matplotlib/histograms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "plt.style.use(\"seaborn\")\r\n",
    "\r\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([164.5707562 , 144.40023841, 171.25676794, 174.10847075,\n       130.73447217, 140.4673074 , 161.91760605, 155.25636111,\n       159.74798264, 147.20434109])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_stats = np.random.default_rng(42).normal(160, 15, 1000)\r\n",
    "height_stats[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide Data into Bins using Integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(array([  1.,  13.,  44., 131., 218., 296., 179.,  87.,  25.,   6.]),\n array([105.27380762, 115.51470738, 125.75560714, 135.99650689,\n        146.23740665, 156.47830641, 166.71920616, 176.96010592,\n        187.20100568, 197.44190543, 207.68280519]),\n <BarContainer object of 10 artists>)"
     },
     "execution_count": 14,
     "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": [
    "num_bin = 10\r\n",
    "plt.hist(height_stats, bins=num_bin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide Data into Bins using Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "interval_bin = [120, 130, 140, 150, 160, 170, 180, 190, 200]\r\n",
    "plt.hist(height_stats, bins=interval_bin,\r\n",
    "         edgecolor=\"black\", lw=1)\r\n",
    "\r\n",
    "plt.title(\"Height Stats\")\r\n",
    "plt.xlabel(\"Heights\")\r\n",
    "plt.ylabel(\"Counts\")\r\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a Line of Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "159.56662673506082"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = np.mean(height_stats)\r\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "interval_bin = [120, 130, 140, 150, 160, 170, 180, 190, 200]\r\n",
    "plt.hist(height_stats, bins=interval_bin,\r\n",
    "         edgecolor=\"black\", lw=1)\r\n",
    "\r\n",
    "\r\n",
    "plt.axvline(mean, c=\"orange\", lw=5, label=\"Height Mean\")\r\n",
    "plt.legend()\r\n",
    "\r\n",
    "plt.title(\"Height Stats\")\r\n",
    "plt.xlabel(\"Heights\")\r\n",
    "plt.ylabel(\"Counts\")\r\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Density Curves\r\n",
    "\r\n",
    "A more efficient way to plot histograms + Density Curve is using `seaborn.displot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([1.18176893e-174])"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as ss\r\n",
    "density = ss.kde.gaussian_kde(height_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "interval_bin = [120, 130, 140, 150, 160, 170, 180, 190, 200]\r\n",
    "plt.hist(height_stats, bins=interval_bin,\r\n",
    "         edgecolor=\"black\", lw=1, density=True)\r\n",
    "\r\n",
    "index = np.arange(120, 200)\r\n",
    "plt.plot(index, \r\n",
    "         density.evaluate(index), \r\n",
    "         color=\"pink\",\r\n",
    "         lw=3,\r\n",
    "         ls=\"--\",\r\n",
    "         label=\"Probability Density\")\r\n",
    "\r\n",
    "plt.axvline(np.mean(height_stats), c=\"orange\", lw=5, label=\"Height Mean\")\r\n",
    "plt.legend()\r\n",
    "\r\n",
    "plt.title(\"Height Stats\")\r\n",
    "plt.xlabel(\"Heights\")\r\n",
    "plt.ylabel(\"Probability Density\")\r\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference\r\n",
    "\r\n",
    "- https://matplotlib.org/stable/gallery/statistics/histogram_features.html\r\n",
    "- https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist\r\n",
    "- https://www.youtube.com/watch?v=XDv6T4a0RNc"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit",
   "name": "python388jvsc74a57bd0910af126f78e4f70975a50f5d0344a29878143e0b01cc32c99ca6cf65dbefcc1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "metadata": {
   "interpreter": {
    "hash": "910af126f78e4f70975a50f5d0344a29878143e0b01cc32c99ca6cf65dbefcc1"
   }
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}