Bar_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0dba1c09-70d0-4bcd-b4c3-6562bf267e5d",
   "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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = [23,45,56,78,213] # Giving data\n",
    "labels = ['Physics','Math', 'Chemistry', 'Biology','Sociology'] # as we know bar plot is Qualitative so that's why this data will be string \n",
    "\n",
    "plt.xticks(range(len(data)) , labels) # making plots and setting string on x axis and givig them labels\n",
    "plt.xlabel('Subject') # Adding label to the graph -> x axis\n",
    "plt.ylabel('People') # Adding label to the graph -> y axis\n",
    "plt.title('Subjects Taken By People') # Adding title to the graph\n",
    "\n",
    "plt.bar(range(len(data)), data, color='red', alpha = 0.7) # Creating bar plots, adding color and then fading a little bit\n",
    "plt.grid(color='#95a5a6', linestyle='--',linewidth=2, axis='y',alpha=0.7)  # Adding grid to the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c25c02b7-d170-4d74-b758-7e591f0d4299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sh69U1Z9POdaKXRAFXlUngJOP9R8E7j9PHutfdUnuBb4OXJfkSJJt0840JTcD72Y40nqs+3n7tENNwTpgX5JvMxzo7KmqC+IrdPJReklq1gUxApeki5EFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhr1f/Twj7u7w1nXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data1 = [23,85,72,43,52]\n",
    "data2 = [42,35,21,16,9]\n",
    "\n",
    "width = 0.3 # Giving a width difference to the two different plots\n",
    "\n",
    "plt.bar(np.arange(len(data1)), data1, width = width) \n",
    "plt.bar(np.arange(len(data2))+width, data2, width = width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2f314e9-5d93-4514-bb72-6061dd1fc412",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}