Code-Example-006-02-Matplotlib-Visualization-Hist-boxplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 2006 paper (Low Birth Weight, a Risk Factor for Cardiovascular Diseases in Later Life, Is Already Associated with Elevated Fetal Glycosylated Hemoglobin at Birth) showed a histogram of the birth weights of newborns in their sample.\n",
    "\n",
    "Scientists collected the weight of 10000 new born babies. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 example weights:  [4051. 3005. 2619. 3715. 3568. 3744. 4175. 3091. 3763. 3478.]\n"
     ]
    },
    {
     "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": [
    "\n",
    "# Let us here assume that the data has a mean of 3500 is with a standard deviation of \n",
    "mu, sigma = 3500, 500 # mean and standard deviation\n",
    "weights_of_new_born_babies_in_gr = np.round(np.random.normal(mu, sigma, 10000))\n",
    "\n",
    "# Print 10 example of the weights. \n",
    "print(\"10 example weights: \", weights_of_new_born_babies_in_gr[:10])\n",
    "\n",
    "\n",
    "# the histogram of the data\n",
    "plt.hist(weights_of_new_born_babies_in_gr, 20, density=False, facecolor='g', alpha=0.75)\n",
    "\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the shape of this distribution has a symetic shape. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)\n",
    "\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.xlim(40, 160)\n",
    "plt.ylim(0, 0.03)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplot shows where the max, min, median, upper and lower quartiles are. \n",
    "We can also see on Boxplots the outliers. Most libraries provide automated outlier detection on boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7ff8339f17f0>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff8339f1b50>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7ff8339f1eb0>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff8339d6250>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7ff8339f1490>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7ff8339d65b0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7ff8339d68b0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 4,
     "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": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Boxplot of New born babies')\n",
    "ax1.boxplot(weights_of_new_born_babies_in_gr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7ff833b2d070>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833b2d3d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7ff833b2d730>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833b2da90>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7ff833b22cd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7ff833b2ddf0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7ff833b37130>],\n",
       " 'means': []}"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax3 = plt.subplots()\n",
    "ax3.set_title('Changed Outlier Symbols')\n",
    "ax3.boxplot(weights_of_new_born_babies_in_gr, flierprops=green_diamond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7ff833c31400>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833c31760>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7ff833c31ac0>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833c31e20>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7ff833c310a0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7ff833c3a1c0>],\n",
       " 'fliers': [],\n",
       " 'means': []}"
      ]
     },
     "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": [
    "fig4, ax4 = plt.subplots()\n",
    "ax4.set_title('Hide Outlier Points')\n",
    "ax4.boxplot(weights_of_new_born_babies_in_gr, showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7ff833da81c0>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833da8520>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7ff833da8880>,\n",
       "  <matplotlib.lines.Line2D at 0x7ff833da8be0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7ff833d9be20>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7ff833da8f40>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7ff833db3280>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Lu77ock7p1XnIjXO2lgeARzW1w8CvgicMmNjOa7GmRrLoVpO5N4PHGdmHBepcabGEXgIcMjQ+n8wuMiYmbFcpMZVH8uVFnoug7cKP2Hw6vG7wEeBrwLXAJ9eUPQbGHxiez1Dn84CxwE7+7b30//F6oQG85kM3pZcA1zdl5OBhwKfB77WbzdNq85Fapy1sXwicFWvZyfwF719lsZyXI0zNZZDxziRe8NyZsZxkRpnahyBRzIIv68A1wJvmLWxXKTGVR9L/+m/JBXhvxSVpCIMdEkqwkCXpCIMdEkqwkCXpCIMdEkqwkCXpCL+H3i6hXW1CslDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal Boxplot\n",
    "red_square = dict(markerfacecolor='r', marker='s')\n",
    "fig5, ax5 = plt.subplots()\n",
    "ax5.set_title('Horizontal Boxes')\n",
    "ax5.boxplot(weights_of_new_born_babies_in_gr, vert=False, flierprops=red_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}