{
 "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": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSVLHUJAkdQwFSVLHUJAkdQwFSVLn/wO4YzQlJ+3odwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171ede90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#Here you can change bins, labels and title\n",
    "ax.hist(df['Age'],bins = 7)\n",
    "plt.title('Age distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('#Employee')\n",
    "#Show histogram\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}