{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "#Please adjust to your file\n",
    "df=pd.read_excel(\"/Users/davidmika/Desktop/First.xlsx\",\"Sheet1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YLgDmAY8C2L6Z6pt+MwxcIWmFpDmlbZztjWV4EzCuDE8A1jUsu760RUREGwy4BQE8w/Zy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b64f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group sum of sales at gender level\n",
    "var = df.groupby('Gender').Sales.sum()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#Here you can change labels and title\n",
    "ax.set_xlabel('Gender')\n",
    "ax.set_ylabel('Sum of Sales')\n",
    "ax.set_title(\"Gender wise Sum of Sales\")\n",
    "var.plot(kind='bar')\n",
    "#Show bar chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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