Notebook1_Pandas_Histogram.ipynb

{
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 "metadata": {
  "colab": {
   "name": "CS361_Notebook1.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bfrTa52jPCJx",
    "colab_type": "text"
   },
   "source": [
    "# Visualizing Data with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3wyBp79wPmtS",
    "colab_type": "text"
   },
   "source": [
    "Let's first import some packages into our environment. Note that `%matplotlib inline` is an `IPython` magic command, which is not a part of standard Python syntax. Here `%matplotlib inline` enables the inline display of matplotlib figures so you don't need to do `plt.show()`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "LOS-vU4pJ9iB",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ],
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ijh1_82NQdCU",
    "colab_type": "text"
   },
   "source": [
    "This is another `IPython` magic command that enables we to run bash commands in this environment. Let's first clone the \"Motor Trend Car Road Tests\" dataset. Note that the storage provided by Colab is not permanent (unless we [mount our Google Drive](https://colab.research.google.com/notebooks/io.ipynb)) so we need to be careful. If you would like to do your homework in this environment, please remember to save your work (like plotted figures) locally."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "jEH1CNLDKP7V",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "outputId": "1ce5fa85-a468-4e0f-9b08-e0e1109b5ca2"
   },
   "source": [
    "![ ! -f \"./mtcars/mtcars.csv\" ] && git clone https://gist.github.com/a412dfbd88b3db70b74b.git mtcars"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'mtcars'...\r\n",
      "remote: Enumerating objects: 3, done.\u001B[K\r\n",
      "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 3\u001B[K\r\n",
      "Unpacking objects: 100% (3/3), done.\r\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a1I9vzqNRk14",
    "colab_type": "text"
   },
   "source": [
    "Now let's import the `mtcars` dataset. It's in standard CSV format with header so we can import directly. Sometimes data may be separated with tabs (or even worse a mixture of tabs and spaces) so we might need to designate a separator. Please refer to [this document](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) if you need more information."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "d4uIfsMeKh28",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "cars = pd.read_csv('./mtcars/mtcars.csv')"
   ],
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Wp_kVG0wK2hI",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "outputId": "d687aa97-dcbc-49ac-9dc5-ec632e65c4de"
   },
   "source": [
    "cars.head()"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n\n   carb  \n0     4  \n1     4  \n2     1  \n3     1  \n4     2  ",
      "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>model</th>\n      <th>mpg</th>\n      <th>cyl</th>\n      <th>disp</th>\n      <th>hp</th>\n      <th>drat</th>\n      <th>wt</th>\n      <th>qsec</th>\n      <th>vs</th>\n      <th>am</th>\n      <th>gear</th>\n      <th>carb</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Mazda RX4</td>\n      <td>21.0</td>\n      <td>6</td>\n      <td>160.0</td>\n      <td>110</td>\n      <td>3.90</td>\n      <td>2.620</td>\n      <td>16.46</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>Mazda RX4 Wag</td>\n      <td>21.0</td>\n      <td>6</td>\n      <td>160.0</td>\n      <td>110</td>\n      <td>3.90</td>\n      <td>2.875</td>\n      <td>17.02</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>Datsun 710</td>\n      <td>22.8</td>\n      <td>4</td>\n      <td>108.0</td>\n      <td>93</td>\n      <td>3.85</td>\n      <td>2.320</td>\n      <td>18.61</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>Hornet 4 Drive</td>\n      <td>21.4</td>\n      <td>6</td>\n      <td>258.0</td>\n      <td>110</td>\n      <td>3.08</td>\n      <td>3.215</td>\n      <td>19.44</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Hornet Sportabout</td>\n      <td>18.7</td>\n      <td>8</td>\n      <td>360.0</td>\n      <td>175</td>\n      <td>3.15</td>\n      <td>3.440</td>\n      <td>17.02</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "2H-cUmIzLDll",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "outputId": "bc9a3b25-ea4f-4227-c2ce-311f632fd330"
   },
   "source": [
    "num_cylinders = cars['cyl'].value_counts()\n",
    "num_cylinders"
   ],
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "8    14\n4    11\n6     7\nName: cyl, dtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bjqhk0Z5LJVz",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "outputId": "0dec445d-c4d4-472c-82d7-a9eebe740eec"
   },
   "source": [
    "plt.bar(num_cylinders.keys(), num_cylinders, color=['black',  'blue', 'orange'])\n",
    "plt.title('Count of cars by Cylinder')\n",
    "plt.xlabel('Num. of Cylinder')\n",
    "plt.ylabel('Count')"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "nHQDjFPCLNR-",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "outputId": "5c966b02-f0de-4930-ce29-79722a1dc531"
   },
   "source": [
    "![ ! -f \"./iris/iris.data\" ] && mkdir ./iris/ && wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\" -O ./iris/iris.data"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-08-09 14:54:13--  https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\r\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\r\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 4551 (4.4K) [application/x-httpd-php]\r\n",
      "Saving to: ‘./iris/iris.data’\r\n",
      "\r\n",
      "./iris/iris.data    100%[===================>]   4.44K  --.-KB/s    in 0s      \r\n",
      "\r\n",
      "2020-08-09 14:54:13 (217 MB/s) - ‘./iris/iris.data’ saved [4551/4551]\r\n",
      "\r\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0PZUBlmlZ1gR",
    "colab_type": "text"
   },
   "source": [
    "This dataset doesn't include the attribute names in its header so we need to specify them manually, or the first line of the file will be regarded as the header."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "4yNWtgfxMTmB",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "iris = pd.read_csv('./iris/iris.data', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])"
   ],
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "D5XmiXLWMqZP",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "outputId": "765aef33-0094-4192-cda4-6794a3e85999"
   },
   "source": [
    "iris.sample(10)"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width            class\n90            5.5          2.6           4.4          1.2  Iris-versicolor\n30            4.8          3.1           1.6          0.2      Iris-setosa\n88            5.6          3.0           4.1          1.3  Iris-versicolor\n95            5.7          3.0           4.2          1.2  Iris-versicolor\n53            5.5          2.3           4.0          1.3  Iris-versicolor\n27            5.2          3.5           1.5          0.2      Iris-setosa\n106           4.9          2.5           4.5          1.7   Iris-virginica\n108           6.7          2.5           5.8          1.8   Iris-virginica\n86            6.7          3.1           4.7          1.5  Iris-versicolor\n43            5.0          3.5           1.6          0.6      Iris-setosa",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>90</td>\n      <td>5.5</td>\n      <td>2.6</td>\n      <td>4.4</td>\n      <td>1.2</td>\n      <td>Iris-versicolor</td>\n    </tr>\n    <tr>\n      <td>30</td>\n      <td>4.8</td>\n      <td>3.1</td>\n      <td>1.6</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <td>88</td>\n      <td>5.6</td>\n      <td>3.0</td>\n      <td>4.1</td>\n      <td>1.3</td>\n      <td>Iris-versicolor</td>\n    </tr>\n    <tr>\n      <td>95</td>\n      <td>5.7</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.2</td>\n      <td>Iris-versicolor</td>\n    </tr>\n    <tr>\n      <td>53</td>\n      <td>5.5</td>\n      <td>2.3</td>\n      <td>4.0</td>\n      <td>1.3</td>\n      <td>Iris-versicolor</td>\n    </tr>\n    <tr>\n      <td>27</td>\n      <td>5.2</td>\n      <td>3.5</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <td>106</td>\n      <td>4.9</td>\n      <td>2.5</td>\n      <td>4.5</td>\n      <td>1.7</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <td>108</td>\n      <td>6.7</td>\n      <td>2.5</td>\n      <td>5.8</td>\n      <td>1.8</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <td>86</td>\n      <td>6.7</td>\n      <td>3.1</td>\n      <td>4.7</td>\n      <td>1.5</td>\n      <td>Iris-versicolor</td>\n    </tr>\n    <tr>\n      <td>43</td>\n      <td>5.0</td>\n      <td>3.5</td>\n      <td>1.6</td>\n      <td>0.6</td>\n      <td>Iris-setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "t0zJWRw0M2yd",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "outputId": "9147a55e-1da2-484c-b806-dd48a32cf346"
   },
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(iris['sepal_length'], bins=50)\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Sepal length with 50 bins')\n",
    "plt.subplots_adjust(bottom=0, top=1, left=-0.5, right=1.5)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(iris['sepal_length'], bins=20)\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Sepal length with 20 bins')\n",
    "plt.subplots_adjust(bottom=0, top=1, left=-0.5, right=1.5)"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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fAWj8zaw/Zc5lmzccMsJItD3mO47gsZQkSdLC7IGUJEmSJHViASlJkiRJ6sQCUpKkCZLkuCTXJrl4lmW/l6SS7N1HbJKk6WcBKUnSZDkeOHh4ZpIHAs8Grhh1QJKklcMCUpKkCVJVZwHfm2XR/wZeD9RoI5IkrSQWkJIkTbgkzwe+XVVf7TsWSdJ082c8JEmaYEl2Ad4I/GKHddcB6wDWrFmzzJFpWi30s1Bd+fNR0mSyB1KSpMn2s8CDga8m2QzsC5yX5D8Mr1hVG6tqbVWtXb169YjDlCRNA3sgJUmaYFV1EXDfrdNtEbm2qq7vLShJ0tSyB1KSpAmS5ETgi8ABSbYkeVXfMUmSVg57ICVJmiBV9ZIFls+MKBRJ0gpkD6QkSZIkqRMLSEmSJElSJxaQkiRJkqROLCAlSZIkSZ1YQEqSJEmSOrGAlCRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpEwtISZIkSVInFpCSJEmSpE4sICVJkiRJnazqY6dJXge8GijgIuAVVfXDPmKRJElaCWbWn9J3CJKmwMh7IJPsA/w2sLaqHgHsABw+6jgkSZIkSYvT1xDWVcDOSVYBuwBX9RSHJEmSJKmjkQ9hrapvJ3k7cAVwG3BqVZ06vF6SdcA6gDVr1ow2SK048w3r2bzhkBFGIvB4SJIkjas+hrDuBRwKPBh4ALBrkpcNr1dVG6tqbVWtXb169ajDlCRJkiQN6WMI67OAy6rquqr6MXAy8KQe4pAkSZIkLUIfBeQVwIFJdkkS4CBgUw9xSJIkSZIWYeQFZFWdDZwEnEfzEx73ADaOOg5JkiRJ0uL08juQVfVm4M197FuSJEmStG36+hkPSZIkSdKEsYCUJEmSJHViASlJkiRJ6sQCUpIkSZLUiQWkJEkTIslxSa5NcvHAvP+Z5NIkFyb5aJI9+4xRkjTdLCAlSZocxwMHD837LPCIqnok8K/AG0YdlCRp5bCAlCRpQlTVWcD3huadWlV3tJNfAvYdeWCSpBXDAlKSpOnxSuCf+g5CkjS9LCAlSZoCSd4I3AGcMM8665Kck+Sc6667bnTBSZKmhgWkJEkTLskRwPOAl1ZVzbVeVW2sqrVVtXb16tWjC1CSNDVW9R2AJEnadkkOBv4A+IWq+kHf8UiSpps9kJIkTYgkJwJfBA5IsiXJq4C/BnYHPpvkgiTv6jVISdJUswdSkqQJUVUvmWX2e0ceiCRpxbIHUpIkSZLUiQWkJEmSJKkTC0hJkiRJUicWkJIkSZKkTiwgJUmSJEmdWEBKkiRJkjqxgJQkSZIkdWIBKUmSJEnqxAJSkiRJktTJqr4DkBZrZv0pcy7bvOGQke5vufY5bkb9mEuSJGk82QMpSZIkSerEAlKSJEmS1IlDWCVJkqQptNDXcLry6yoaZA+kJEmSJKkTC0hJkiRJUicWkJIkSZKkTiwgJUmSJEmdWEBKkiRJkjqxgJQkSZIkdWIBKUmSJEnqxAJSkiRJktSJBaQkSZIkqRMLSEmSJElSJxaQkiRJkqROLCAlSZIkSZ1YQEqSJEmSOrGAlCRpgiQ5Lsm1SS4emHefJJ9N8o32/159xihJml4WkJIkTZbjgYOH5q0HTquq/YHT2mlJkpacBaQkSROkqs4Cvjc0+1Dgfe3t9wEvGGlQkqQVY1XfAUiSpO12v6q6GqCqrk5y39lWSrIOWAewZs2aEYYn3d3M+lOWZDubNxyy3dtYqliklcAeSEmSVoiq2lhVa6tq7erVq/sOR5I0gSwgJUmafNckuT9A+//anuORJE0pC0hJkibfx4Ej2ttHAP/YYyySpCnWSwGZZM8kJyW5NMmmJE/sIw5JkiZNkhOBLwIHJNmS5FXABuDZSb4BPLudliRpyfV1EZ1jgU9X1WFJdgJ26SkOSZImSlW9ZI5FB400EEnSijTyAjLJHsDTgCMBqup24PZRxyFJkiRJWpw+eiD3A64D/i7Jo4BzgaOr6vuDK3mp8W0zLZehXo52jNtjM188812SfFvvJ0mSJG2vPr4DuQp4LPDOqnoM8H1g/fBKXmpckiRJksZLHwXkFmBLVZ3dTp9EU1BKkiRJksbYyAvIqvoOcGWSA9pZBwFfG3UckiRJkqTF6esqrEcBJ7RXYP0W8Iqe4pAkSZIkddRLAVlVFwBr+9i3JEmSJGnb9PEdSEmSJEnSBLKAlCRJkiR10qmATPLkLvMkSVI35lZJ0iTq2gP5Vx3nSZKkbsytkqSJM+9FdJI8EXgSsDrJ7wws2gPYYTkDkyRpGplbJUmTbKGrsO4E7Naut/vA/JuBw5YrKEmSppi5dYWYWX/Kkmxn84ZDlmQ7krQU5i0gq+pM4Mwkx1fV5SOKSZKkqWVulSRNsq6/A3nPJBuBmcH7VNUzlyMoSZJWAHOrJGnidC0g/x54F/Ae4CfLF44kSSuGuVWSNHG6FpB3VNU7lzUSSZJWFnOrJGnidP0Zj08keU2S+ye5z9a/ZY1MkqTpZm6VJE2crj2QR7T/f39gXgH7LW04kiStGOZWSdLE6VRAVtWDlzsQSZJWEnOrJGkSdSogk/zqbPOr6v1LG44kSSuDuVWSNIm6DmF9/MDtewEHAecBJjlJkraNuVWSNHG6DmE9anA6yb2BDyxLRJIkrQDmVknSJOp6FdZhPwD2X8pAJEla4cytkqSx1/U7kJ+guTIcwA7AzwEfWa6gJEmaduZWSdIk6vodyLcP3L4DuLyqtixDPJIkrRRLmluTvA54NU1RehHwiqr64faFKEnSXXUawlpVZwKXArsDewG3L2dQkiRNu6XMrUn2AX4bWFtVj6Dp0Tx8KeKUJGlQpwIyyYuBLwMvAl4MnJ3ksOUMTJKkabYMuXUVsHOSVcAuwFXbH6UkSXfVdQjrG4HHV9W1AElWA/8MnLRcgUmSNOWWLLdW1beTvB24ArgNOLWqTl3KYCVJgu4F5D22JrjWd9n2K7iqg5n1p8y5bPOGQ0YYiTQ/n6vSNluy3JpkL+BQ4MHAjcDfJ3lZVX1waL11wDqANWvWbFPQkqSVrWsB+ekknwFObKd/BfjU8oQkSdKKsJS59VnAZVV1HUCSk4EnAXcpIKtqI7ARYO3atTW8EUmSFjJvAZnkIcD9qur3k/wX4ClAgC8CJ4wgPkmSpsoy5dYrgAOT7EIzhPUg4JyliFeSpEELDZU5BrgFoKpOrqrfqarX0ZwhPWa5g5MkaQoteW6tqrNpvjt5Hs1PeNyDtqdRkqSltNAQ1pmqunB4ZlWdk2RmWSKSJGm6LUturao3A2/ejrgkSVrQQj2Q95pn2c5LGYgkSSuEuVWSNLEWKiC/kuTXhmcmeRVw7vKEJEnSVDO3SpIm1kJDWF8LfDTJS7kzqa0FdgJeuJyBSZI0pcytkqSJNW8BWVXXAE9K8gzgEe3sU6rqc8semSRJU8jcKkmaZJ1+B7KqTgdOX+ZYJElaMcytkqRJtNB3ICVJkiRJAiwgJUmSJEkdWUBKkiRJkjqxgJQkSZIkdWIBKUmSJEnqxAJSkiRJktSJBaQkSZIkqZNOvwMpSZI0CWbWn9J3CNJ283mscWYPpCRJkiSpEwtISZIkSVInFpCSJEmSpE4sICVJkiRJnVhASpIkSZI66a2ATLJDkvOTfLKvGCRJkiRJ3fXZA3k0sKnH/UuSJEmSFqGXAjLJvsAhwHv62L8kSZIkafH66oE8Bng98NOe9i9JkiRJWqRVo95hkucB11bVuUmePs9664B1AGvWrBlRdEtrZv0pcy7bvOGQEUayfOZrI0xPOyfFQsdjpVsJr8nl4OMmSZK26qMH8snA85NsBj4EPDPJB4dXqqqNVbW2qtauXr161DFKkiRJkoaMvICsqjdU1b5VNQMcDnyuql426jgkSZIkSYvj70BKkjQFkuyZ5KQklybZlOSJfcckSZo+I/8O5KCqOgM4o88YJEmaEscCn66qw5LsBOzSd0CSpOnTawEpSZK2X5I9gKcBRwJU1e3A7X3GJEmaTg5hlSRp8u0HXAf8XZLzk7wnya59ByVJmj72QEqSNPlWAY8Fjqqqs5McC6wH3jS40jT8RJY0zJ+wkkbLHkhJkibfFmBLVZ3dTp9EU1DehT+RJUnaXhaQkiRNuKr6DnBlkgPaWQcBX+sxJEnSlHIIqyRJ0+Eo4IT2CqzfAl7RczySpClkASlJ0hSoqguAtX3HIUmabg5hlSRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpEwtISZIkSVInFpCSJEmSpE4sICVJkiRJnVhASpIkSZI6sYCUJEmSJHViASlJkiRJ6sQCUpIkSZLUiQWkJEmSJKmTVX0HIEmSNLP+lL5DGFs+NtL4WqrX5+YNhyzJdkbBHkhJkiRJUicWkJIkSZKkTiwgJUmSJEmd+B3I1nzjlydpTLLGi99bWXoLPabj9Hr1fUWSJE0beyAlSZIkSZ1YQEqSJEmSOrGAlCRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpEwtISZIkSVInFpCSJEmSpE4sICVJkiRJnVhASpI0JZLskOT8JJ/sOxZJ0nSygJQkaXocDWzqOwhJ0vSygJQkaQok2Rc4BHhP37FIkqbXqr4DkCRJS+IY4PXA7nOtkGQdsA5gzZo1S7LTmfWnLMl2JI2vpXqdb95wyJJsZyniWapYViJ7ICVJmnBJngdcW1XnzrdeVW2sqrVVtXb16tUjik6SNE0sICVJmnxPBp6fZDPwIeCZST7Yb0iSpGlkASlJ0oSrqjdU1b5VNQMcDnyuql7Wc1iSpClkASlJkiRJ6sSL6EiSNEWq6gzgjJ7DkCRNKXsgJUmSJEmdWEBKkiRJkjqxgJQkSZIkdWIBKUmSJEnqZOQFZJIHJjk9yaYklyQ5etQxSJIkSZIWr4+rsN4B/G5VnZdkd+DcJJ+tqq/1EIskSZIkqaOR90BW1dVVdV57+xZgE7DPqOOQJEmSJC1Or9+BTDIDPAY4u884JEmSJEkL62MIKwBJdgP+AXhtVd08y/J1wDqANWvWjDi65Tez/pS+Q5CW3XzP880bDhlhJJIkSVoKvfRAJtmRpng8oapOnm2dqtpYVWurau3q1atHG6AkSZIk6W76uAprgPcCm6rqHaPevyRJkiRp2/TRA/lk4OXAM5Nc0P49t4c4JEmSJEmLMPLvQFbV54GMer+SJEmSpO3T61VYJUmSJEmTwwJSkiRJktSJBaQkSZIkqRMLSEmSJElSJxaQkiRJkqROLCAlSZIkSZ1YQEqSJEmSOrGAlCRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpEwtISZIkSVInFpCSJE24JA9McnqSTUkuSXJ03zFJkqbTqr4DkCRJ2+0O4Her6rwkuwPnJvlsVX2t78AkSdPFHkhJkiZcVV1dVee1t28BNgH79BuVJGkaWUBKkjRFkswAjwHO7jcSSdI0cgirJElTIsluwD8Ar62qm2dZvg5YB7BmzZoRRydppZtZf0rfIWgJ2AMpSdIUSLIjTfF4QlWdPNs6VbWxqtZW1drVq1ePNkBJ0lSwgJQkacIlCfBeYFNVvaPveCRJ08sCUpKkyfdk4OXAM5Nc0P49t++gJEnTx+9ASpI04arq80D6jkOSNP3sgZQkSZIkdbKieiC39cpP891v84ZDtjWcFcHHTpNiW98ftvV5vND+5tvutr6u+ng9+h4gSdJ0sQdSkiRJktSJBaQkSZIkqRMLSEmSJElSJxaQkiRJkqROLCAlSZIkSZ1YQEqSJEmSOrGAlCRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpk1V9ByBJkiRJozSz/pS+Q7iLpYpn84ZDlmQ787EHUpIkSZLUiQWkJEmSJKkTC0hJkiRJUicWkJIkSZKkTiwgJUmSJEmdWEBKkiRJkjqxgJQkSZIkdWIBKUmSJEnqxAJSkiRJktSJBaQkSZIkqRMLSEmSJElSJxaQkiRJkqROeikgkxyc5OtJvplkfR8xSJI0TcytkqRRGHkBmWQH4G+A5wAPA16S5GGjjkOSpGlhbpUkjUofPZBPAL5ZVd+qqtuBDwGH9hCHJEnTwtwqSRqJPgrIfYArB6a3tPMkSdK2MbdKkkYiVTXaHSYvAv5zVfGSuoIAAAkwSURBVL26nX458ISqOmpovXXAunbyAODrIw20m72B6/sOYjtNQxtgOtoxDW2A6WjHNLQBVk47HlRVq0cVzDgyt46daWgD2I5xMg1tgOloxzS0AbYjt65annjmtQV44MD0vsBVwytV1UZg46iC2hZJzqmqtX3HsT2moQ0wHe2YhjbAdLRjGtoAtmOFMbeOkWloA9iOcTINbYDpaMc0tAG2rx19DGH9CrB/kgcn2Qk4HPh4D3FIkjQtzK2SpJEYeQ9kVd2R5LeAzwA7AMdV1SWjjkOSpGlhbpUkjUofQ1ipqk8Bn+pj30tsrIcBdTQNbYDpaMc0tAGmox3T0AawHSuKuXWsTEMbwHaMk2loA0xHO6ahDbAd7Rj5RXQkSZIkSZOpj+9ASpIkSZImkAVkB0l2SHJ+kk/OsuzIJNcluaD9e3UfMS4kyeYkF7UxnjPL8iT5yyTfTHJhksf2Eed8OrTh6UluGjgWf9xHnAtJsmeSk5JcmmRTkicOLR/7YwGd2jHWxyPJAQOxXZDk5iSvHVpn7I9Fx3aM9bEASPK6JJckuTjJiUnuNbT8nkk+3B6Ls5PM9BOploq5dXxMQ341t46Paciv05JbYXnyay/fgZxARwObgD3mWP7hqvqtEcazrZ5RVXP93stzgP3bv58H3tn+HzfztQHg/1XV80YWzbY5Fvh0VR2W5mqJuwwtn5RjsVA7YIyPR1V9HXg0NB9kgW8DHx1abeyPRcd2wBgfiyT7AL8NPKyqbkvyEZqriB4/sNqrgBuq6iFJDgfeBvzKyIPVUjK3jpdJz6/m1jExDfl1GnIrLF9+tQdyAUn2BQ4B3tN3LMvsUOD91fgSsGeS+/cd1LRJsgfwNOC9AFV1e1XdOLTa2B+Lju2YJAcB/1ZVlw/NH/tjMWSudkyCVcDOSVbRfGAa/g3DQ4H3tbdPAg5KkhHGpyVkbtVSMreOtWnIr5OcW2EZ8qsF5MKOAV4P/HSedX657X4/KckD51mvTwWcmuTcJOtmWb4PcOXA9JZ23jhZqA0AT0zy1ST/lOThowyuo/2A64C/a4duvSfJrkPrTMKx6NIOGP/jsdXhwImzzJ+EYzFornbAGB+Lqvo28HbgCuBq4KaqOnVotX8/FlV1B3AT8DOjjFNLytw6XiY9v5pbx9c05NeJzK2wfPnVAnIeSZ4HXFtV586z2ieAmap6JPDP3FnBj5snV9VjaYYM/LckTxtaPtuZhnG7RO9CbTgPeFBVPQr4K+Bjow6wg1XAY4F3VtVjgO8D64fWmYRj0aUdk3A8aIcIPR/4+9kWzzJv3I4FsGA7xvpYJNmL5gzog4EHALsmednwarPcdSyPheZnbh3L5+2k51dz6xiahvw6ybkVli+/WkDO78nA85NsBj4EPDPJBwdXqKrvVtWP2sl3A48bbYjdVNVV7f9racZwP2FolS3A4Bnefbl7F3evFmpDVd1cVbe2tz8F7Jhk75EHOr8twJaqOrudPokmWQyvM9bHgg7tmJDjAc0HpvOq6ppZlk3CsdhqznZMwLF4FnBZVV1XVT8GTgaeNLTOvx+LdhjOvYHvjTRKLRVz65iZgvxqbh1P05BfJzm3wjLlVwvIeVTVG6pq36qaoem+/lxV3aVqHxqv/XyaCwKMlSS7Jtl9623gF4GLh1b7OPCraRxI08V99YhDnVOXNiT5D1vHbCd5As3z+7ujjnU+VfUd4MokB7SzDgK+NrTaWB8L6NaOSTgerZcw99CUsT8WA+ZsxwQciyuAA5Ps0sZ5EHd/L/04cER7+zCa9+OxO1uthZlbx+s9ZBryq7l1fI7FkGnIr5OcW2GZ8qtXYd0GSf47cE5VfRz47STPB+6gqdaP7DO2OdwP+Gj7HF8F/N+q+nSS3wCoqncBnwKeC3wT+AHwip5inUuXNhwG/GaSO4DbgMPH9APmUcAJ7bCIbwGvmLBjsdVC7Rj745FkF+DZwK8PzJu4Y9GhHWN9LKrq7CQn0QwHugM4H9g49F77XuADSb5J8157eG8Ba1mYW3szLfnV3DpGpiG/TnpuheXLrxmzdkqSJEmSxpRDWCVJkiRJnVhASpIkSZI6sYCUJEmSJHViASlJkiRJ6sQCUpIkSZLUiQWktA2SvDHJJUkuTHJBkp9f4u0/Pcknu85f4n3/4cDtmSTDv2smSdKSM7dKk8ECUlqkJE8Engc8tqoeCTwLuLLfqJbUHy68iiRJS8fcKk0OC0hp8e4PXF9VPwKoquur6iqAJI9LcmaSc5N8Jsn92/lnJDkmyReSXJzkCe38J7Tzzm//H7AtAS2w37cl+XKSf03y1Hb+Lkk+0p7l/XCSs5OsTbIB2Lk983tCu/kdkry7PSt8apKdt+vRkyTp7syt0oSwgJQW71TggW3S+NskvwCQZEfgr4DDqupxwHHAnw7cb9eqehLwmnYZwKXA06rqMcAfA3+22GA67HdVVT0BeC3w5nbea4Ab2rO8bwUeB1BV64HbqurRVfXSdt39gb+pqocDNwK/vNgYJUlagLlVmhCr+g5AmjRVdWuSxwFPBZ4BfDjJeuAc4BHAZ5MA7ABcPXDXE9v7n5VkjyR7ArsD70uyP1DAjtsQ0gEL7Pfk9v+5wEx7+ynAsW08Fye5cJ7tX1ZVF8yyDUmSloS51dyqyWEBKW2DqvoJcAZwRpKLgCNoEsAlVfXEue42y/RbgdOr6oVJZtptLlYW2O+P2v8/4c7XfBax/R8N3P4J4DAbSdKSM7dKk8EhrNIiJTmgPau51aOBy4GvA6vbCwGQZMckDx9Y71fa+U8Bbqqqm4B7A99ulx+5jSEttN/ZfB54cbv+w4D/NLDsx+3QHUmSRsLcKk0OeyClxdsN+Kt2mMwdwDeBdVV1e5LDgL9Mcm+a19cxwCXt/W5I8gVgD+CV7by/oBlm8zvA5zru/6AkWwamXwTMt9/Z/G273wuB84ELgZvaZRuBC5OcB7yxY0ySJG0Pc6s0IVI13PMvaaklOQP4vao6p+9YAJLsAOxYVT9M8rPAacBDq+r2nkOTJKkTc6vUD3sgpZVpF+D0djhNgN80wUmStF3MrVoR7IGUJEmSJHXiRXQkSZIkSZ1YQEqSJEmSOrGAlCRJkiR1YgEpSZIkSerEAlKSJEmS1IkFpCRJkiSpk/8PxAmrANQYalQAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "5gKrt2pqOM6a",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "outputId": "4718a445-cfd0-4255-eafa-23d86ebd47ce",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "n_bins = 20\n",
    "\n",
    "setosa = iris[iris['class'] == 'Iris-setosa']\n",
    "versicolor = iris[iris['class'] == 'Iris-versicolor']\n",
    "virginica = iris[iris['class'] == 'Iris-virginica']\n",
    "\n",
    "\n",
    "colors = ['orange', 'blue', 'black']\n",
    "labels = ['setosa','versicolor','virginica']\n",
    "x_multi = [setosa['sepal_length'],versicolor['sepal_length'],virginica['sepal_length']]\n",
    "plt.hist(x_multi, n_bins, histtype='bar',color=colors,label= labels)\n",
    "plt.legend(prop={'size': 10})\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Count')"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Count')"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ]
  }
 ]
}