08-Histograms with Matplotlib in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "polish-harbor",
   "metadata": {},
   "source": [
    "# Histograms with Matplotlib in Python "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74152dc6",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about histogram with Matplotlib.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "developed-tribune",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "simplified-queue",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bizarre-insurance",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatty-preserve",
   "metadata": {},
   "source": [
    "## How to Plot Histograms ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "moved-hopkins",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.random.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "streaming-place",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  54., 155., 281., 252., 172.,  60.,  17.,   1.,   1.]),\n",
       " array([-3.00177717, -2.26872876, -1.53568035, -0.80263194, -0.06958353,\n",
       "         0.66346488,  1.39651329,  2.12956169,  2.8626101 ,  3.59565851,\n",
       "         4.32870692]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "forty-redhead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequencies')"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)\n",
    "plt.title(\"My Histograms\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequencies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "narrative-overhead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequencies')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, edgecolor=\"black\")\n",
    "plt.title(\"My Histograms\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequencies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "special-toronto",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequencies')"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, edgecolor=\"black\", bins=10)\n",
    "plt.title(\"My Histograms\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequencies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fundamental-server",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[-3,-2,-1,0,1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "champion-interim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequencies')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, edgecolor=\"black\", bins=bins)\n",
    "plt.title(\"My Histograms\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequencies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "marked-robertson",
   "metadata": {},
   "outputs": [],
   "source": [
    "median=np.median(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "annual-redhead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1b7da1e3e80>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, edgecolor=\"black\", bins=bins)\n",
    "plt.title(\"My Histograms\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequencies\")\n",
    "plt.axvline(median, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "tutorial-cartridge",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.random.normal(0, 0.8, 1000)\n",
    "x2 = np.random.normal(-3, 2, 1000)\n",
    "x3 = np.random.normal(2, 1, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "declared-eleven",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  0.,  0.,  1.,  5.,  3.,  6.,  4.,  4., 10., 14., 19., 16.,\n",
       "        31., 31., 42., 42., 45., 57., 60., 64., 57., 59., 59., 61., 50.,\n",
       "        49., 39., 38., 31., 31., 18., 15., 13., 13.,  4.,  2.,  2.,  1.,\n",
       "         3.]),\n",
       " array([-1.50590162, -1.34706095, -1.18822027, -1.0293796 , -0.87053892,\n",
       "        -0.71169825, -0.55285757, -0.3940169 , -0.23517622, -0.07633555,\n",
       "         0.08250513,  0.2413458 ,  0.40018648,  0.55902716,  0.71786783,\n",
       "         0.87670851,  1.03554918,  1.19438986,  1.35323053,  1.51207121,\n",
       "         1.67091188,  1.82975256,  1.98859323,  2.14743391,  2.30627458,\n",
       "         2.46511526,  2.62395593,  2.78279661,  2.94163729,  3.10047796,\n",
       "         3.25931864,  3.41815931,  3.57699999,  3.73584066,  3.89468134,\n",
       "         4.05352201,  4.21236269,  4.37120336,  4.53004404,  4.68888471,\n",
       "         4.84772539]),\n",
       " [<matplotlib.patches.Polygon at 0x1b7da399490>])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s2WhtbYXZbEZaWpqzjlqtRmpqKurr62UPloiIlGlAKwicPHkS6enp6OrqQkhICN544w1MnTrVmVA0Go1LfY1Gg7a2NrfnNJlMXgXqbX2lCtR26qw22c9pFXDOQBRo7XQ4PP8f0zaImL09xqFyuD+f7YLz9/85/33MSdde9i4wmdls8l/PjkuB+b735vNIr9e73T+gZKPX63H06FGcP38eBw4cQG5uLg4dOuTcr1KpXOpLktSrzNvAejKZTF7VV6pAbmfw5xGyns9qtSEyUt5zBqJAbKe6+98e60R4GbPNavP6GLVD7T6GiO/v2fSMuWe5r9lsNkREyH89dXGB976X+/NoQMkmODgYcXFxAIAZM2bg73//O7Zt24aCggIAgMViQVRUlLN+e3t7r94OERGNXIN6zuby5cvo7u5GbGwstFotampqnPu6urpQW1uLlJQU2YIkIiJl89izefbZZ5Geno7rr7/eOcvs2LFj2Lt3L1QqFXJzc1FaWgq9Xo/4+HiUlJQgJCQEOTk5voifiIgUwGOyMZvNWL16NSwWC8aOHYupU6eiqqoK8+bNAwDk5eXB4XCgsLAQdrsdSUlJqK6u5jM2RETk5DHZGI1Gt/tVKhUMBgMMBoNsQRER0fDCL08jGgGOtnmegUYkEhfiJCIi4ZhsiIhIOCYbIiISjsmGiIiEY7IhIiLhOBuNaJhS+gy0Y+eUHT+5Ys+GiIiEY7IhIiLhmGyIiEg43rMhol7Savd4Vd/hcECt/v77aY7ccp/cIZHCsWdDRETCMdkQEZFwTDZERCQckw0REQnHZENERMJxNhrRMKL0VQNo+GLPhoiIhGOyISIi4ZhsiIhIOCYbIiISjsmGiIiE85hstm7dijvuuAPR0dGYNGkSlixZglOnTrnUkSQJxcXFSEhIgE6nQ0ZGBpqamoQFTUREyuJx6vOxY8ewcuVKzJw5E5Ik4fnnn8eiRYtQX1+PcePGAQDKyspQXl6O8vJy6PV6bN68GVlZWWhsbERYWJjwRpA8gt96zd8hENEw5THZVFdXu2xv374dMTExqKurw8KFCyFJEoxGI/Lz85GZmQkAMBqN0Ov1qKqqwvLly8VETkREiuH1PZvOzk5cvnwZ4eHhAIDW1laYzWakpaU566jVaqSmpqK+vl62QImISLm8XkFg3bp1uPnmmzF79mwAgNlsBgBoNBqXehqNBm1tbf2ex2QyefW63tZXKn+2U2e1+fT1rD5+PX/xZTsdDv/N+XE4HM7fbQNos0Pl8FinLx+c+f64pGsvD+ocQ2GzyX89Oy4F5uebN59Her3e7X6vks369etRV1eH9957D0FBQS77VCqVy7YkSb3KvAmsJ5PJ5FV9pfJ3O4M/j/DZa1mtNkRG+u71/MXX7VR3+2e5mqu/PC1iAG1WO9Qe63gSETF6yOfwhs1mQ0SE/NdTFxd4n29yfx4N+L9BBoMB+/fvx4EDBzBx4kRnuVarBQBYLBaX+u3t7b16O0RENDINKNkUFRWhqqoKBw4cwOTJk132xcbGQqvVoqamxlnW1dWF2tpapKSkyBstESlKimOv84dGNo/DaAUFBXjzzTfxxhtvIDw83HmPJiQkBKGhoVCpVMjNzUVpaSn0ej3i4+NRUlKCkJAQ5OTkCG8AEREFPo/JpqKiAgCc05q/U1RUBIPBAADIy8uDw+FAYWEh7HY7kpKSUF1dzWdsiIgIwACSjd1u93gSlUoFg8HgTD5ERDRw3c27XLaD45b5KRJxuDYaEREJx2RDRETCMdkQEZFwXq8gQEQUiI6d+/6B1lt1vn3Ykzxjz4aIiIRjsiEiIuE4jDaMePo+mu4sft3DcHS0zT/roXlLxCoCPYfOKLCxZ0NERMIx2RARkXAcRhtB+LXPRMrQc0WB4bKaAHs2REQkHJMNEREJx2RDRETC8Z4NkQIF6nTnCeObAIiZ5kzKxp4NEREJx2RDRETCcRhNQTh1mZTihrOf9bvvTNTNPoxE+a7+YrWrKWVqNHs2REQkHJMNEREJx2E0IvIpd0NsAIfZhiv2bIiISLgBJZu//vWv+MUvfoEpU6YgPDwcu3fvdtkvSRKKi4uRkJAAnU6HjIwMNDU1CQmYiIiUZ0DJ5sKFC0hMTMQLL7wAtVrda39ZWRnKy8uxadMmHDlyBBqNBllZWejo6JA9YCIiUp4BJZv09HQ8/fTTyMzMxA9+4HqIJEkwGo3Iz89HZmYmEhMTYTQa0dnZiaqqKiFBExGRsgz5nk1rayvMZjPS0tKcZWq1Gqmpqaivrx/q6YmIaBgYcrIxm80AAI1G41Ku0WhgsViGenoiIhoGZJv6rFKpXLYlSepV1pPJZPLq/N7WVyp37dRZbT6MRDzrMGtPf4bSzv85H9gTRn+k+xIAMO7aK9v/+c/Qz+lwOIZ8DpvtwtAD6ffcgfXvtuOSuM9Gbz539Xq92/1DTjZarRYAYLFYEBUV5Sxvb2/v1dvxJrCeTCaTV/WVylM7gz+P8GE0YlmtNkRGDp/29Geo7VR3B+bqzt8ZNerKR8h//vNf5+9D1dckJG9FRIyWIZLebDYbIiIC69+tLk7MZ6Pcn7tD/m9TbGwstFotampqnGVdXV2ora1FSkrKUE9PRETDwID+K9LZ2Ynm5mYAwOXLl3H27FmcOHEC48aNQ3R0NHJzc1FaWgq9Xo/4+HiUlJQgJCQEOTk5QoMnouFH7hUGjp37vnd4q05Mj4c8G1CyOX78OO6++27ndnFxMYqLi7F06VIYjUbk5eXB4XCgsLAQdrsdSUlJqK6uRlhYmLDAiYhIOQaUbObOnQu73d7vfpVKBYPBAIPBIFdcREQ0jAT2VBciIhoWmGyIiEg4JhsiIhKOyYaIiIRjsiEiIuGYbIiISDgmGyIiEk62hTiJaHiYMN71W3Yt7VP8FAkNJ+zZEBGRcEw2REQkHIfRfCz4rdf63aez2gD9//FhNBSIjraJ/1oBDpWRr7FnQ0REwjHZEBGRcBxGCzDuhtmI/OHqITd/k/v7bpSuu3mX8/fguGV+jMQ99myIiEg4JhsiIhKOw2hEPtRzptnc60b3We4PgTZUNhQ3nP0M/+9sj+0e+47h+yG2vr4iOuh/P3XZ/r+d/3X+HhN6DTAhRq4wA4Ivh+DYsyEiIuGYbIiISDgmGyIiEk6x92zcTRHuzlruw0iIrujrvovD8QOkRw68PonVc9p0kF2xH3/96nkPpi/+nBrNng0REQkna7KpqKjAtGnToNVqcfvtt+OTTz6R8/RERKRQsvUjq6ursW7dOpSWlmLOnDmoqKjA4sWLUVdXh+joaLleZkA8PYU/lGE2PuFP3hI1XObNYprDaWqzXHpOa+4pJnTwH4tXT532xqWE6YM+dqA8DbOJJFvPpry8HPfddx8efPBB3HjjjdiyZQu0Wi0qKyvlegkiIlIold1ul4Z6ku7ublx33XV49dVXsWjRImd5QUEBTp06hcOHDw/1JYiISMFk6dlYrVZcunQJGo3GpVyj0cBiscjxEkREpGCyThBQqVQu25Ik9SojIqKRR5ZkExkZiaCgoF69mPb29l69HSIiGnlkSTbBwcGYPn06ampqXMpramqQkpIix0sQEZGCyTb1ee3atXj44YeRlJSElJQUVFZW4ty5c1i+nE/zExGNdLLds8nOzkZxcTG2bNmCuXPnoq6uDnv37kVMjPsluf/whz/g5z//OWJiYhAeHo7W1tZedex2O1avXo2YmBjExMRg9erVsNvtHmMK5IdMW1tbER4e3ufPiy++2O9xu3fv7vOYrq4uH0bvnYyMjF7xrlixwuNxb7/9NlJSUjBhwgSkpKTg4MGDPoh2cL755hsUFhZi1qxZ0Ol0mDp1Kp544gnYbDa3xynlenr7Xjp58iTuuusu6HQ6TJkyBZs2bYIkDXniqzBbt27FHXfcgejoaEyaNAlLlizBqVOn3B7T33v4ww8/9FHUg1NcXNwr5smTJ7s9Ro7rKeviQKtWrcKqVau8OubixYtIS0vDXXfdhfXr1/d73rNnz2Lfvn1QqVR47LHH8PDDD+PNN9/s97yB9JBpX6KionD69GmXskOHDqGgoAD33HOP22PHjBmD48ePu5T98Ic/lD1GOd1///14+umnndue4m1oaMCKFStgMBhw99134+DBg3jooYfw/vvvIzk5WXS4Xmtra0NbWxs2bNiAhIQEfP311ygoKMDKlSvx1ltvuT020K+nt++lb7/9FllZWUhNTcWRI0dgMpmwdu1ajBkzBr/61a/80ALPjh07hpUrV2LmzJmQJAnPP/88Fi1ahPr6eowbN87tsfv378dNN93k3PZUPxDo9XocOnTIuR0UFNRvXbmup99XoluzZg0A9Hqzfef06dP48MMP8d577znv//z2t7/FwoULYTKZoNfr+zyu50OmALBlyxZ89NFHqKysxDPPPCOgJd4JCgqCVqt1KTt48CB++tOfYuLEiW6PValUvY4NdGPGjPEqZqPRiLlz56KgoAAAcOONN+Lo0aMwGo149dVXRYU5aImJiXjjjTec23Fxcdi4cSOWLFmCb7/9FmPHju332EC/nt6+l/bt2weHwwGj0Qi1Wo3ExER88cUX2LZtGx599NGAnKFaXV3tsr19+3bExMSgrq4OCxcudHtsREREQF+/vlxzzTUDjlmu6xnwC3E2NDQgNDTUZaLBnDlzEBISgvr6+j6P6e7uxqeffoq0tDSX8rS0tH6P8beWlhZ8/PHHeOihhzzWdTgcuOmmm5CYmIglS5bgH//4h/gAh2j//v2Ii4vDnDlz8OSTT6Kjo8Nt/cbGxl7Xb968eQF7/frS0dGB0aNHY8yYMW7rBfL1HMx7qaGhAbfccgvUarWzbN68eWhra+tzmDwQdXZ24vLlywgPD/dYd9myZYiPj8eCBQvw9ttviw9OBi0tLZgyZQqmTZuGFStWoKWlpd+6cl3PgE82FosFkZGRLtlTpVJh/Pjx/T4wqsSHTHfu3InIyEjcddddbuvp9Xq8/PLL2LNnDyoqKjB69Gjceeed+Oc//+mjSL23ePFi7NixAwcPHkRhYSEOHDiAZcvcL3VuNpsVdf2uZrfb8Zvf/AYPPPAArrmm/wGEQL+eg3kvWSyWPut/t08J1q1bh5tvvhmzZ8/ut05oaCh+/etf47XXXsO+fftw2223Yfny5W6H9wNBcnIytm3bhn379uHFF1+E2WxGenp6v/cX5bqeQobRnnvuOZSUlLitc/DgQcydO3dA5+urmzaQB0b98ZDpYNr+3//+F3v27MF9992HUaNGuT129uzZLm+AlJQUzJ07F9u3b8fmzZuHFrwXvGlnz97a1KlTMXHiRMybNw+ffvoppk+f3u/xgfCQ8GCu54ULF7B06VJcd9112Lhxo9tjA+V6euLtteirfl/lgWj9+vWoq6vDe++95/ZeRmRkpMs9ixkzZsBms6GsrAxLlizxRaiDMn/+fJft5ORkTJ8+HXv27MGjjz7a5zFyXE8hySY3Nxf33nuv2zpRUVEDOteECRPQ3t7u8o9bkiRYrdZ+Hxj150Omg2n7u+++i3PnzuGBBx7w+vWCgoIwffp0NDc3e33sUAzlGs+YMQNBQUFobm7uN9lotdqAeEjY23Z2dnZi8eLFAIA333zT6xv9/rqe/RnMe2nChAl91gcQ8A95GwwGVFdX4+DBgx7vnfYlKSkJu3fvlj8wgUJDQ5GQkNDvvzm5rqeQZBMZGYnIyH6+ntBLs2fPRmdnJxoaGpz3bRoaGnDhwoV+Hxjt+ZBpz4VBa2pqPM70GqrBtH3nzp34yU9+gvj4eK9fT5IknDx50mU2jC8M5RqfPHkSly5dcnuDctasWaipqcFjjz3mLPPHQ8LetLOjowOLFy+GJEmoqqpCaGio16/nr+vZn8G8l2bPno1nn30WXV1dzmRbU1OD6667DrGxsb4Ie1CKiopQXV2NQ4cOeZwK3J/PPvtMcZMFurq6YDKZ+h1pkut6+v2ejdlsxokTJ/Dll18CuDL77MSJE/jmm28AXJmF9LOf/QyPP/44Ghsb0dDQgMcffxwLFixwmYk2a9YsvPLKK87ttWvXYs+ePdi5cydOnz6NoqKigHzI9KuvvsJHH33knOlztXvuuQcbNmxwbr/wwgv46KOP0NLSghMnTuDRRx/FyZMnB/Tcij+cOXMGmzZtwvHjx9Ha2oo//elPWLlyJaZNm4Y5c+Y4613dzkceeQR/+ctfsHXrVnzxxRfYunUrjh49itzcXH80w6OOjg5kZ2fDbrdj27ZtuHjxIsxmM8xmM7q7u531lHg9Pb2XNmzY4JJ4cnJyoFarsWbNGpw6dQoHDhzA7373O6xZsyZgh9EKCgqc983Cw8Od166zs9NZ5+p27tmzB/v27cPp06dhMpnw0ksvoaKiAqtXr/ZHEwbsySefxLFjx9DS0oK//e1vePDBB3Hx4kUsXboUgLjr6fepz5WVldi0aZNz+7shi/Lyctx///0AgB07dqCoqAjZ2dkAgIULF/YazzaZTLBarc7t7Oxs2Gw2bNmyBWazGVOmTBnQQ6a+tmvXLowdO7bf/yWeOXMG119/vXP7/PnzyMvLg8ViwdixYzFt2jQcPnwYSUlJvgrZK6NGjcLHH3+M3//+97hw4QKuv/56pKenY926dS7j4Ve387tVKJ577jkUFxfjhhtuQGVlZUA+YwMAn376KRobGwGg17XoeU9HidfT03vp3LlzOHPmjLP+tddei7feegsFBQW44447EB4ejrVr1/Z7PyAQVFRUAAAyMzNdyouKimAwGAD0bicAlJSU4KuvvkJQUBAmTZqEl19+OaDv1wDA119/jVWrVsFqtWL8+PFITk7GBx98IPx6yvJ9NkRERO74fRiNiIiGPyYbIiISjsmGiIiEY7IhIiLhmGyIiEg4JhsiIhKOyYaIiIRjsiEiIuGYbIiISLj/D0AexfURNw2vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x1, histtype=\"stepfilled\", \n",
    "         alpha=0.5, bins=40)\n",
    "plt.hist(x2, histtype=\"stepfilled\", \n",
    "         alpha=0.5, bins=40)\n",
    "plt.hist(x3, histtype=\"stepfilled\", \n",
    "         alpha=0.5, bins=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-checkout",
   "metadata": {},
   "source": [
    "## Two Dimensional Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "sustained-citation",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, \n",
    "                                     cov, \n",
    "                                     10000).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "heard-williams",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[  0.,   0.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,\n",
       "           0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   1.,   1.,   1.,   5.,   0.,   4.,   0.,   0.,\n",
       "           1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  1.,   0.,   0.,   2.,   0.,   2.,   5.,   3.,   5.,   3.,   6.,\n",
       "           1.,   2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   2.,   6.,   5.,  11.,   9.,   9.,  11.,\n",
       "           3.,   2.,   1.,   2.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   8.,   6.,  15.,  12.,  23.,  18.,  10.,\n",
       "          16.,  15.,   4.,   7.,   1.,   1.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   1.,   0.,   1.,   4.,   8.,  13.,  14.,  17.,  23.,  22.,\n",
       "          28.,  32.,   8.,  11.,   3.,   2.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   1.,   3.,   5.,  17.,  22.,  36.,  45.,  45.,\n",
       "          44.,  40.,  29.,  20.,   9.,   4.,   5.,   1.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   1.,   2.,   0.,   6.,  11.,  16.,  27.,  50.,  62.,\n",
       "          74.,  59.,  67.,  37.,  25.,  21.,   9.,   1.,   1.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   3.,   4.,   5.,  21.,  33.,  52.,  74.,\n",
       "          86., 100.,  91.,  74.,  50.,  30.,  23.,  17.,   1.,   1.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   1.,   1.,   5.,  13.,  18.,  50.,  67.,\n",
       "         111., 101., 107., 120.,  89.,  45.,  38.,  23.,  14.,   5.,   0.,\n",
       "           2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   1.,   0.,   1.,   2.,   6.,  15.,  33.,  57.,\n",
       "          95., 114., 143., 153., 113., 108.,  59.,  34.,  23.,  14.,   0.,\n",
       "           1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   2.,   3.,   1.,  13.,  25.,  44.,\n",
       "          82., 105., 144., 150., 144., 131., 111.,  64.,  37.,  13.,   9.,\n",
       "           4.,   1.,   1.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   1.,   0.,   6.,   6.,  22.,  24.,\n",
       "          51.,  94., 127., 152., 177., 171., 129.,  88.,  44.,  28.,   9.,\n",
       "           3.,   1.,   1.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.,   4.,  18.,\n",
       "          29.,  62.,  93., 147., 158., 152., 143., 110.,  83.,  37.,  22.,\n",
       "           7.,   2.,   1.,   1.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   2.,   2.,   2.,   7.,\n",
       "          16.,  36.,  52.,  93.,  99., 129., 131., 115.,  87.,  68.,  26.,\n",
       "          17.,   4.,   0.,   1.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   2.,   5.,\n",
       "           5.,  11.,  35.,  69.,  62.,  92., 118., 111.,  77.,  51.,  35.,\n",
       "          22.,   7.,   6.,   1.,   1.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,\n",
       "           3.,   9.,  20.,  27.,  52.,  47.,  78.,  81.,  62.,  45.,  36.,\n",
       "          23.,  20.,   4.,   4.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,\n",
       "           0.,   2.,   7.,   8.,  22.,  29.,  45.,  62.,  63.,  49.,  37.,\n",
       "          20.,  17.,  14.,   1.,   2.,   1.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   1.,   3.,   3.,   8.,  18.,  29.,  37.,  28.,  41.,  16.,\n",
       "          22.,  14.,  10.,   4.,   4.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   1.,   7.,   7.,  12.,  11.,  19.,  17.,  23.,\n",
       "          25.,  14.,   8.,   8.,   3.,   1.,   0.,   1.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   1.,   0.,   2.,   2.,   6.,   8.,  10.,  10.,\n",
       "          10.,   6.,   8.,   5.,   5.,   1.,   1.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   1.,   4.,   4.,   3.,   7.,\n",
       "          10.,   6.,   5.,   4.,   1.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   1.,   3.,\n",
       "           3.,   2.,   3.,   1.,   1.,   1.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   1.,\n",
       "           2.,   2.,   1.,   3.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,\n",
       "           1.,   1.,   1.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "           0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.]]),\n",
       " array([-4.2022814 , -3.925143  , -3.64800459, -3.37086619, -3.09372778,\n",
       "        -2.81658938, -2.53945098, -2.26231257, -1.98517417, -1.70803576,\n",
       "        -1.43089736, -1.15375895, -0.87662055, -0.59948214, -0.32234374,\n",
       "        -0.04520533,  0.23193307,  0.50907148,  0.78620988,  1.06334829,\n",
       "         1.34048669,  1.6176251 ,  1.8947635 ,  2.1719019 ,  2.44904031,\n",
       "         2.72617871,  3.00331712,  3.28045552,  3.55759393,  3.83473233,\n",
       "         4.11187074]),\n",
       " array([-5.70579738, -5.33195692, -4.95811646, -4.584276  , -4.21043554,\n",
       "        -3.83659508, -3.46275462, -3.08891416, -2.7150737 , -2.34123323,\n",
       "        -1.96739277, -1.59355231, -1.21971185, -0.84587139, -0.47203093,\n",
       "        -0.09819047,  0.27564999,  0.64949045,  1.02333091,  1.39717137,\n",
       "         1.77101183,  2.14485229,  2.51869275,  2.89253321,  3.26637367,\n",
       "         3.64021413,  4.01405459,  4.38789505,  4.76173551,  5.13557597,\n",
       "         5.50941643]),\n",
       " <matplotlib.collections.QuadMesh at 0x1b7db276370>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(x, y, bins=30, cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "approved-ethnic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(x, y, bins=30, cmap='Reds')\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Number of rectangles')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-anaheim",
   "metadata": {},
   "source": [
    "## Application with real data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "exciting-guinea",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies=pd.read_csv(\"imdbratings.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "destroyed-plate",
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "appropriate-korean",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 25., 168., 301., 259., 111.,  66.,  27.,  14.,   5.,   3.]),\n",
       " array([ 64. ,  81.8,  99.6, 117.4, 135.2, 153. , 170.8, 188.6, 206.4,\n",
       "        224.2, 242. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies.duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "accessible-suspension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 25., 168., 301., 259., 111.,  66.,  27.,  14.,   5.,   3.]),\n",
       " array([ 64. ,  81.8,  99.6, 117.4, 135.2, 153. , 170.8, 188.6, 206.4,\n",
       "        224.2, 242. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    movies.duration, edgecolor=\"black\", color=\"m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "silent-identity",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=np.arange(70,200,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "referenced-platinum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1b7dc46c850>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc46c820>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc45caf0>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b2430>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b2940>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b2e50>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b93a0>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b98b0>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b9dc0>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b95e0>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4b2670>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4be340>,\n",
       "  <matplotlib.axis.XTick at 0x1b7dc4be7f0>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies.duration, edgecolor=\"black\", \n",
    "         color=\"m\", bins=bins)\n",
    "plt.xticks(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "saving-twenty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequencies')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies.duration, edgecolor=\"black\", \n",
    "         color=\"m\", bins=bins)\n",
    "plt.xticks(bins)\n",
    "plt.title(\"Histograms of movie durations in imdb\")\n",
    "plt.xlabel(\"Durations\")\n",
    "plt.ylabel(\"Frequencies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-kidney",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}