Data Visualizations 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77423f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83b471ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d43013ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "student_data={\"s_id\":[1,2,3,4,5],\"s_name\":[\"Raji\",\"Ashwini\",\"Madhu\",\"Shekar\",\"Anil\"],\"s_marks\":[95,90,89,98,75]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "130ac61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>s_name</th>\n",
       "      <th>s_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Raji</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ashwini</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madhu</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Shekar</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Anil</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s_id   s_name  s_marks\n",
       "0     1     Raji       95\n",
       "1     2  Ashwini       90\n",
       "2     3    Madhu       89\n",
       "3     4   Shekar       98\n",
       "4     5     Anil       75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(student_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1234a235",
   "metadata": {},
   "source": [
    "##### A bar Graph for single Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4aaa11d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,8))\n",
    "x=df[\"s_id\"]\n",
    "y=df[\"s_marks\"]\n",
    "plt.bar(x,y,label=\"student Data\",color=\"black\")\n",
    "plt.xlabel(\"Student_id\")\n",
    "plt.ylabel(\"student_marks\")\n",
    "plt.title(\"Osmania University\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2589744",
   "metadata": {},
   "source": [
    "##### A bar Graph for 2 two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c29cab53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,8))\n",
    "x=df[\"s_id\"]\n",
    "x1=[1,3,5,7,9]\n",
    "y1=[1000,800,5000,4300,2800]\n",
    "x2=[2,4,6,8,10]\n",
    "y2=[500,1500,3000,4500,2000]\n",
    "plt.bar(x1,y1,label=\"Sales Department\",color=\"black\")\n",
    "plt.bar(x2,y2,label=\"Production Department\",color=\"orange\")\n",
    "plt.xlabel(\"EmployeeData\")\n",
    "plt.ylabel(\"Salaries\")\n",
    "plt.title(\"Micosoft Company\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30a87f0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57d1d0e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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