visualizations/matplotlib/matplotlib-bar-chart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b15fa0b-d3a3-4f65-9f2f-2960f56a96e5",
   "metadata": {},
   "source": [
    "## Using only Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82ae333d-c3de-49c3-a685-e2bf38efd14f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import packages\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create data\n",
    "x = [1,2,3,4,5]\n",
    "y = [3.5,4.8,5.0,8.1,1.9]\n",
    "\n",
    "# plot\n",
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9a1e94-8844-4b22-83f4-1c0286d32983",
   "metadata": {},
   "source": [
    "## Using Matplotlib and Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a59c8b22-bafa-4a24-a2e3-8a1f1ea07a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# create data\n",
    "x = 1 + np.arange(8)\n",
    "y = np.array([4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0])\n",
    "\n",
    "# plot\n",
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38385a02-1602-4a7c-b7a1-aa65d112dd99",
   "metadata": {},
   "source": [
    "## Using Matplotlib and Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "735f6f40-b3c2-4873-9494-b9a8ef4deeee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# create data \n",
    "df = pd.DataFrame({'count': [1,2,3,4,5,6,7,8], 'value': [3.5,4.8,5.0,8.1,1.9,3.8,2.9,7.1]})\n",
    "\n",
    "#plot\n",
    "plt.bar(df['count'], df['value'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}