Day_12_Data_Visualisation_Part_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Day_12_Data_Visualisation_Part_2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLZYSy7r_5YA"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfgxD0UE_88o",
        "outputId": "bccd9eb8-018e-4c17-ffc7-53bad0c65bd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "df = pd.read_csv('/content/pokemon.csv')\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(807, 14)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>species</th>\n",
              "      <th>generation_id</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>base_experience</th>\n",
              "      <th>type_1</th>\n",
              "      <th>type_2</th>\n",
              "      <th>hp</th>\n",
              "      <th>attack</th>\n",
              "      <th>defense</th>\n",
              "      <th>speed</th>\n",
              "      <th>special-attack</th>\n",
              "      <th>special-defense</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>bulbasaur</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>6.9</td>\n",
              "      <td>64</td>\n",
              "      <td>grass</td>\n",
              "      <td>poison</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>45</td>\n",
              "      <td>65</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>ivysaur</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>142</td>\n",
              "      <td>grass</td>\n",
              "      <td>poison</td>\n",
              "      <td>60</td>\n",
              "      <td>62</td>\n",
              "      <td>63</td>\n",
              "      <td>60</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>venusaur</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>236</td>\n",
              "      <td>grass</td>\n",
              "      <td>poison</td>\n",
              "      <td>80</td>\n",
              "      <td>82</td>\n",
              "      <td>83</td>\n",
              "      <td>80</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>charmander</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>8.5</td>\n",
              "      <td>62</td>\n",
              "      <td>fire</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39</td>\n",
              "      <td>52</td>\n",
              "      <td>43</td>\n",
              "      <td>65</td>\n",
              "      <td>60</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>charmeleon</td>\n",
              "      <td>1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>142</td>\n",
              "      <td>fire</td>\n",
              "      <td>NaN</td>\n",
              "      <td>58</td>\n",
              "      <td>64</td>\n",
              "      <td>58</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id     species  generation_id  ...  speed  special-attack  special-defense\n",
              "0   1   bulbasaur              1  ...     45              65               65\n",
              "1   2     ivysaur              1  ...     60              80               80\n",
              "2   3    venusaur              1  ...     80             100              100\n",
              "3   4  charmander              1  ...     65              60               50\n",
              "4   5  charmeleon              1  ...     80              80               65\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRJu9JH8ALmp"
      },
      "source": [
        "## **Pie Charts:**\n",
        "A pie chart is a common univariate plot type that is used to depict relative frequencies for levels of a categorical variable. Frequencies in a pie chart are depicted as wedges drawn on a circle: the larger the angle or area, the more common the categorical value taken."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lqXY76gAYvV"
      },
      "source": [
        "![l3-c07-piecharts2.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAFoCAYAAAAxXpjgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd81PXhx/H3XTZkAkkIBAiQyAiREAIouBDBUQUFqzgYKlIREbVa0Z9W0VbBvW1RVKoUKqhgFStLpSArQEB2GIEkhAwgJCH7cr8/0tJSQJNwd5+7y+v5ePCQhBuvtCi88/3e9yx2u90uAAAAAIDTWU0HAAAAAEBTwQADAAAAABdhgAEAAACAizDAAAAAAMBFGGAAAAAA4CIMMAAAAABwEQYYAAAAALgIAwwAAAAAXIQBBgAAAAAuwgADAAAAABdhgAEAAACAizDAAAAAAMBFGGAAAAAA4CIMMAAAAABwEQYYAAAAALgIAwwAAAAAXIQBBgAAAAAuwgADAAAAABdhgAEAAACAizDAAAAAAMBFGGAAAAAA4CIMMAAAAABwEQYYAAAAALgIAwwAAAAAXIQBBgAAAAAuwgADAAAAABdhgAEAAACAizDAAAAAAMBFGGAAAAAA4CIMMAAAAABwEQYYAAAAALgIAwwAAAAAXIQBBgAAAAAuwgADAAAAABdhgAEAAACAizDAAAAAAMBFGGAAAAAA4CIMMAAAAABwEQYYAAAAALgIAwwAAAAAXIQBBgAAAAAuwgADAAAAABfxNR0AAIC3atWqleLi4kxnAECjZGZmqrCw0HSG12GAAQDgJHFxcUpLSzOdAQCNkpqaajrBK3EKIgAAAAC4CAMMAAAAAFyEAQYAAAAALsIAAwAAAAAXYYABAAAAgIswwAAAAADARRhgAAAAAOAiDDAAAAAAcBEGGAAAAAC4CAMMAAAAAFyEAQYAAAAALuJrOgCoLx8fHyUlJam6ulq+vr4aM2aMHnjgAVmtZ/8+QmZmpq699lpt3bpV6enpOnTokK655hoXVgPA2fV+5C+mE1xuw4ujTScAgFEMMHiMoKAgpaenS5Ly8/N166236vjx45o6dWq97p+enq60tLQmM8BqKypUnZOj6kO5sh07KltxiWpLimUrLpGtpFi1//5nWZlkq5W91ibZavXCoHuVpSD5WC2yWCwK8rMqNNBPoUF+CgvyU2ign8KCfE9+HB0aqHYtmiksyM/0lwwAAOD2GGDwSFFRUZoxY4b69Omjp59+WrW1tZoyZYq+//57VVZWauLEifrNb35z8vZVVVX6/e9/r/Lycq1cuVKPPfaYOnbsqAceeEDl5eUKCgrShx9+qC5duhj8qhrOVlqqyp07Vblnr6pzslWdk6OqnBxV5xySrbCwUY95MKlY22uqG3y/0EBftWvRTO0imqldi6C6n7dopm6tQ9U6LLBRLQAAAN6GAQaP1alTJ9XW1io/P18LFy5UWFiY1q9fr8rKSg0YMEBDhgyRxWKRJPn7++uZZ55RWlqa3nrrLUlScXGxVqxYIV9fXy1dulSPP/64PvvsM5Nf0s+qzstXxY7tqty5UxXbd6hi505VZ2VJdrvpNElScUWNth0q1rZDxaf9WqvgACW2CVWPtqHq0SZMPdqGqV2LZgYqAQAAzGKAwaPZ/zU+Fi9erC1btmj+/PmSpOPHjysjI0PnnXfeWe97/PhxjRkzRhkZGbJYLKqubvhRH2eqOnhQJ9auVdnadSpbt041+fmmkxqtsLRSP+wu0A+7C05+LryZn86PDVf/zi01oHMrJbYJldVqMViJpubOO+/UV199paioKG3dulWSdPToUd18883KzMxUXFycPv30U0VERMhut2vy5MlatGiRmjVrpo8++kgpKSmGvwIAgCdigMFj7du3Tz4+PoqKipLdbtebb76pK6+88pTbZGZmnvX+Tz75pAYOHKgvvvhCmZmZuuyyy5wb/Auqc3N1Ys1ala1ZoxPr1qkmN9doj7MVlVVrxe4CrfjXKAtv5qcLO7VU//hWGtC5pTpFBhsuhLcbO3as7rvvPo0e/Z+LQkybNk2DBg3SlClTNG3aNE2bNk3Tp0/XN998o4yMDGVkZGjt2rWaMGGC1q5da7AeAOCpGGDwSAUFBbrnnnt03333yWKx6Morr9S7776ryy+/XH5+ftq9e7fatm17yn1CQkJUUlJy8uPjx4+fvM1HH33kyvyTKvfsUfHixSpZslSVO3YYaXAXRWXV+mbrYX2z9bAkqU1YoK7oHq1rkmLUN64FR8fgcJdccslp36RZuHChvv/+e0nSmDFjdNlll2n69OlauHChRo8eLYvFogsuuEBFRUXKzc1VTEyM68MBAB6NAQaPUV5eruTk5JOXoR81apQeeughSdK4ceOUmZmplJQU2e12RUZGasGCBafcf+DAgZo2bZqSk5P12GOP6Xe/+53GjBmjV155RZdffrnrvo6t21SyZIlKlixR1b59LnteT3PoeIX+svqA/rL6gCJDAnRVYmtdkxSjfh0ZY3CevLy8k6MqJiZG+f869TcnJ0ft2rU7ebvY2Fjl5OQwwAAADcYAg8ew2Wxn/TWr1arnnntOzz333CmfDwsLO/najhYtWmj9+vWn/Pru3btP/vzZZ591YO2pqrJzVPTZfBV/+XdV5+Q47Xm8VUFJpT5ec0AfrzmgVsEBuqpHtIYlt1WfuBam09BE2M9wsZt/X+Tnf82YMUMzZsyQVHe0HgCA/8YAA5zEXlWlkmXLVDRvvk6sXu02Vyv0dIWllfpkzUF9suagEqKCdWu/9hqeEsv7kMEhoqOjT55amJubq6ioKEl1R7yysrJO3i47O1tt2rQ542OMHz9e48ePlySlpqY6PxoA4FGspgMAb1O5d6/ypk1XxqWXKefBh3Tixx8ZX06SkV+qqX/frgueW6ZH5m1WelaR6SR4uKFDh2rWrFmSpFmzZmnYsGEnP/+Xv/xFdrtda9asUVhYGKcfAgAahSNggIOcWL1aR957v25wwaXKq22atyFb8zZkK7FNqEZd0EE3pLRVgK+P6TS4sVtuuUXff/+9CgsLFRsbq6lTp2rKlCm66aabNHPmTLVv317z5s2TJF1zzTVatGiR4uPj1axZM3344YeG6wEAnooBBpwDe22tShYv0ZH331fFv15rBrO2HSrWlM9/0qtLd+vuizvptn4dFOTPEMPp5syZc8bPL1u27LTPWSwWvf32285OAgA0AQwwoBFqq6p0fMECHZ35gaoOHDCdgzPIK67UH77eoXe/36s7L+qoURd2UGggrxMDAABmMcCABrBXVenY3z7VkRkzVMPVzTzCkRNVevHbXfrTD3s15sI43XVRR0U09zedBQAAmigGGFAP9tpaFf/97yp48y1VZ2ebzkEjlFTU6K3v9ujDVfs1/pLOGn9JJ05NBAAALscAA37BiR9/VN4LL6py507TKXCAE1U2vbp0t2avPaCHBp+nX6e2kw9v7AwAAFyEy9ADZ1G5Z48O/uY3OnjnXYwvL5RfUqkpn/+ka99cqdV7j5jOAQAATQQDDPgftRUVyn/5Fe27/gad+GGF6Rw42Y7cYt3y3hpN+GSDso6Wmc4BAABejgEG/JfSlau077qhOvLee1JNjekcuNA3Ww9ryKsrNHPlftXW8sbZAADAORhggKSaI0eU8/Ajyho3TtVZWaZzYEh5tU3PfrVdN/7pR+3JLzGdAwAAvBADDE1e0fz52nfNr1T81VemU+AmNh4s0jVvrNRbyzNUY6s1nQMAALwIAwxNVs2RIzo4frxyn3hStuPHTefAzVTV1Oqlxbs19K1V2prD7w8AAOAYDDA0SaX/XKl9w67XiRX/NJ0CN7c9t1jXv71Kby3P4LVhAADgnDHA0KTYq6qU9/zzyho/XrbCQtM58BA1tXa9tHi3xny4ToWllaZzAACAB+ONmNFkVO7bp5zfPqzKHTtMp8BD/TOjUNe8/k+9PrKXLuzc0nQO0OT0fuQvphNcbsOLo00nAHAwjoChSShasED7R9zI+MI5yy+p1O0z1+r1pZySCAAAGo4BBq9mt9mU9/w05U55TPbyctM58BK2WrteXbpboz5Yq4ISTkkEAAD1xwCD17IVFyvrN/fo6KxZplPgpVbtOaLr316lnYeLTacAAAAPwQCDV6rct0+ZN92sEytXmk6Bl8spKteN767W97vyTacAAAAPwACD1yldsUKZN49UVWam6RQ0EaWVNbprVpo+Xp1pOgUAALg5Bhi8yrE5c5R1zwTVlpSYTkETY6u168mF2zT179u4OAcAADgrBhi8RsE77+jw1Gek2lrTKWjCPlyVqfEfp6msqsZ0CgAAcEMMMHg8u92uvOefV+Ebb5pOASRJS3fk6/b316q4otp0CgAAcDMMMHg0u82m3Mce19FZTe/NOeHeNh4s0m3vrVVRWZXpFAAA4EYYYPBYtVVVyr5/so4vWGA6BTijn3KO65b31upIKe8VBgAA6jDA4JFqKyuV9ZvfqHTZMtMpwM/akVuskTPWKL+kwnQKAABwAwwweBx7dbWy779fZavXmE4B6iUjv1Qj/7xGh48zwgAAaOoYYPAodptNOQ8/ohM/rDCdAjTIvsITuunPq5VXzAgDAKApY4DBY9jtduU+/n8q+fZb0ylAoxw8WqYxH6zj6ogAADRhDDB4jMPPPKPjCxeazgDOyc7DJRo3K00V1TbTKQAAwAAGGDxC/ksvqWjOXNMZgEOs239Uk+dukq3WbjoFAAC4GAMMbu/o7Nk68v5M0xmAQ327LU9PLNhqOgMAALgYAwxurXTlKuU997zpDMAp5qw7qFcW7zKdAQAAXIgBBrdVuXevch58ULLxWhl4rzeW79GnaVmmMwAAgIswwOCWao4dU9aEe1VbUmI6BXC6JxZs1easItMZAADABRhgcDv2qirlTLpf1QcPmk4BXKKqplb3fLJBhaWVplMAAICTMcDgdnKfeUZlaWmmMwCXyj1eoYmzN6rGVms6BQAAOBEDDG6l6PMvdHz+Z6YzACPW7j+qPy7aYToDAAA4EQMMbqNy3z4d/sMfTGcARn24KlNfbMp22OP5+PgoOTlZPXv2VEpKin788cefvX1RUZHeeecdhz0/AAA4FQMMbqG2slI5Dzwoe1mZ6RTAuMc+/0m78xxzAZqgoCClp6dr8+bNev755/XYY4/97O0ZYAAAOBcDDG4h7/nnVbl7t+kMwC1UVNfqgbnpqqpx7OvBiouLFRERcfLjF198UX369NH555+vp556SpI0ZcoU7d27V8nJyXrkkUdUWlqqQYMGKSUlRUlJSVq4cKFDmwAAaGp8TQcAxf/4VkVz/2Y6A3Ar23OL9erS3Xr0qq7n9Djl5eVKTk5WRUWFcnNztXz5cknS4sWLlZGRoXXr1slut2vo0KFasWKFpk2bpq1btyo9PV2SVFNToy+++EKhoaEqLCzUBRdcoKFDh8pisZzz1wgAQFPEAINRVdk5yn3ySdMZgFv68w97NahrlFLjWjT6Mf59CqIkrV69WqNHj9bWrVu1ePFiLV68WL169ZIklZaWKiMjQ+3btz/l/na7XY8//rhWrFghq9WqnJwc5eXlqXXr1o3/wgAAaMI4BRHG2O125T7+OG+2DJxFrV168NN0lVbWOOTxLrzwQhUWFqqgoEB2u12PPfaY0tPTlZ6erj179uiuu+467T6zZ89WQUGBNmzYoPT0dEVHR6uiosIhPe7s1VdfVWJionr06KFbbrlFFRUV2r9/v/r166eEhATdfPPNqqqqMp0JAPBADDAYUzR3rsrWrTOdAbi1rKPleubv2xzyWDt37pTNZlPLli115ZVX6oMPPlBpaakkKScnR/n5+QoJCVHJf31T5Pjx44qKipKfn5++++47HThwwCEt7iwnJ0dvvPGG0tLStHXrVtlsNs2dO1ePPvqoHnzwQWVkZCgiIkIzZ840nQoA8ECcgggjqnNylP/iS6YzAI/waVq2rugWrSGJDT/t79+vAZPqjjrPmjVLPj4+GjJkiHbs2KELL7xQkhQcHKxPPvlEnTt31oABA9SjRw9dffXVevTRR3XdddcpNTVVycnJ6tr13F6T5ilqampUXl4uPz8/lZWVKSYmRsuXL9df//pXSdKYMWP09NNPa8KECYZLAQCehgEGI5766UVdc0l3tfx2g2S3m84B3N6TC7fqws4tFRLo16D72Wy2s/7a5MmTNXny5NM+/++R8W+rV69u0HN6urZt2+rhhx9W+/btFRQUpCFDhqh3794KDw+Xr2/dH5uxsbHKyckxXAoA8EScggiX+8f+f+jvecs0oVe6Zt/XVZZ2bU0nAW4vr7hSLy/mrRpc4dixY1q4cKH279+vQ4cO6cSJE/rmm29Ou93ZrgQ5Y8YMpaamKjU1VQUFBc7OBQB4GAYYXKq4qljT108/+fHC4AzdcdsxZV/fV/LlgCzwc/6yOlNbsotMZ3i9pUuXqmPHjoqMjJSfn5+GDx+uH3/8UUVFRaqpqbsgSnZ2ttq0aXPG+48fP15paWlKS0tTZGSkK9MBAB6AAQaXem3DayosLzzlc6WWKj3UbaPevS9OOq+jmTDAA9TapScXbJWd03adqn379lqzZo3Kyspkt9u1bNkyde/eXQMHDtT8+fMlSbNmzdKwYcMMlwIAPBEDDC7zU8FPmr97/ll//bugTN0+/JB2jewrS0CAC8sAz7E5+7g+TcsyneHV+vXrpxtvvFEpKSlKSkpSbW2txo8fr+nTp+uVV15RfHy8jhw5csbL9gMA8Es45wsu8+rGV2XXz3/nvspi05MdNyp1Yhv9dlmQfDbvclEd4Dle/HaXrk6KUWgDL8iB+ps6daqmTp16yuc6deqkdbx1BgDgHHEEDC6xKmeV1h9eX+/bpwUc0q1X79WG0X1kCW7uxDLA8xSWVun1pRmmMwAAQCMwwOB0drtdr298veH3s0jT227S4xOCVXlBkhPKAM/18ZoDOlRUbjoDAAA0EAMMTvftgW+14+iORt8/w/eIRg3coe/GpcjSIsKBZYDnqqqp1ZvLOQoGAICnYYDBqWpqa/T2prcd8ljvRm7RA+MsKr48xSGPB3i6eWnZyiw8YToDAAA0AAMMTrVgzwJlFmc67PFyfIo1rt8WLbg3SZaYaIc9LuCJamrtem0pb84MAIAnYYDBaSptlXp387tOeey/hu3Q+NFlyv9VH8nKb2M0XV9uPqTdeSWmMwAAQD3xN1c4zdydc5Vflu+0xz9mLdd952/SR/clyBLXzmnPA7izWrv08mLergEAAE/BAINTlFaV6v2f3nfJcy1qvldjbi7UgRF9JV/e2g5Nz7fb8rQ157jpDAAAUA8MMDjFF3u+UFFlkcuer8xarUfO26g3JrWTvVtnlz0v4C5mrtxvOgEAANQDAwwOZ7fb9bddfzPy3CsDs3TbsCxtu7WvLEGBRhoAE77ackh5xRWmMwAAwC9ggMHhVuas1IHiA8aev8ZSq6kdNurZe1uqJqW7sQ7Alaptdv1ldabpDAAA8AsYYHC4OTvnmE6QJG3xz9OtV+7W2rGpsoSEmM4BnO6vaw+qotpmOgMAAPwMBhgcKqs4SytzVprOOMXLMel69J5AlQ3oaToFcKpjZdX6fGOO6QwAAPAzGGBwqDm75sguu+mM0+zzPaaxl2zT4vHJsrZqYToHcJoPVu2X3e5+/w4CAIA6DDA4TFl1mRbsWWA642e933Kr7r3LpqLBvU2nAE6xJ79UKzIKTWcAAICzYIDBYb7a95VKqkpMZ/yifOsJjU/drHkTE2VpG2M6B3C4eWlZphMAAMBZMMDgMO5y8Y36mhe6S+NGlSj3uj6Sj4/pHMBhlmzPU3FFtekMAABwBgwwOMTGvI3aU7THdEaDHbdUaHKPTXp/YmepcwfTOYBDVNbUatGWXNMZAADgDBhgcIhvM781nXBOFjffp1G/Pqy9v+4r+fmZzgHO2eebuBoiAADuiAGGc2a327X04FLTGees0mLTY/Eb9fKkNqrtcZ7pHOCcrM88qqyjZaYzAADA/2CA4ZxtLtis/LJ80xkOszYgR7ddt19bbu8jS7NmpnOARrHbpS84CgYAgNthgOGcLTmwxHSCw9lk1x/abdJT94apqk8P0zlAozDAAABwPwwwnLNlB5eZTnCa7X4Fuv2KnVp5Z4os4WGmc4AG2V94Qjtyi01nAACA/+JrOgCebVvhNuWUev932d+I3qIF48P19NpeCv5hk+kcoN6W78xXt5hQ0xkADOj9yF9MJ7jchhdHm04AfhFHwHBOvPH0w7M56FOkO/v/pK/v6SlLVCvTOUC9LN/pPa/PBADAGzDAcE684eqHDTUrYpsm3FGtI1elShaL6RzgZ206eEzHTlSZzgAAAP/CAEOj7Tq6SweKD5jOMKLQekITeqVr9n1dZWnX1nQOcFa1dun73RwFAwDAXTDA0GjfZX1nOsG4hcEZuuO2Y8oe1kfy5SWVcE/LdxaYTgAAAP/CAEOjbcjbYDrBLZRaqvRQ901697446byOpnOA06zYXSBbrd10BgAAEAMMjWSrtWlLwRbTGW7lu6BM3T78kHaN7CtLQIDpHOCk4+XV2nTwmOkMAAAgBhgaaeexnSqrKTOd4XaqLDY92XGjpk+Mlq1nF9M5wEnrMxlgAAC4AwYYGiU9P910gltLCzikW6/eqw2j+8gS3Nx0DqANBxhgAAC4AwYYGmVj3kbTCW7PbpGmt92kxycEq/KCJNM5aOI4BREAAPfAAEOjcASs/jJ8j2jUwB36blyKLC0iTOegiTpyokr7C0+YzgAAoMljgKHBskqylF/O+wo11LuRW/TAOIuKL08xnYImitMQAQAwjwGGBuPoV+Pl+BRrXL8tWnBvkiwx0aZz0MQwwAAAMI8BhgbbmM/rv87VX8N2aPzoMuX/qo9k5V9DuMZGBhgAAMbxNz802OaCzaYTvMIxa7nuO3+TProvQZa4dqZz0ARk5JeootpmOgMAgCaNAYYGqbXX6sDxA6YzvMqi5ns15uZCHRjRV/L1NZ0DL1Zrl/YVcCEOAABMYoChQXJP5Kqqtsp0htcps1brkfM26o1J7WTv1tl0DrzYnoJS0wkAADRpDDA0CEe/nGtlYJZuG5albbf2lSUo0HQOvNCefAYYAAAmMcDQIAdKGGDOVmOp1dQOG/XsvS1Vk9LddA68zF4GWL0UFRXpxhtvVNeuXdWtWzetXr1aR48e1eDBg5WQkKDBgwfr2DEuagIAaDgGGBrkYPFB0wlNxhb/PN165W6tHZsqS0iI6Rx4CY6A1c/kyZN11VVXaefOndq8ebO6deumadOmadCgQcrIyNCgQYM0bdo005kAAA/EAEODHCjmCJirvRyTrkfvCVTZgJ6mU+AF9heekK3WbjrDrRUXF2vFihW66667JEn+/v4KDw/XwoULNWbMGEnSmDFjtGDBApOZAAAPxQBDgxws4QiYCft8j2nsJdu0eHyyrK1amM6BB6uy1erg0TLTGW5t3759ioyM1B133KFevXpp3LhxOnHihPLy8hQTEyNJiomJUX5+vuFSAIAnYoCh3mpqa5RTkmM6o0l7v+VW3XuXTUWDe5tOgQc7VFRuOsGt1dTUaOPGjZowYYI2bdqk5s2bN+h0wxkzZig1NVWpqakqKChwYikAwBMxwFBvh0oPqcZeYzqjycu3ntD41M2aNzFRlrYxpnPggQpKKk0nuLXY2FjFxsaqX79+kqQbb7xRGzduVHR0tHJzcyVJubm5ioqKOuP9x48fr7S0NKWlpSkyMtJl3QAAz8AAQ73x+i/3Mi90l8aNKlHudX0kHx/TOfAgDLCf17p1a7Vr1067du2SJC1btkzdu3fX0KFDNWvWLEnSrFmzNGzYMJOZAAAP5Ws6AJ6jsLzQdAL+x3FLhSb32KQhHTtr3NeV0l5GMn5ZQan3DbBBgwZp2bJlv/i5+nrzzTd12223qaqqSp06ddKHH36o2tpa3XTTTZo5c6bat2+vefPmOSIdANDEMMBQb6XVXL7aXS1uvk8//NpHT+/tq84LNknV1aaT4MbyiytMJzhMRUWFysrKVFhYqGPHjslur7vCY3FxsQ4dOtTox01OTlZaWtppn2/soAMA4N8YYKi30ioGmDurtNj0WPxG9ZvUVg8uDpB1627TSXBT3nQE7M9//rNee+01HTp0SL179z45wEJDQzVx4kTDdQAAnI4BhnorqS4xnYB6WBuQo9uus+ix5D7q+fk22cu45DhO5U2vAZs8ebImT56sN998U5MmTTKdAwDAL2KAod44AuY5bLLrD+02qfu9kXr8h07yX7/VdBLcSGFplekEh5s0aZJ+/PFHZWZmqqbmP1drHT16tMEqAABOxwBDvfEaMM+z3a9At19RoPuTUnTx53tlLzpuOgluoLTS+95OYtSoUdq7d6+Sk5Pl86+rglosFgYYAMDtMMBQbxwB81xvRG/RwrvD9dS6Xgr+YZPpHBhWVVMrW61dPlaL6RSHSUtL0/bt22WxeM/XBADwTrwPGOqNI2Ce7YBvke7s/5O+vqenLFGtTOfAsPJqm+kEh+rRo4cOHz5sOgMAgF/EETDUW0kVF+HwBrMitunrO5rr2c2pavntBulfV41D01JeZVNwgPf8EVBYWKju3burb9++CggIOPn5L7/80mAVAACn854/feF0HAHzHoXWE5rQK13DErrq9gXFsmflmE6Ci1Xbak0nONTTTz9tOgEAgHphgKHeKmq8581bUWdhcIaW3eavZ3b0UezXm6Qa77s4A86sxuZdRz4vvfRS0wkAANQLAwz15mvlt4s3KrVU6aHumzSwY5wmLPKu1wXh7KprvesIWEhIyMkLcFRVVam6ulrNmzdXcXGx4TIAAE7F36hRb/5Wf9MJcKLvgjJ1fEy8Xj62RA8evkI7S5uZToIT2Wq96whYScmpr1FdsGCB1q1bZ6gGAICz4yqIqDc/Hz/TCXCibiEd9OaeDeqWPVffWO7XZwmLFRtYaToLThLg693/+b/++uu1fPly0xkAAJyGI2CotwCfgF++ETxSfHA7/XnvdoWW171Rs6W6TL2zPtI/A77QP9vdqgcPXKAjVQxwbxLo52M6waE+//zzkz+vra2R2l9lAAAgAElEQVRVWloa7wkGAHBLDDDUm78PpyB6o7jmbfTe/gxFnDhy2q9ZKo/rkqx3tT54vv7R4nY9sr+3Tti8+8hJUxHo610D7O9///vJn/v6+iouLk4LFy40WAQAwJkxwFBvvAbM+7RtFq33srPUqjT/Z29nLSvQNWWvakjLdpoffLuezOyh6lqOLniyAD/vGtIffvih6QQAAOrFu/4EhlPxGjDv0jooUjNz89S6qP7vAeZbnKWRh57Xtuin9XjcbifWwZksFu87BTE7O1s33HCDoqKiFB0drREjRig7O9t0FgAAp2GAod44AuY9IgNbaGb+UbU9erBR9/c/lqHxh5/W9thpmtAu07FxcDp/H+/7T/8dd9yhoUOH6tChQ8rJydF1112nO+64w3QWAACn8b4/heE0XITDO7QICNf7R06ofeH+c36sZoVb9GjB49rc4Q2NjMl1QB1cwduOfklSQUGB7rjjDvn6+srX11djx45VQUGB6SwAAE7DAEO9cQqi5wv1D9GMohp1ys9w6OOG5a3RtGO/1fqO7+nqyEKHPjYcr7m/9w2wVq1a6ZNPPpHNZpPNZtMnn3yili1bms4CAOA0DDDUW1hAmOkEnINgv+aaUWpVl8PbnfYckbnf6Z2SyVoZP1sDIo477XlwbiJDA00nONwHH3ygTz/9VK1bt1ZMTIzmz5/PhTkAAG6JqyCi3iKDIk0noJGa+TbTu+WBSszZ5PTnssiu2Oyv9Yn1W+1JuF4PHb5SP5U0d/rzov6iQrzvdOInn3xSs2bNUkREhCTp6NGjevjhh/XBBx8YLgPgKXo/8hfTCS634cXRphOaJI6AuaEvvvhCFotFO3fulCRlZmaqR48eP3uf+tzmXEU2Y4B5okCfAL1VHaLkLOePr/9mqa1RQtZ8fWmfpIUJ3yguqMKlz4+z88YBtmXLlpPjS5JatGihTZtc+3seAID6YIC5oTlz5uiiiy7S3LlzTaecgiNgnsff6q/XaluqT+Z6Yw2Wmgr1zPpY3/lP1l8TvldUQLWxFtSJ9sJTEGtra3Xs2LGTHx89elQ1NTUGiwAAODMGmJspLS3VqlWrNHPmzDMOsI8++kjDhg3TVVddpS5dumjq1Kknf81ms+nuu+9WYmKihgwZovLycknSe++9pz59+qhnz54aMWKEysrKGtXGETDP4mv11UvW1hqwb43pFEmSpbJE/bNmaE2zhzQjfo1CfPnLsSneeATst7/9rfr3768nn3xSv//979W/f3/97ne/M50FAMBpGGBuZsGCBbrqqqt03nnnqUWLFtq4ceNpt1m3bp1mz56t9PR0zZs3T2lpaZKkjIwMTZw4Udu2bVN4eLg+++wzSdLw4cO1fv16bd68Wd26ddPMmTMb1da6WevGf2FwKR+Lj6b5ttPAjJWmU05jLT+iIdlvKD18il7uvEkB1lrTSU1OVKj3DbDRo0frs88+U3R0tCIjI/X5559r1KhRprMAADgNA8zNzJkzRyNHjpQkjRw5UnPmzDntNoMHD1bLli0VFBSk4cOHa+XKur9kd+zYUcnJyZKk3r17KzMzU5K0detWXXzxxUpKStLs2bO1bdu2RrW1DGqpIN+gRt0XrmO1WPVsQEdduesH0yk/y6f0kEbkvKifon6vp+J2yGKxm05qMqJCvO8UREnq3r277rvvPk2aNEndu3c3nQMAwBlxFUQ3cuTIES1fvlxbt26VxWKRzWaTxWLRvffee8rtLBbLGT8OCPjPd7V9fHxOnoI4duxYLViwQD179tRHH32k77//vtGNbYPbak/RnkbfH85lkUW/D4rXdduWmk6pN/+ifbpDz2pkm0T9yXqLXs/qZDrJ68VG8I0UAABM4QiYG5k/f75Gjx6tAwcOKDMzU1lZWerYsaOys7NPud2SJUt09OhRlZeXa8GCBRowYMDPPm5JSYliYmJUXV2t2bNnn1Nj2+C253R/ONeU4K4a4UHj678FHdmmBwue0E/tX9HoNodM53itVsEBCm/mbzoDAIAmiwHmRubMmaMbbrjhlM+NGDFCzz333Cmfu+iiizRq1CglJydrxIgRSk1N/dnHffbZZ9WvXz8NHjxYXbt2PafG2JDYc7o/nOehkETd+tO3pjPOWUh+mp45+rA2dPyThkXnm87xOglRwaYTAABo0jgF0Y2c6dTA+++/X/fff/8pn4uKitJbb711yufi4uK0devWkx8//PDDJ38+YcIETZgwwSGN7ULaOeRx4Fj3hvbQHZsXmc5wqJa5K/Sa/qkpnYdoStFQ/XAk4pfvhF+UEM0AAwDAJI6AoUG6t+SF7e5mXHiSJnjZ+Po3i+yKyflWH5Xdr+Xx85QSVmo6yeNxBAwAALMYYB5m7Nixpx39cqWuLbrKx+Jj7PlxqtvDz9fkTV+bznA6i92mTtlf6LOaSfo64St1blZuOsljxUeFmE4AAKBJY4ChQYJ8g9QpnKvUuYObIpL06KavTGe4lMVWqcSsv2qp7/36NGGZYgKrTCd5HE5BBADALAYYGiyxZaLphCZvWESSntjonacd1oel6oT6Zs3UqsAH9UHCKoX51ZhO8ggtmvurVbD3vQkzAACehAGGBmOAmXV1RA89s+kbWcQbF1srjunyrLe1MfQRvRG/QUE+NtNJbu382DDTCQAANHkMMDQYA8ycKyIS9Vz6YlnttaZT3IrPiTwNzX5ZW1o+oT922iofC//7nElqB64kCQCAaQwwNFiXFl3ka+UdDFzt4vBuemHzMvnWcrrd2fgVH9Bth57T9phn9UiHDNM5bqd3hxamEwAAaPIYYGgwfx9/JYQnmM5oUvqFn6dXf/pBfjYuOlEfAUd3aWLeU9rW7gWNi80yneMWfK0WJbcLN50BAECTxwBDoyS24jREV0kJi9eb235UQE2F6RSP07wgXU8UPqr0uLd0Y+s80zlGJbYJVZA/byEBAIBpDDA0SlKrJNMJTcL5oZ30zo71CqoqM53i0cIP/6iXih7U2k4faHCro6ZzjOD0QwAA3AMDDI1yQcwFphO8XreQDnp39yY1rywxneI1og8t1YwT9+uH+LnqG15sOselUuO4AAcAAO6AAYZGaRPcRvHh8aYzvFZ8cDvN2LNNoeXHTad4HYu9Vh2yv9Tfqibp24SF6hrs/UcXLRapTxxHwBrKZrOpV69euvbaayVJ+/fvV79+/ZSQkKCbb75ZVVW8JhMA0HAMMDTaJbGXmE7wSnHN2+q9/RkKL2uap8q5iqW2Wl2y/qZvLPfr84TFig2sNJ3kNEltwxQZwhswN9Trr7+ubt26nfz40Ucf1YMPPqiMjAxFRERo5syZBusAAJ6KAYZGuzT2UtMJXie2WWu9n3VArUrzTac0GZbqMqVkfaR/BkzWxwkr1NK/2nSSw13eNcp0gsfJzs7W119/rXHjxkmS7Ha7li9frhtvvFGSNGbMGC1YsMBkIgDAQzHA0Gg9I3sqLCDMdIbXiAmK1MxDhxV9/JDplCbJUlmsi7P+pPXBv9W78evU3Md73sz5im7RphM8zgMPPKAXXnhBVmvdH5NHjhxReHi4fH3r3gMxNjZWOTk5JhMBAB6KAYZG87H6aECbAaYzvEJUYEu9n39UbY4dNJ3S5FnLCnV19mva3GKKpnfaIj+r3XTSOWkdGqgebflGSUN89dVXioqKUu/evU9+zm4//feBxWI54/1nzJih1NRUpaamqqCgwGmdAADPxADDOeE0xHPXIiBc7x0pVfvC/aZT8F98S7J186Fp2hb1lP4vbpcsFs8cYgM5/bDBVq1apS+//FJxcXEaOXKkli9frgceeEBFRUWqqamRVHeKYps2bc54//HjxystLU1paWmKjIx0ZToAwAMwwHBOBrQdIB8Lb+7aWGH+oZpRVK1O+RmmU3AW/kV7dPfhqdrWdpomtss0ndNgV3RjgDXU888/r+zsbGVmZmru3Lm6/PLLNXv2bA0cOFDz58+XJM2aNUvDhg0zXAoA8EQMMJyTsIAw9YzsaTrDI4X4BevPpRZ1ObzDdArqoVnhT3qk4HFt6fC6bo3JNZ1TL4F+Vg2Ib2U6w2tMnz5dr7zyiuLj43XkyBHdddddppMAAB7I13QAPN9l7S7TxvyNpjM8SjPfZnqnPECJOZtMp6CBQvPW6jmt1UOdLtPvS4ZrUYH7DpxB3aIV6McR6nNx2WWX6bLLLpMkderUSevWrTMbBADweBwBwzm7uuPVnIbYAIE+AXqrKljJWYwvT9bq0Pd6u2SyVnb+RAMi3PMNs29MiTWdAAAA/gcDDOesdfPW6t+mv+kMj+Bv9dfrtS3V50Ca6RQ4gEV2xeYs0ieVk7Q04TMlhZwwnXRSZEiALjmPC0AAAOBuGGBwiBEJI0wnuD1fq69etrRW/31rTKfAwSy1NYrP+kxf2ifpy4RF6tSswnSSrk9uIx/rmS+TDgAAzGGAwSEubXepWga2NJ3htnwsPpruG6vL9qw0nQInstRU6PysT7TM937NTfhOUQHVxlpG9Ob0QwAA3BEDDA7ha/XVsHguyXwmVotVf/DvqCG7VphOgYtYqkp1QdZ7WtPsIb0Xv1ohvjUuff7ENqHq2jrUpc8JAADqhwEGh+E0xNNZZNFTgfG6dudy0ykwwFp+RIOz31R6+BS90nmTAqy1LnneEVx8AwAAt8UAg8O0D22v1OhU0xlu5bHmXTV8+1LTGTDMp/SQhue8qK2RT+rpjjtksdid9lwBvlZd36ut0x4fAACcGwYYHGp4wnDTCW7jtyGJumXrt6Yz4Eb8ju/X2NxntaPNH/Vg+31OeY4berVVi+b+TnlsAABw7hhgcKghcUMU6s9rTyaG9tDYLd+YzoCbCjyyXZPzn9DW9i9rbJtshz2uxSLddVFHhz0eAABwPAYYHCrAJ0BDOw81nWHU3WFJumfzItMZ8ADB+Rv09NHfaWPcO7o+Ov+cH++ShEglRIc4oAwAADgLAwwONyZxjPysfqYzjBgVfr7uT//adAY8TIvDK/Xq8Qe1uvNHuqzFsUY/zriLOfoFAIC7Y4DB4Vo3b63r4683neFyN0ck6XebvjKdAQ9lkV0xOYv1Yfn9+i7hU6WElTbo/l1bh+jihEgn1QEAAEdhgMEpxiWNk6/V13SGy1wfkaT/28hphzh3FrtNHbMW6LOa+7Qo4e9KaF5er/vx2i8AADwDAwxO0Sa4jYZ1bhpvzHx1RA9N3fSNLHLepcXR9FhsVeqeNUeLrfdrfsISxQRWnfW20aEBGpbMpecBAPAEDDA4zbikcfK1ePdRsCsiEvVc+mJZ7a55g100PZbqE0rN+lCrAh/QRwkrFeFXc9pt7hsYL39f/nMOAIAn4E9sOE1sSKyu7Xyt6QynuSS8m15IXyrf2tP/Qgw4mrWiSJdlvaO00Ef0Zuc0BfnYJEltw4N0c5/2husAAEB9McDgVOOTxsvH4mM6w+EuCO+iV7d8L7/aatMpaGJ8TuTpupxX9FPL/9NzHX/S5Ms7c/QLAAAPwp/acKp2oe30q06/Mp3hUL3DEvTGtlXyt1WaTkET5lt8ULdWzdNNvXntFwAAnoQBBqcbf773HAU7P7Sz3t6xTkFVZaZTAOnyJyQf736dJQAA3oYBBqfrENpBvz7v16Yzzlm3kDj9addGNa8sMZ0CSG17S4k3mK4AAAANxACDS0xKmaQWgS1MZzRaQnB7zdizVSEVx02nAJIs0pA/mo4AAACNwACDS4T6h+q3qb81ndEocc3b6r39uxRedtR0ClCn50ipw4WmKwAAQCMwwOAyQzsPVUpUiumMBolt1lrvZx1Qy9IC0ylAnYAwafCzpisAAEAjMcDgUk9c8ITHvDlzTFCkZh46rOjjh0ynAP9x+f9JwZGmKwAAQCMxwOBSCREJurXbraYzflFUYEvNzDuiNscOmk4B/qN1ktRnnOkKAABwDhhgcLmJyRMV1SzKdMZZtQiI0HtHStTuSKbpFOC/WKRrXpas3vGWDgAANFUMMLhcM79meqTPI6YzzijMP1QziqrUKX+P6RTgVKl3SO37ma4AAADniAEGI66Ku0oXxrjXVdxC/IL15xKpy+EdplOAU0V0lIb8wXQFAABwAAYYjJnaf6pC/UNNZ0iSmvk20zvl/ko8tNV0CnAqi1W6/l3Jv7npEgAA4AAMMBgTExyjqf2nms5QoE+A3qoKVnJWuukU4HQX3sd7fgEA4EUYYDDqig5X6KbzbjL2/P5Wf71ua6E+B9KMNQBnFdVduvwJ0xUAAMCBGGAw7nd9f6f48HiXP6+v1VevWKLVf/9alz838IusftINf5J8A0yXAAAAB2KAwbgAnwC9eMmLCvQJdNlz+lh89IJPrC7ds8plzwk0yBVPSzE9TVcAAAAHY4DBLcRHxLvs0vRWi1V/9I/T4N0rXPJ8QIN1Hyb1v890BQAAcAIGGNzGTV1u0uAOg536HBZZ9HRgvH618zunPg/QaC0TpGFvm64AAABOwgCDW3m6/9Nq07yN0x7/8eZddMP2pU57fOCc+DWXbv5YCggxXQIAAJyEAQa3EuofqhcvfVEBPo6/8MDDId01cutihz8u4DDXvS5FdTNdAQAAnIgBBrdzfuT5eu6i52SRxWGPeV9oD43Z8g+HPR7gcH3HS+f/2nQFAABwMgYY3NKQuCH6bepvHfJYd4cl6TebFznksQCnSBgiXTXNdAUAAHABBhjc1pjEMRrZZeQ5Pcbo8PN1f/rXDioCnKD1+dKNH0pWH9MlAADABRhgcGtT+k7RZbGXNeq+N0ck6ZFNXzk2CHCk0FjptnlSQLDpEgAA4CIMMLg1H6uPpl8yXd1bdm/Q/W6ISNL/beS0Q7ixgLC68RXS2nQJAABwIQYY3F4zv2Z6e9Db9b48/TURPfT0pm9kkd3JZUAjWf2km2ZJ0Q37xgJcIysrSwMHDlS3bt2UmJio119/XZJ09OhRDR48WAkJCRo8eLCOHTtmuBQA4IkYYPAIrYJa6Z0r3lGI/8+/P9LgiEQ9t+lbWe21LioDGsjiI414X+o80HQJzsLX11cvv/yyduzYoTVr1ujtt9/W9u3bNW3aNA0aNEgZGRkaNGiQpk3jwikAgIZjgMFjdA7vrDcvf1NBvkFn/PVLw7tpevpS+dhtLi4D6slilYbPkBKvN12CnxETE6OUlBRJUkhIiLp166acnBwtXLhQY8aMkSSNGTNGCxYsMJkJAPBQDDB4lN7RvfXuFe+qmW+zUz5/YXgXvbLle/nVVhsqA36BxSoNe0dKutF0CRogMzNTmzZtUr9+/ZSXl6eYmBhJdSMtPz/fcB0AwBMxwOBx/neE9Q5L0OvbVsnfVmm4DDgbi3Tta1LyLaZD0AClpaUaMWKEXnvtNYWGhtb7fjNmzFBqaqpSU1NVUFDgxEIAgCdigMEjpUSn6E+D/6T+LRL1zo61CqoqM50EnN2vXpJ6jzFdgQaorq7WiBEjdNttt2n48OGSpOjoaOXm5kqScnNzFRUVdcb7jh8/XmlpaUpLS1NkZKTLmgEAnoEBBo/VK6qX/tzrYTWz+JpOAc7M4iMNfUvqM850CRrAbrfrrrvuUrdu3fTQQw+d/PzQoUM1a9YsSdKsWbM0bNgwU4kAAA/GAINni02Vxn4tNee7zHAzvoHSzZ9IKaNMl6CBVq1apY8//ljLly9XcnKykpOTtWjRIk2ZMkVLlixRQkKClixZoilTpphOBQB4IA4dwPO17iHd8Q/pk+FS0QHTNYAUGCbdMlfq0N90CRrhoosukt1+5vcRXLZsmYtrAADehiNg8A6t4qVxy6S2qaZL0NQFt5bGLmJ8AQCAM2KAwXsER0pjv5K687oMGNKis3TXt3VHZQEAAM6AAQbv4hck/XqWNGCy6RI0NZ0HSXcvlyLiTJcAAAA3xgCD97FYpMHPSNe9Lll5mSNcoP8k6bZ5UlC46RIAAODmGGDwXr3HSqMXSsHRpkvgrXwDpeHvSUP+IFl9TNcAAAAPwACDd4u7SPrNP6W4i02XwNuExkp3/kM6/ybTJQAAwIMwwOD9QqLrjoRd9JAki+kaeINOA6Xx30ttepkuAQAAHoYBhqbB6iNd8ZR069+koAjTNfBUVl/piqelUV/UXXUTAACggRhgaFrOu1L6zQqpw0WmS+BpIjrWveH3RQ/WXegFAACgERhgaHrC29e9X9hV0yTfINM18AS9Rkn3rJTa9TFdAgAAPBwDDE2TxSJdMEGasEqK7Wu6Bu4qJEYa+Vdp2FtSQLDpGgAA4AUYYGjaWnaW7vy27n3DfAJM18BtWKTUu6SJ66SuvzIdAwAAvAgDDLBapQGTpXu4XD0kRXatu7z8ta9IgaGmawAAgJdhgAH/Ftml7rVhI2ZKIW1M18DVfAKkyx6ve9+49heYrgEAAF6KAQb8r6QbpfvW1x0V8/E3XQNX6PIracKP0mWPSr78fw4AAJyHAQacSUBw3evCJvwodR5kugbO0iZFGrtIuuWvUqt40zUAAKAJYIABP6dVgjTqc2nUAqltqukaOEpYe2n4+9Ldy6W4AaZrAABAE+JrOgDwCJ0H1v3Y9Y20/I9S3k+mi9AYzVpK/e+vewsCX656CQAAXI8BBjREl6ul866Stn0hffecdCTDdBHqIyRG6j9J6j1W8m9uugYAADRhDDCgoSwWqcdwqfuwuiG2+i3p0CbTVTiT8PbSgAekXrdzxAsAALgFBhjQWFafuismJt0oHfhRWv22tGuRZK81XYao7nVHvJJuknz4zxwAAHAf/M0EcIQO/et+HNkrrXlXSv+rVH3CdFXT4uMvdRsq9bmr7v8LAAAAN8QAAxypZWfpVy9Jlz8hbf1MSp8t5WwwXeXdwtpLqWOlXqOl4EjTNQAAAD+LAQY4Q1B43ZGYPndJ+Tvqhtjmv0kn8k2XeQe/ZnUXRDn/Zil+sGTlHTUAAIBnYIABzhbVTRryB2nQ09KeJdLmuVLGEk5RbCgffyn+CqnHiLrxxdUMAQCAB2KAAa7i41s3HLpcLVWXS3uWSTu+lHZ/K1UUma5zT76BUtxFUvfrpW7X1R1ZBAAA8GAMMMAEvyCp27V1P2w1Utaaujd53ru87pRF2U0XmtMyvu5IV/wVdePLL8h0EQAAgMMwwADTfHzrhkbcRXUfnyiUMlf+50fBTnn1IAuJkWL7SB0vqRtdLTqaLgIAAHAaBhjgbpq3khKvr/sh/WeQ5WyQDm+RcrdI5UfNNjaWj78U07NucMX2kdr1lcJiTVcBAAC4DAMMcHf/O8gk6XjOf8bY4S3SkT1S0UGpusxc53+z+Ejh7aVWCVJk17o3Ro7qVvfDN8B0HQAAgDEMMMAThbWt+9Hl6lM/X5ovHcuUjh2o+2dRplRaIJUfqztqVn5MKi+S7LbGP7fVT2rWQgppXXf6YEhrKaTNfz6O6CBFdJR8/c/hCwQAAPBODDDAmwRH1f1o1/fst7Hb6666WH5Mqq6QamvqBlmtre7n//6nj7/k30zya/6vfzaru/S7j5/rvh4AAAAvwwADmhqLRQqKqPsBAAAAl7KaDgAAAACApoIBBgAAAAAuwgADAAAAABdhgAEAAACAizDAALhMXl6ebr31VnXq1Em9e/fWhRdeqC+++MJ0FgAAgMswwAC4hN1u1/XXX69LLrlE+/bt04YNGzR37lxlZ2ebTgMAAHAZBhgAl1i+fLn8/f11zz33nPxchw4dNGnSJGVmZuriiy9WSkqKUlJS9OOPP0qSvv/+e1166aW66aabdN5552nKlCmaPXu2+vbtq6SkJO3du1eSNHbsWE2YMEEDBw5Up06d9MMPP+jOO+9Ut27dNHbs2JPPN2HCBKWmpioxMVFPPfWUS79+AAAAifcBA+Ai27ZtU0pKyhl/LSoqSkuWLFFgYKAyMjJ0yy23KC0tTZK0efNm7dixQy1atFCnTp00btw4rVu3Tq+//rrefPNNvfbaa5KkY8eOafny5fryyy913XXXadWqVXr//ffVp08fpaenKzk5WX/84x/VokUL2Ww2DRo0SFu2bNH555/vsv8NAAAAOAIGwIiJEyeqZ8+e6tOnj6qrq3X33XcrKSlJv/71r7V9+/aTt+vTp49iYmIUEBCgzp07a8iQIZKkpKQkZWZmnrzdddddJ4vFoqSkJEVHRyspKUlWq1WJiYknb/fpp58qJSVFvXr10rZt2055HqC+/vGPf6hLly6Kj4/XtGnTTOcAADwMR8AAuERiYqI+++yzkx+//fbbKiwsVGpqql599VVFR0dr8+bNqq2tVWBg4MnbBQQEnPy51Wo9+bHValVNTc1pt/vv2/z37fbv36+XXnpJ69evV0REhMaOHauKigqnfb3wTjabTRMnTtSSJUsUGxurPn36aOjQoerevbvpNACAh+AIGACXuPzyy1VRUaF333335OfKysokScePH1dMTIysVqs+/vhj2Ww2hz9/cXGxmjdvrrCwMOXl5embb75x+HPA+61bt07x8fHq1KmT/P39NXLkSC1cuNB0FgDAg3AEDIBLWCwWLViwQA8++KBeeOEFRUZGqnnz5po+fbpSUlI0YsQIzZs3TwMHDlTz5s0d/vw9e/ZUr169lJiYqE6dOmnAgAEOfw54v5ycHLVr1+7kx7GxsVq7dq3BIgCAp7HY7Xa76QgA/9/e/cdEXf9xAH9+OLUakBoCopUCm9LO444DZPgjOkwoC3UQ0dyQG0tTgUksE4LUxvpdWkkZSIUhA8QGrk0nNMBS21QWCCZBwjk1Iw7GCQrjuHt///DLzftafmXC54Sej7/4fHi/3/d6vcdn47X3+/M+IhoPysrKcPToUeTn5wMACgsLcerUKezevdvWJi8vD3l5eQCA5uZm+Pn5OSTWO+ns7IS7u7ujwxg3OF8jw/kauft1zgwGA4xGo6PDmHC4AkZERHSXHn30UVy6dMl2ffnyZcyaNcuuzfr167F+/Xq5QxuRoKAg20mj9P9xvkaG8zVynLN/F74DRkREdFNhl/sAAAtESURBVJeCg4PR2tqK9vZ2DA4OoqSkBCtXrnR0WERENI5wBYyIiOguTZo0CTk5OYiMjITFYkFiYiKUSqWjwyIionGEBRgREdEIrFixAitWrHB0GPfkft8ieb/hfI0M52vkOGf/LjyEg4iIiIiISCZ8B4yIiIiIiEgmLMCIiIgmCIVCAY1GA7VaDa1Wi5MnT96xfU9PD7744guZopNfR0cH1qxZAx8fHwQGBiI0NBTl5eWODmvcKS8vhyRJaG5uBnDzaPIFCxbcsc/dtJmohp9DpVIJtVqNnTt3wmq13rHPrfNVX1+Pw4cPyxEqOQgLMCIiognioYceQn19PRoaGvDuu+8iIyPjju0ncgEmhMDq1avx5JNPoq2tDXV1dSgpKcHly5cdHdq4U1xcjCVLlqCkpMTRoYwLw8/huXPnUFVVhcOHD+Ott9666/4swCY+FmBEREQT0LVr1zB9+nTb9Ycffojg4GD4+/tj+/btAID09HRcuHABGo0GW7ZsQV9fH5YtWwatVguVSoVDhw45Kvx7Vl1djSlTpmDDhg22e3PmzEFKSgoMBgOWLl0KrVZrt1JYW1uLsLAwvPjii5g3bx7S09NRVFSEhQsXQqVS4cKFCwAAvV6PjRs3QqfTwcfHB8eOHUNiYiKeeOIJ6PV62+dt3LgRQUFBUCqVtjkfb/r6+nDixAl89dVXf1uAFRQUYNWqVXjmmWcwf/58u0LDYrFg3bp1UCqViIiIQH9/PwBg7969CA4OhlqtRkxMDG7cuCFbPnLz8PBAXl4ecnJyIISAxWLBli1bbM9ibm6uXfvBwUFs27YNpaWl0Gg0KC0txalTp7Bo0SIEBARg0aJF+O233xyUDY0aQURERBOCk5OTUKvVYv78+eLhhx8WZ86cEUIIcfToUbFu3TphtVqFxWIRzz33nDh27Jhob28XSqXS1t9sNguTySSEEKKzs1P4+voKq9XqkFzu1aeffipSU1P/9nfXr18X/f39QgghWlpaRGBgoBBCiJqaGjF16lTxxx9/iIGBATFr1iyxbds2IYQQn3zyidi8ebMQQoiEhAQRFxcnrFarqKioEK6uruLs2bPCYrEIrVYrfvnlFyGEEF1dXUIIIYaGhkRYWJhoaGgY05zHQmFhoUhMTBRCCBEaGirq6urs/m6++eYbMXPmTGE0GsWNGzeEUqkUp0+fFu3t7UKhUNjmIjY2VhQWFgohhDAajbbxMzMzxWeffSZzVmPL2dn5tnvTpk0Tf/75p8jNzRXZ2dlCCCEGBgZEYGCgaGtru21Ok5KSbH1NJpMwm81CCCGqqqpEdHS0DFnQWOIx9ERERBPE8NYnAPj555+xdu1aNDU1obKyEpWVlQgICABwc1WjtbUVjz/+uF1/IQTeeOMN/Pjjj3BycsKVK1fQ0dGBmTNnyp7LaEtKSsLx48cxZcoU/PDDD0hOTkZ9fT0UCgVaWlps7YKDg+Hl5QUA8PX1RUREBABApVKhpqbG1i4qKgqSJEGlUsHT0xMqlQoAoFQqYTAYoNFocODAAeTl5WFoaAhXr17Fr7/+Cn9/fxmzvnfFxcVITU0FALz00ksoLi5GUlKSXZvly5fDzc0NABAdHY3jx49j9erV8Pb2hkajAQAEBgbCYDAAAJqampCVlYWenh709fUhMjJSvoQcRPz30PHKykqcPXsWBw8eBACYTCa0trZi3rx5/9jXZDIhISEBra2tkCQJZrNZlphp7LAAIyIimoBCQ0NhNBrR2dkJIQQyMjLwyiuv2LUZ/od4WFFRETo7O1FXV4fJkydj7ty5GBgYkDHq0aNUKvHdd9/Zrj///HMYjUYEBQVh165d8PT0RENDA6xWKx588EFbuwceeMD2s5OTk+3ayckJQ0NDt7W7tc2t7drb2/HRRx/h9OnTmD59OvR6/biby66uLlRXV6OpqQmSJMFisUCSJGzatMmunSRJf3t967woFArbFkS9Xo+Kigqo1WoUFBSgtrZ2bBNxsLa2NigUCnh4eEAIgd27d99WdP7vs3irN998EzqdDuXl5TAYDHjqqafGNmAac3wHjIiIaAJqbm6GxWKBm5sbIiMj8fXXX6Ovrw8AcOXKFfz1119wdXVFb2+vrY/JZIKHhwcmT56MmpoaXLx40VHh37Pw8HAMDAxgz549tnvD7xqZTCZ4eXnByckJhYWFsFgso/75165dg7OzM6ZOnYqOjg4cOXJk1D9jrB08eBBr167FxYsXYTAYcOnSJXh7e992kElVVRW6u7vR39+PiooKLF68+I7j9vb2wsvLC2azGUVFRWOZgsN1dnZiw4YNSE5OhiRJiIyMxJ49e2yrWC0tLbh+/bpdn797LmfPng3g5jt3NP5xBYyIiGiC6O/vt235EkJg3759UCgUiIiIwPnz5xEaGgoAcHFxwf79++Hr64vFixdjwYIFePbZZ7F161ZERUUhKCgIGo0Gfn5+jkznnkiShIqKCrz66qv44IMP4O7uDmdnZ7z//vvQarWIiYlBWVkZdDodnJ2dR/3z1Wo1AgICoFQq4ePj83+LkvtRcXEx0tPT7e7FxMTgnXfesbu3ZMkSxMfH4/fff8eaNWsQFBR0xxWd7OxshISEYM6cOVCpVHbFxkQw/ByazWZMmjQJ8fHxSEtLAwC8/PLLMBgM0Gq1EELA3d0dFRUVdv11Oh3ee+89aDQaZGRk4PXXX0dCQgJ27tyJ8PBwR6REo0wSw5tSiYiIiIhGoKCgAGfOnEFOTo6jQyEaN7gFkYiIiIiISCZcASMiIiIiIpIJV8CIiIiIiIhkwgKMiIiIiIhIJizAiIiIiO5jtbW1OHnypKPDIKJRwgKMiIiI6D421gXYrV8wTURjjwUYERERkQN8++238Pf3h1qtRnx8PL7//nuEhIQgICAATz/9NDo6OmAwGPDll19i165d0Gg0+Omnn24bx2QyYe7cubBarQBufuH0Y489BrPZjL179yI4OBhqtRoxMTG2L6PW6/VIS0uDTqfD1q1bZc2b6N+OpyASERERyezcuXOIjo7GiRMnMGPGDHR3d0OSJEybNg2SJCE/Px/nz5/Hxx9/jB07dsDFxQWvvfbaP463atUqpKamQqfTobS0FFVVVcjPz0dXVxfc3NwAAFlZWfD09ERKSgr0ej2MRiMOHToEhUIhV9pEBGCSowMgIiIi+reprq7GCy+8gBkzZgAAHnnkETQ2NiIuLg5Xr17F4OAgvL2973q8uLg4lJaWQqfToaSkBJs2bQIANDU1ISsrCz09Pejr60NkZKStT2xsLIsvIgfgFkQiIiIimQkhIEmS3b2UlBQkJyejsbERubm5GBgYuOvxVq5ciSNHjqC7uxt1dXUIDw8HcHOrYU5ODhobG7F9+3a7MZ2dnUcnGSIaERZgRERERDJbtmwZDhw4gK6uLgBAd3c3TCYTZs+eDQDYt2+fra2rqyt6e3vvOJ6LiwsWLlyIzZs34/nnn7etbPX29sLLywtmsxlFRUVjlA0RjQQLMCIiIiKZKZVKZGZmIiwsDGq1GmlpadixYwdiY2OxdOlS29ZEAIiKikJ5efk/HsIxLC4uDvv370dcXJztXnZ2NkJCQrB8+XL4+fmNaU5EdHd4CAcREREREZFMuAJGREREREQkE56CSERERDROvP322ygrK7O7Fxsbi8zMTAdFREQjxS2IREREREREMuEWRCIiIiIiIpmwACMiIiIiIpIJCzAiIiIiIiKZsAAjIiIiIiKSCQswIiIiIiIimbAAIyIiIiIikgkLMCIiIiIiIpmwACMiIiIiIpIJCzAiIiIiIiKZsAAjIiIiIiKSCQswIiIiIiIimbAAIyIiIiIikgkLMCIiIiIiIpn8B3VooNUS9efRAAAAAElFTkSuQmCC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ij10kjHsAegX"
      },
      "source": [
        "###### Pie chart and barchart displaying the same categorical counts."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fp1-hCKZApCC"
      },
      "source": [
        "Unfortunately, pie charts are a fairly limited plot type in the range of scenarios where they can be used, and it is easy for chart makers to try and spice up pie charts in a way that makes them more difficult to read. If you want to use a pie chart, try to follow certain guidelines:\n",
        "\n",
        "- Make sure that your interest is in relative frequencies. Areas should represent parts of a whole, rather than measurements on a second variable (unless that second variable can logically be summed up into some whole).\n",
        "- Limit the number of slices plotted. A pie chart works best with two or three slices, though it's also possible to plot with four or five slices as long as the wedge sizes can be distinguished. If you have a lot of categories, or categories that have small proportional representation, consider grouping them together so that fewer wedges are plotted, or use an 'Other' category to handle them.\n",
        "- Plot the data systematically. One typical method of plotting a pie chart is to start from the top of the circle, then plot each categorical level clockwise from most frequent to least frequent. If you have three categories and are interested in the comparison of two of them, a common plotting method is to place the two categories of interest on either side of the 12 o'clock direction, with the third category filling in the remaining space at the bottom.\n",
        "\n",
        "If these guidelines cannot be met, then you should probably make use of a bar chart instead. A bar chart is a safer choice in general. The bar heights are more precisely interpreted than areas or angles, and a bar chart can be displayed more compactly than a pie chart. There's also more flexibility with a bar chart for plotting variables with a lot of levels, like plotting the bars horizontally.\n",
        "\n",
        "You can create a pie chart with matplotlib's ```pie``` function. This function requires that the data be in a summarized form: the primary argument to the function will be the wedge sizes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nMSOatdAPu2",
        "outputId": "0f7dd2b0-6688-4461-cc6a-c752899c5dd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# code for the pie chart seen above\n",
        "sorted_counts = df['generation_id'].value_counts()\n",
        "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
        "        counterclock = False);\n",
        "plt.axis('square');"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1-SyZRaBiHH"
      },
      "source": [
        "To follow the guidelines in the bullet points above, I include the \"startangle = 90\" and \"counterclock = False\" arguments to start the first slice at vertically upwards, and will plot the sorted counts in a clockwise fashion. The axis function call and 'square' argument makes it so that the scaling of the plot is equal on both the x- and y-axes. Without this call, the pie could end up looking oval-shaped, rather than a circle.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFETAkVgBlUN"
      },
      "source": [
        "## **Additional Variation:**\n",
        "\n",
        "A sister plot to the pie chart is the donut plot. It's just like a pie chart, except that there's a hole in the center of the plot. Perceptually, there's not much difference between a donut plot and a pie chart, and donut plots should be used with the same guidelines as a pie chart. Aesthetics might be one of the reasons why you would choose one or the other. For instance, you might see statistics reported in the hole of a donut plot to better make use of available space.\n",
        "\n",
        "To create a donut plot, you can add a \"wedgeprops\" argument to the pie function call. By default, the radius of the pie (circle) is 1; setting the wedges' width property to less than 1 removes coloring from the center of the circle."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwaiRfqzBONw",
        "outputId": "04512e0b-4cde-4915-af47-a6caad042fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "sorted_counts = df['generation_id'].value_counts()\n",
        "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
        "        counterclock = False, wedgeprops = {'width' : 0.4});\n",
        "plt.axis('square');"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAPTAuHcCLPZ"
      },
      "source": [
        "## **Histograms:**\n",
        "\n",
        "A histogram is used to plot the distribution of a numeric variable. It's the quantitative version of the bar chart. However, rather than plot one bar for each unique numeric value, values are grouped into continuous bins, and one bar for each bin is plotted depicting the number. For instance, using the default settings for matplotlib's hist function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CyulfjZFB5w_",
        "outputId": "f599cb59-c27d-41bb-b85a-63d3f0d3745d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "source": [
        "plt.hist(data = df, x = 'speed')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 31.,  93., 163., 159., 128., 115.,  81.,  26.,   6.,   5.]),\n",
              " array([  5. ,  20.5,  36. ,  51.5,  67. ,  82.5,  98. , 113.5, 129. ,\n",
              "        144.5, 160. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKrWcYMceQTF"
      },
      "source": [
        "Overall, a generally bimodal distribution is observed (one with two peaks or humps). The direct adjacency of the bars in the histogram, in contrast to the separated bars in a bar chart, emphasize the fact that the data takes on a continuous range of values. When a data value is on a bin edge, it is counted in the bin to its right. The exception is the rightmost bin edge, which places data values equal to the uppermost limit into the right-most bin (to the upper limit's left).\n",
        "\n",
        "By default, the hist function divides the data into 10 bins, based on the range of values taken. In almost every case, we will want to change these settings. Usually, having only ten bins is too few to really understand the distribution of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJ5lVR-_CWmP",
        "outputId": "db4d1c50-ca9c-4834-d27f-a1090b88a128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        }
      },
      "source": [
        "plt.hist(data = df, x = 'speed', bins = 20)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  6.,  25.,  21.,  72.,  66.,  97.,  53., 106.,  64.,  64.,  69.,\n",
              "         46.,  52.,  29.,  18.,   8.,   5.,   1.,   4.,   1.]),\n",
              " array([  5.  ,  12.75,  20.5 ,  28.25,  36.  ,  43.75,  51.5 ,  59.25,\n",
              "         67.  ,  74.75,  82.5 ,  90.25,  98.  , 105.75, 113.5 , 121.25,\n",
              "        129.  , 136.75, 144.5 , 152.25, 160.  ]),\n",
              " <a list of 20 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCXi8fDHes7R"
      },
      "source": [
        "You can use descriptive statistics (e.g. via df['num_var'].describe()) to gauge what minimum and maximum bin limits might be appropriate for the plot. These bin edges can be set using numpy's arange function.\n",
        "\n",
        "The first argument to arange is the leftmost bin edge, the second argument the upper limit, and the third argument the bin width. Note that even though I've specified the \"max\" value in the second argument, I've added a \"+1\" (the bin width). That is because arange will only return values that are strictly less than the upper limit. Adding in \"+1\" is a safety measure to ensure that the rightmost bin edge is at least the maximum data value, so that all of the data points are plotted. The leftmost bin is set as a hardcoded value to get a nice, interpretable value, though you could use functions like numpy's around if you wanted to approach that end programmatically."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OClaNf9XCgWc",
        "outputId": "21401599-08be-4491-cddd-19ed60d8e30e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "bins = np.arange(0, df['speed'].max()+1, 5)\n",
        "plt.hist(data = df, x = 'speed', bins = bins)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0.,  3.,  4., 11., 19., 18., 43., 38., 57., 46., 53., 48., 62.,\n",
              "        55., 53., 27., 45., 32., 39., 42., 36., 22., 18., 12.,  8.,  5.,\n",
              "         5.,  0.,  1.,  1.,  3.,  1.]),\n",
              " array([  0,   5,  10,  15,  20,  25,  30,  35,  40,  45,  50,  55,  60,\n",
              "         65,  70,  75,  80,  85,  90,  95, 100, 105, 110, 115, 120, 125,\n",
              "        130, 135, 140, 145, 150, 155, 160]),\n",
              " <a list of 32 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMeCkGcme6Fr"
      },
      "source": [
        "When creating histograms, it's useful to play around with different bin widths to see what represents the data best. Too many bins, and you may see too much noise that interferes with identification of the underlying signal. Too few bins, and you may not be able to see the true signal in the first place.\n",
        "\n",
        "This example puts two plots side by side through use of the subplot function, whose arguments specify the number of rows, columns, and index of the active subplot (in that order). The figure() function is called with the \"figsize\" parameter so that we can have a larger figure to support having multiple subplots. (More details on figures and subplots are coming up next in the lesson.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAy4BZDVe9pc",
        "outputId": "0c1e629f-f110-45fd-f2e8-f7a38cae70e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "plt.figure(figsize = [10, 5]) # larger figure size for subplots\n",
        "\n",
        "# histogram on left, example of too-large bin size\n",
        "plt.subplot(1, 2, 1) # 1 row, 2 cols, subplot 1\n",
        "bin_edges = np.arange(0, df['speed'].max()+1, +5)\n",
        "plt.hist(data = df, x = 'speed', bins = bin_edges);\n",
        "\n",
        "# histogram on right, example of too-small bin size\n",
        "plt.subplot(1, 2, 2) # 1 row, 2 cols, subplot 2\n",
        "bin_edges = np.arange(0, df['speed'].max()++1, 1)\n",
        "plt.hist(data = df, x = 'speed', bins = bin_edges);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3aaxzTJfR2E"
      },
      "source": [
        "## **Alternative Approach**\n",
        "The seaborn function distplot can also be used to plot a histogram, and is integrated with other univariate plotting functions.\n",
        "\n",
        "When we specify the data to be plotted, note that the first argument must be the Series or array with the points to be plotted. This is in contrast to our ability to specify a data source and column as separate arguments, like we've seen with and countplot and hist."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SwwVFSHdHsF",
        "outputId": "60e8e4fb-bb41-4b62-e689-91d655edee76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "sb.distplot(df['speed']);"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-agNforgTki"
      },
      "source": [
        "The distplot function has built-in rules for specifying histogram bins, and by default plots a curve depicting the kernel density estimate (KDE) on top of the data. The vertical axis is based on the KDE, rather than the histogram: you shouldn't expect the total heights of the bars to equal 1, but the area under the curve should equal 1. If you want to learn more about KDEs, check out the extra page at the end of the lesson.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmItC9FUdQK_",
        "outputId": "b010b3b6-a2a9-4782-944f-13f3b84a83c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "sb.distplot(df['speed'], kde=False);"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0rKzfLugida"
      },
      "source": [
        "Despite the fact that the default bin-selection formula used by distplot might be better than the choice of ten bins that .hist uses, you'll still want to do some tweaking to align the bins to 'round' values. You can use other parameter settings to plot just the histogram and specify the bins like before:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpuaoJzwdZKN",
        "outputId": "3e9c3ab5-4595-41fd-ac64-be979959f919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "bin_edges = np.arange(0, df['speed'].max()+1, 5)\n",
        "sb.distplot(df['speed'], bins = bin_edges, kde = False,\n",
        "            hist_kws = {'alpha' : 1});"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEGCAYAAAB8Ys7jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARmElEQVR4nO3de4zlZX3H8fdHEEG0Lsi4XVnaXSuiROWSKYVgTQUvCERoahBq6lpptjUt1dbULpo0MTUpaKPVxGg3gt1UFCiCEExVXKDWxIDD/bIgiIsusuzQgtdURb/94/xWxtlZ5szOucyz+34lk/O7nTnffXbOZ555zu/3/FJVSJLa87RxFyBJ2jUGuCQ1ygCXpEYZ4JLUKANckhq19yhf7KCDDqpVq1aN8iUlqXk33XTTo1U1MXv7SAN81apVTE1NjfIlJal5SR6ca7tDKJLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1KiRXokprVr3hb6O23zeKUOuRGqfPXBJapQBLkmN6ivAkyxLclmSe5JsSnJckgOTXJPkvu7xgGEXK0l6Ur898I8AX6yqFwNHAJuAdcDGqjoU2NitS5JGZN4AT/Ic4JXABQBV9bOqehw4DdjQHbYBOH1YRUqSdtRPD3w1MA18KsktST6ZZH9geVU93B2zFVg+15OTrE0ylWRqenp6MFVLkvoK8L2Bo4GPV9VRwI+ZNVxSVQXUXE+uqvVVNVlVkxMTO9xQQpK0i/oJ8C3Alqq6oVu/jF6gP5JkBUD3uG04JUqS5jJvgFfVVuC7SQ7rNp0I3A1cBazptq0BrhxKhZKkOfV7JeY5wEVJ9gEeAP6UXvhfmuRs4EHgjOGUKEmaS18BXlW3ApNz7DpxsOVIkvrllZiS1CgDXJIaZYBLUqMMcElqlPOB76acd1va/dkDl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY3yNEItSf2cBukpkNrT2QOXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjfI0Qs1rqc5suFTrkkbFHrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqVF+nESbZDPwQ+AXwRFVNJjkQuARYBWwGzqiqx4ZTpiRptoX0wF9VVUdW1WS3vg7YWFWHAhu7dUnSiCxmCOU0YEO3vAE4ffHlSJL61W+AF/DlJDclWdttW15VD3fLW4Hlcz0xydokU0mmpqenF1muJGm7fi+lf0VVPZTkecA1Se6ZubOqKknN9cSqWg+sB5icnJzzGEnSwvXVA6+qh7rHbcAVwDHAI0lWAHSP24ZVpCRpR/MGeJL9kzx7+zLwWuBO4CpgTXfYGuDKYRUpSdpRP0Moy4Erkmw//jNV9cUk3wAuTXI28CBwxvDKlCTNNm+AV9UDwBFzbP8f4MRhFCVJmp/zgWtg+p2fW9JgeCm9JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapSnEe7hPPVPapc9cElqlAEuSY1yCEWi/6GkzeedMuRKpP7ZA5ekRhngktQoA1ySGmWAS1KjDHBJapQBLkmN8jTCJcRT2SQthD1wSWqUAS5JjTLAJalRjoFLY9LPZx5+3qGnYg9ckhplgEtSo/oO8CR7JbklydXd+uokNyS5P8klSfYZXpmSpNkW0gN/B7Bpxvr5wIer6oXAY8DZgyxMkvTU+grwJCuBU4BPdusBTgAu6w7ZAJw+jAIlSXPrtwf+L8C7gV92688FHq+qJ7r1LcDBcz0xydokU0mmpqenF1WsJOlJ8wZ4klOBbVV10668QFWtr6rJqpqcmJjYlW8hSZpDP+eBHw+8IcnJwL7AbwAfAZYl2bvrha8EHhpemZKk2ebtgVfVuVW1sqpWAWcC11bVm4HrgDd2h60BrhxalZKkHSzmSsy/By5O8n7gFuCCwZQkta3fWSWlxVpQgFfV9cD13fIDwDGDL0mS1A+vxJSkRjmZVYP8E10S2AOXpGYZ4JLUKANckhplgEtSowxwSWqUAS5JjfI0QmkJ6/eUUe+duWeyBy5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRjmZlbQA3o9US4k9cElqlAEuSY2aN8CT7JvkxiS3Jbkryfu67auT3JDk/iSXJNln+OVKkrbrpwf+U+CEqjoCOBI4KcmxwPnAh6vqhcBjwNnDK1OSNNu8AV49P+pWn959FXACcFm3fQNw+lAqlCTNqa8x8CR7JbkV2AZcA3wLeLyqnugO2QIcPJwSJUlz6SvAq+oXVXUksBI4Bnhxvy+QZG2SqSRT09PTu1imJGm2BZ2FUlWPA9cBxwHLkmw/j3wl8NBOnrO+qiaranJiYmJRxUqSntTPWSgTSZZ1y/sBrwE20QvyN3aHrQGuHFaRkqQd9XMl5gpgQ5K96AX+pVV1dZK7gYuTvB+4BbhgiHVKkmaZN8Cr6nbgqDm2P0BvPFySNAZeiSlJjTLAJalRBrgkNcoAl6RGGeCS1Chv6KDdnjdh0O7KHrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNcj5waTfQz5znm887ZQSVaJTsgUtSowxwSWqUAS5JjZo3wJMckuS6JHcnuSvJO7rtBya5Jsl93eMBwy9XkrRdPz3wJ4B3VdXhwLHAXyY5HFgHbKyqQ4GN3bokaUTmDfCqeriqbu6WfwhsAg4GTgM2dIdtAE4fVpGSpB0taAw8ySrgKOAGYHlVPdzt2gos38lz1iaZSjI1PT29iFIlSTP1HeBJngV8DnhnVf1g5r6qKqDmel5Vra+qyaqanJiYWFSxkqQn9RXgSZ5OL7wvqqrLu82PJFnR7V8BbBtOiZKkucx7JWaSABcAm6rqQzN2XQWsAc7rHq8cSoVLXD9XwIFXwUkavH4upT8e+BPgjiS3dtveQy+4L01yNvAgcMZwSpQkzWXeAK+qrwHZye4TB1uOJKlfXokpSY1yNsIR6XesXBoWP6/Z/dgDl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhrlPTEl/Zp+7p3pfTOXBnvgktQoA1ySGjXvEEqSC4FTgW1V9dJu24HAJcAqYDNwRlU9Nrwyx6OfPyUlaVz66YH/G3DSrG3rgI1VdSiwsVuXJI3QvAFeVV8F/nfW5tOADd3yBuD0AdclSZrHro6BL6+qh7vlrcDynR2YZG2SqSRT09PTu/hykqTZFv0hZlUVUE+xf31VTVbV5MTExGJfTpLU2dUAfyTJCoDucdvgSpIk9WNXA/wqYE23vAa4cjDlSJL6NW+AJ/ks8HXgsCRbkpwNnAe8Jsl9wKu7dUnSCM17HnhVnbWTXScOuBZJ0gJ4JaYkNcrJrCQtWL9XKTvp1XDZA5ekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEZ5JaakoRnkfWW9qnNH9sAlqVEGuCQ1ao8cQhnkn3WSNC72wCWpUQa4JDXKAJekRu12Y+COb0sahBZuWmEPXJIaZYBLUqMMcElqlAEuSY0ywCWpUbvdWSiS9lzjOAutn9cc1pkqi+qBJzkpyb1J7k+yblBFSZLmt8sBnmQv4GPA64HDgbOSHD6owiRJT20xPfBjgPur6oGq+hlwMXDaYMqSJM1nMWPgBwPfnbG+Bfi92QclWQus7VZ/lOTeXXy9g4BHd/G5w2RdC2NdC2NdnZzf12FLsr1y/qLr+u25Ng79Q8yqWg+sX+z3STJVVZMDKGmgrGthrGthrGth9rS6FjOE8hBwyIz1ld02SdIILCbAvwEcmmR1kn2AM4GrBlOWJGk+uzyEUlVPJPkr4EvAXsCFVXXXwCrb0aKHYYbEuhbGuhbGuhZmj6orVTWM7ytJGjIvpZekRhngktSoJgJ8qVyyn+SQJNcluTvJXUne0W0/MMk1Se7rHg8YQ217JbklydXd+uokN3Rtdkn3QfPIJVmW5LIk9yTZlOS4JdJef9P9H96Z5LNJ9h1HmyW5MMm2JHfO2DZn+6Tno119tyc5esR1fbD7f7w9yRVJls3Yd25X171JXjfKumbse1eSSnJQtz7W9uq2n9O12V1JPjBj+2Daq6qW9Be9D0i/BbwA2Ae4DTh8TLWsAI7ulp8NfJPeNAIfANZ129cB54+htr8FPgNc3a1fCpzZLX8CePuY2mwD8Gfd8j7AsnG3F72L0L4N7Dejrd46jjYDXgkcDdw5Y9uc7QOcDPwnEOBY4IYR1/VaYO9u+fwZdR3evS+fAazu3q97jaqubvsh9E6oeBA4aIm016uArwDP6NafN+j2GtmbZhENcxzwpRnr5wLnjruurpYrgdcA9wIrum0rgHtHXMdKYCNwAnB19wP76Iw326+14Qjrek4XlJm1fdzttf0q4gPpnYl1NfC6cbUZsGrWG3/O9gH+FThrruNGUdesfX8IXNQt/9p7sgvS40ZZF3AZcASweUaAj7W96HUIXj3HcQNrrxaGUOa6ZP/gMdXyK0lWAUcBNwDLq+rhbtdWYPmIy/kX4N3AL7v15wKPV9UT3fq42mw1MA18qhve+WSS/Rlze1XVQ8A/A98BHga+D9zE0mgz2Hn7LKX3wtvo9W5hzHUlOQ14qKpum7Vr3O31IuD3u2G5/0ryu4Ouq4UAX3KSPAv4HPDOqvrBzH3V+5U6snMzk5wKbKuqm0b1mguwN70/Kz9eVUcBP6Y3JPAro24vgG5M+TR6v2CeD+wPnDTKGvo1jvaZT5L3Ak8AFy2BWp4JvAf4h3HXMoe96f2Vdyzwd8ClSTLIF2ghwJfUJftJnk4vvC+qqsu7zY8kWdHtXwFsG2FJxwNvSLKZ3oyQJwAfAZYl2X6h1rjabAuwpapu6NYvoxfo42wvgFcD366q6ar6OXA5vXZcCm0GO2+fsb8XkrwVOBV4c/fLZdx1/Q69X8S3de+BlcDNSX5zzHVB7+f/8uq5kd5fyAcNsq4WAnzJXLLf/fa8ANhUVR+asesqYE23vIbe2PhIVNW5VbWyqlbRa5trq+rNwHXAG8dR04zatgLfTXJYt+lE4G7G2F6d7wDHJnlm93+6va6xt1lnZ+1zFfCW7uyKY4HvzxhqGbokJ9EbqntDVf1kVr1nJnlGktXAocCNo6ipqu6oqudV1aruPbCF3okGWxlzewGfp/dBJkleRO9D/EcZZHsNa0B/wB8OnEzvjI9vAe8dYx2voPfn7O3Ard3XyfTGnDcC99H71PnAMdX3Bzx5FsoLuh+K+4H/oPskfAw1HQlMdW32eeCApdBewPuAe4A7gX+nd0bAyNsM+Cy9cfif0wufs3fWPvQ+nP5Y9z64A5gccV330xu73f6z/4kZx7+3q+te4PWjrGvW/s08+SHmuNtrH+DT3c/YzcAJg24vL6WXpEa1MIQiSZqDAS5JjTLAJalRBrgkNcoAl6RGGeDSLkpyfZIldwNd7TkMcElqlAGu3UqS/ZN8Iclt3Vzfb0qyOckHktyR5MYkL+yOnUjyuSTf6L6On/E9LuyOvaWbLIkk+yW5OL15za8A9hvjP1Xa9ZsaS0vUScD3quoUgCTPoTd39fer6mVJ3kJv9sZT6c0Z8+Gq+lqS36I3redL6F0ld21Vva27acGNSb4C/Dnwk6p6SZKX07u6Thobr8TUbqWbc+LLwCX0phX4726SoxOq6oFuMrKtVfXcJNuA7814+gRwGHA9sC+9GfegN6Pc64B/Aj5aVdd2r3UzsLaqpob/L5N2ZA9cu5Wq+mZ366yTgfcn2bh918zDusenAcdW1f/N/B7dBFd/VFX3zto+pKqlXeMYuHYrSZ5Pb5jj08AH6U1fC/CmGY9f75a/DJwz47lHdotfAs7ZPndzkqO67V8F/rjb9lLg5UP6Z0h9sQeu3c3LgA8m+SW9meHeTm8e8gOS3A78FDirO/avgY912/emF9B/AfwjvXHy25M8jd5t4U4FPk7v7kKbgE307uIjjY1j4NrtdWPgk1X16LhrkQbJIRRJapQ9cElqlD1wSWqUAS5JjTLAJalRBrgkNcoAl6RG/T9iopRy0QCTvAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tMzZpUIgqpy"
      },
      "source": [
        "The alpha (transparency) setting must be associated as a dictionary to \"hist_kws\" since there are other underlying plotting functions, like the KDE, that have their own optional keyword parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B52DTvlcgldh"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    }
  ]
}