020-06-14T19:57:13.353Z"
    },
    "executionInfo": {
     "elapsed": 154,
     "status": "ok",
     "timestamp": 1687818325739,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "HtDdUJUdTG6c",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "b7d1e959-23db-4106-f296-d3190f318e81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ugwtR9guTG6c"
   },
   "source": [
    "Anyway, these data generated from the `hist` function is calculated from `numpy`'s `histogram` function. https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html\n",
    "\n",
    "Note that the result of `np.histogram()` is same as that of `plt.hist()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:14.303Z",
     "iopub.status.busy": "2020-06-14T19:57:14.287Z",
     "iopub.status.idle": "2020-06-14T19:57:14.334Z",
     "shell.execute_reply": "2020-06-14T19:57:14.349Z"
    },
    "executionInfo": {
     "elapsed": 201,
     "status": "ok",
     "timestamp": 1687818328266,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "xn-WpuKiTG6c",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "05d56921-f033-4a88-dd19-eda81353c5ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9,  39,  76, 133, 293, 599, 784, 684, 323,  48]),\n",
       " array([1.4 , 2.18, 2.96, 3.74, 4.52, 5.3 , 6.08, 6.86, 7.64, 8.42, 9.2 ]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(movies.IMDB_Rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:14.903Z",
     "iopub.status.busy": "2020-06-14T19:57:14.885Z",
     "iopub.status.idle": "2020-06-14T19:57:14.997Z",
     "shell.execute_reply": "2020-06-14T19:57:15.015Z"
    },
    "executionInfo": {
     "elapsed": 319,
     "status": "ok",
     "timestamp": 1687818328581,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "a1DPjJI7TG6c",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "f7213626-edd6-4584-f7ef-96070d480917"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9.,  39.,  76., 133., 293., 599., 784., 684., 323.,  48.]),\n",
       " array([1.4 , 2.18, 2.96, 3.74, 4.52, 5.3 , 6.08, 6.86, 7.64, 8.42, 9.2 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies.IMDB_Rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o4ArxCNETG6d"
   },
   "source": [
    "If you look at the documentation, you can see that `numpy` uses simply 10 as the default number of bins. But you can set it manually or set it to be `auto`, which is the \"Maximum of the `sturges` and `fd` estimators.\". Let's try this `auto` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:15.850Z",
     "iopub.status.busy": "2020-06-14T19:57:15.833Z",
     "iopub.status.idle": "2020-06-14T19:57:15.998Z",
     "shell.execute_reply": "2020-06-14T19:57:16.015Z"
    },
    "executionInfo": {
     "elapsed": 316,
     "status": "ok",
     "timestamp": 1687818329586,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "g04dPJySTG6d",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "868eaedc-074b-45df-ea31-5d3a0648d9e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(movies.IMDB_Rating, bins='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TM_jST11TG6d"
   },
   "source": [
    "## Consequences of the binning parameter\n",
    "\n",
    "Let's explore the effect of bin size using small multiples. In `matplotlib`, you can use [subplot](https://www.google.com/search?client=safari&rls=en&q=matplotlib+subplot&ie=UTF-8&oe=UTF-8) to put multiple plots into a single figure.\n",
    "\n",
    "For instance, you can do something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:17.609Z",
     "iopub.status.busy": "2020-06-14T19:57:17.591Z",
     "iopub.status.idle": "2020-06-14T19:57:17.858Z",
     "shell.execute_reply": "2020-06-14T19:57:17.878Z"
    },
    "executionInfo": {
     "elapsed": 1004,
     "status": "ok",
     "timestamp": 1687818421168,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "4lliCeD6TG6d",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "88567283-2f7e-470d-bccd-e19734730225"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "movies.IMDB_Rating.hist(bins=3)\n",
    "plt.subplot(1,2,2)\n",
    "movies.IMDB_Rating.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XXIbKfeWTG6d"
   },
   "source": [
    "What does the argument in `plt.subplot(1,2,1)` mean? If you're not sure, check out: http://stackoverflow.com/questions/3584805/in-matplotlib-what-does-the-argument-mean-in-fig-add-subplot111\n",
    "\n",
    "**Q: create 8 subplots (2 rows and 4 columns) with the following `binsizes`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 872
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:19.049Z",
     "iopub.status.busy": "2020-06-14T19:57:19.029Z",
     "iopub.status.idle": "2020-06-14T19:57:20.201Z",
     "shell.execute_reply": "2020-06-14T19:57:20.255Z"
    },
    "executionInfo": {
     "elapsed": 2347,
     "status": "ok",
     "timestamp": 1687818423649,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "jlYvPcu-TG6d",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "cb6e99e3-6332-420f-e3d1-c0bec8422308"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = [2, 3, 5, 10, 30, 40, 60, 100 ]\n",
    "figsize = (18, 10)\n",
    "\n",
    "# TODO\n",
    "\n",
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhkbFVmOTG6d"
   },
   "source": [
    "Do you see the issues with having too few bins or too many bins? In particular, do you notice weird patterns that emerge from `bins=30`?\n",
    "\n",
    "**Q: Can you guess why do you see such patterns? What are the peaks and what are the empty bars? What do they tell you about choosing the binsize in histograms?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:21.486Z",
     "iopub.status.busy": "2020-06-14T19:57:21.468Z",
     "iopub.status.idle": "2020-06-14T19:57:21.640Z",
     "shell.execute_reply": "2020-06-14T19:57:21.689Z"
    },
    "executionInfo": {
     "elapsed": 637,
     "status": "ok",
     "timestamp": 1687818426810,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "_RYXN95UTG6e",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "6079008d-ce20-459c-8381-b0ef63548fe4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:22.246Z",
     "iopub.status.busy": "2020-06-14T19:57:22.230Z",
     "iopub.status.idle": "2020-06-14T19:57:22.280Z",
     "shell.execute_reply": "2020-06-14T19:57:22.297Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1687818426810,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "okuF4FOzTG6e",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "fad130a5-cc9d-4832-d9f2-b52d8d95313b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YhF5JL3mTG6e"
   },
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OVCQtwOLTG6e"
   },
   "source": [
    "## Formulae for choosing the number of bins.\n",
    "\n",
    "We can manually choose the number of bins based on those formulae."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:26.087Z",
     "iopub.status.busy": "2020-06-14T19:57:26.071Z",
     "iopub.status.idle": "2020-06-14T19:57:26.506Z",
     "shell.execute_reply": "2020-06-14T19:57:26.556Z"
    },
    "executionInfo": {
     "elapsed": 1146,
     "status": "ok",
     "timestamp": 1687818431715,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "My4M97X8TG6f",
    "outputId": "17eb04e4-63fd-4e9e-d880-138c1a92f87a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'F-D, 35 bins'}>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = len(movies)\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "# Sqrt\n",
    "nbins = int(np.sqrt(N))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"SQRT, {} bins\".format(nbins))\n",
    "movies.IMDB_Rating.hist(bins=nbins)\n",
    "\n",
    "# Sturge's formula\n",
    "nbins = int(np.ceil(np.log2(N) + 1))\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"Sturge, {} bins\".format(nbins))\n",
    "movies.IMDB_Rating.hist(bins=nbins)\n",
    "\n",
    "# Freedman-Diaconis\n",
    "iqr = np.percentile(movies.IMDB_Rating, 75) - np.percentile(movies.IMDB_Rating, 25)\n",
    "width = 2*iqr/np.power(N, 1/3)\n",
    "nbins = int((max(movies.IMDB_Rating) - min(movies.IMDB_Rating)) / width)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.title(\"F-D, {} bins\".format(nbins))\n",
    "movies.IMDB_Rating.hist(bins=nbins)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8RKA0F83TG6f"
   },
   "source": [
    "But we can also use built-in formulae too. Let's try all of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:27.570Z",
     "iopub.status.busy": "2020-06-14T19:57:27.554Z",
     "iopub.status.idle": "2020-06-14T19:57:28.976Z",
     "shell.execute_reply": "2020-06-14T19:57:28.994Z"
    },
    "executionInfo": {
     "elapsed": 1582,
     "status": "ok",
     "timestamp": 1687818433290,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "qoEJofYATG6f",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "2c4f4f9c-1a22-422f-9865-2cae93d50bfc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "plt.subplot(161)\n",
    "movies.IMDB_Rating.hist(bins='fd')\n",
    "\n",
    "plt.subplot(162)\n",
    "movies.IMDB_Rating.hist(bins='doane')\n",
    "\n",
    "plt.subplot(163)\n",
    "movies.IMDB_Rating.hist(bins='scott')\n",
    "\n",
    "plt.subplot(164)\n",
    "movies.IMDB_Rating.hist(bins='rice')\n",
    "\n",
    "plt.subplot(165)\n",
    "movies.IMDB_Rating.hist(bins='sturges')\n",
    "\n",
    "plt.subplot(166)\n",
    "movies.IMDB_Rating.hist(bins='sqrt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "33_5ZPO2TG6f"
   },
   "source": [
    "Some are decent, but several of them tend to overestimate the good number of bins. As you have more data points, some of the formulae may overestimate the necessary number of bins. Particularly in our case, because of the precision issue, we shouldn't increase the number of bins too much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oX4mgRmVTG6f"
   },
   "source": [
    "### Then, how should we choose the number of bins?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nwa4u4gjTG6f"
   },
   "source": [
    "So what's the conclusion? use Scott's rule or Sturges' formula?\n",
    "\n",
    "No, I think the take-away is that you **should understand how the inappropriate number of bins can mislead you** and you should **try multiple number of bins** to obtain the most accurate picture of the data. Although the 'default' may work in most cases, don't blindly trust it! Don't judge the distribution of a dataset based on a single histogram. Try multiple parameters to get the full picture!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fevnnf2fTG6f"
   },
   "source": [
    "## CDF (Cumulative distribution function)\n",
    "\n",
    "Drawing a CDF is easy. Because it's very common data visualization, histogram has an option called `cumulative`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:33.036Z",
     "iopub.status.busy": "2020-06-14T19:57:32.985Z",
     "iopub.status.idle": "2020-06-14T19:57:33.475Z",
     "shell.execute_reply": "2020-06-14T19:57:33.492Z"
    },
    "executionInfo": {
     "elapsed": 590,
     "status": "ok",
     "timestamp": 1687818472742,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "tGx6OxKATG6g",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "908dae60-f150-4f40-9149-8faa5670e7ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.IMDB_Rating.hist(cumulative=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ylVvfe-eTG6g"
   },
   "source": [
    "You can also combine with options such as `histtype` and `density`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:34.566Z",
     "iopub.status.busy": "2020-06-14T19:57:34.550Z",
     "iopub.status.idle": "2020-06-14T19:57:34.671Z",
     "shell.execute_reply": "2020-06-14T19:57:34.687Z"
    },
    "executionInfo": {
     "elapsed": 336,
     "status": "ok",
     "timestamp": 1687818473325,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "V-qGVKxVTG6g",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "9fd0e1f5-e2fb-49dc-ebb8-9d7a17bc5e29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.IMDB_Rating.hist(histtype='step', cumulative=True, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5LkuToB3TG6g"
   },
   "source": [
    "And increase the number of bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:35.726Z",
     "iopub.status.busy": "2020-06-14T19:57:35.709Z",
     "iopub.status.idle": "2020-06-14T19:57:37.266Z",
     "shell.execute_reply": "2020-06-14T19:57:37.319Z"
    },
    "executionInfo": {
     "elapsed": 2968,
     "status": "ok",
     "timestamp": 1687818476463,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "d0fgONoxTG6g",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "3f3cb362-f263-4565-f72a-33ea6be4e2cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.IMDB_Rating.hist(cumulative=True, density=True, bins=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JrytXZBQTG6g"
   },
   "source": [
    "This method works fine. By increasing the number of bins, you can get a CDF in the resolution that you want. But let's also try it manually to better understand what's going on. First, we should sort all the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:38.193Z",
     "iopub.status.busy": "2020-06-14T19:57:38.177Z",
     "iopub.status.idle": "2020-06-14T19:57:38.229Z",
     "shell.execute_reply": "2020-06-14T19:57:38.245Z"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1687818476464,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "hQmzLYonTG6g",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "e986fb7e-dd88-412e-8d7b-7652dee31d4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1247    1.4\n",
       "406     1.5\n",
       "1754    1.6\n",
       "1590    1.7\n",
       "1515    1.7\n",
       "Name: IMDB_Rating, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_sorted = movies.IMDB_Rating.sort_values()\n",
    "rating_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GwGp6rXDTG6g"
   },
   "source": [
    "We need to know the number of data points,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:39.585Z",
     "iopub.status.busy": "2020-06-14T19:57:39.569Z",
     "iopub.status.idle": "2020-06-14T19:57:39.617Z",
     "shell.execute_reply": "2020-06-14T19:57:39.635Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1687818476465,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "wzlbCrkcTG6h",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "788c365c-1eb4-446c-9a6e-3627f7a758e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2988"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = len(rating_sorted)\n",
    "N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TB-YgQwDTG6h"
   },
   "source": [
    "And I think this may be useful for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:40.533Z",
     "iopub.status.busy": "2020-06-14T19:57:40.515Z",
     "iopub.status.idle": "2020-06-14T19:57:40.565Z",
     "shell.execute_reply": "2020-06-14T19:57:40.581Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1687818476989,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ta5S5g-OTG6h",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "69f7a2c3-e913-4d5a-c10c-e66abf2dc7c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 , 0.22,\n",
       "       0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42, 0.44,\n",
       "       0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64, 0.66,\n",
       "       0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86, 0.88,\n",
       "       0.9 , 0.92, 0.94, 0.96, 0.98, 1.  ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 50\n",
    "np.linspace(1/n, 1.0, num=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTkw7L1ETG6h"
   },
   "source": [
    "**Q: now you're ready to draw a proper CDF. Draw the CDF plot of this data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:41.498Z",
     "iopub.status.busy": "2020-06-14T19:57:41.481Z",
     "iopub.status.idle": "2020-06-14T19:57:41.677Z",
     "shell.execute_reply": "2020-06-14T19:57:41.693Z"
    },
    "executionInfo": {
     "elapsed": 345,
     "status": "ok",
     "timestamp": 1687818478045,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "mS2e8Ji4TG6h",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "0e2fd594-524b-4d3a-df83-aad9d593a30e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_yRa45NKTG6h"
   },
   "source": [
    "## A bit more histogram with altair\n",
    "\n",
    "As you may remember, you can get a pandas dataframe from `vega_datasets` package and use it to create visualizations. But, if you use `altair`, you can simply pass the URL instead of the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:43.058Z",
     "iopub.status.busy": "2020-06-14T19:57:43.042Z",
     "iopub.status.idle": "2020-06-14T19:57:43.092Z",
     "shell.execute_reply": "2020-06-14T19:57:43.108Z"
    },
    "executionInfo": {
     "elapsed": 138,
     "status": "ok",
     "timestamp": 1687818484772,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "i5tVwCoUTG6h",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "f2d50db3-0a7e-4dbb-8e4c-73112fb813e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vega_datasets.data.movies.url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:43.771Z",
     "iopub.status.busy": "2020-06-14T19:57:43.755Z",
     "iopub.status.idle": "2020-06-14T19:57:43.806Z",
     "shell.execute_reply": "2020-06-14T19:57:43.821Z"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1687818484928,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ovK3CJblTG6i",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "148eff64-dbe9-44d0-bcca-a4e775aac584"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('jupyterlab')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose based on your environment\n",
    "alt.renderers.enable('jupyterlab')\n",
    "# alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lwcVA0LxTG6i"
   },
   "source": [
    "As mentioned before, in `altair` histogram is not special. It is just a plot that use bars (`mark_bar()`) where X axis is defined by `IMDB_Rating` with bins (`bin=True`), and Y axis is defined by `count()` aggregation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:44.724Z",
     "iopub.status.busy": "2020-06-14T19:57:44.708Z",
     "iopub.status.idle": "2020-06-14T19:57:44.757Z",
     "shell.execute_reply": "2020-06-14T19:57:44.817Z"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1687818484929,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "h048hIgETG6i",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "2729bc70-08cd-4c5b-e569-76c73bf8cbb6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "data": {
        "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
       },
       "encoding": {
        "x": {
         "bin": true,
         "field": "IMDB_Rating",
         "type": "quantitative"
        },
        "y": {
         "aggregate": "count",
         "type": "quantitative"
        }
       },
       "mark": {
        "type": "bar"
       }
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X(\"IMDB_Rating:Q\",  bin=True),\n",
    "    alt.Y('count()')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KrhwH6I1TG6i"
   },
   "source": [
    "Have you noted that it is `IMDB_Rating:Q` not `IMDB_Rating`? This is a shorthand for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:45.826Z",
     "iopub.status.busy": "2020-06-14T19:57:45.808Z",
     "iopub.status.idle": "2020-06-14T19:57:45.861Z",
     "shell.execute_reply": "2020-06-14T19:57:45.890Z"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1687818485080,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "Jna8_X-MTG6i",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "11a8b01b-03f5-4d69-c124-cce4b583a2d9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "data": {
        "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
       },
       "encoding": {
        "x": {
         "bin": true,
         "field": "IMDB_Rating",
         "type": "quantitative"
        },
        "y": {
         "aggregate": "count",
         "type": "quantitative"
        }
       },
       "mark": {
        "type": "bar"
       }
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X('IMDB_Rating', type='quantitative', bin=True),\n",
    "    alt.Y(aggregate='count', type='quantitative')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PXWdvT7XTG6i"
   },
   "source": [
    "In altair, you want to specify the data types using one of the four categories: quantitative, ordinal, nominal, and temporal. https://altair-viz.github.io/user_guide/encoding.html#data-types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "07Qhm4sZTG6i"
   },
   "source": [
    "Although you can adjust the bins in `altair`, it does not encourage you to set the bins directly. For instance, although there is `step` parameter that directly sets the bin size, there are parameters such as `maxbins` (maximum number of bins) or `minstep` (minimum allowable step size), or `nice` (attemps to make the bin boundaries more human-friendly), that encourage you not to specify the bins directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:48.350Z",
     "iopub.status.busy": "2020-06-14T19:57:48.333Z",
     "iopub.status.idle": "2020-06-14T19:57:48.386Z",
     "shell.execute_reply": "2020-06-14T19:57:48.414Z"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1687818485080,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "6fg-viz1TG6i",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "3c6ba6e4-afc4-4819-dc3f-ed276f5fbc59"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "data": {
        "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
       },
       "encoding": {
        "x": {
         "bin": {
          "step": 0.09
         },
         "field": "IMDB_Rating",
         "type": "quantitative"
        },
        "y": {
         "aggregate": "count",
         "type": "quantitative"
        }
       },
       "mark": {
        "type": "bar"
       }
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from altair import Bin\n",
    "\n",
    "alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X(\"IMDB_Rating:Q\",  bin=Bin(step=0.09)),\n",
    "    alt.Y('count()')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:49.339Z",
     "iopub.status.busy": "2020-06-14T19:57:49.320Z",
     "iopub.status.idle": "2020-06-14T19:57:49.373Z",
     "shell.execute_reply": "2020-06-14T19:57:49.401Z"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1687818485081,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "lhrilAaATG6j",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "530289a0-fa70-4eda-a05e-90caa03bc646"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "data": {
        "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
       },
       "encoding": {
        "x": {
         "bin": {
          "maxbins": 20,
          "nice": true
         },
         "field": "IMDB_Rating",
         "type": "quantitative"
        },
        "y": {
         "aggregate": "count",
         "type": "quantitative"
        }
       },
       "mark": {
        "type": "bar"
       }
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X(\"IMDB_Rating:Q\",  bin=Bin(nice=True, maxbins=20)),\n",
    "    alt.Y('count()')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6I7ZhwUTG6j"
   },
   "source": [
    "### Composing charts in altair\n",
    "\n",
    "`altair` has a very nice way to compose multiple plots. Two histograms side by side? just do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:50.814Z",
     "iopub.status.busy": "2020-06-14T19:57:50.798Z",
     "iopub.status.idle": "2020-06-14T19:57:50.842Z",
     "shell.execute_reply": "2020-06-14T19:57:50.859Z"
    },
    "executionInfo": {
     "elapsed": 156,
     "status": "ok",
     "timestamp": 1687818529552,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "xPmv3BSrTG6j"
   },
   "outputs": [],
   "source": [
    "chart1 = alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X(\"IMDB_Rating:Q\",  bin=Bin(step=0.1)),\n",
    "    alt.Y('count()')\n",
    ").properties(\n",
    "    width=300,\n",
    "    height=150\n",
    ")\n",
    "chart2 = alt.Chart(vega_datasets.data.movies.url).mark_bar().encode(\n",
    "    alt.X(\"IMDB_Rating:Q\",  bin=Bin(nice=True, maxbins=20)),\n",
    "    alt.Y('count()')\n",
    ").properties(\n",
    "    width=300,\n",
    "    height=150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:52.890Z",
     "iopub.status.busy": "2020-06-14T19:57:52.874Z",
     "iopub.status.idle": "2020-06-14T19:57:52.923Z",
     "shell.execute_reply": "2020-06-14T19:57:52.953Z"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1687818530196,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ysVDBmkHTG6j",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "cb97ec72-7ae3-4472-e672-08aa74e3b5af"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "hconcat": [
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "step": 0.1
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        },
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "maxbins": 20,
            "nice": true
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        }
       ]
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart1 | chart2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:53.433Z",
     "iopub.status.busy": "2020-06-14T19:57:53.415Z",
     "iopub.status.idle": "2020-06-14T19:57:53.469Z",
     "shell.execute_reply": "2020-06-14T19:57:53.502Z"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1687818530827,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "4V9T406iTG6j",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "e90872a0-239a-4dca-d2a3-87edae091c74"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "hconcat": [
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "step": 0.1
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        },
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "maxbins": 20,
            "nice": true
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        }
       ]
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.hconcat(chart1, chart2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J2H-DmilTG6j"
   },
   "source": [
    "Vertical commposition?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:55.011Z",
     "iopub.status.busy": "2020-06-14T19:57:54.929Z",
     "iopub.status.idle": "2020-06-14T19:57:55.080Z",
     "shell.execute_reply": "2020-06-14T19:57:55.113Z"
    },
    "executionInfo": {
     "elapsed": 447,
     "status": "ok",
     "timestamp": 1687818531762,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "XqiQlLJwTG6j",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "b4f414f9-d5af-4630-801c-b1f0980d1338"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "vconcat": [
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "step": 0.1
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        },
        {
         "data": {
          "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
         },
         "encoding": {
          "x": {
           "bin": {
            "maxbins": 20,
            "nice": true
           },
           "field": "IMDB_Rating",
           "type": "quantitative"
          },
          "y": {
           "aggregate": "count",
           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        }
       ]
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.vconcat(chart1, chart2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "execution": {
     "iopub.execute_input": "2020-06-14T19:57:56.070Z",
     "iopub.status.busy": "2020-06-14T19:57:56.051Z",
     "iopub.status.idle": "2020-06-14T19:57:56.107Z",
     "shell.execute_reply": "2020-06-14T19:57:56.143Z"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1687818531763,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
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     "user_tz": 240
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    "id": "lNNGnNF9TG6k",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "c3de22f3-1815-45c9-85d8-279f43b6fb59"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
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         "continuousWidth": 300
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       "vconcat": [
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           "bin": {
            "step": 0.1
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           "field": "IMDB_Rating",
           "type": "quantitative"
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           "type": "quantitative"
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         "height": 150,
         "mark": {
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           "type": "quantitative"
          }
         },
         "height": 150,
         "mark": {
          "type": "bar"
         },
         "width": 300
        }
       ]
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "chart1 & chart2"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "jAIuSQfmTG6k"
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    "Shall we avoid some repetitions? You can define a *base* empty chart first and then assign encodings later when you put together multiple charts together. Here is an example: https://altair-viz.github.io/user_guide/compound_charts.html#repeated-charts\n",
    "\n",
    "**Q: Using the base chart approach to create a 2x2 chart where the top row shows the two histograms of `IMDB_Rating` with `maxbins`=10 and 50 respectively, and the bottom row shows another two histograms of `IMDB_Votes` with `maxbins`=10 and 50.**"
   ]
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    "jupyter": {
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    "outputId": "28095dd2-5f7b-4134-b4ef-9f5d92dd447b"
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   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v5+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.17.0.json",
       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
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       "data": {
        "url": "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/movies.json"
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             "type": "quantitative"
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            "y": {
             "aggregate": "count",
             "type": "quantitative"
            }
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           "height": 150,
           "mark": {
            "type": "bar"
           },
           "width": 200
          },
          {
           "encoding": {
            "x": {
             "bin": {
              "maxbins": 50
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             "field": "IMDB_Rating",
             "type": "quantitative"
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             "type": "quantitative"
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           },
           "height": 150,
           "mark": {
            "type": "bar"
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           "width": 200
          }
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        },
        {
         "hconcat": [
          {
           "encoding": {
            "x": {
             "bin": {
              "maxbins": 10
             },
             "field": "IMDB_Votes",
             "type": "quantitative"
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             "aggregate": "count",
             "type": "quantitative"
            }
           },
           "height": 150,
           "mark": {
            "type": "bar"
           },
           "width": 200
          },
          {
           "encoding": {
            "x": {
             "bin": {
              "maxbins": 50
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             "field": "IMDB_Votes",
             "type": "quantitative"
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            "y": {
             "aggregate": "count",
             "type": "quantitative"
            }
           },
           "height": 150,
           "mark": {
            "type": "bar"
           },
           "width": 200
          }
         ]
        }
       ]
      },
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE\n"
   ]
  }
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