plot-histogram-python-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ___How to plot a histogram in Python (using pandas)___\n",
    "_Find the tutorial article here: https://data36.com/plot-histogram-python-pandas_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step #1: Import pandas and numpy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step #2: Get the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = 168 #mean\n",
    "sigma = 5 #stddev\n",
    "sample = 250\n",
    "np.random.seed(0)\n",
    "height_f = np.random.normal(mu, sigma, sample).astype(int)\n",
    "\n",
    "mu = 176 #mean\n",
    "sigma = 6 #stddev\n",
    "sample = 250\n",
    "np.random.seed(1)\n",
    "height_m = np.random.normal(mu, sigma, sample).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176, 170, 172, 179, 177, 163, 172, 167, 167, 170, 168, 175, 171,\n",
       "       168, 170, 169, 175, 166, 169, 163, 155, 171, 172, 164, 179, 160,\n",
       "       168, 167, 175, 175, 168, 169, 163, 158, 166, 168, 174, 174, 166,\n",
       "       166, 162, 160, 159, 177, 165, 165, 161, 171, 159, 166, 163, 169,\n",
       "       165, 162, 167, 170, 168, 169, 164, 166, 164, 166, 163, 159, 168,\n",
       "       165, 159, 170, 163, 168, 171, 168, 173, 161, 170, 164, 163, 165,\n",
       "       166, 168, 162, 172, 170, 160, 175, 177, 173, 167, 162, 173, 165,\n",
       "       174, 169, 172, 169, 171, 168, 176, 168, 170, 177, 161, 161, 172,\n",
       "       162, 177, 165, 164, 177, 175, 177, 172, 163, 177, 166, 172, 172,\n",
       "       167, 171, 172, 169, 162, 169, 174, 164, 167, 165, 177, 171, 170,\n",
       "       164, 170, 164, 168, 164, 171, 170, 166, 169, 162, 160, 170, 168,\n",
       "       171, 179, 172, 163, 173, 161, 165, 167, 176, 164, 163, 167, 164,\n",
       "       173, 162, 162, 165, 165, 177, 172, 168, 161, 172, 162, 160, 173,\n",
       "       169, 172, 169, 172, 164, 162, 171, 163, 164, 165, 168, 166, 161,\n",
       "       164, 156, 171, 159, 162, 168, 164, 175, 161, 169, 167, 162, 170,\n",
       "       167, 171, 172, 178, 174, 166, 166, 173, 171, 171, 159, 167, 164,\n",
       "       169, 167, 172, 169, 171, 165, 163, 165, 167, 169, 179, 167, 163,\n",
       "       166, 165, 170, 160, 168, 168, 169, 165, 166, 160, 165, 165, 170,\n",
       "       162, 171, 175, 157, 170, 171, 164, 166, 167, 166, 166, 159, 173,\n",
       "       173, 163, 160])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step #3: Prepare the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gym = pd.DataFrame({'height_f': height_f, 'height_m': height_m})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height_f</th>\n",
       "      <th>height_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>177</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>159</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>173</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>173</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>163</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>160</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     height_f  height_m\n",
       "0         176       185\n",
       "1         170       172\n",
       "2         172       172\n",
       "3         179       169\n",
       "4         177       181\n",
       "..        ...       ...\n",
       "245       159       180\n",
       "246       173       179\n",
       "247       173       173\n",
       "248       163       186\n",
       "249       160       168\n",
       "\n",
       "[250 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gym"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [OPTIONAL] Basics: Plotting in Python using pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Messy line chart_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116d88950>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Nicer line chart_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117bd7d10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T05t+8N6hUc50Dpx3A9NFRLh8QSblWpGpPKQJXIWt8Ra4F8MIwfcEvrOqGRG4dpn76ssLuWYl9GZ1nmrnIg4X3sB0KS3KoKa1j5YeXfVeTU8TuApbriTmaQs83mYlI9Hmczn9jqpmLpuf7vFN04LMREQcS6N5yrWIw7LJEnixo+9du1GUJzSBq7DlaRn9RHPSEnxqgbf0DPFmQ+eUxTsXirdZmZee4NWNzONNPcTFWCjITHT7/Oq5acTbLNqNojyiCVyFLU/L6CeakxrnUwv8pWPNGIPH/d8u3o5EOd7cy+LcZKyTXFNsjIU1Bela0KM8oglchS1vqjBdfG2B7zrWTF5qHKvmpnr1uoXZSdS29nlcfFPd1ON2BMpEpUWZHGnspm9o1KtYVPTRBK7CljfzoLjMSY2nrW+YoVHPZwkcHrXzt+OtbFieh4jnrX1wrM7TMzRKa+/0KwF1DYxwtmuQJW5GoExUWpTJmN2wv77Tq1hU9NEErsKWN1WYLq6FHZq9KKkvr22nd2h02vJ5d1xDCT3pRjnhXMRhshuYLmsL07GIFvSo6WkCV2HJGEObD10oeT4s7PDH/WdIirVy5eIsr84F3s1K6JoDZboulJR4GyvnpupIFDUtTeAqLHUPjDI8Zve4CtPF22rM7sERnn3zLDeumUtirPvFG6YyLyMBm1U8mpXweFMPCc6RK9MpWZDJ/vpORnSBBzUFTeAqLLX0OhKw133grvlQPEzgTx1oZGBkjM2lhd4F6GS1CIWZiR5VY1Y39bIkL9mjUTWlRZkMjIxxpLHbp7hUdNAErsKSt2X0LqnxMSTYrB63wLeW17MiP5VL56d5HaNLcXayR33gx5t6WJI7dfeJy/gCDzXajaImpwlchSVvy+hdRIT8NM9W5jl8povDZ7q5tazA69EnEy3MSaK2rZ8x++RDCTv7h2nuGXI7B4o7uanxLMhK1BV61JSmTeAi8gsRaRaRwxO23SsiZ0TkgPPrvcENU0Wbt2Yi9C6Bg2NOFE9W5tlaXk9cjIWbLpvn9TkmKs5OYnjUTmPn5Occv4E5x7MWODi6USrqdIEHNTlPWuCPAje42f7fxpg1zq/nAhuWinauMvp0L8roXRwt8KmHEfYPj/Lk/kbed0k+aYnen2OiYg8mtTrunMRquhEoE5UWZdDeN8xJL+ZaUdFl2gRujPkboJ/j1Ixq6fG+jN4lz9mFYp+iS+PZN8/SMzTK5jLfbl5O5MlY8OqmHpLjYpjrvMnqCdeiEjqcUE3Gnz7wz4vIm84uloyARaRmldExO1f9107+99Uar17nSxm9S35aPKN2Q2vf5K3wbeWnWZiTNH6z0B+5KXEkxlqnnJXweJNjDhRv+tqLs5PITo7Vgh41KV8T+M+ARcAa4Czwvcl2FJEtIlIhIhUtLS0+nk5FqqpzPTR0DPCn/We8ep0vVZgurnnBm7rcJ/Dqph4q6jrYXOrfzUsXEZl2Uqvq5h6Pb2BOPG7JgkwqdGZCNQmfErgxpskYM2aMsQMPA2VT7PuQMabEGFOSk+PZRPlq9tjjHAZ3sKGL5h7PqyNbfZgHxcVVTj/Zjcyt5aexWYUPrZvv0/HdmSqBt/UO0do77FX/t0tJUQb17f1+rfOpZi+fEriI5E/48Wbg8GT7quhWUddOvM3xa/bSMc8+gTlWo/e9C8VVjeku6Q2NjvGHfQ28a2Wez8d3pzg7iYaOfoZHL66c9LSE3h1XP7gOJ1TueDKM8HFgN7BMRBpE5A7gARE5JCJvAtcBXw5ynCoCGWPYU9PBDavmMCc1np2VzR69ztcyepes5DhiLOK2mOfFI0109I/4XHk5meLsJOwG6tv7L3quutn7ESguq+amkhhr1YIe5da0kz8YY251s/mRIMSiZpm6tn5ae4coK84iITaGpw6cYXjUTmzM1O2GFucYcF+7UKwWITfF/cIOW8vrmZ+RwFWLs3069mQmjkRZnHt+X/fxph5S4mPIS/X+emKsFtYWpusKPcotrcRUQeP62F9alMHG5bn0DY+N94lPxVXE420Z/URz0uIvWtihrq2PV0+0samkwKfhiVN5K4FfPCvh8aZelual+HzDtLQok6pz3XQPjvgVo5p9NIGroCmvbSc90cainGSuWJxFbIyFnVXTd6O4FjP2tox+ojlp8Re1wH9XcRqLwEdKAnfz0iU9MZbMpFhqWs/vQjHGeLQKz1RKizKxG9hXp61wdT5N4CpoKmo7KFmQicUiJMbGcMWiLHZUNU1bGu5PGb1LXqqjBe461+iYne0VDVy3LJf8tOmnc/VFUVbiRS3wlt4hOvpHvB5CONHawnSsFuEN7QdXF9AEroKipWeIU6195xXKbFieS11b/7RzZ7f2DmH1sYzeJT8tnv7hMXqc60rurGqmuWcoIJWXk3E3K2G1HyNQXFz/+f38lVNsrzjtV4xqdtEEroJir3NV9dLizPFt1y1zLFm2a5pulNaeYbKSfCujd3EV87j6wbeWnyY3JY7rlgWvFmFhThJN3UPnLUbsmgNlunUwp/PjW9dRVpzJP//+Tf7rhaoppwlQ0UMTuAqKPTUdxNssrJ771jzbBZmJLMtLYcc0wwlben0v4nFxdZOc6xrkbNcALx1r5paS+cRYg/cr725OlONNvaQn2vy6IQuQlmjj0U+WcWtZIT976SR3/XYfA8OeL9ysZidN4CooKuraWVOQftGQweuW51Je2z7liAp/yuhd5kxogW+vaMBuYFNJ8LpPAIqyLp6VsLqph6W5vo9AmchmtXDfzav5+vtW8MKRc2x6aDfNWqEZ1TSBq4DrGxrlSGP3eBXhRBtX5DJqN7xyvHXS17f2+J/Ac51jrhu7BthWfpqrFmdTmJXo1zGnU5TtOH6Nc1IrYwzHm3pYOse/7pOJRIRPX72Qhz5RwonmXm76yascaewK2PFVZNEErgJuf30nY3ZDiZsEvrYgnbQE26TDCcfL6FN8q8J0ibdZyUyK5Y/7z3Cmc4BNpQV+Hc8TibEx5KfFj3ehNHUP0T046tcNzMm8a2Ue2+9cD8AtD+7mr0ebAn4OFf40gauA21PbjkVgXWH6Rc/FWC1cuyyHl441u12CzFVG72+fMTi6Uera+slItHH9qjy/j+eJ4uyk8VE24zcwPVwH01ur5qbx5F1Xsjg3mc/8uoKfv3JKV++JMprAVcBV1LazIj+VlHj3wwA3LM+lrW+Ygw2dFz3nbxn9RK4V6j+8bj5xMVa/j+eJ4uyk8T7wt1bhCVwXyoVyU+PZtmU97145h/94tpJ7/niYkbGLJ9RSs5MmcBVQI2N29td3uu3/dnnH0hws4n44YSCKeFxcCXxzWfC7T1yKs5Po7B+ho2+Y6qZespJiyQrgrIfuJMRa+elt6/jstYt4fE89X952IKjnU+FDE7gKqCON3QyMjE2ZwNMTY7l8QYbb4YSBTOC3va2Qb964isVB6sJwxzWU8FRrH8eb/Suh94bFIvzLDcv57LWLeObNs9S3XTwropp9NIGrgKqYMIHVVDYsz+Po2e6LJpxyzYMSiC6UVXPTuP2KIr+P443xBN7SS3VTb1C7T9z5h/ULsAhsq6if0fOq0NAErgJqT007C7ISyU2devHejSscVZkXjkYJRBl9KBVkJmK1CLtPttE7NMqSGWqBu+SnJXDdsly2VzQwqn3hs54mcBUwxhgq6jqm7D5xWZKbzLz0hIsTeADK6EPJZrVQkJHADud1zVQXykSbywpp7hnyaOZHFdk0gauAOdnSR3vfsEcrvYsIG1fk8uqJVgZH3ioJD0QVZqgVZyfRNeCoNJ3pLhSA65blkJsSx7ZynfhqttMErgLG1f/troDHneuW5zIwMsbrp9rGtwViHpRQK852JO2clDjSE/0rSPJFjNXCLSXz2XWsedKFndXsoAlcBcye2naykmJZ6LyRN531C7NIsFnP+6gfiDL6UCvOcVz/shB0n7hsKinEbmB7RUPIYlDBpwlcBUxFbQclRRkeT9wUb7Ny5eIsdlQ2Y4wJWBl9qLn+A/N3Cll/FGYlcuXiLLaVn9apZ2cxTeAqIJq6B6lv7/foBuZEG5bncaZzgOrmXroHA1dGH0pL8pKxWYU1BRdPJTCTNpcWcqZzgL+fmHziMBXZNIGrgHhrAWNvE7hjOOGOyuaAjgEPpdyUeP72tev4wKVzQxrH9avyyEi0sbVcx4TPVprAVUBU1HaQGGtl1dxUr143Jy2elfmp7KpqDmgVZqjlpyWEfChkXIyVD6+bz1+ONo3/26rZRRO4Cog9Ne2sLUz3acWbjStyqahr52SLY/3I2ZDAw8XmsgJGxgxP7NWbmbORJnDlt+7BEarOuV/AwRPXLc/FbhhPMtnJkX0TM5wszk2hZEEG28pP61Szs5AmcOW3fXUd2I33/d8ul81PJyspln31nVgtQkYIxk7PZpvLCjnV2seemvZQh6ICTBO48ltFbQdWi++jLqwW4R3O1eIjuYw+XL3vknxS4mLYqpWZs44mcOW3PbXtrJ6bSlJcjM/H2LjcsWKO9n8HXkKslZvWzuW5Q2fp6p98MWkVeTSBK78MjY5x8HSnx+Xzk7l6aTYxFiE7wocQhqvNpYUMjdr504EzoQ5FBZAmcOWXw2e6GRq1+9z/7ZIab+Oz1y7ixstCO3Z6tlo9L41L5qXx+J56vZk5i2gCV34pH5/AavoZCKfzleuX8ZHL5/t9HOXeptICqs71cLChK9ShqADRBK78UlHbzsKcJO27jgA3rZlLgs3K1j1amTlbaAJXPrPbDeW1HZQu8K/7RM2MlHgb7780n6cONtI7NBrqcFQAaAJXPjvR0kvXwEhAuk/UzNhcVkj/8BjPHGwMdSgqAKZN4CLyCxFpFpHDE7ZlishfRKTa+V3/gqOQqzCkrFhb4JFiXWE6S3KTeVzHhM8KnrTAHwVuuGDb3cAOY8wSYIfzZxVlKmrbyUmJozAzMdShKA+JCJvLCjl4upPKs92hDkf5adoEboz5G3BhDe5NwC+dj38JfDDAcSkPGWNCNmF/eW0HZUWZHi/goMLDh9bOI9Zq0ZuZs4CvfeB5xpizAM7vuYELSXnjwZdPUXbfX3ljwrqSM+F35ac50zng0QLGKrxkJMXy7tVz+OP+M+ctKK0iT9BvYorIFhGpEJGKlpaWYJ8u6rxw5BytvcN8/JE3ZmTKULvdcP/zVXztiTe5anE2t5QUBP2cKvBuLS2ge3CU5w+fDXUoyg++JvAmEckHcH5vnmxHY8xDxpgSY0xJTk6Oj6dT7vQPj3LkTBe3va2Q0qJMvrL9IN/5c1XQulT6h0f53GP7ePDlk3zsbYX87ydL/Zr/RIXO2xdmsSArkcf36M3MSOZrAn8KuN35+HbgycCEo7xxoL6TUbvhnSvz+OWnyri1rICf7DrJ5x/fx8BwYD8aN3UPsul/XufPR8/x9fet4D8/uBqbD4s3qPBgsQibSgvYU9POKedCGiryeDKM8HFgN7BMRBpE5A7gfuBdIlINvMv5s5ph5bUdiMC6wgxsVgv33XwJ//beFTx/+BybH9pNc89gQM5z+EwXN/34VU629PLwJ0r49NUL9cblLPCRdfOxWoRtOqQwYnkyCuVWY0y+McZmjJlvjHnEGNNmjNlojFni/K4zxYdAeW07y/JSSEuwAY4hYp+5ZiH/8/HLOd7Uywd//KrfQ8X+crSJj/7PbkTg93dewTtX5gUidBUGclPj2bg8l9/vbWB41B7qcJQP9DNwhBods7OvvsNtEc31q+aw/c712A185GevsbOqyevjG2N4+G+n2PLrChbnJvPkXVey0ssFi1X4u7WskLa+Yf5a6f3viAo9TeAR6ujZbvqHxyadh3v1vDT+dNeVFOck8elfVvCLv9d4PI3oyJide/54mP98rpIbVs1h25b15KbGBzJ8FSauWZrD3LR4Xa0nQukQgghVXtsBMOU47Dlp8fzun9bzpa0H+NYzR9l/upNFOUnTHnv3yTbeqGnnc9cu4qvXL9MlzmYxq0W4paSAH+6s5nR7PwVaVRtRNIFHqPKaduZnJJCfljDlfomxMTz48ct54M/HePiVU4x5MMQwMdbKdz5yqY7xjhK3lMznhzur2V5xmv97/bJQh6O8oAk8AhljqKhr55olntRvgukAABZASURBVI2rt1iEu9+znH+5wfM/Th1lEj3mZyRyzZIcflfRwP/ZuIQYHR4aMfSdikC1bf209g57vQ6liHj8paLLrWUFnOse5G/VWi0dSTSBR6Dy8WlcdR4SFRgbV+SRnRyrlZkRRhN4BCqvbScj0cainORQh6JmCZvVwocvn8/OqmaauwNTAKaCTxN4BCqvbadEp3FVAba5tJAxu2H7DEyKpgJDE3iEae4ZpLatX6dxVQFXnJ3E2xdmsq38dMjmmFfe0QQeYSqc47+9vYGplCduLSukvr2f3TM8v7zyjSbwCFNe2068zcLquWmhDkXNQu9eNYe0BBuP62o9EUETeIQpr21nTUE6sTH61qnAi7dZuXntPF480kR733Cow1HT0CwQQXqHRjna2E2Zdp+oILq1rJDhMTt/2Kc3M8OdJvAIsq+uA7vR/m8VXMvmpLC2MJ2t5ac9ngBNhYYm8AhSUduORWDdAh2BooJrc2kBJ5p72VvXEepQ1BQ0gUeQ8toOVs5NJVnXoVRB9v5L55IUa9VpZsOcJvAIMTxqZ//pDkq1+0TNgKS4GG5cM49n3myke3Ak1OGoSWgCjxBHGrsYHLFrAlcz5tayAgZH7Dx5oDHUoahJaAKPEOW1jgmsSrQCU82QS+alsSI/la06JjxsaQKPEOW1HRRlJZKbokubqZkhImwqmc+Rxm5OtfSGOhzlhibwCGC3Gypq27X7RM24q5c6Fg1xfQJU4UUTeAQ41dpLR/+IJnA14xZmJ5GZFMueGh1OGI40gUcA1x+P9n+rmSYilCzIoKJOW+DhSBN4BKiobSc7OZbi7OlXlFcq0MqKM6lr69eFHsKQJvAIUF7XTskCXcBBhYZr6obyWu1GCTeawMPcua5BTrcPUFqs/d8qNFbNTSXBZtUbmWFIE3iYc/3R6Ao8KlRsVgtrC9M1gYchTeBhrry2ncRYKyvzU0MdiopipUWZVJ7tpsfPsvqRMTtf/9Mhqs51Byiy6KYJPMyV13awrjCDGKu+VSp0SosysRvYV9/p13H21LTzm9fr+eftbzKm6276TbNCGOsaGKHqXLeO/1Yht7YwHatFqPCzG2VnVTMAh8508bsKnenQX5rAw9i++g6M0f5vFXpJcTGsmpvqdz/4zqpmrlmaQ1lRJt/58zG6+nWmQ39oAg9j5TXtxFiENYXpoQ5FKUoWZLK/vpPhUbtPrz/V0ktNax/vXJHLvTeuorN/mO//5ViAo4wumsDDWEVtB6vmpZEYqws4qNArK85gaNTO4cYun17v6j65blkuK+emctvbFvDr1+uoPKs3NH3lVwIXkVoROSQiB0SkIlBBKRgaHeNAQyelunyaChOXL3AW9NT41o2ys6qZpXnJFGQmAvCV65eSlmDjG08d0bU3fRSIFvh1xpg1xpiSABxLOR1q6GJ41K4FPCps5KTEsTA7yaeKzJ7BEfbUtHPd8tzxbemJsXz13cvYU9PO02+eDWSoUUO7UMKU64+kRFvgKoyUFDkmtrJ7OQTwlepWRu2Gjcvzztu+ubSQ1fNSue/ZSvqGRgMZalTwN4Eb4EUR2SsiWwIRkAJjDC8ePceinCSykuNCHY5S40qLMunsH+Gklws87KxqJi3BxroLbshbLcI3b1zFue5BfrLrRCBDjQr+JvArjTHrgPcAd4nINRfuICJbRKRCRCpaWlr8PF10eOpgI/vrO7njqoWhDkWp87hqEvZ4MZzQbje8dKyZdyzNcVuQdvmCTD60bh4Pv3KKmta+gMUaDfxK4MaYRuf3ZuCPQJmbfR4yxpQYY0pycnL8OV1U6Bsa5b7nKlk9L5VNpQWhDkep8yzISiQ7OY4KL/rB3zzTRWvvMBsm9H9f6O73LCcuxsq3nj4SiDCjhs8JXESSRCTF9Ri4HjgcqMCi1Y93naCpe4hv3rgaq0Wnj1XhRUQoK87wqqBnZ2UTFoF3LJ28AZebEs8XNy5h17EWdlQ2BSLUqOBPCzwP+LuIHAT2AM8aY14ITFjRqaa1j5+/cooPrZvH5XrzUoWpkgWZNHQMcLZrwKP9d1Q1c/mCDDKSYqfc7/YriliUk8S3njnK4MhYIEKd9XxO4MaYU8aYy5xfq4wx/xnIwKLRt54+QlyMlbvfszzUoSg1qbJizxd4ONc1yJHG7vOGD04mNsbCvTeuoq6tn0f+XuN3nNFAhxGGiR2VTew61sIXNy4hNyU+1OEoNanlc1JIirV6VNCz65ij+vLC4YOTuXpJDu9elcePd56gsdOzFn400wQeBgZHxvjm00dZlJPE7VcUhTocpaYUY7WwboFn/eA7q5qZl57A0rxkj4//9fetxG4M9z1X6U+YUUEn2QgDP3/lFPXt/fz6jjJiY/T/VBX+Sosy+e+/HqdrYIS0BJvbfQZHxvh7dSsfuXy+V+u5FmQmcuc7FvGDHdVkJx8hMdY67Ws2LM8dX7szmmgCD7HGzgF+susk716Vx9VLdJiligwlRRkYA/vqOibt336jpp2BkbEphw9O5rPXLuLl4y089kbdtPuO2g3PHjrLrq9ciyXKRm5pAg+x/3yuErsxfP19K0MdilIeW1uQQYxFKK9tnzSB76xsIt5mYf2iLK+PH2+z8qe7rvRo3z/ub+DL2w7y+qk2rlic7fW5Ipl+Xg+h10628uybZ7nzHYvGZ2hTKhIkxFpZPS9t0n5wYww7qpq5clE28bbpu0D88Z7V+aTGx7C1PPpW+NEEHiKjY3a++dRR5qUn8NlrF4U6HKW8VlacycHTXW7HbJ9o7qWhY4ANK7zvPvFWvM3KzWvn8cLhc3T0DQf9fOFEE3iI/Pr1Oo419fD/3r8y6C0UpYKhZEEGw2N2Dp25eIGHHRMWb5gJm8sKGR6z84f9Z2bkfOFCE3gItPYO8f2/HOfqJdm8e5Vn42OVCjeuUR/uulF2VjWzIj+VuekJMxLLivxULitIZ+ue+qhaHEITeAh854VjDAyP8Y0PrPJqeJVS4SQzKZbFuckXFfR09Y+wt66DjT6MPvHHraUFVDf3sq++c0bPG0qawGfYwdOd/G7vaT55ZRGLcz0vblAqHJUWZVBR13HeAg8vV7cwZjcelc8H0vsvm0tirJWte+pn9LyhpAl8BjV3D/K1379JdnIc/2fjklCHo5TfSosy6Rkc5VhTz/i2nZVNZCbFsqYgfYpXBl5yXAw3XjaXZ948S8/giM/H+f3eBm596HX6h8N/hSBN4DPkSGMXN/3kVU539PO9Wy4jJd599ZpSkcS1wEOFsx98zG546XgL1y7NCcl0yJvLChkYGePJA40+vb65Z5B7nzrC7lNt/OylkwGOLvA0gc+Avx5t4pYHdwOw/c71XDPFvMhKRZL5GQnMSY0fn5lwf30Hnf0jMzJ80J3L5qexfE4K23wcE/5fzx9jaHSMKxdn8T9/O0V9W3+AIwwsTeBBZIzh56+c4jO/rmBxbjJP3nUlq+amhTospQJGRCgpckxs5SresVokZNNCiAibSws4dKaLw26GN05lb10HT+xr4I6rFvL9j67BZhG+9czRIEUaGJrAg2RkzM49fzzMfzxbybtXzmHblvXkpuo0sWr2KSvO5GzXIGc6B9hV1UxpUcakE1zNhJvXzicuxsLWcs9vZo7ZDfc+dYS81Di+sGExeanxfGHjEv5a2cRLzilxw5Em8CDoGhjhk/9bzuN76vnstYv46W3rSPBgRjWlIlHJAkc/+JMHGqk61+PT5FWBlJZo472X5PPk/kYGhj1b2ed3Fac5dKaLe967gqQ4xxRRn7qymIXZSXzz6aMMjYbnCkGawAOsrq2PD/30Vd6oaeOBj1zKv9ywPOpmSFPRZdmcFFLiY3jwZcdNvw0eLt4QTJtLC+gZGuXZQ2en3bezf5gHXqiirCiTGy+bO749NsbCv39gJTWtffzi77VBjNZ3msADaE9NOx/8yau09Q3z6zvexkdLdFV5NftZLcLlCzLoGRylMDORRTlJoQ6JsuJMFmYneTQm/Pt/ccxrfu+NFxfWXbssl3euyONHO6s51zUYrHB9FpXTyY6M2Xn5WAuDAfxY1Ng5wHf/fJz5GQk88o+lFGeH/pdYqZlSWpTJS8da2LA8Nyyqi0WETaUFfPv5KqqbeliSl+J2v6ON3fzm9Tpue9sCVs5NdbvPv79/Je/875f59vOV/GDz2mCG7bWoS+Bd/SPc+Zu97D7VFvBjr1+Yxc8+vo70xKlX31ZqtrlmSQ7fe/EY770kP9ShjPvw5fP57ovH2FZ+mq+//+L59o1x3LhMS7DxleuXTnqcwqxE7rxmIT/ceYLb3rZgfFHncBBVCby2tY9PPVrO6Y5+vv2hSyhZkBGwY1ssQnFWkvZ3q6h0yfw09v2/d4VV4yU7OY53rczjiX0N/PMNy4iLOX8gwVMHG9lT2859N18ybdyfvXYxT+w7wzeeOsIzX7gqJEVK7kRNAn/jVBv/9Ju9CPDYp98eVv+LKjUbhFPydtlUWshzh87x4pEmPjDhBmXf0Cj3PVfJ6nmpbCqd/l5VQqyVf3vfCj732D5++0Ydn1hfFMSoPRcVNzF/v7eBjz/yBllJsfzpris1eSsVJa5enM289ISLxoT/eNcJmrqH+OaNqz1uTb9n9RyuWJTFd188TnuYLBwxqxO43W544IUqvrr9IGXFmfzhc1eyIEtvLioVLSwWx83MV0+0jZfF17T28fNXTvGhdfO43ItuVBHh3htX0Ts0yndfPBaskL0yaxP4wPAYn398Hz996SS3lhXy6CfLQlodppQKjVtK5mMR2FbhaIV/6+kjxMVYufs9y70+1tK8FG5fX8Tje+o51OBdqX4wzMoE3tw9yOaHdvP84XN8/X0ruO/m1diss/JSlVLTyE9L4NpluWyvaODPR86x61gLX9y4hNwU36a2+NK7lpCVFMs3njp83jzooTDrstrRxm4++JNXqW7u5aFPlPDpqxeGxbhUpVTobC4toLlniC9tPcCinCRuv6LI52Olxtv42g3L2VffyR9DvAZnRIxC+cXfa/hrZZNH+x443Ulago3td67Xmf+UUgBsWJ5LbkoczT1D3HvjKmJj/Gu7fmTdfH77Rj3ffPoIT+xr8Og1X7thecAXuYiIBG43hpExu0f7Xrcsl298YKXO/KeUGhdjtXDPe1dwqqU3IFPdWizCAx+5lP94tpIBD1fuCcZiyzKTKziXlJSYioqKGTufUkrNBiKy1xhTcuH2WdcHrpRS0UITuFJKRShN4EopFaH8SuAicoOIHBOREyJyd6CCUkopNT2fE7iIWIGfAO8BVgK3isjFczYqpZQKCn9a4GXACWPMKWPMMLAVuCkwYSmllJqOPwl8HnB6ws8Nzm1KKaVmgD8J3F19+kWDykVki4hUiEhFS0uLH6dTSik1kT+VmA3AxJnQ5wONF+5kjHkIeAhARFpEpO6CXbKBVj/iCCez5Vpmy3WAXku4mi3XMlPXscDdRp8rMUUkBjgObATOAOXAx4wxR7w8ToW7CqNINFuuZbZcB+i1hKvZci2hvg6fW+DGmFER+TzwZ8AK/MLb5K2UUsp3fk1mZYx5DnguQLEopZTyQjhUYj4U6gACaLZcy2y5DtBrCVez5VpCeh0zOhuhUkqpwAmHFrhSSikfBD2Bi8gvRKRZRA5fsP0LznlUjojIAxO2/6tzbpVjIvLuYMfnKW+uQ0SKRGRARA44vx4MTdTuubsWEdk2Id5aETkw4bmwfE/Au2uJ0PdljYi87oy3QkTKnNtFRH7ofF/eFJF1oYv8fF5ex7Ui0jXhPfn30EV+sUmu5TIR2S0ih0TkaRFJnfDczP6tGGOC+gVcA6wDDk/Ydh3wVyDO+XOu8/tK4CAQBxQDJwFrsGMMwnUUTdwv3L7cXcsFz38P+Pdwf098uJaIe1+AF4H3OB+/F3hpwuPncRTUvR14I9Tx+3gd1wLPhDpmL6+lHHiH8/GngP/P+XjG/1aC3gI3xvwNaL9g82eB+40xQ859mp3bbwK2GmOGjDE1wAkcc66EnJfXEdYmuRbA0bIDPgo87twUtu8JeH0tYW2SazGAq4WXxlvFcjcBvzIOrwPpIpI/M5FOzcvrCGuTXMsy4G/Ox38BPux8PON/K6HqA18KXC0ib4jIyyJS6tweafOrTHYdAMUist+5/epQBeiDq4EmY0y18+dIe08muvBaIPLely8B3xGR08B3gX91bo+092Wy6wBYLyIHReR5EVkVmvC8chi40fn4Ft6qSJ/x9yRUCTwGyMDx0e+fgd85W0seza8SRia7jrNAoTFmLfB/gd9O7CcLc7dyfos10t6TiS68lkh8Xz4LfNkYUwB8GXjEuT3S3pfJrmMfsMAYcxnwI+BPIYrPG58C7hKRvUAKMOzcPuPvSagSeAPwB+fHvz2AHcecAh7NrxJG3F6H8yNUG4AxZi+OvrClIYzTI+KYHuFDwLYJmyPtPQHcX0uEvi+3A39wPt7OWx/JI+19cXsdxphuY0yv8/FzgE1EskMTomeMMVXGmOuNMZfjaCCcdD414+9JqBL4n4ANACKyFIjFMSHMU8BmEYkTkWJgCbAnRDF6wu11iEiOOBa8QEQW4riOUyGL0nPvBKqMMQ0TtkXae+Jy0bVE6PvSCLzD+XgD4OoOegr4B+dolLcDXcaYs6EI0ENur0NE5jg/teIcmWIB2kISoYdEJNf53QJ8HXCNZpr5v5UZuIv7OI6PriM4/oe6A0ei+w2OvqR9wIYJ+/8bjv/RjuG8ax0OX95cB46bGkdw3JHeB3wg1PFPdy3O7Y8Cd7rZPyzfE2+vJRLfF+AqYK8z5jeAy537Co4VsU4Ch4CSUMfv43V8fsJ78jpwRajj9+BavohjIr/jwP04CyKd+8/o34pWYiqlVITSSkyllIpQmsCVUipCaQJXSqkIpQlcKaUilCZwpZSKUJrAlVIqQmkCVxHFOSXs4en3HN//ThH5h2n2+UcR+fEkz93jbYxKzRRN4GpWM8Y8aIz5lR+H0ASuwpYmcBWJrCLysDgW0XhRRBJEZJGIvCAie0XkFRFZDiAi94rIV52PS52LH+wWke9c0JKf63x9tby1MMf9QIJzoYHH3AXi/ERQJSI/F5HDIvKYiLxTRF51Hitspt5Vs48mcBWJlgA/McasAjpxlMg/BHzBOCYY+irwUzev+18c5fXrgbELnlsDbAIuATaJSIEx5m5gwBizxhhz2xTxLAZ+AFwKLAc+hqN0/KtoC14FUUyoA1DKBzXGGNeSb3txrLRzBbDdOS8SOFZFGSci6UCKMeY156bfAu+fsMsOY0yXc9+jwALOn9t5ungOOV97xHksIyKHnLEpFRSawFUkGprweAzIAzqNMWumeI27uZqnOqY3fxsTX2uf8LPdy+Mo5RXtQlGzQTdQIyK3wPiCv5dN3MEY0wH0OKdeBdjs4bFHRMQWuFCVChxN4Gq2uA24Q0QO4pie9CY3+9wBPCQiu3G0yLs8OO5DwJuT3cRUKpR0OlkVNUQk2ThXfxGRu4F8Y8wXQxyWUj7T/jkVTd4nIv+K4/e+DvjH0IajlH+0Ba6UB0QkC9jh5qmNxrnOplIzTRO4UkpFKL2JqZRSEUoTuFJKRShN4EopFaE0gSulVITSBK6UUhHq/wcCFDxYaXi5cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.groupby('height_m').count().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Bar charts_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117d71e10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.groupby('height_m').count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117eff190>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.groupby('height_f').count().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step #4: Plot a histogram in Python!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117e59350>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11806cbd0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAATvUlEQVR4nO3dfZAkdX3H8feXRwmigMjW8VAeVUFLkosoGySFxhHBJyyhEqGwqOSupHKlpYkPW5FDU4nmqU4TYspoohchnqXhQaMBpYgiMhorCnKKHAQID17JyeWuCA9xSIIufvPH9MG4N3s7Ozsz3b/d96tqart7e3a+Pfvbz3b/uvs3kZlIksqzT90FSJKGY4BLUqEMcEkqlAEuSYUywCWpUAa4JBXKAF+kiNgWEacP8bzbI6I1ztfo83MiIv4hIh6OiJuW+vO0cpTUzlcyA3xCMvOXMrO91J8TEa2I2D7g6i8BzgCOycyTl/ra0kJqaucrlgG+vD0H2JaZj9VdiKTRM8CHc2JE3BoRj0bEFRHxNICIeF1E3BIRj0TEv0XEr+x+Qu/hYkQcFBGbq66NOyLi3X32NvZ4jYg4GLgWOCoiOtXjqH4FRsQFwCeAX6vWe/9Y3gktZ41v59XrvC8iPhsRn46IH0fE1oh4bkRcFBG7IuL+iHjlGN6f+mWmj0U8gG3ATcBRwOHAHcCbgRcBu4AXA/sCa6t1D+x53unV9Ebg68BhwDHArcD2hV6j+l6rd90Fal0HfLPu98xHeY/C2vn7gP8DXgXsB3wK+AHwXmB/4HeAH9T9no7j4R74cD6cmQ9k5kPAF4ET6TaSj2fmjZn5RGZuBh4HTunz/HOBP8/MhzNzO/DhAV9DmqSS2vm/ZuaXM3MW+CzwbGBjZv4UuBxYHRGHDvmzG8sAH85/9kz/D/B0uv3NM9Vh5SMR8QhwLN29i7mOAu7vmb+/zzr9XkOapJLa+c6e6f8FHszMJ3rmWcLPbiwDfHTuB/4sMw/tefxCZl7WZ90ddA8pdzt2Ea/j8JGqk+28QQzw0fl74M0R8eLq+uuDI+LMiDikz7pXAhdFxGERcTTwtkW8zk7gWRHxzFEULS2S7bxBDPARycyb6fYPfgR4GLiH7knEfv4Y2E73RMtXgc/R7Ucc5HXuBC4D7qsOYec9Oy+Nmu28WaI6i6saRcRbgPMy82V11yKNi+189NwDr0FErIqIUyNin4h4HjADfKHuuqRRsp2PnwFejwOAjwM/Br4GXAX87TA/KCKu7bnZoffxnhHWKw3Ddj5mdqFIUqH2q7sAqYkiYhvdPccngNnMnI6Iw4ErgNV07yI8NzMfrqtGaaJ74EcccUSuXr16Yq83n8cee4yDDz647jLGYrlv25133vlgZj573K9VBfh0Zj7Ys+yDwEOZuTEiNgCHZeaFe/s5u9t8ab8X6x2/xdS8ZcuW/u1+kvftn3TSSdkEN9xwQ90ljM1y3zbg5pzcWCBHzFl2F7Cqml4F3LXQz9nd5kv7vVjv+C2m5vnavV0oUn8JfCUiku7YH5uAqczcAZCZOyLiyH5PjIj1wHqAqakp2u02nU6Hdrs9odKXznrHbxQ1G+BSf6dm5gNVSF8XEXcO+sQq7DcBTE9PZ6vVot1u02q1xlTq6Fnv+I2iZi8jlPrIzAeqr7voXrt8MrAzIlZB9xpnusOqSrUxwKU5qvE9Dtk9DbwSuA24mu7411Rfr6qnQqnLLhRpT1PAFyICun8j/5iZ/xIR3wGurD7t6IfAOTXWKBng0lyZeR/wgj7L/wt4xeQrkvqzC0WSCmWAS1KhDHBJKpR94A2zesM1i37Oto1njqESafGGab/DsM13uQcuSYUywCWpUAa4JBXKAJekQg10EtPB7SWpeRazB/7yzDwxM6er+Q3A9Zl5PHB9NS9JmpCldKGcBWyupjcDZy+9HEnSoAa9Dnykg9vXrcmDv8+smV30c3q3pcnbtlSdTqfuEtQQc683n1kzy7oFrkFfjteODxrgIx3cvm5NHvx9oUbYz7bzW09ON3nblmq5/mOShjVQF4qD20tS8ywY4A5uL0nNNEgXioPbS1IDLRjgDm4vSc3knZiSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANcmkdE7BsR34uIL1Xzx0XEjRFxd0RcEREH1F2jVjYDXJrf24E7euY/AHwoM48HHgYuqKUqqWKAS31ExDHAmcAnqvkATgM+V62yGTi7nuqkrkE+lV5aif4aeDdwSDX/LOCRzJyt5rcDR/d7YkSsB9YDTE1N0W636XQ6tNvt8VY8QsPWO7NmduGVxmDqoIVfu2nv/yjahAEuzRERrwN2ZeaWiGjtXtxn1ez3/MzcBGwCmJ6ezlarRbvdptVq9Vu9kYatd92Ga0ZfzABm1sxy8da9x9m281uTKWZAo2gTBri0p1OB10fEa4GnAc+gu0d+aETsV+2FHwM8UGONkn3g0lyZeVFmHpOZq4HzgK9l5vnADcAbqtXWAlfVVKIEGODSYlwIvCsi7qHbJ35JzfVohbMLRdqLzGwD7Wr6PuDkOuuRerkHLkmFMsAlqVAGuCQVygCXpEINHOAO7CNJzbKYPXAH9pGkBhkowB3YR5KaZ9A98N0D+/ysmh94YB9J0ngseCPPUgf26TcyW92aPDLcMKO59W5Lk7dtqTqdTt0lSI0yyJ2YSxrYp9/IbHVr8shww4zm1jvKWpO3bamW6z8maVgLdqE4sI8kNdNSrgN3YB9JqtGiBrNyYB9Jag7vxJSkQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVB+qLGkPWz90aNDDeugyXIPXJIKZYBLUqHsQhmT1R5+Shoz98AlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl+aIiKdFxE0R8f2IuD0i3l8tPy4iboyIuyPiiog4oO5atbI5Fsoy0Dvuysya2YGGAd228cxxllS6x4HTMrMTEfsD34yIa4F3AR/KzMsj4mPABcDf1VmoVjb3wKU5sqtTze5fPRI4DfhctXwzcHYN5UlPcg9c6iMi9gW2AL8IfBS4F3gkM2erVbYDR8/z3PXAeoCpqSna7TadTod2uz32ukdl6qDu0VwpBqm3ae//KNqEAS71kZlPACdGxKHAF4Dn91ttnuduAjYBTE9PZ6vVot1u02q1xlXuyP3NZ67i4q3lxMPMmtkF6912fmsyxQxoFG3CLhRpLzLzEaANnAIcGhG7U+IY4IG66pLAAJf2EBHPrva8iYiDgNOBO4AbgDdUq60FrqqnQqmrnGMkaXJWAZurfvB9gCsz80sR8e/A5RHxp8D3gEvqLFIywKU5MvNW4IV9lt8HnDz5iqT+DPABLMfPtxxmm7x2XGqWBfvAvStNkpppkJOYu+9KewFwIvDqiDgF+ADdu9KOBx6me1eaJGlCFgxw70qTpGYaqA981Hel1Wnrjx5l6qDujQqDmlkzxoJGbJx30NX9u+t0OguvJK0gAwX4qO9Kq9O6DdcMdNdWqca5bXXfyVb3PxCpaRZ1I493pUlScwxyFYp3pUlSAw1yrO1daZLUQAsGuHelSVIzOZiVJBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEItzzFVJT1pmM8/LWkM/JXMPXBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeDSHBFxbETcEBF3RMTtEfH2avnhEXFdRNxdfT2s7lq1shng0p5mgZnMfD5wCvDWiDgB2ABcn5nHA9dX81JtDHBpjszckZnfraZ/DNwBHA2cBWyuVtsMnF1PhVKXAS7tRUSsBl4I3AhMZeYO6IY8cGR9lUl+Io80r4h4OvBPwDsy878jYtDnrQfWA0xNTdFut+l0OrTb7bHVujcza2YX/Zypg4Z7Xl0Gqbeu938+o2gTBrjUR0TsTze8P5OZn68W74yIVZm5IyJWAbv6PTczNwGbAKanp7PVatFut2m1WpMofQ/rhvpItVku3lpOPAxS77bzW5MpZkCjaBN2oUhzRHdX+xLgjsz8q55vXQ2srabXAldNujap14IB7iVVWoFOBX4LOC0ibqkerwU2AmdExN3AGdW8VJtBjpF2X1L13Yg4BNgSEdcB6+heUrUxIjbQvaTqwvGVKk1GZn4TmK/D+xWTrEWjs3qIriSAbRvPHHElo7PgHriXVElSMy3qLMXeLqmKiL6XVPU7I1+nmTWzxZ1hX4xxblvdv7tOp1Pr60tNM3CAD3tJVb8z8nVat+Ga4s6wL8Y4t63us/h1/wORmmagq1D2dklV9f15L6mSJI3HIFeheEmVJDXQIMfauy+p2hoRt1TL3kP3EqorI+IC4IfAOeMpUZLUz4IB7iVVktRM3okpSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUqOX5sTSSNCLDfBjypD4I2QDX2DX5D0AqmV0oklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS71ERGXRsSuiLitZ9nhEXFdRNxdfT2szholA1zq75PAq+cs2wBcn5nHA9dX81JtDHCpj8z8BvDQnMVnAZur6c3A2RMtSprD8cA1sGHG9V5mpjJzB0Bm7oiII+suSCvbggEeEZcCrwN2ZeYvV8sOB64AVgPbgHMz8+HxlSmVIyLWA+sBpqamaLfbdDod2u12LfXMrJld9HOmDhrueXVpWr2D/K5H0SYG2QP/JPAR4FM9y3b3BW6MiA3V/IVLqkRqvp0Rsara+14F7Oq3UmZuAjYBTE9PZ6vVot1u02q1JljqU9YNceQ0s2aWi7eWc4DetHq3nd9acJ1RtIkF+8DtC5SedDWwtppeC1xVYy3S0H3gA/cF9jucrNPMmtnGHW6N0nLZtn7tpNPpTOz1I+IyoAUcERHbgT8CNgJXRsQFwA+BcyZWkNTH2I85+h1O1mndhmsad7g1Sstl2/odgk7yn39mvnGeb71iYkVICxj2MsKdVR8ge+sLlCSNz7ABbl+gJNVswQCv+gK/BTwvIrZX/X8bgTMi4m7gjGpekjRBC3aW2hcoSc3krfSSVCgDXJIKVfT1Zo7NIWklcw9ckgpV9B64tNJ41Kle7oFLUqEMcEkqlAEuSYWyD1ySRmyQcxUza2Z/bqz2bRvPXPTruAcuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQ38kg1cWAqLZV74JJUKANckgplgEtSoQxwSSpUI05iejJHkhbPPXBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoZYU4BHx6oi4KyLuiYgNoypKairbvJpk6ACPiH2BjwKvAU4A3hgRJ4yqMKlpbPNqmqXsgZ8M3JOZ92XmT4DLgbNGU5bUSLZ5NcpSxkI5Gri/Z3478OK5K0XEemB9NduJiLuW8Joj8XtwBPBg3XWMw3LZtvhA38VHAM+ZbCU/ZyltvqjfS2ntqLR6Yc+a52nzu/Vt90sJ8OizLPdYkLkJ2LSE1xm5iLg5M6frrmMcVsC2ra6zhD7LBmrzpf1erHf8RlHzUrpQtgPH9swfAzywlGKkhrPNq1GWEuDfAY6PiOMi4gDgPODq0ZQlNZJtXo0ydBdKZs5GxNuALwP7Apdm5u0jq2y8GtWlM2Ju25gssc2X9nux3vFbcs2RuUcXniSpAN6JKUmFMsAlqVDLMsAj4tKI2BURt81Z/rvVbdC3R8QHe5ZfVN0afVdEvGryFQ+u37ZFxBURcUv12BYRt/R8r/RtOzEivl1t280RcXK1PCLiw9W23RoRL6qv8vLaXGntqLS2MU+9L4iIb0XE1oj4YkQ8o+d7w72/mbnsHsCvAy8CbutZ9nLgq8CB1fyR1dcTgO8DBwLHAfcC+9a9DYvZtjnfvxj4w+WybcBXgNdU068F2j3T19K9NvsU4MYG1t7YNldaOyqtbcxT73eAl1XTbwL+ZKnv77LcA8/MbwAPzVn8FmBjZj5erbOrWn4WcHlmPp6ZPwDuoXvLdCPNs21Ad88DOBe4rFq0HLYtgd17Ks/kqeuuzwI+lV3fBg6NiFWTqXRPpbW50tpRaW1jnnqfB3yjmr4O+M1qeuj3d1kG+DyeC7w0Im6MiK9HxK9Wy/vdHn30xKsbjZcCOzPz7mp+OWzbO4C/iIj7gb8ELqqWl7Btpba5UtpRaW3jNuD11fQ5PHVT2ND1rqQA3w84jO4h1e8DV1Z7GgPdHl2IN/LUXhMsj217C/DOzDwWeCdwSbW8hG0rtc2V0o5KaxtvAt4aEVuAQ4CfVMuHrnclBfh24PPVYdVNwM/oDiazLG6Pjoj9gN8AruhZvBy2bS3w+Wr6szx1aFnCthXX5gprR0W1jcy8MzNfmZkn0f0HeW/1raHrXUkB/s/AaQAR8VzgALojgV0NnBcRB0bEccDxwE21VTm804E7M3N7z7LlsG0PAC+rpk8Ddh/WXw38dnXFwSnAo5m5o44C96LENldSOyqqbUTEkdXXfYA/AD5WfWv493fSZ2cndAb4MmAH8FO6/90uoPvH82m6/VDfBU7rWf+9dP8b3kV1Vrupj37bVi3/JPDmPusXvW3AS4AtdM/S3wicVK0bdD9c4V5gKzDdwNob2+ZKa0eltY156n078B/VYyPVnfBLeX+9lV6SCrWSulAkaVkxwCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1Kh/h9B4UK0R8kMpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117fc1a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1181e2690>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Plot your histograms on the same chart!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118367710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1184cdf90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gym.plot.hist(bins=20, alpha=0.7)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}