examples/Discrete_histogram_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This example shows how to plot discrete histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plot_utils as pu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete histogram on numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 11, 11, 11, 11, 11, 11, 11, 11, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "discrete_values = [-10, -1, 11, 3, 5, 2, 4, 0]  # define 8 discrete values\n",
    "counts = [10, 20, 8, 14, 22, 15, 14, 17]   # how many times each value occurs\n",
    "\n",
    "x = []\n",
    "for j, val in enumerate(discrete_values):\n",
    "    x.extend([val] * counts[j])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot histogram; note that the numerical values are automatically sorted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pu.discrete_histogram(x);  # generate discrete histogram with default arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete histogram on categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cat', 'cat', 'cat', 'cat', 'cat', 'cat', 'cat', 'cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'dog', 'fish', 'fish', 'fish', 'fish', 'fish', 'fish', 'fish', 'fish', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'sheep', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'bird', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'rabbit', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'pig', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow', 'cow']\n"
     ]
    }
   ],
   "source": [
    "discrete_values = ['cat', 'dog', 'fish', 'sheep', 'bird', 'rabbit', 'pig', 'cow']  # define 8 discrete values\n",
    "counts = [10, 20, 8, 14, 22, 15, 14, 17]   # how many times each value occurs\n",
    "\n",
    "x = []\n",
    "for j, val in enumerate(discrete_values):\n",
    "    x.extend([val] * counts[j])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot histogram, with some modified argument values; note that the categories are also automatically sorted (alphabetically):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "ax  = plt.axes()\n",
    "fig, ax, _ = pu.discrete_histogram(x, fig, ax, color='orange', alpha=0.6, rot=45);\n",
    "ax.grid(lw=0.5, ls=':')\n",
    "ax.set_ylabel('Number of each animals');"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}