{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled97.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN0RT3Vs7wjXNmRpTqsi+IN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nurfnick/Data_Viz/blob/main/Content/Data_Visualization/25_Histograms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Histograms\n"
      ],
      "metadata": {
        "id": "4B2jSkit0e34"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we look at many quantititative variables.  Looking at the histogram allows us to get an idea of the shape of the distribution."
      ],
      "metadata": {
        "id": "X0lRD_Zi0iF8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d2Wts3iV0Xlr"
      },
      "outputs": [],
      "source": [
        "import pandas as pa\n",
        "\n",
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/iris.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "qHgG-PO302P9",
        "outputId": "90a17e8b-28bf-4f78-85a4-76eafa872714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf0139b590>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can get all the data in a histogram rather quickly!"
      ],
      "metadata": {
        "id": "-O1Lokq0JbUY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist()"
      ],
      "metadata": {
        "id": "q1LSIN8I4LHA",
        "outputId": "2dc719d0-d56e-41bf-a48d-84c79811d625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbf020eb0d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbf01fb9d90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbf018340d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbf019916d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Number of Bins"
      ],
      "metadata": {
        "id": "VPuZsuM9IgPk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remember to look at multiple bin widths when doing a histogram it may make a difference in the display of your data!  The default is 10 which is not bad here."
      ],
      "metadata": {
        "id": "ldQem-fA09dU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot.hist(bins = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "KsR5yxQ3073l",
        "outputId": "ebff096e-09f8-4690-e657-2729d49a2f7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda6826bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD4CAYAAAAEhuazAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQA0lEQVR4nO3de5AlZX3G8e8jq+ESBQ0TVGBcTCgMiRhxvRJNFE28IJhIIlYwSqlrEu9JlaJl1EpVqkyVUdFKjBu84I2o6yUY0YAaTVKVgCyQCKwURBFZUFATF5US0V/+OL0wTHZme2enT5/Z9/upmprTfXq7n3k5PNPTp093qgpJUlvuMnYASdL0Wf6S1CDLX5IaZPlLUoMsf0lq0LqxA/Rx8MEH1/r168eOIUlrypYtW75dVXM7e25NlP/69eu56KKLxo4hSWtKkq8v9ZyHfSSpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDBiv/JO9KcmOSyxbMu1eS85Nc1X2/51DblyQtbcg9//cAT1w073Tgc1V1JPC5blqSNGWDlX9V/Qvw3UWzTwLO6h6fBTxtqO1LkpY27U/4HlJVN3SPvwkcstSCSTYCGwHm5+enEG1tW3/6p25/fM0bnjJiEklrwWhv+NbkFmJL3kasqjZV1Yaq2jA3t9NLU0iSVmja5f+tJPcB6L7fOOXtS5KYfvmfAzy7e/xs4B+mvH1JEsOe6nk28O/AUUmuS/Jc4A3AE5JcBTy+m5YkTdlgb/hW1TOXeOr4obYpSerHT/hKUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZN+x6+GpH3+Z0t/vfQmNzzl6QGWf6S1CDLX5IaZPlLUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAaNUv5JXp7k8iSXJTk7yb5j5JCkVk29/JMcCrwE2FBVvwLsA5wy7RyS1LKxDvusA/ZLsg7YH7h+pByS1KSp38C9qrYleSNwLXALcF5Vnbd4uSQbgY0A8/Pz0w2pmeVNz1efY9qmMQ773BM4CTgCuC9wQJJTFy9XVZuqakNVbZibm5t2TEnaq41x2OfxwNeq6qaq+jHwMeBRI+SQpGaNUf7XAo9Isn+SAMcDW0fIIUnNmnr5V9UFwGbgYuDLXYZN084hSS2b+hu+AFX1OuB1Y2xbkuQnfCWpSZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWrQKNfzV7sW3iwchrlh+N52Q/K97efRbHDPX5IaZPlLUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kN6lX+SR44dBBJ0vT03fP/myQXJvnjJAcOmkiSNLhe5V9VjwZ+Hzgc2JLkg0meMGgySdJgeh/zr6qrgNcArwR+HXhrkq8k+Z2hwkmShtH3mP8xSd4MbAUeBzy1qn6pe/zm3d1okoOSbO5+eWxN8sjdXYckaeX63snrbcCZwKur6pYdM6vq+iSvWcF2zwA+U1UnJ7kbsP8K1iFJWqG+5f8U4Jaq+glAkrsA+1bVD6vqfbuzwe4N48cAzwGoqluBW3dnHZKkPdP3mP9ngf0WTO/fzVuJI4CbgHcnuSTJmUkOWOG6JEkr0HfPf9+q+v6Oiar6fpKVHqpZBxwLvLiqLkhyBnA68GcLF0qyEdgIMD8/v8JN7R3Wyg28VzPnLPzMfTLMQk5pJfru+f8gybE7JpI8BLhlmeWXcx1wXVVd0E1vZvLL4E6qalNVbaiqDXNzcyvclCRpZ/ru+b8M+EiS64EA9waesZINVtU3k3wjyVFVdSVwPHDFStYlSVqZXuVfVV9K8gDgqG7WlVX14z3Y7ouBD3Rn+nwVOG0P1iVJ2k199/wBHgqs7/7NsUmoqveuZKNVdSmwYSX/VpK053qVf5L3Ab8AXAr8pJtdwIrKX5I0rr57/huAo6uqhgwjSZqOvmf7XMbkTV5J0l6g757/wcAVSS4EfrRjZlWdOEgqSdKg+pb/64cMIUmarr6nen4xyf2AI6vqs92ne/cZNpokaSh9L+n8fCafxH1HN+tQ4BNDhZIkDavvG74vBI4DtsPtN3b5+aFCSZKG1bf8f9RdehmAJOuYnOcvSVqD+pb/F5O8Gtivu3fvR4BPDhdLkjSkvuV/OpNr8H8ZeAFwLpP7+UqS1qC+Z/v8FPi77kuStMb1vbbP19jJMf6quv+qJ5IkDW53ru2zw77A7wL3Wv04kqRp6HXMv6q+s+BrW1W9hclN3SVJa1Dfwz4Lb7N4FyZ/CezOvQC0BO8Bq9Xg60i7q2+B/9WCx7cB1wC/t+ppJElT0fdsn8cOHUSSND19D/v8yXLPV9WbVieOJGkadudsn4cC53TTTwUuBK4aIpQkaVh9y/8w4NiquhkgyeuBT1XVqUMFkyQNp+/lHQ4Bbl0wfWs3T5K0BvXd838vcGGSj3fTTwPOGiaSJGlofc/2+YsknwYe3c06raouGS6WJGlIfQ/7AOwPbK+qM4DrkhwxUCZJ0sD63sbxdcArgVd1s+4KvH+oUJKkYfXd8/9t4ETgBwBVdT1w96FCSZKG1bf8b62qorusc5IDhoskSRpa3/L/cJJ3AAcleT7wWbyxiyStWbs82ydJgA8BDwC2A0cBr62q8wfOJkkayC7Lv6oqyblV9UDAwpekvUDfwz4XJ3nooEkkSVPT9xO+DwdOTXINkzN+wuSPgmOGCiZJGs6y5Z9kvqquBX5rtTecZB/gImBbVZ2w2uuXJC1tV3v+n2ByNc+vJ/loVT19Fbf9UmArcI9VXKckqYddHfPPgsf3X62NJjmMyQ3gz1ytdUqS+tvVnn8t8XhPvQV4Bct8SjjJRmAjwPz8/CpuenXtbTfO3tt+nrG0OI4t/sxLWQtjsas9/wcl2Z7kZuCY7vH2JDcn2b6SDSY5AbixqrYst1xVbaqqDVW1YW5ubiWbkiQtYdk9/6raZ4BtHgecmOTJwL7APZK837uCSdL07M4lnVdFVb2qqg6rqvXAKcDnLX5Jmq6pl78kaXx9P+Q1iKr6AvCFMTNIUovc85ekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBo17Pf0xr4QbLGo7//XfPrIzXrOTYG7jnL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGWf6S1CDLX5IaZPlLUoMsf0lq0NTLP8nhSf45yRVJLk/y0mlnkKTWjXEnr9uAP62qi5PcHdiS5PyqumKELJLUpKnv+VfVDVV1cff4ZmArcOi0c0hSy0a9h2+S9cCDgQt28txGYCPA/Pz8VHMNwXuPagy+7rSU0d7wTfKzwEeBl1XV9sXPV9WmqtpQVRvm5uamH1CS9mKjlH+SuzIp/g9U1cfGyCBJLRvjbJ8A7wS2VtWbpr19SdI4e/7HAc8CHpfk0u7rySPkkKRmTf0N36r6NyDT3q4k6Q5+wleSGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGjXoD92nYkxtYe/PrOzgWWg1LvY5W8/XVZ12z3AvT+n/NPX9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGWf6S1CDLX5IaZPlLUoMsf0lq0Cjln+SJSa5McnWS08fIIEktm3r5J9kH+GvgScDRwDOTHD3tHJLUsjH2/B8GXF1VX62qW4G/B04aIYckNStVNd0NJicDT6yq53XTzwIeXlUvWrTcRmBjN3kUcOVUg97ZwcC3R9x+X+ZcfWslqzlX31rJulzO+1XV3M6eWDdcnj1TVZuATWPnAEhyUVVtGDvHrphz9a2VrOZcfWsl60pzjnHYZxtw+ILpw7p5kqQpGaP8vwQcmeSIJHcDTgHOGSGHJDVr6od9quq2JC8C/gnYB3hXVV0+7Ry7aSYOP/VgztW3VrKac/Wtlawryjn1N3wlSePzE76S1CDLX5IaZPkvkGSfJJck+cedPPecJDclubT7et4YGbss1yT5cpfjop08nyRv7S6f8V9Jjp3RnL+R5HsLxvS1Y+TsshyUZHOSryTZmuSRi56flTHdVc7RxzTJUQu2f2mS7UletmiZ0cezZ87Rx3NBlpcnuTzJZUnOTrLvoud/JsmHujG9IMn65dY3s+f5j+SlwFbgHks8/6HFH0Yb0WOraqkPdjwJOLL7ejjw9u77GJbLCfCvVXXC1NIs7QzgM1V1cncW2v6Lnp+VMd1VThh5TKvqSuBX4fbLuWwDPr5osdHHs2dOmIHXaJJDgZcAR1fVLUk+zORMyfcsWOy5wP9U1S8mOQX4S+AZS63TPf9OksOApwBnjp1lFZwEvLcm/gM4KMl9xg41q5IcCDwGeCdAVd1aVf+7aLHRx7RnzllzPPDfVfX1RfNHH89Flso5S9YB+yVZx+SX/vWLnj8JOKt7vBk4PkmWWpnlf4e3AK8AfrrMMk/v/kTdnOTwZZYbWgHnJdnSXQZjsUOBbyyYvq6bN227ygnwyCT/meTTSX55muEWOAK4CXh3d9jvzCQHLFpmFsa0T06YjTHd4RTg7J3Mn4XxXGipnDAD41lV24A3AtcCNwDfq6rzFi12+5hW1W3A94CfW2qdlj+Q5ATgxqrassxinwTWV9UxwPnc8Rt2DL9WVccy+dP5hUkeM2KW5ewq58VMrj3yIOBtwCemHbCzDjgWeHtVPRj4ATCLlxrvk3NWxpTusNSJwEfGytDHLnLOxHgmuSeTPfsjgPsCByQ5dU/WaflPHAecmOQaJlcZfVyS9y9coKq+U1U/6ibPBB4y3Yh3yrKt+34jk2OUD1u0yExcQmNXOatqe1V9v3t8LnDXJAdPOyeTvc7rquqCbnozk5JdaBbGdJc5Z2hMYfJL/+Kq+tZOnpuF8dxhyZwzNJ6PB75WVTdV1Y+BjwGPWrTM7WPaHRo6EPjOUiu0/IGqelVVHVZV65n8+ff5qrrTb9VFxyNPZPLG8NQlOSDJ3Xc8Bn4TuGzRYucAf9CdUfEIJn8i3jBrOZPce8cxySQPY/J6XPLFOpSq+ibwjSRHdbOOB65YtNjoY9on56yMaeeZLH0oZfTxXGDJnDM0ntcCj0iyf5fneP5/B50DPLt7fDKTHlvyU7ye7bOMJH8OXFRV5wAvSXIicBvwXeA5I8U6BPh493pcB3ywqj6T5A8BqupvgXOBJwNXAz8ETpvRnCcDf5TkNuAW4JTlXqwDezHwge4QwFeB02ZwTPvknIkx7X7hPwF4wYJ5MzeePXLOxHhW1QVJNjM5DHUbcAmwaVFHvRN4X5KrmXTUKcut08s7SFKDPOwjSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KD/g9lgPbeI63fVgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "100 was too many and 5 too few."
      ],
      "metadata": {
        "id": "4aURrlCC2hxP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot.hist(bins = 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "h1p6QCKY1MgM",
        "outputId": "1342ab00-c178-45a5-c378-1cfa4183fed7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda620acd0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "25 bins gave a little more clarity to how the data was distributed."
      ],
      "metadata": {
        "id": "6DBGf7hd2ndz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot.hist(bins = 25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "1aNBelTY1J2E",
        "outputId": "221178c9-353c-4398-bb7a-3eb548fbec2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda639b9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Density Shows Much the Same as Histograms"
      ],
      "metadata": {
        "id": "xoTAmwsULkH-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Densitied can give you a similar view to the histogram."
      ],
      "metadata": {
        "id": "5pXSFJH_PnwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot(kind = 'density')"
      ],
      "metadata": {
        "id": "d0B4I02bLtXH",
        "outputId": "830fc182-8b7e-4a2c-848a-39fc1f0beb47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd931bf910>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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s/8IFWanx5KUn8NYBKxhm6FnBMMOurNY6vN108dR03rZ+DOMCKxhm2JXVNBPri2JaZpLXUUakpVPTaG7vZo+tj2GGmBUMM+zKapuZkZVEjM++/NzQ049hw2vNUHP1O1ZElonIXhEpF5H7+zl+n4iUicgOEXlFRKb0OuYXkW3Ox9q+15rIpKpnRkgZd2SPHUNu2hjeto5vM8RcKxgi4gMeBW4GCoE7RaSwz2lbgSJVnQ88C/yw17HTqrrQ+ViOGRHqWjpobO20guGypfnBfgxb59sMJTdbGMVAuaoeUNVOYDWwovcJqvqaqrY5m28BOS7mMWHgr1Oa26JJblqan8aJti72153yOooZQdwsGJOAyl7bVc6+s/k08Ide2/EiUiIib4nIB9wIaIZfzwipWVnJHicZ2Xr6Mey2lBlKYdHrKCJ3AUXAj3rtnqKqRcBHgZ+IyLR+rlvlFJWS+vr6YUprLkRZTTNT0hNItjUwXJWbNoaJqfG8ddA6vs3QcbNgVAO5vbZznH3vIiLXA98ClqtqR89+Va12Ph8A/gQs6nutqj6mqkWqWpSZmTm06Y0rbA2M4SEiFOensenAcVStH8MMDTcLxmagQETyRSQWWAm8a7STiCwCfkGwWNT12j9OROKc1xnAZUCZi1nNMDizBoYVjGGxND+dhlMdHGxo9TqKGSFcKxiq2g3cC6wHdgNrVLVURB4UkZ5RTz8CkoBn+gyfnQ2UiMh24DXgIVW1ghHhzqyBYU94D4u/ro9ht6XM0Ih2881VdR2wrs++B3q9vv4s170JzHMzmxl+tgbG8JqakUhGUhybDjRyZ/Fkr+OYESAsOr3N6FBW28y4hBiyUmwNjOEgIizNT2PTQevHMEPDCoYZNmU1zRRm2xoYw2np1DRqm9qpOnHa6yhmBLCCYYZFzxoYs7PsdtRwKs63fgwzdKxgmGFxqNHWwPDCjPHJjE2IYZOtj2GGgBUMMyxKnQ7v2TakdlhFRQnFeWnWwjBDIqSCISL/IyK3iIgVGDMou2tbbA0MjxTnp3HkeBu1TdaPYS5MqAXgXwlO0bFfRB4SkZkuZjIjUFltM9PHJxEbbb9zDLeLp/bMK2WtDHNhQvruVdU/qurHgMXAIeCPIvKmiHxSRGxSIDOg3bXN1n/hkdkTU0iOi7YFlcwFC/nXPRFJBz4BfIbgOhaPECwgL7uSzIwY9S0d1Ld0WP+FR3xRQlHeOJu51lywUPswngf+DCQA71fV5ar636r6RYJTexhzVrtrezq8bUpzryydmk5FfSv1LR0Dn2zMWYTawvilqhaq6j+rai1Az+SAzhTkxpxVzxoYNumgd5Y6z2NYP4a5EKEWjH/sZ9/GoQxiRq7dtc1kp8YzNiHW6yij1txJqSTE+uy2lLkg55x8UESyCK6SN8aZirxnTocUgrenjBmQdXh7L8YXxZIp4+x5DHNBBpqt9iaCHd05wMO99rcAf+9SJjOCtHf5qahv5aY5WV5HGfWW5qfx45f2caK1k3GJ1toz5++cBUNVnwCeEJEPqepzw5TJjCD7j53CH1DrvwgDxc4635sPHedGK+BmEAa6JXWXqv4GyBOR+/oeV9WH+7nMmDPKapsAmxIkHCzITSUuOopNB61gmMEZqNM70fmcBCT383FOIrJMRPaKSLmI3N/P8ftEpExEdojIKyIypdexu0Vkv/Nxd8h/IxNWdte2kBjrY3KadXl5LS7ax6LJY9lkHd9mkAa6JfUL5/N3z/eNRcQHPArcAFQBm0VkbZ+lVrcCRaraJiL/B/ghcIeIpAHfBooABbY415443xzGW2U1zcyamEJUlK2BEQ6K89P52av7aW7vIiXeJmkw5yfUB/d+KCIpIhLjtATqReSuAS4rBspV9YCqdgKrgRW9T1DV11S1zdl8i2DnOgQ7219W1eNOkXgZWBbqX8qEB1Vld22zPbAXRi7OTyOgsOWQ/e5lzl+oz2HcqKrNwK0E55KaDvzdANdMAip7bVc5+87m08AfBnmtCUNVJ07T0tFN4cRUr6MYx6LJ44jxiQ2vNYMy0LDavufdAjyjqk1Ducym01opAq46z+tWAasAJk+2Re7DTZlNCRJ2xsT6mJ9j/RhmcEJtYfxORPYAS4BXRCQTaB/gmmogt9d2jrPvXUTkeuBbwHJV7Tifa1X1MVUtUtWizMzMEP8qZrjsrm0mSmCWLcsaVpbmp7Gzqom2zm6vo5gIE+r05vcDlxLsoO4CWunTH9GPzUCBiOSLSCywEljb+wTn6fFfECwWdb0OrQduFJFxIjIOuNHZZyJIWU0zeRmJjIn1eR3F9FKcn0Z3QHnn8Emvo5gIE+otKYBZBJ/H6H3Nf57tZFXtFpF7Cf6g9wGPq2qpiDwIlKjqWuBHBIfsPuPc4jrizIR7XES+R7DoADyoqnbTNcLsPtrMgpyxXscwfRTlpeGLEjYdbOTyggyv45gIElLBEJEngWnANsDv7FbOUTAAVHUdsK7Pvgd6vb7+HNc+DjweSj4Tfprbu6g8fpqVF1nfUrhJiotm3qRU/lLewFdvtMUzTehCbWEUAYWqqm6GMSPHntoWwKY0D1eXT8/g3zZU2PMY5ryE2um9C7C5BEzIympsSpBwdnlBBv6AsrHCRkuZ0IXawsgAykTkbeDMkl2qutyVVCbildY0k5EUy4SUOK+jmH4snjyOhFgfb+xvsJmETchCLRjfcTOEGXl21TQzJzuVoXxexwyd2Ogoluan8UZ5g9dRTAQJdVjtBoJPeMc4rzcD77iYy0Sw9i4/+4+1MHeS3Y4KZ5cXZHKwoZWqE20Dn2wMoc8l9VngWYLPTEBwmo7/dSuUiWx7j7bQHVDmZtuUIOHsCmdI7Rv7rZVhQhNqp/c9wGVAM4Cq7gfGuxXKRLZdTof33ElWMMJZwfgkJqTE8We7LWVCFGrB6HBmnAXAeXjPhtiafu2qbiZ1TAw548Z4HcWcg4hw2fQM3ixvIBCwb2czsFALxgYR+XtgjIjcADwDvOBeLBPJSmuamDspxTq8I8AVBRmcaOuitKbZ6ygmAoRaMO4H6oGdwOcIPr39D26FMpGrszvAntoW67+IEJdPD07auWFf3QBnGhP6KKkAwU7uL6jq7ar6S3vq2/Rnf10Lnf6A9V9EiMzkOObnpPLqHisYZmDnLBgS9B0RaQD2Anud1fYeONd1ZvQqrQ7e2rCCETmunTWerZUnaTzVMfDJZlQbqIXxFYKjoy5S1TRVTQOWApeJyFdcT2cizq6aJpLiopmSluB1FBOia2eNRxU27Kv3OooJcwMVjL8B7lTVgz07VPUAcBfwcTeDmci0q7qJwuwUoqKswztSzM1OJTM5jlfstpQZwEAFI0ZV3zNIW1XrAZvi0rxLtz9AWW2zdXhHmKgo4dqZ43l9Xz1d/oDXcUwYG6hgdA7ymBmFDjS00t4VYF6OTQkSaa6ZNZ6W9m5KDp3wOooJYwNNPrhARPoboC1AvAt5TATbVe084W0tjIhzeUEGsb4oXttbxyXT0r2OY8LUOVsYqupT1ZR+PpJVdcBbUiKyTET2iki5iNzfz/ErReQdEekWkdv7HPOLyDbnY23fa0342VHVxJgYH1Mzk7yOYs5TUlw0S6em8ceyY9iIeXM2oT64d95ExAc8CtwMFAJ3ikhhn9OOAJ8AnurnLU6r6kLnw9bdiADbq04yb1IqPuvwjkjL5mZxoKGVfcdOeR3FhCnXCgZQDJSr6gFnHqrVwIreJ6jqIVXdAVhPW4Tr7A5QWtPMwsljvY5iBunGwixEYN3OWq+jmDDlZsGYBFT22q5y9oUqXkRKROQtEflAfyeIyCrnnJL6ehtD7qU9R5vp7A6wIMcKRqTKTI6jOC+NP+yygmH652bBuFBTVLUI+CjwExGZ1vcEVX1MVYtUtSgzM3P4E5oztleeBGBBrnV4R7Kb52ax79gpyuvstpR5LzcLRjWQ22s7x9kXElWtdj4fAP4ELBrKcGZobatsIiMplkljbUrzSLZs7kQAXrRWhumHmwVjM1AgIvkiEgusBEIa7SQi40QkznmdQXB6kjLXkpoLtq3yBAtzx9qU5hEuKzWeJVPG8fudR72OYsKQawVDVbuBe4H1wG5gjaqWisiDIrIcQEQuEpEq4MPAL0Sk1Ll8NlAiItuB14CHVNUKRphqbu+ior7V+i9GiFvmTWR3bTP7jrV4HcWEmYEe3LsgqrqO4NoZvfc90Ov1ZoK3qvpe9yYwz81sZujsrAo+sLcg1wrGSLB8YTbfX7eb596p4ps3z/Y6jgkj4dzpbSLEtp4Ob2thjAgZSXFcPSOT/91ajd+WbjW9WMEwF2xb5UmmZiSSmmDzUY4Uty3O4VhzB29WvGfuUTOKWcEwF0RV2VZ50m5HjTDXzR5PSnw0//NOyAMbzShgBcNckKPN7dS3dLDQCsaIEh/j49YF2by46ygt7V1exzFhwgqGuSDvHO55YM8KxkhzR1Eup7v8PL/VWhkmyAqGuSAlh48THxPFnGxbA2OkWZA7lvk5qTy58bDNYGsAKxjmApUcCj6wF+OzL6WR6G8unsL+ulNsOnjc6ygmDNh3uRm01o5uymqbKZqS5nUU45L3L8gmdUwMT2487HUUEwasYJhB2155En9AWZI3zusoxiXxMT5WXpTLi6VHqTze5nUc4zErGGbQNh86gQgsnmwFYyT71OX5+ER47PUDXkcxHrOCYQat5PBxZk5IJnWMPbA3kk1Iiee2xZNYU1JJfUuH13GMh6xgmEHxB5StR06yZIq1LkaDz101jU5/gF+/cdDrKMZDVjDMoOw52sypjm4uyrMO79EgPyORW+dn88Sbh6hrafc6jvGIFQwzKFsOnwCwFsYo8tUbZtDlD/DTV/Z7HcV4xAqGGZTNh04wISWOnHG2wt5okZeRyJ3Fk3n67UoO1NsSrqORFQxz3lSVtw40sjQ/3VbYG2W+dF0BcdFRfP/3u+3p71HI1YIhIstEZK+IlIvI/f0cv1JE3hGRbhG5vc+xu0Vkv/Nxt5s5zfmpqG+lvqWDS6alex3FDLPM5Dj+9voCXtlTx/rSY17HMcPMtYIhIj7gUeBmoBC4U0QK+5x2BPgE8FSfa9OAbwNLgWLg2yJiN8vDxMYDjQBcMtUKxmj0ycvymZWVzHdfKOVUR7fXccwwcrOFUQyUq+oBVe0EVgMrep+gqodUdQcQ6HPtTcDLqnpcVU8ALwPLXMxqzsNbFY1MTI1nSnqC11GMB2J8UfzTbfM42tzO939f5nUcM4zcLBiTgMpe21XOviG7VkRWiUiJiJTU19cPOqgJXSAQ7L+4ZKr1X4xmiyeP43NXTuPptytZX3rU6zhmmER0p7eqPqaqRapalJmZ6XWcUWFfXQuNrZ1cbP0Xo959N8xg7qQUvvHcDmpOnvY6jhkGbhaMaiC313aOs8/ta42LNlYE+y8utYIx6sVGR/HIykV0+5VVT5ZwutPvdSTjMjcLxmagQETyRSQWWAmsDfHa9cCNIjLO6ey+0dlnPLaxopHctDHkjLP+CwPTMpP4yR0LKa1p5hvP7bChtiOcawVDVbuBewn+oN8NrFHVUhF5UESWA4jIRSJSBXwY+IWIlDrXHge+R7DobAYedPYZD3X7A2w80MilUzO8jmLCyPWFE/jajTNZu72G//vyPq/jGBdFu/nmqroOWNdn3wO9Xm8meLupv2sfBx53M585P9sqT9LS3s1VM62/yLzbF66exuHGVn76ajnjEmP55GX5XkcyLnC1YJiRZcO+enxRwmXTrYVh3k1E+KcPzqPpdBfffaGMsQkxfHBRv78LmggW0aOkzPDasK+eRbljbf0L069oX7AT/NJp6XztmR28uKvW60hmiFnBMCFpONXBjqomrppht6PM2cXH+Pjlx4tYkJPKF5/eymt76ryOZIaQFQwTkjf2NwBY/4UZUGJcNP/xqWJmZaXwud9s4S/lDV5HMkPECoYJyYZ99aQlxjI3O9XrKCYCpMTH8J+fKiY/PZHPPFFCySEb5DgSWMEwA/IHlNf31XNFQQZRUTYdiAnNuMRYfvOZpUxMjeeT/76ZHVUnvY5kLpAVDDOgrUdO0NjayXWzJ3gdxUSYzOQ4/uuzSxmbGMPf/Pptdtc2ex3JXAArGGZA60uPEvY7H2AAAA5nSURBVOuL4hrrvzCDMDF1DE995mLGxPj4m19vorzOVuuLVFYwzDmpKutLj3Hp9HSS4204rRmc3LQEnvrsUkD42K/e4nBjq9eRzCBYwTDntPdYC0eOt3FjYZbXUUyEm5qZxH99Zimd3QE++stNVNsMtxHHCoY5p/W7jiECNxRa/4W5cDOzknny00tpbu/i0/+xmVZbsS+iWMEw57S+9ChLJo8jMznO6yhmhJg7KZV//dhi9h1r4b412wgEbIbbSGEFw5zV4cZWymqbuXGOtS7M0LqiIJNv3VLI+tJjPPLKfq/jmBBZwTBn9dttNQDcMj/b4yRmJPrUZXl8aHEOj7yyn5dsmdeIYAXD9EtV+d9t1RTnpzFp7Biv45gRSET4/gfnMm9SKl97ZjtVJ9q8jmQGYAXD9GtXdTMH6lv5wMJJXkcxI1h8jI+ffXQRAYUvPr2VLn/A60jmHFwtGCKyTET2iki5iNzfz/E4Eflv5/gmEclz9ueJyGkR2eZ8/NzNnOa9frutmhif8L55NpzWuGtKeiIPfWgeW4+c5Mcv7fU6jjkH1xZQEhEf8ChwA1AFbBaRtapa1uu0TwMnVHW6iKwEfgDc4RyrUNWFbuUzZ9ftD7B2ew1XzRjP2IRYr+OYUeDW+dlsrGjkFxsOcHF+OtfMGu91JNMPN1sYxUC5qh5Q1U5gNbCizzkrgCec188C14mIzW7nsVf21FHX0sFHimzFNDN8/r9bC5k9MYWvPrOduuZ2r+OYfrhZMCYBlb22q5x9/Z6jqt1AE5DuHMsXka0iskFErujvDxCRVSJSIiIl9fX1Q5t+FHv67SNMSInjWvstzwyj+Bgf/3LnIk53+vmKPZ8RlsK107sWmKyqi4D7gKdEJKXvSar6mKoWqWpRZqZNjDcUKo+3sWFfPXcU5RLtC9cvDzNSTR+fxHeWF/KX8kZ+/nqF13FMH27+RKgGcntt5zj7+j1HRKKBVKBRVTtUtRFAVbcAFcAMF7Max5qSSgS4o3iy11HMKPWRolxumT+R//+lfbxz5ITXcUwvbhaMzUCBiOSLSCywEljb55y1wN3O69uBV1VVRSTT6TRHRKYCBcABF7MaoL3Lz1ObjnDNzPH27IXxjIjwTx+cR1ZKPF9evZXm9i6vIxmHawXD6ZO4F1gP7AbWqGqpiDwoIsud034NpItIOcFbTz1Db68EdojINoKd4Z9XVVvj0WXPvVNFY2snn7liqtdRzCiXOiaGn965kJqT7Xzr+V2oWn9GOHBtWC2Aqq4D1vXZ90Cv1+3Ah/u57jngOTezmXfzB5Rf/fkg83NSuXhqmtdxjGHJlDS+cn0BP35pH1cWZPDhotyBLzKusl5NA8DLZcc42NDKqiunYiObTbj4P1dP5+KpaXx7bSkV9bZSn9esYBgCAeWRV/YzJT2BZXPsyW4TPnxRwk/uWERsdBRfenorHd1+ryONalYwDC/sqGF3bTP33TDDhtKasJOVGs+Pbl9AaU0zP3zRpg7xkv10GOW6/AEefnkfs7KSeb9NY27C1A2FE/jEpXn8+o2DvLanzus4o5YVjFHuN28d5nBjG99YNouoKOu7MOHr/ptnMSsrma/Z1CGesYIxitU1t/PwS/u4oiCDq2fak/ImvPVMhd7a2c3nf7OF9i7rzxhuVjBGsQd/V0aHP8D3Vsy1kVEmIkwfn8zDH1nIO0dO8vVnd9jzGcPMCsYotXZ7Db/bUcu910wnLyPR6zjGhOx98ybydzfNZO32Gv7vH2098OHk6oN7JjxVnWjjW8/vZNHksXzh6mlexzHmvH3h6mkcbGjlp6/sZ1xCDJ+8LN/rSKOCFYxRpq2zm889uQVVeOSORTaM1kQkEeGfb5tHS3sX332hjLhoHx9dahNmus1+Wowi/oDy1TXbKatt5qd3LmRyeoLXkYwZtBhfFP9y52KumZnJ3z+/k1/92eYndZsVjFHCH1D+7tnt/GHXUb71vtlcO2uC15GMuWCx0VH8211LuGXeRP7x97t58IUy/LbwkmvsltQo0N7l56vPbOf3O2q574YZNhutGVF6VuqbkBLP4385yO7aZh5ZuZDxKfFeRxtxrIUxwh1ubOWOx95i3c5a/v59s/jSdQVeRzJmyEVFCQ+8v5Af3T6fbZUnufmRP/PC9hobdjvErGCMUF3+AP+58RA3P/JnDtSf4ud3LWHVlTYiyoxsHy7K5YUvXsbEsfF88emtfOxXm9hV3eR1rBFDRkoFLioq0pKSEq9jeK69y8/vd9Ty6GvlHGho5YqCDH7woflk2wp6ZhTxB5Sn3j7Cj17cQ3N7N1cUZPCpy/O5YnqGjQzsQ0S2qGpRSOe6WTBEZBnwCOADfqWqD/U5Hgf8J7AEaATuUNVDzrFvAp8G/MCXVHX9uf6s0VwwTnf62XiggVf31PHC9lqaTncxY0ISX79pFtfNHm9PcZtRq+l0F/+16TCPv3GQhlOdZCTFsWzuBC6fnsHFU9MZmxDrdUTPhUXBcNbk3gfcAFQRXOP7TlUt63XOF4D5qvp5EVkJfFBV7xCRQuBpoBjIBv4IzFDVs04eM1ILRpc/QFuHn1Od3bR2dFPf0kFtUztHm05TUd9KaU0TFfWt+APKmBgf184ez8eKJ3Px1HSbTNAYR0e3nz/tref5d6p5fX89bZ3BHyU548YwKyuF6eOTmJgaz4SUeCakxJEyJoakuGgS46JJiPGN6O+l8ykYbo6SKgbKVfWAE2o1sAIo63XOCuA7zutngZ9J8NfhFcBqVe0ADjprfhcDG4c65Mm2Tm7/+UZUlTOlU0HhTIdZ8HXPIUX1r9v0c17PO6nzPpy5Xnu9z1nevyeHQkd3gE5/4KzZJ6TEMSc7lRsLsyjOT6M4P434GN8g/yWMGbnion3cNCeLm+Zk0eUPsL3yJJsOHmd3bTN7j7bwp711dJ9lOK4IxEf7iI4SoqKE6CjB1/PZJ/hE+m3F91ti+tnZ33khv59j1sQU/uXORec4Y2i4WTAmAZW9tquApWc7R1W7RaQJSHf2v9Xn2kl9/wARWQWsApg8eXBPefqihJkTkp03/Ot/ioggBL9YnENn/hPlzLnO9ruuC+4/c52cueLc5/HuLxKR4Bd5YqyPxLhoEuN8JMRGk5kcd+Y3ISsOxpy/GF8URXlpFOX9de36QEBpbO3kWHM7x5rbOdXRHfxoD7bsT3f58QfAHwjQHVACqnT7FX9A+y00/ZWe/u7m9Fui+tmp/Z95Ru644emjjOjnMFT1MeAxCN6SGsx7JMfH8OjHFg9pLmNMZImKEjKT48hMjmPupFSv44QtN4cLVAO5vbZznH39niMi0UAqwc7vUK41xhgzjNwsGJuBAhHJF5FYYCWwts85a4G7nde3A69qsN22FlgpInEikg8UAG+7mNUYY8wAXLsl5fRJ3AusJzis9nFVLRWRB4ESVV0L/Bp40unUPk6wqOCct4ZgB3k3cM+5RkgZY4xxnz24Z4wxo9j5DKu1Rx6NMcaExAqGMcaYkFjBMMYYExIrGMYYY0IyYjq9RaQeOOx1jj4ygAavQ4TAcg69SMlqOYdWpOSEv2adoqqZoVwwYgpGOBKRklBHH3jJcg69SMlqOYdWpOSEwWW1W1LGGGNCYgXDGGNMSKxguOsxrwOEyHIOvUjJajmHVqTkhEFktT4MY4wxIbEWhjHGmJBYwTDGGBMSKxhDTETiReRtEdkuIqUi8l2vM52LiPhEZKuI/M7rLOciIodEZKeIbBORsJ1lUkTGisizIrJHRHaLyCVeZ+qPiMx0/i17PppF5G+9ztUfEfmK8720S0SeFpF4rzP1R0S+7GQsDbd/SxF5XETqRGRXr31pIvKyiOx3Po8b6H2sYAy9DuBaVV0ALASWicjFHmc6ly8Du70OEaJrVHVhmI9zfwR4UVVnAQsI039bVd3r/FsuBJYAbcDzHsd6DxGZBHwJKFLVuQSXSljpbar3EpG5wGeBYoL/77eKyHRvU73LfwDL+uy7H3hFVQuAV5ztc7KCMcQ06JSzGeN8hOXIAhHJAW4BfuV1lpFARFKBKwmu84KqdqrqSW9TheQ6oEJVw22mhB7RwBhnVc4EoMbjPP2ZDWxS1TZV7QY2ALd5nOkMVX2d4JpDva0AnnBePwF8YKD3sYLhAuc2zzagDnhZVTd5neksfgJ8HQh4HSQECrwkIltEZJXXYc4iH6gH/t25zfcrEUn0OlQIVgJPex2iP6paDfwYOALUAk2q+pK3qfq1C7hCRNJFJAF4H+9eZjocTVDVWuf1UWDCQBdYwXCBqvqdpn4OUOw0V8OKiNwK1KnqFq+zhOhyVV0M3AzcIyJXeh2oH9HAYuDfVHUR0EoIzXwvOcsnLwee8TpLf5z76isIFuNsIFFE7vI21Xup6m7gB8BLwIvANiBiVgl1lsYe8E6IFQwXObcjXuO99w7DwWXAchE5BKwGrhWR33gb6eyc3zRR1TqC99qLvU3UryqgqleL8lmCBSSc3Qy8o6rHvA5yFtcDB1W1XlW7gP8BLvU4U79U9dequkRVrwROAPu8zjSAYyIyEcD5XDfQBVYwhpiIZIrIWOf1GOAGYI+3qd5LVb+pqjmqmkfwlsSrqhp2v7kBiEiiiCT3vAZuJHgLIKyo6lGgUkRmOruuI7gufTi7kzC9HeU4AlwsIgkiIgT/TcNyIIGIjHc+TybYf/GUt4kGtBa423l9N/DbgS6IdjXO6DQReEJEfAQL8hpVDeshqxFgAvB88OcF0cBTqvqit5HO6ovAfzm3eg4An/Q4z1k5xfcG4HNeZzkbVd0kIs8C7wDdwFbCd/qN50QkHegC7gmnAQ8i8jRwNZAhIlXAt4GHgDUi8mmCS0N8ZMD3salBjDHGhMJuSRljjAmJFQxjjDEhsYJhjDEmJFYwjDHGhMQKhjHGmJBYwTDGGBMSKxjGGGNC8v8AsmrnvA5vyX4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Playing with the method can give you more detail but can also lead to overfitting."
      ],
      "metadata": {
        "id": "YIykemCMPtwQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLength.plot(kind = 'density',bw_method = 0.3)"
      ],
      "metadata": {
        "id": "JZ_DPht0MmIu",
        "outputId": "966ed325-c467-4f07-ece2-746c9f1bc9e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbef10f7510>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multiple Data Points"
      ],
      "metadata": {
        "id": "V0WdtPF3Ij0C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Often we want to see if the distributions differ on another categorical variable.  Here we look at SepalLength by Class."
      ],
      "metadata": {
        "id": "HjbThH5WIoMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['SepalLength'].hist(by = df['Class'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "G0RFBpzh1QmV",
        "outputId": "6f01b73f-9f51-4a06-cacf-19706828433a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbef1000610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbef0fb6410>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbef0f79a10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbef0f25b10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One of the issues in the above graph is that the axis do not match.  Can you fix that?\n",
        "\n",
        "Below I put them all together on the same axis.  The `alpha` value makes them transparent."
      ],
      "metadata": {
        "id": "7E31VTB66lLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Class').SepalLength.plot.hist(alpha = .7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "4yszDgrb1_ds",
        "outputId": "d6f2a98c-8792-4af1-ae28-8c4532107673"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "Iris-setosa        AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Iris-versicolor    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Iris-virginica     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Name: SepalLength, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Some Things to Avoid"
      ],
      "metadata": {
        "id": "aqOmw1oQLNCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Putting non-comparable data on the same axis is easy but not appropriate!  The following don't really make any sense since we should not be comparing length and width on the same axis!"
      ],
      "metadata": {
        "id": "tLR3KR3CI6T6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Class').plot.hist(alpha = .7, title = \"Bad Graphs!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "ukavrHMg3BtC",
        "outputId": "0488edf4-cb84-43dc-9e87-be25f72b5e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "Iris-setosa        AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Iris-versicolor    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Iris-virginica     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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pr7zyCsOGDWP8+PGceOKJPPzww2zZsoU+ffpw0kkn8eCDD9KgQQNWrlzJ0qVLy4utQexJXkcddRS7d+9m8ODBLF26lK5dux603XHjxnHNNdfQv39/PvvsM4YOHcrKlSsB+OCDD5g7dy7bt2+nffv2XHbZZRQVFTFz5kyWLFlCSUkJPXr0oGfPnpx11lnce++9+ww9NWvWjPfee4/777+fCRMm8NBDD9Xoc1LiF5G0V1xcXF4HZ8CAAVx00UXk5eXxwgsvMGHCBAB27drFZ599xhtvvMFVV10FQNeuXb+T2J9++mkmT55MaWkp69evZ8WKFXEl/jlz5nznaVrbtm1jx44dAAwfPpx69epRr149mjdvzoYNG1iwYAGnn346mZmZZGZmcuqppx7w+BXLNT/77LNV+GT2T4lfRNJe2Rh/Re7OzJkz4y6V/MknnzBhwgTeffddmjRpwqhRo9i1a1dc++7Zs4e3336bzMzMfd4rK7UM1S+3nOhyzRrjF5FaaejQoUyaNImyQpSLFy8GYg9fmTZtGgDLly9n6dKlQKyXfsQRR9CoUSM2bNjAK6+8EndbQ4YMYdKkSeXLe/8R2lt+fj4vvvgiu3btYseOHcyePbv8vWSUa1aPX0QSKp7pl8lw8803c/XVV9O1a1f27NlDmzZtmD17NpdddhkXXnghHTt2pGPHjvTs2ROAbt260b17dzp06ECrVq3Kn5+7P127duWww2L95l/84hfcc889XHHFFXTt2pXS0lIGDhx4wJlFvXv35rTTTqNr164cffTRdOnShUaNGgEwatQoLr30UurXr1/pg2RqKtSyzImisswih650K8t8qNixYwcNGzbkm2++YeDAgUyePPk7F5qrqiplmdXjFxFJgTFjxrBixQp27drFyJEja5T0q0qJX0QkBcquM6SCLu6KiESMEr+ISMQo8YuIRIwSv4hIxOjirogkVKKnUMczNTpZZZkXLFjAJ598wsSJEwG45JJLWL16NXPmzAFixdRWrVrFL3/5Sx577LHvFGUrU1ZmuU6dOkybNo3LL78ciD0LeMKECd+5mSss6vGLSNpLVlnm/Px8CgoKypeXLFnC1q1b2b17NwAFBQXk5eXRq1ev/Sb9irZs2cL9998fSpwHo8QvIrVKmGWZc3Jy+OijjyguLmbr1q3Ur1+fnJwcli1bBsQSf35+PvPmzeOUU04BYPPmzQwZMoTOnTtz8cUXl5eQuOGGG1i9ejU5OTlcd911QOymrrPOOosOHTpw3nnnEdYNthrqEZFaI+yyzHXq1KF79+68++67FBcX07dvX9q2bUtBQQFZWVm4O61atWL16tXl+9x2223079+fP/zhD7z00ktMmRJ7zPidd97J8uXLy+v6zJs3j8WLF/P+++/TsmVL8vPzWbBgAf3790/456TELyJpL5llmfPy8igoKKC4uJjc3Fzatm3Ln/70J7KyssjLy9sntjfeeKO8lPLw4cNp0qRJpefRp08fsrOzgdi3izVr1ijxi4jsTzLLMufn5/PAAw+wa9currjiCrKyslixYkWlib8qElHCOR4a4xeRWimsssy5ubm8/fbbbNq0iebNm2NmZGVl8fzzz++3omfF9l555RW+/vprIDnllyujHr+IJNShUpk2rLLMTZo0ISsri86dO5evy83NZcGCBXTr1m2f7W+55RZGjBhB586dycvL49hjjwWgadOm5Ofnc8IJJ/CTn/yE4cOHh/Ap7J/KMofkUPmfXyRsKst8aKhKWWYN9YiIRIwSv4hIxCjxi0iNpcOQcW1W1c9fiV9EaiQzM5PNmzcr+aeIu7N582YyMzPj3kezekSkRrKzs1m3bh2bNm1KdSiRlZmZWX7jVzyU+EWkRurWrUubNm1SHYZUgYZ6REQiRolfRCRilPhFRCJGiV9EJGKU+EVEIkaJX0QkYpT4RUQiJrTEb2aZZrbQzJaY2ftmdluwvo2ZvWNmH5vZdDM7PKwYRERkX2H2+P8FnOju3YAcYJiZ9QP+A7jL3X8IfA1cFGIMIiKyl9ASv8fsCBbrBj8OnAjMCNY/CpwRVgwiIrKvUMf4zSzDzIqAjcCrwGpgi7uXPUhyHXBMJfuOMbNFZrZINUBERBIn1MTv7rvdPQfIBvoAHaqw72R37+XuvbKyskKLUUQkapIyq8fdtwBzgVygsZmVFYfLBj5PRgwiIhIT5qyeLDNrHLyuD5wMrCT2B+CsYLORwPNhxSAiIvsKsyxzC+BRM8sg9gfmaXefbWYrgKfM7A5gMTAlxBhERGQvoSV+d18KdN/P+n8QG+8XEZEUiGuox8y6hB2IiIgkR7xj/PcHd+FebmaNQo1IRERCFVfid/cBwHlAK6DQzKaZ2cmhRiYiIqGIe4zf3VeZ2U3AIuAeoLuZGfB7d382rAAljUw7O3Vtnzs9dW2LpJl4x/i7mtldxKZjngic6u4dg9d3hRifiIgkWLw9/knAQ8R698VlK939i+BbgIiIpIl4E/9woNjddwOY2WFAprt/4+7/HVp0IiKScPHO6pkD1K+w3CBYJyIiaSbexJ9ZocQywesG4YQkIiJhijfx7zSzHmULZtYTKD7A9iIicoiKd4z/auAZM/sCMOD7QArn7omISHXFlfjd/V0z6wC0D1Z96O4l4YUlIiJhqUqRtt5A62CfHmaGuz8WSlQiIhKauBK/mf03cDxQBOwOVjugxC8ikmbi7fH3Ajq5u4cZjIiIhC/eWT3LiV3QFRGRNBdvj78ZsMLMFgL/Klvp7qeFEpWIiIQm3sR/a5hBiKSrsa+NTVnb9w6+N2VtS3qLdzrn62b2A6Ctu88xswZARrihiYhIGOIty/wrYAbwYLDqGGBWWEGJiEh44r24ewWQD2yD2ENZgOZhBSUiIuGJN/H/y92/LVswszrE5vGLiEiaiTfxv25mvwfqB8/afQZ4MbywREQkLPEm/huATcAy4BLgZUBP3hIRSUPxzurZA/wt+BERkTQWb62eT9jPmL67H5fwiEREJFRVqdVTJhP4P8BRiQ9HRETCFtcYv7tvrvDzubtPJPYAdhERSTPxDvX0qLB4GLFvAFWp5S8iIoeIeJP3/63wuhRYA/wi4dGIiEjo4p3V8+OwAxERkeSId6jn2gO97+5/TUw4IiIStqrM6ukNvBAsnwosBFaFEZSIiIQn3sSfDfRw9+0AZnYr8JK7nx9WYCIiEo54SzYcDXxbYfnbYJ2IiKSZeHv8jwELzey5YPkM4NFwQhIRkTDFO6tnvJm9AgwIVl3o7ovDC0tERMIS71APQANgm7vfDawzszYH2tjMWpnZXDNbYWbvm9m4YP1RZvaqma0KfjepQfwiIlJF8T568Rbgt8DvglV1gccPslsp8Gt37wT0A64ws07ESjy/5u5tgdeCZRERSZJ4e/xnAqcBOwHc/QvgyAPt4O7r3f294PV2YCWxZ/Wezr+vDzxK7HqBiIgkSbwXd791dzczBzCzI6rSiJm1BroD7wBHu/v64K1/UsnsIDMbA4wBOPbYY6vSnEw7O9URiMghLN4e/9Nm9iDQ2Mx+BcwhzoeymFlDYCZwtbtvq/ieuzuVPLvX3Se7ey9375WVlRVnmCIicjAH7fGbmQHTgQ7ANqA98Ad3fzWOfesSS/pPuPuzweoNZtbC3debWQtgY7WjFxGRKjto4g+GeF529y7AQZN9meAPxhRg5V61fF4ARgJ3Br+fr1rIIiJSE/EO9bxnZr2reOx84ALgRDMrCn5+Sizhn2xmq4CTgmUREUmSeC/u9gXON7M1xGb2GLEvA10r28Hd3wy225/BVQlSREQS54CJ38yOdffPgKFJikdEREJ2sB7/LGJVOT81s5nu/vNkBCUiIuE52Bh/xaGa48IMREREkuNgPX6v5LWISNJdNPXdlLQ7ZVRV57Yc2g6W+LuZ2TZiPf/6wWv498Xd74UanYiIJNwBE7+7ZyQrEBERSY6qlGUWEZFaQIlfRCRi4r2BS0QESN0FVkkc9fhFRCJGiV9EJGKU+EVEIkaJX0QkYpT4RUQiRolfRCRilPhFRCJGiV9EJGKU+EVEIkaJX0QkYpT4RUQiRolfRCRiVKRNJE2NfW1siloemaJ2JVHU4xcRiRglfhGRiFHiFxGJGCV+EZGIUeIXEYkYJX4RkYhR4hcRiRglfhGRiFHiFxGJGCV+EZGIUeIXEYkYJX4RkYhR4hcRiRhV5xQROYiLpr6bknanjOodynHV4xcRiZjQEr+ZPWxmG81seYV1R5nZq2a2KvjdJKz2RURk/8Ls8U8Fhu217gbgNXdvC7wWLIuISBKFlvjd/Q3gq71Wnw48Grx+FDgjrPZFRGT/kj3Gf7S7rw9e/xM4urINzWyMmS0ys0WbNm1KTnQiIhGQsou77u6AH+D9ye7ey917ZWVlJTEyEZHaLdmJf4OZtQAIfm9McvsiIpGX7MT/AjAyeD0SeD7J7YuIRF6Y0zmfBN4C2pvZOjO7CLgTONnMVgEnBcsiIpJEod256+4jKnlrcFhtiojIwenOXRGRiFHiFxGJGCV+EZGIUXVOqRXGvjY21SGIpA31+EVEIkaJX0QkYpT4RUQiRolfRCRilPhFRCJGiV9EJGKU+EVEIkaJX0QkYpT4RUQiRolfRCRilPhFRCJGiV9EJGKU+EVEIkbVOUOy9tLLUtZ2q4Epa1pE0oB6/CIiEaPELyISMUr8IiIRozF+kTS1ZO2WlLTbLiWtSiKpxy8iEjFK/CIiEaPELyISMUr8IiIRo4u7kjBjSz9LYetZKWxbJL2oxy8iEjFK/CIiEaPELyISMUr8IiIRo8QvIhIxSvwiIhGjxC8iEjFK/CIiEaMbuMLyeWEKG2+TwrZTJFWf9zE9U9OuSA2oxy8iEjEpSfxmNszMPjSzj83shlTEICISVUlP/GaWAdwH/AToBIwws07JjkNEJKpS0ePvA3zs7v9w92+Bp4DTUxCHiEgkmbsnt0Gzs4Bh7n5xsHwB0Nfdx+613RhgTLDYHviwmk02A76s5r6HmtpyLrXlPEDncqiqLedS0/P4gbvvU7r2kJ3V4+6Tgck1PY6ZLXL3XgkIKeVqy7nUlvMAncuhqracS1jnkYqhns+BVhWWs4N1IiKSBKlI/O8Cbc2sjZkdDpwDvJCCOEREIinpQz3uXmpmY4H/B2QAD7v7+yE2WePhokNIbTmX2nIeoHM5VNWWcwnlPJJ+cVdERFJLd+6KiESMEr+ISMTU2sRvZg+b2UYzW57qWGrCzFqZ2VwzW2Fm75vZuFTHVF1mlmlmC81sSXAut6U6ppoyswwzW2xms1MdS02Y2RozW2ZmRWa2KNXxVJeZNTazGWb2gZmtNLPcVMdUHWbWPvhvUfazzcyuTtjxa+sYv5kNBHYAj7n7CamOp7rMrAXQwt3fM7MjgULgDHdfkeLQqszMDDjC3XeYWV3gTWCcu7+d4tCqzcyuBXoB33P3U1IdT3WZ2Rqgl7un9U1PZvYoMN/dHwpmDTZw9y2pjqsmgjI3nxO70fXTRByz1vb43f0N4KtUx1FT7r7e3d8LXm8HVgLHpDaq6vGYHcFi3eAnbXseZpYNDAceSnUsAmbWCBgITAFw92/TPekHBgOrE5X0oRYn/trIzFoD3YF3UhtJ9QVDI0XARuBVd0/bcwEmAtcDe1IdSAI48L9mVhiUS0lHbYBNwCPB8NtDZnZEqoNKgHOAJxN5QCX+NGFmDYGZwNXuvi3V8VSXu+929xxid2z3MbO0HIYzs1OAje6eyifuJFJ/d+9BrGruFcFQabqpA/QA/svduwM7gbQu+x4MV50GPJPI4yrxp4FgPHwm8IS7P5vqeBIh+Ao+FxiW6liqKR84LRgbfwo40cweT21I1efunwe/NwLPEauim27WAesqfIucQewPQTr7CfCeu29I5EGV+A9xwQXRKcBKd/9rquOpCTPLMrPGwev6wMnAB6mNqnrc/Xfunu3urYl9Ff+7u5+f4rCqxcyOCCYOEAyNDAHSbjacu/8TWGtm7YNVg4G0mwSxlxEkeJgHDuHqnDVlZk8Cg4BmZrYOuMXdp6Q2qmrJBy4AlgVj4wC/d/eXUxhTdbUAHg1mKRwGPO3uaT0NspY4Gngu1segDjDN3f8ntSFV25XAE8EQySWxtswAAABFSURBVD+AC1McT7UFf4RPBi5J+LFr63ROERHZPw31iIhEjBK/iEjEKPGLiESMEr+ISMQo8YuIRIwSv4hIxCjxi4hEzP8HqUVPnFg098oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is another that doesn't make any sense as the data should not be comparable!"
      ],
      "metadata": {
        "id": "UNPbzzDaJTu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.hist(title = \"Bad Graph!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "6IXZZ5bp3-HA",
        "outputId": "5ab15b79-6ed2-480d-852e-7bb52a48fde7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda38f49d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Your Turn\n",
        "\n",
        "Look at NYC Air B&B, provide a histogram of the price and a histogram of the price broken down by 'neighbourhood_group'.  Make sure to give titles!  Play with bin size to find appropriate shape.  Comment on why you are seeing so little of the data and how you might see more."
      ],
      "metadata": {
        "id": "Mmuokpa9E5D3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "a-SQEa2cJkAz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}