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ibm_hr_data_w_python_1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Exploring IBM HR data using Python</h1>\n",
"<h2>Intro</h2>\n",
"<p>This tutorial series explores the IBM HR data set. This data is typically used to demonstrate the ability of various machine learning algorithms applied to HR data.</p>\n",
"<p>In this series, I'll use it to demonstrate the awesome power Python can bring to HR data</p>\n",
"<p><b>Sections</b></p>\n",
"<ul>\n",
" <li>Statistics</li>\n",
" <li><font color='#1569C7'>Matplotlib</font></li>\n",
" <li>Pandas</li>\n",
" <li>Seaborn</li>\n",
" <li>Plotly</li>\n",
" <li>Findings</li>\n",
" </ul>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"__author__ = \"adam\"\n",
"__version__ = \"1.0.0\"\n",
"__maintainer__ = \"adam\"\n",
"__email__ = \"adam@datapluspeople.com\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# imports \n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# if continuing on from the previous section, read the data from saved file\n",
"\n",
"empl_data = pd.read_excel(\"WA_Fn-UseC_-HR-Employee-Attrition.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# read the data directly from IBM Watson Analytics\n",
"# using pandas read excel file into dataframe\n",
"# url = \"https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-HR-Employee-Attrition.xlsx\"\n",
"# empl_data = pd.read_excel(url)\n",
"\n",
"# save data for later\n",
"# empl_data.to_excel(\"WA_Fn-UseC_-HR-Employee-Attrition.xlsx\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>matplotlib</h2>\n",
"\n",
"Matplotlib is the backbone of plotting in Python. If you're familiar with R, think Base Graphics. They're similar in that they are powerful alone, but become even more powerful when higher-level packages build on top of these respective base packages. \n",
"\n",
"As Matplotlib is the base of many higher-level Python plotting packages, we'll start our EDA with it.\n",
"\n",
"As you'll see next, it's very easy to get started. And, as we've loaded our data into a DataFrame, it's easy to pass the information we want to Matplotlib using techniques we learned briefly in the first seciton of this series.\n",
"\n",
"We'll skip all of the statistics and get right into visualizations, where we'll explore the 'Age' dimension of our data.\n",
"\n",
"Now, show me the data!\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"36.923809523809524"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first, determine the average age\n",
"avg_age = empl_data['Age'].mean()\n",
"avg_age"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BarContainer object of 1 artists>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot average age\n",
"plt.bar('Age',avg_age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>With just a few short lines, we've been able to create a plot of the average age of employees</p>\n",
"<p>We can improve the readability of this plot by adding items such as the actual value and a title</p>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# average employee age\n",
"\n",
"#create the figure \n",
"plt.figure(figsize=(4,4))\n",
"\n",
"# calculate average age\n",
"avg_age = empl_data['Age'].mean()\n",
"\n",
"# round avg_age for display\n",
"display_age = round(avg_age, 2)\n",
"\n",
"# create the plot\n",
"plt.bar('Age', avg_age)\n",
"\n",
"# add the value label\n",
"plt.text('Age', avg_age, display_age, va='bottom', ha='center')\n",
"\n",
"# add a title\n",
"plt.title('Average Employee Age')\n",
"\n",
"# display the plot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>With a few additional lines, we added a lot more clarity to the plot in order to share and communicate the information properly.</p>\n",
"<p>Next, let's look at the average age within each department.</p>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# average employee age by department\n",
"\n",
"plt.figure(figsize=(9,4))\n",
"bars = plt.bar(empl_data['Department'].unique(),empl_data.groupby('Department')['Age'].mean())\n",
"\n",
"plt.title('Average Age by Department')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The plot looks good, and gives us the insight that the average age doesn't change much across the 3 departments.</p>\n",
"<p>Adding labels to this type of plot can be accomplished a few ways, I'll show one below.</p>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# average employee age by department\n",
"\n",
"plt.figure(figsize=(9,4))\n",
"bars = plt.bar(empl_data['Department'].unique(),empl_data.groupby('Department')['Age'].mean())\n",
"\n",
"# create a function to calculate and add the labels\n",
"def label(bars):\n",
" for bar in bars:\n",
" height = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2., 1.0*height,\n",
" '%0.2f' % height, va='bottom', ha='center')\n",
"\n",
"label(bars)\n",
"\n",
"plt.title('Average Age by Department')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>This method works for what we've done so far. But the way that we've implemented this so far only works so long as we are completing everything within the single cell.</p>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 648x288 with 0 Axes>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 648x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,4))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bars = plt.bar(empl_data['Department'].unique(),empl_data.groupby('Department')['Age'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>When the figure is defined in a separate step, the bars are not placed on a figure size of 9x4. To correct for this, we'll introduce subplots.</p>"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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78r4k/1RVf53ZKYF3+J/a7VFVX8jsFOXe+Xt0PpTkxUnS3Z/M7D07NyY5k+TpJO+c9Lz2CwAYyRVYAYChxAgAMJQYAQCGEiMAwFBiBAAYSowAAEOJEQBgqP8DQDFd4yRIZsgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1,1,figsize=(9,4))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BarContainer object of 3 artists>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax.bar(empl_data['Department'].unique(),empl_data.groupby('Department')['Age'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Average Age by Department')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax.set_title('Average Age by Department')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>Using a subplot, we were able to continually add and build the plot - we didn't have to complete the entire plot in one step.</p>\n",
"<p>Shortly, we'll use subplots to split out three distinct plots for each department, rather than putting all in one plot.</p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The average does not appear to change across the departments, what about the median age?.</p>"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# median employee age by department\n",
"\n",
"plt.figure(figsize=(9,4))\n",
"bars = plt.bar(empl_data['Department'].unique(),empl_data.groupby('Department')['Age'].median())\n",
"\n",
"# create a function to calculate and add the labels\n",
"def label(bars):\n",
" for bar in bars:\n",
" height = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2., 1.0*height,\n",
" '%0.2f' % height, va='bottom', ha='center')\n",
"\n",
"label(bars)\n",
"\n",
"plt.title('Median Age by Department')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>Not significant, but we do observe a change in the median age greater than the average age.</p>\n",
"<p>Next, let's take a look at the distribution of ages.</p>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# histogram showing distribution of employee ages\n",
"\n",
"plt.figure(figsize=(9,4))\n",
"plt.hist(empl_data['Age'])\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Age Distribution of Employees')\n",
"plt.grid(False)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAEWCAYAAAB47K3ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHU1JREFUeJzt3XucXWV97/HPl4siYBsxwzXAQIsIUgWMFIutFKqCIGBFCwc1VZTaYmuvGtAW20oL57SKHusF8QJ4QUQpVGiVImh7WsGAKCJQEFMSEyAIyEWFAr/zx1ojm8kks5PMnr1X+Lxfr/2avdZ69lq/mTWZ/c3zPHutVBWSJEldssGwC5AkSVpTBhhJktQ5BhhJktQ5BhhJktQ5BhhJktQ5BhhJktQ5BhhpPZLkxCRnzOD+7k+yc/v8E0neNYP7/lCSP5+p/a3BcX83ye3t9/b02T5+Tx37J1k6rONLXWeAkWZAksuT3J3kyQM+xk+T3Jfk3iRXJVnYe8yq+puqekOf+5q2XVVtXlW3zEDtv53k3yft+01V9dfruu81rGNj4N3Ai9vv7YeTto8nqTbc9D5+azbrlDQ9A4y0jpKMA78KFHDYgA/35qp6KrAN8CfAUcDFSTKTB0my0Uzub4RsBWwCXDdNuzltwJl4fHYWapO0Bgww0rp7LfB14BPAgt4NSZ6e5J/aHpNvJHlXb09EkmcmuSTJXUluTPKqfg5YVQ9U1eU0gen5wCHt/t6Z5JPt802SfDLJD5Pc0x5/qyQn0wSu97e9C+9v21eS45PcBNzUs+4Xew49t633viRfTbJj226i5+JnwWeilyfJbsCHgOe3x7un3f64Iakkb0xyc/uzuDDJtj3bKsmbktzU9nT9w6pCW5InJzktybL2cVq77hnAjW2ze5J8pZ+f9aR9fyLJB5L8c/u9/L8kW7fHuDvJDUn26mm/OMkJSb7bbv94kk1Wse/d2p/ZPUmuS3JYu/557ZBX78/2FUmuaZ9v0PbEfa891+cm2aKn7b5J/qPd77eS7N+z7beT3NKez+8nOWZNfybSsBhgpHX3WuBT7eMlSbbq2fYPwAPA1jTh5mcBJ8lmwCXAp4EtgaOBDyR5Vr8HrqpbgUU0gWSyBcDPA9sDTwfeBPykqt4O/BtNb87mVfXmntccAfwysPsqDnkM8NfAXOCa9nuersbr22P/Z3u8OZPbJDkA+FvgVTS9S/8NnDOp2aHA84DntO1esopDvh3YF9izbbsP8I6q+i9g4mc7p6oOmK72VXgV8A6an8GDwH8CV7fL59EMUfU6pq31F4BntK99nHZo65+AL9P8Lvw+8Kkku1bVN4AfAi/qecmrgbPb539Ac95eCGwL3E3ze0eS7YCLgHcBWwB/Cnw+yVj7+/c+4OC2V+9XaM6p1AkGGGkdJHkBsCNwblVdBXwP+F/ttg2BVwAnVdWPq+q7wJk9Lz8UWFxVH6+qh6vqauDzwJFrWMYymjenyf6HJrj8YlU9UlVXVdW90+zrb6vqrqr6ySq2X1RVX6uqB2mCwvOTbL+G9U7lGOBjVXV1u+8T2n2P97Q5paruaUPbZTQBZVX7+ququqOqVgB/CbxmDeu5s+2xmHjs1rPt/PZn+VPgfOCnVXVWVT0CfBbYa9K+3l9VS6rqLuBkmqA62b7A5u33+FBVfQX4Yk/bM2lCC23vyktogi/A7wBvr6ql7c/uncCRbY/Nq4GLq+riqnq0qi6hCbwvbV/7KLBHkqdU1fKqmm5oTRoZBhhp3SwAvlxVd7bLn+axXpYxYCNgSU/73uc7Ar/c+0ZJ8+a79RrWsB1w1xTrzwa+BJzTDqX87/Z/+quzpN/tVXV/e9xtV928b9vS9Lr07vuHNN/bhNt6nv+Y5g1/2n21z9e0xrlVNafncX3Pttt7nv9kiuXJdfX+TFdVy7bAkqp6dFLbie//k8DLkmxO0wP0b1W1vN22I3B+z+/Q9cAjNPN9dgReOel37AXANlX1APBbNL1jy5NclOSZq/6RSKPFACOtpSRPoXkzeWGS25LcBvwR8JwkzwFWAA8D83pe1ttbsQT46qQ3ys2r6nfXoIbtgefSDAk9TlX9T1X9ZVXtTjM8cCjNcBc0E46nMt3t6X9Wf/tmugVND9AD7epNe9r2BrHp9ruM5s12Yt+b0fQe/WCa1027L2CHdt2w9J7zVdWyDNg+yQaT2v4AoKp+QDNU9XKa3qSze9otoRkG6v092qR9zRLg7EnbNquqU9r9fqmqXkQzbHcD8JEZ+Y6lWWCAkdbeETT/092dZjhjT2A3mjDx2nZI4QvAO5Ns2v7v9rU9r/8i8Iwkr0mycft43qThiim1+3shcAFwJXDxFG1+PckvtUNZ99IMKT3Sbr4d2HktvueXJnlBkifRzIW5oh0eWUHzZvvqJBsmeT3NnI8JtwPz2tdN5dPA65LsmeZj4X/T7nvxWtT4GeAd7TyPucBf0PRgDMvxSea1Qz8n0gwzTXYFTQh8a/t7sD/wMh4/D+gs4K3AL9EMXU34EHByHptQPZbk8HbbRM/NS9rzskma68/MSzOh+7A2LD4I3M9jvx/SyDPASGtvAfDxqrq1qm6beADvB45p5yC8mWYi7W00/2v+DM2bBVV1H/Bimo9CL2vbnAqs7loy709yH00gOI1mzsxBk4YeJmxNM6n0Xpphha/y2Bv5e2nmSdyd5H1r8D1/GjiJZujouTRDXhPeCPwZzdDPs4D/6Nn2FZqPLt+W5E4mqapLgT9vv5/lNOHnqDWoq9e7aOZ5fBu4lmaC7ZpegO+ePP46MH+8lrVA8zP7MnBL+1iplqp6iOYTZQcDdwIfoAnBN/Q0O592uKgd/pnwXuBC4Mvt78bXaSZiU1VLgMNpgtMKmh6ZP6P5278BzUfxl9GczxcCv7cO36c0q1I1Xc+upJmS5FRg66paMG1jdV6SxcAbqupfZ2h/3wN+Z6b2J3WZPTDSAKW5zsuz09gHOJbHd/9LfUnyCpq5RGt8/RppfbS+Xm1TGhVPpRk22ha4A/h7mnkrUt+SXE4z1+o1qxgulJ5wHEKSJEmd4xCSJEnqnE4PIc2dO7fGx8eHXYYkSZoBV1111Z1VNdZP204HmPHxcRYtWjTsMiRJ0gxI8t/Tt2o4hCRJkjrHACNJkjrHACNJkjrHACNJkjrHACNJkjrHACNJkjrHACNJkjrHACNJkjrHACNJkjqn01filbTmxhdeNCP7WXzKITOyH0laG/bASJKkzjHASJKkzjHASJKkzjHASJKkzjHASJKkzhlYgEnysSR3JPnOFNv+NEklmdsuJ8n7ktyc5NtJ9h5UXZIkqfsG2QPzCeCgySuTbA+8CLi1Z/XBwC7t4zjggwOsS5IkddzAAkxVfQ24a4pN7wHeClTPusOBs6rxdWBOkm0GVZskSeq2WZ0Dk+Qw4AdV9a1Jm7YDlvQsL23XSZIkrWTWrsSbZFPg7cCLp9o8xbqaYh1JjqMZZmKHHXaYsfokSVJ3zGYPzC8AOwHfSrIYmAdcnWRrmh6X7XvazgOWTbWTqjq9quZX1fyxsbEBlyxJkkbRrAWYqrq2qrasqvGqGqcJLXtX1W3AhcBr208j7Qv8qKqWz1ZtkiSpWwb5MerPAP8J7JpkaZJjV9P8YuAW4GbgI8DvDaouSZLUfQObA1NVR0+zfbzneQHHD6oWSZK0fvFKvJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMMMJIkqXMGdisBqcvGF140I/tZfMohM7IfSdLj2QMjSZI6xwAjSZI6xwAjSZI6xwAjSZI6xwAjSZI6xwAjSZI6xwAjSZI6xwAjSZI6xwAjSZI6Z2ABJsnHktyR5Ds96/5PkhuSfDvJ+Unm9Gw7IcnNSW5M8pJB1SVJkrpvkD0wnwAOmrTuEmCPqno28F/ACQBJdgeOAp7VvuYDSTYcYG2SJKnDBhZgquprwF2T1n25qh5uF78OzGufHw6cU1UPVtX3gZuBfQZVmyRJ6rZhzoF5PfDP7fPtgCU925a261aS5Lgki5IsWrFixYBLlCRJo2god6NO8nbgYeBTE6umaFZTvbaqTgdOB5g/f/6UbaRR4V2tJWkwZj3AJFkAHAocWFUTAWQpsH1Ps3nAstmuTZIkdcOsDiElOQh4G3BYVf24Z9OFwFFJnpxkJ2AX4MrZrE2SJHXHwHpgknwG2B+Ym2QpcBLNp46eDFySBODrVfWmqrouybnAd2mGlo6vqkcGVZskSeq2gQWYqjp6itUfXU37k4GTB1WPJElaf3glXkmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkGGEmS1DkDuxeSpPXb+MKLZmQ/i085ZEb2I+mJxR4YSZLUOQYYSZLUOQYYSZLUOQYYSZLUOQYYSZLUOQYYSZLUOQYYSZLUOQMLMEk+luSOJN/pWbdFkkuS3NR+fVq7Pknel+TmJN9Osveg6pIkSd03yB6YTwAHTVq3ELi0qnYBLm2XAQ4GdmkfxwEfHGBdkiSp4wYWYKrqa8Bdk1YfDpzZPj8TOKJn/VnV+DowJ8k2g6pNkiR122zPgdmqqpYDtF+3bNdvByzpabe0XbeSJMclWZRk0YoVKwZarCRJGk2jMok3U6yrqRpW1elVNb+q5o+NjQ24LEmSNIpmO8DcPjE01H69o12/FNi+p908YNks1yZJkjpitgPMhcCC9vkC4IKe9a9tP420L/CjiaEmSZKkyTYa1I6TfAbYH5ibZClwEnAKcG6SY4FbgVe2zS8GXgrcDPwYeN2g6pK0fhpfeNGM7WvxKYfM2L4kDcbAAkxVHb2KTQdO0baA4wdViyRJWr8MLMBI/fJ/zpKkNTUqn0KSJEnqW18BJskegy5EkiSpX/32wHwoyZVJfi/JnIFWJEmSNI2+AkxVvQA4huZaLYuSfDrJiwZamSRJ0ir0PQemqm4C3gG8DXgh8L4kNyT5zUEVJ0mSNJV+58A8O8l7gOuBA4CXVdVu7fP3DLA+SZKklfT7Mer3Ax8BTqyqn0ysrKplSd4xkMokSZJWod8A81LgJ1X1CECSDYBNqurHVXX2wKqTJEmaQr9zYP4VeErP8qbtOkmSpFnXb4DZpKrun1hon286mJIkSZJWr98A80CSvScWkjwX+Mlq2kuSJA1Mv3Ng/hD4XJJl7fI2wG8NpiRJkqTV6yvAVNU3kjwT2BUIcENV/c9AK5MkSVqFNbkb9fOA8fY1eyWhqs4aSFWSJEmr0VeASXI28AvANcAj7eoCDDCSJGnW9dsDMx/YvapqkMVIkiT1o99PIX0H2HqQhUiSJPWr3x6YucB3k1wJPDixsqoOG0hVkiRJq9FvgHnnTB40yR8Bb6CZR3Mt8Dqaj2afA2wBXA28pqoemsnjSpKk9UNfQ0hV9VVgMbBx+/wbNCFjjSXZDvgDYH5V7QFsCBwFnAq8p6p2Ae4Gjl2b/UuSpPVfXwEmyRuB84APt6u2A/5xHY67EfCUJBvR3JJgOXBAewyAM4Ej1mH/kiRpPdbvJN7jgf2AewGq6iZgy7U5YFX9APg74Faa4PIj4Crgnqp6uG22lCYkrSTJcUkWJVm0YsWKtSlBkiR1XL8B5sHe+Shtz8lafaQ6ydOAw4GdgG2BzYCDp2g65f6r6vSqml9V88fGxtamBEmS1HH9BpivJjmRZtjnRcDngH9ay2P+BvD9qlrR3o7gC8CvAHPaYAQwD1i2qh1IkqQntn4/hbSQZlLttcDvABcDZ6zlMW8F9k2yKc0drQ8EFgGXAUfSfBJpAXDBWu5fktbJ+MKLZmQ/i085ZEb2I2ll/d7M8VHgI+1jnVTVFUnOo/kU08PAN4HTgYuAc5K8q1330XU9lrS+mKk3VElaX/R7L6TvM8WclKraeW0OWlUnASdNWn0LsM/a7E+SJD2xrMm9kCZsAryS5oJzkiRJs67fC9n9sOfxg6o6jea6LZIkSbOu3yGkvXsWN6DpkXnqQCqSJEmaRr9DSH/f8/xhmtsKvGrGq5EkrcRPRUkr6/dTSL8+6EIkSZL61e8Q0h+vbntVvXtmypEkSZremnwK6XnAhe3yy4CvAUsGUZQkSdLq9Btg5gJ7V9V9AEneCXyuqt4wqMIkSZJWpd97Ie0APNSz/BAwPuPVSJIk9aHfHpizgSuTnE9zRd6XA2cNrCpJkqTV6PdTSCcn+WfgV9tVr6uqbw6uLEmSpFXrtwcGYFPg3qr6eJKxJDtV1fcHVZi0NrzpoSQ9MfQ1BybJScDbgBPaVRsDnxxUUZIkSavT7yTelwOHAQ8AVNUyvJWAJEkakn4DzENVVTQTeEmy2eBKkiRJWr1+58Ccm+TDwJwkbwReD3xkcGWpC5xvIkkaln4/hfR3SV4E3AvsCvxFVV0y0MokSZJWYdoAk2RD4EtV9RuAoUWSJA3dtHNgquoR4MdJfn4W6pEkSZpWv3Ngfgpcm+QS2k8iAVTVH6zNQZPMAc4A9qCZGPx64EbgszS3KFgMvKqq7l6b/UuSpPVbvwHmovYxU94L/EtVHZnkSTQXyTsRuLSqTkmyEFhIc+0ZSZKkx1ltgEmyQ1XdWlVnztQBk/wc8GvAbwNU1UPAQ0kOB/Zvm50JXI4BRpIkTWG6OTD/OPEkyedn6Jg7AyuAjyf5ZpIz2uvKbFVVywHar1tO9eIkxyVZlGTRihUrZqgkSZLUJdMFmPQ833mGjrkRsDfwwarai2ZOzcJ+X1xVp1fV/KqaPzY2NkMlSZKkLpkuwNQqnq+LpcDSqrqiXT6PJtDcnmQbgPbrHTN0PEmStJ6ZLsA8J8m9Se4Dnt0+vzfJfUnuXZsDVtVtwJIku7arDgS+C1wILGjXLQAuWJv9S5Kk9d9qJ/FW1YYDOu7vA59qP4F0C/A6mjB1bpJjgVuBVw7o2JIkqeP6/Rj1jKqqa4D5U2w6cLZrkSRJ3dPv3aglSZJGhgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1jgFGkiR1zlBuJSBJEsD4wotmZD+LTzlkRvaj7rAHRpIkdY4BRpIkdY4BRpIkdY4BRpIkdY4BRpIkdY4BRpIkdY4BRpIkdY7XgZGkJ4iZuuYKeN0VDd/QemCSbJjkm0m+2C7vlOSKJDcl+WySJw2rNkmSNNqG2QPzFuB64Ofa5VOB91TVOUk+BBwLfHBYxUmaHTPZKyDpiWMoPTBJ5gGHAGe0ywEOAM5rm5wJHDGM2iRJ0ugb1hDSacBbgUfb5acD91TVw+3yUmC7qV6Y5Lgki5IsWrFixeArlSRJI2fWA0ySQ4E7quqq3tVTNK2pXl9Vp1fV/KqaPzY2NpAaJUnSaBvGHJj9gMOSvBTYhGYOzGnAnCQbtb0w84BlQ6hNkiR1wKz3wFTVCVU1r6rGgaOAr1TVMcBlwJFtswXABbNdmyRJ6oZRupDd24A/TnIzzZyYjw65HkmSNKKGeiG7qrocuLx9fguwzzDrkSR1kxfpe+LxSrySJA3ATIUqA9XURmkISZIkqS8GGEmS1DkGGEmS1DnOgZGkAfE+T9Lg2AMjSZI6xwAjSZI6xwAjSZI6xwAjSZI6x0m8HeJFkSRJatgDI0mSOscAI0mSOscAI0mSOscAI0mSOsdJvE9AXh1UktR19sBIkqTOMcBIkqTOcQhpwByukbQ+8m+bhs0eGEmS1DmzHmCSbJ/ksiTXJ7kuyVva9VskuSTJTe3Xp812bZIkqRuG0QPzMPAnVbUbsC9wfJLdgYXApVW1C3BpuyxJkrSSWQ8wVbW8qq5un98HXA9sBxwOnNk2OxM4YrZrkyRJ3TDUOTBJxoG9gCuArapqOTQhB9hyeJVJkqRRNrQAk2Rz4PPAH1bVvWvwuuOSLEqyaMWKFYMrUJIkjayhBJgkG9OEl09V1Rfa1bcn2abdvg1wx1SvrarTq2p+Vc0fGxubnYIlSdJImfXrwCQJ8FHg+qp6d8+mC4EFwCnt1wtmuzZJkkbtGjczVc/iUw6Zkf2MimFcyG4/4DXAtUmuadedSBNczk1yLHAr8Moh1CZJkjpg1gNMVf07kFVsPnA2a1mdUUvgkiTpMV6JV5IkdY4BRpIkdY43c5Qk6QlgJqdGjMKEYHtgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS5xhgJElS54xcgElyUJIbk9ycZOGw65EkSaNnpAJMkg2BfwAOBnYHjk6y+3CrkiRJo2akAgywD3BzVd1SVQ8B5wCHD7kmSZI0YjYadgGTbAcs6VleCvxyb4MkxwHHtYv3J7lxlmqbKXOBO4ddhPrm+eoez1n3eM46JqcO7Jzt2G/DUQswmWJdPW6h6nTg9NkpZ+YlWVRV84ddh/rj+eoez1n3eM66ZxTO2agNIS0Ftu9ZngcsG1ItkiRpRI1agPkGsEuSnZI8CTgKuHDINUmSpBEzUkNIVfVwkjcDXwI2BD5WVdcNuayZ1tnhrycoz1f3eM66x3PWPUM/Z6mq6VtJkiSNkFEbQpIkSZqWAUaSJHWOAWZAkmyf5LIk1ye5Lslb2vVbJLkkyU3t16cNu1Y1kmyS5Mok32rP2V+263dKckV7zj7bTjDXiEiyYZJvJvliu+z5GmFJFie5Nsk1SRa16/y7OMKSzElyXpIb2ve054/COTPADM7DwJ9U1W7AvsDx7W0RFgKXVtUuwKXtskbDg8ABVfUcYE/goCT7AqcC72nP2d3AsUOsUSt7C3B9z7Lna/T9elXt2XMdEf8ujrb3Av9SVc8EnkPz723o58wAMyBVtbyqrm6f30dzwrejuTXCmW2zM4EjhlOhJqvG/e3ixu2jgAOA89r1nrMRkmQecAhwRrscPF9d5N/FEZXk54BfAz4KUFUPVdU9jMA5M8DMgiTjwF7AFcBWVbUcmpADbDm8yjRZOxxxDXAHcAnwPeCeqnq4bbKUJohqNJwGvBV4tF1+Op6vUVfAl5Nc1d4aBvy7OMp2BlYAH2+Has9IshkjcM4MMAOWZHPg88AfVtW9w65Hq1dVj1TVnjRXgd4H2G2qZrNblaaS5FDgjqq6qnf1FE09X6Nlv6raGziYZmj914ZdkFZrI2Bv4INVtRfwACMyxGeAGaAkG9OEl09V1Rfa1bcn2abdvg3N//Q1Ytou0stp5i/NSTJx0UdvbzE69gMOS7KY5s71B9D0yHi+RlhVLWu/3gGcT/MfBf8ujq6lwNKquqJdPo8m0Az9nBlgBqQdi/8ocH1Vvbtn04XAgvb5AuCC2a5NU0sylmRO+/wpwG/QzF26DDiybeY5GxFVdUJVzauqcZrbjnylqo7B8zWykmyW5KkTz4EXA9/Bv4sjq6puA5Yk2bVddSDwXUbgnHkl3gFJ8gLg34BreWx8/kSaeTDnAjsAtwKvrKq7hlKkHifJs2kmo21IE+7Praq/SrIzzf/wtwC+Cby6qh4cXqWaLMn+wJ9W1aGer9HVnpvz28WNgE9X1clJno5/F0dWkj1pJso/CbgFeB3t30iGeM4MMJIkqXMcQpIkSZ1jgJEkSZ1jgJEkSZ1jgJEkSZ1jgJEkSZ1jgJE0EpK8PEkleeawa5E0+gwwkkbF0cC/01yUTpJWywAjaejae4btBxxLG2CSbJDkA0muS/LFJBcnObLd9twkX21vCPiliUuaS3riMMBIGgVHAP9SVf8F3JVkb+A3gXHgl4A3AM+Hn91j7P8CR1bVc4GPAScPo2hJw7PR9E0kaeCOprkRIzS3ATga2Bj4XFU9CtyW5LJ2+67AHsAlzS3H2BBYPrvlSho2A4ykoWrvg3MAsEeSogkkxWP3zFnpJcB1VfX8WSpR0ghyCEnSsB0JnFVVO1bVeFVtD3wfuBN4RTsXZitg/7b9jcBYkp8NKSV51jAKlzQ8BhhJw3Y0K/e2fB7YFlgKfAf4MM2d3H9UVQ/RhJ5Tk3wLuAb4ldkrV9Io8G7UkkZWks2r6v52mOlKYL+qum3YdUkaPufASBplX0wyB3gS8NeGF0kT7IGRJEmd4xwYSZLUOQYYSZLUOQYYSZLUOQYYSZLUOQYYSZLUOf8fjI56B5xf8TMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# matplotlib allows us to control the number of bins in the histogram\n",
"\n",
"plt.figure(figsize=(9,4))\n",
"plt.hist(empl_data['Age'], bins=25)\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Age Distribution of Employees')\n",
"plt.grid(False)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# age distribution by department\n",
"\n",
"# create subplots\n",
"f, axs = plt.subplots(1,3, sharey='row', figsize=(16,4))\n",
"\n",
"# label each subplot\n",
"axs[0].set_title('Human Resources')\n",
"axs[1].set_title('Sales')\n",
"axs[2].set_title('Research & Development')\n",
"\n",
"# add histogram to each subplot\n",
"axs[0].hist(empl_data[empl_data['Department']=='Human Resources']['Age'], bins=10)\n",
"axs[1].hist(empl_data[empl_data['Department']=='Sales']['Age'], bins=10)\n",
"axs[2].hist(empl_data[empl_data['Department']=='Research & Development']['Age'], bins=10)\n",
"\n",
"# show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# allow each y-axis scale to be independent of other plots\n",
"\n",
"fig = plt.figure(figsize = (16,4))\n",
"sub1 = plt.subplot(1,3,1)\n",
"sub1.set_title('Human Resources')\n",
"sub1.hist(empl_data[empl_data['Department']=='Human Resources']['Age'], bins=10)\n",
"sub2 = plt.subplot(1,3,2)\n",
"sub2.set_title('Sales')\n",
"sub2.hist(empl_data[empl_data['Department']=='Sales']['Age'], bins=10)\n",
"sub3 = plt.subplot(1,3,3)\n",
"sub3.set_title('Research & Development')\n",
"sub3.hist(empl_data[empl_data['Department']=='Research & Development']['Age'], bins=10)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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rWdcBSJIkSVo0HwM+BHx8vCDJ84GTgKdW1V1J9m/LDwdOBZ4MHAT8XZLHV9W9ix61JGlGJngkSZKkJaKqvpJk5YTi/wysraq72jo72vKTgE+35d9PsgU4CvjaIoWrRbJyzYauQ5A0D5yiJUmSJC1tjwf+fZKNSf4+yTPa8uXALT31trVlD5JkdZJNSTaNjY0tcLiSpMmY4JEkSZKWtmXAPsDRwNuBC5MEyCR1a7IXqKp1VTVaVaMjIyMLF6kkaUomeCRJkqSlbRtwcTW+DtwH7NeWH9xTbwVwawfxSZL6YIJHkiRJWtr+FjgWIMnjgV2BHwKXAqcm2S3JocBhwNc7i1KSNC0XWZYkSZKWiCQXAMcA+yXZBrwbOB84v711+t3Aqqoq4PokFwI3APcAr/MOWpI0uEzwSJIkSUtEVZ02xa5XTlH/fcD7Fi4iSdJ8mXGKVpLzk+xoM/rjZfsmuTzJje3vfRY2TEmSJEmSJE2lnzV4PgYcP6FsDXBFVR0GXNFuS5IkSZIkqQMzJniq6ivAjyYUnwSsbx+vB06e57gkSZIkSZLUp9neReuAqtoO0P7ef6qKSVYn2ZRk09jY2CwPJ0mSJEmSpKks+G3Sq2pdVY1W1ejIyMhCH06SJEmSJGnJmW2C5/YkBwK0v3fMX0iSJEmSJEnaGbNN8FwKrGofrwIumZ9wJEmSJEmStLP6uU36BcDXgCck2ZbkdGAt8MIkNwIvbLclSZIkSZLUgWUzVaiq06bYddw8xyJJkiRJS9LKNRu6DkHSkFvwRZYlSZIkSZK0sGYcwSNJkiRJ0kNNl6Omtq49obNj66HLETySJEmSJElDzgSPJEmSJEnSkDPBI0mSJEmSNORM8EiSHiTJm5Ncn+Q7SS5IsnuSQ5NsTHJjks8k2bXrOCVJkiQ1TPBIkh4gyXLgjcBoVT0F2AU4FTgLOLuqDgN+DJzeXZSSJEmSepngkSRNZhnwiCTLgD2A7cCxwEXt/vXAyR3FJkmSJGkCEzySpAeoqh8A7wdupkns/AS4Brijqu5pq20DlncToSRptpKcn2RHku9Msu9tSSrJfu12kvxlki1JvpXkyMWPWJLULxM8kqQHSLIPcBJwKHAQsCfw4kmq1hTPX51kU5JNY2NjCxeoJGk2PgYcP7EwycHAC2mS++NeDBzW/qwGPrwI8UmSZskEjyRpohcA36+qsar6FXAx8Gxg73bKFsAK4NbJnlxV66pqtKpGR0ZGFidiSVJfquorwI8m2XU28A4emLw/Cfh4Na6mOQ8cuAhhSpJmwQSPJGmim4Gjk+yRJMBxwA3AlcApbZ1VwCUdxSdJmkdJXgb8oKq+OWHXcuCWnm2n50rSADPBI0l6gKraSLOY8rXAt2nOFeuAdwJvSbIFeDRwXmdBSpLmRZI9gD8E/miy3ZOUOT1XkgbUspmrSJKWmqp6N/DuCcU3AUd1EI4kaeH8Js2aa99sBm2yArg2yVE0I3YO7qk77fRcmi8DGB0dnTQJJElaWCZ4hsDKNRu6DmHJWaptvnXtCV2HIEmSFlFVfRvYf3w7yVZgtKp+mORS4PVJPg08E/hJVW3vJlJJ0kycoiVJkiQtEUkuAL4GPCHJtiSnT1P9MprRm1uAjwB/sAghSpJmyRE8kiRJ0hJRVafNsH9lz+MCXrfQMUmS5ocjeCRJkiRJkoacCR5JkiRJkqQhZ4JHkiRJkiRpyLkGjyRJGghd3sHQuwhKkqRh5wgeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkacjNKcGT5M1Jrk/ynSQXJNl9vgKTJEmSJElSf2ad4EmyHHgjMFpVTwF2AU6dr8AkSZIkSZLUn7lO0VoGPCLJMmAP4Na5hyRJkiRJkqSdMesET1X9AHg/cDOwHfhJVX1pYr0kq5NsSrJpbGxs9pFKkiRJkiRpUnOZorUPcBJwKHAQsGeSV06sV1Xrqmq0qkZHRkZmH6kkSZKkOUlyfpIdSb7TU/bnSb6b5FtJPpdk7559ZybZkuR7SV7UTdSSpH7MZYrWC4DvV9VYVf0KuBh49vyEJUmSJGkBfAw4fkLZ5cBTquqpwD8BZwIkOZxmjc0nt8/56yS7LF6okqSdMZcEz83A0Un2SBLgOGDz/IQlSZIkab5V1VeAH00o+1JV3dNuXg2saB+fBHy6qu6qqu8DW4CjFi1YSdJOmcsaPBuBi4BrgW+3r7VunuKSJEmStPh+F/j/2sfLgVt69m1ryx7EdTclqXtzuotWVb27qp5YVU+pqldV1V3zFZgkSZKkxZPkD4F7gE+OF01SrSZ7rutuSlL3lnUdgCRJkqRuJVkFnAgcV1XjSZxtwME91VYAty52bJKk/sxpBI8kSZKk4ZbkeOCdwMuq6t96dl0KnJpktySHAocBX+8iRknSzBzBI0mSJC0RSS4AjgH2S7INeDfNXbN2Ay5v7p3C1VX12qq6PsmFwA00U7deV1X3dhO5JGkmJngkSZKkJaKqTpuk+Lxp6r8PeN/CRSRJmi8meCRJGjAr12zoOgRJkiQNGdfgkSRJkiRJGnImeCRJkiRJkoacCR5JkiRJkqQhZ4JHkiRJkiRpyJngkSRJkiRJGnImeCRJkiRJkoacCR5JkiRJkqQhZ4JHkvQgSfZOclGS7ybZnORZSfZNcnmSG9vf+3QdpyRJkqSGCR5J0mQ+CHyhqp4IPA3YDKwBrqiqw4Ar2m1JkiRJA8AEjyTpAZI8CngecB5AVd1dVXcAJwHr22rrgZO7iVCSJEnSRCZ4JEkTPRYYAz6a5BtJzk2yJ3BAVW0HaH/vP9mTk6xOsinJprGxscWLWpIkSVrCTPBIkiZaBhwJfLiqng78nJ2YjlVV66pqtKpGR0ZGFipGSZIkST1M8EiSJtoGbKuqje32RTQJn9uTHAjQ/t7RUXySJEmSJljWdQCSpMFSVbcluSXJE6rqe8BxwA3tzypgbfv7kg7DlObVyjUbOj3+1rUndHp8SZI0/EzwSJIm8wbgk0l2BW4CXkMz6vPCJKcDNwMv7zA+SZIkST1M8EiSHqSqrgNGJ9l13GLHIkmSJGlmrsEjSZIkLRFJzk+yI8l3esr2TXJ5khvb3/u05Unyl0m2JPlWkiO7i1ySNBMTPJIkSdLS8THg+Alla4Arquow4Aruv3Pii4HD2p/VwIcXKUZJ0iyY4JEkSZKWiKr6CvCjCcUnAevbx+uBk3vKP16Nq4G9x++mKEkaPCZ4JEmSpKXtgKraDtD+3r8tXw7c0lNvW1v2IElWJ9mUZNPY2NiCBitJmpwJHkmSJEmTySRlNVnFqlpXVaNVNToyMrLAYUmSJmOCR5IkSVrabh+fetX+3tGWbwMO7qm3Arh1kWOTJPXJBI8kSZK0tF0KrGofrwIu6Sn/T+3dtI4GfjI+lUuSNHiWdR2AJEmSpMWR5ALgGGC/JNuAdwNrgQuTnA7cDLy8rX4Z8BJgC/BvwGsWPWBJUt9M8EiSJElLRFWdNsWu4yapW8DrFjYiSdJ8cYqWJEmSJEnSkJtTgifJ3kkuSvLdJJuTPGu+ApMkSZIkSVJ/5jpF64PAF6rqlCS7AnvMQ0ySJEmSJEnaCbNO8CR5FPA84NUAVXU3cPf8hCVJkiRJkqR+zWWK1mOBMeCjSb6R5Nwke85TXJIkSZIkSerTXKZoLQOOBN5QVRuTfBBYA/zX3kpJVgOrAQ455JA5HK47K9ds6DoESZIkSZKkKc1lBM82YFtVbWy3L6JJ+DxAVa2rqtGqGh0ZGZnD4SRJkiRJkjSZWSd4quo24JYkT2iLjgNumJeoJEmSJEmS1Le53kXrDcAn2zto3QS8Zu4hSZIkSZIkaWfMKcFTVdcBo/MUiyRJkiRJkmZhLmvwSJIkSZIkaQCY4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSSR5c5Lrk3wnyQVJdk9yaJKNSW5M8pkku3YdpyRpciZ4JEmSpCUuyXLgjcBoVT0F2AU4FTgLOLuqDgN+DJzeXZSSpOmY4JEkSZIEsAx4RJJlwB7AduBY4KJ2/3rg5I5ikyTNwASPJEmStMRV1Q+A9wM30yR2fgJcA9xRVfe01bYBy7uJUJI0ExM8kiRJ0hKXZB/gJOBQ4CBgT+DFk1StKZ6/OsmmJJvGxsYWLlBJ0pSWdR2AJEmSpM69APh+VY0BJLkYeDawd5Jl7SieFcCtkz25qtYB6wBGR0cnTQJJut/KNRs6O/bWtSd0dmwtLEfwSJIkSboZODrJHkkCHAfcAFwJnNLWWQVc0lF8kqQZmOCRJEmSlriq2kizmPK1wLdpPiesA94JvCXJFuDRwHmdBSlJmpZTtCRJkjrmUH0Ngqp6N/DuCcU3AUd1EI4kaSc5gkeSJEmSJGnImeCRJEmSJEkaciZ4JEmSJEmShpwJHknSpJLskuQbST7fbh+aZGOSG5N8JsmuXccoSZIkqWGCR5I0lTOAzT3bZwFnV9VhwI+B0zuJSpIkSdKDmOCRJD1IkhXACcC57XaAY2luoQuwHji5m+gkSZIkTWSCR5I0mXOAdwD3tduPBu6oqnva7W3A8smemGR1kk1JNo2NjS18pJIkSZJM8EiSHijJicCOqrqmt3iSqjXZ86tqXVWNVtXoyMjIgsQoSZIk6YGWdR2AJGngPAd4WZKXALsDj6IZ0bN3kmXtKJ4VwK0dxihJkiSphyN4JEkPUFVnVtWKqloJnAp8uapeAVwJnNJWWwVc0lGIkiRJkiYwwSNJ6tc7gbck2UKzJs95HccjSZIkqeUULUnSlKrqKuCq9vFNwFFdxiNJkiRpco7gkSRJkiRJGnImeCRJkiRJkoacU7QkSZIkCVi5ZkPXIUgLrsv/51vXntDZsZcCR/BIkiRJkiQNuTkneJLskuQbST4/HwFJkiRJkiRp58zHCJ4zgM3z8DqSJEmSJEmahTkleJKsAE4Azp2fcCRJkiR1IcneSS5K8t0km5M8K8m+SS5PcmP7e5+u45QkTW6uiyyfA7wD2GuqCklWA6sBDjnkkDkeTpLmnwvNSZIEwAeBL1TVKUl2BfYA3gVcUVVrk6wB1gDv7DJISdLkZp3gSXIisKOqrklyzFT1qmodsA5gdHS0Zns8SZIWk3dSkbSUJHkU8Dzg1QBVdTdwd5KTgGPaauuBqzDBI0kDaS5TtJ4DvCzJVuDTwLFJPjEvUUmSJElaTI8FxoCPtjdQOTfJnsABVbUdoP29/2RPTrI6yaYkm8bGxhYvaknSr806wVNVZ1bViqpaCZwKfLmqXjlvkUmSJElaLMuAI4EPV9XTgZ/TTMfqS1Wtq6rRqhodGRlZqBglSdOYj7toSZIkSRpu24BtVbWx3b6IJuFze5IDAdrfOzqKT5I0g3lJ8FTVVVV14ny8liRJkqTFVVW3AbckeUJbdBxwA3ApsKotWwVc0kF4kqQ+zPUuWpIkSZIeGt4AfLK9g9ZNwGtovhC+MMnpwM3AyzuMT5I0DRM8kiRJkqiq64DRSXYdt9ixSJJ2nmvwSJIkSZIkDTkTPJIkSZIkSUPOKVqSJEmSJGnBrVyzobNjb117QmfHXiyO4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyy7oOQJIAVq7Z0HUIkiRJkjS0HMEjSZIkSZI05BzBI0mStIR1OYJy69oTOju2JEkPNY7gkSRJkiRJGnImeCRJkiRJkoacCR5JkiRJACTZJck3kny+3T40ycYkNyb5TJJdu45RkjQ5EzySJEmSxp0BbO7ZPgs4u6oOA34MnN5JVJKkGZngkSRJkkSSFcAJwLntdoBjgYvaKuuBk7uJTpI0ExM8kiRJkgDOAd4B3NduPxq4o6ruabe3Acsne2KS1Uk2Jdk0Nja28JFKkh7EBI8kSZK0xCU5EdhRVdf0Fk9StSZ7flWtq6rRqhodGRlZkBglSdNKzDmmAAALv0lEQVRb1nUAkiRJkjr3HOBlSV4C7A48imZEz95JlrWjeFYAty5kECvXbFjIl5ekhzRH8EiSHiDJwUmuTLI5yfVJzmjL901yeXsnlcuT7NN1rJKk+VFVZ1bViqpaCZwKfLmqXgFcCZzSVlsFXNJRiJKkGZjgkSRNdA/w1qp6EnA08LokhwNrgCvaO6lc0W5Lkh7a3gm8JckWmjV5zus4HknSFJyiJUl6gKraDmxvH/80yWaaRTVPAo5pq60HrqK58JckPYRU1VU0fTxVdRNwVJfxSJL6Y4JHkjSlJCuBpwMbgQPa5A9VtT3J/lM8ZzWwGuCQQw5ZnEAlDaUu11vZuvaEzo4tSdJCcIqWJGlSSR4JfBZ4U1Xd2e/zvJOKJEmStPhM8EiSHiTJw2mSO5+sqovb4tuTHNjuPxDY0VV8kiRJkh5o1gmeqe6yIkkabklCs4jm5qr6QM+uS2nuoALeSUWSJEkaKHNZg2f8LivXJtkLuCbJ5VV1wzzFJknqxnOAVwHfTnJdW/YuYC1wYZLTgZuBl3cUnyRJkqQJZp3gmeYuKyZ4JGmIVdVXgUyx+7jFjEWSJElSf+blLloT7rIycZ93U5GGRJd3M5EkSZKkhdL1Z53FuHvjnBM8M91lparWAesARkdHa67HkyQtHV2fiCVJkqRhMae7aE1xlxVJkiRJkiQtorncRWuqu6xIkiRJkiRpEc1lBM/4XVaOTXJd+/OSeYpLkiRJkiRJfZrLXbSmu8uKJEmSJEmSFsmc1uCRJEmSJElS90zwSJIkSZIkDTkTPJIkSZIkSUPOBI8kSZIkSdKQm/Uiy5IkSdKwWrlmQ2fH3rr2hM6OLUl66HIEjyRJkrTEJTk4yZVJNie5PskZbfm+SS5PcmP7e5+uY5UkTc4EjyRJkqR7gLdW1ZOAo4HXJTkcWANcUVWHAVe025KkAWSCR5IkSVriqmp7VV3bPv4psBlYDpwErG+rrQdO7iZCSdJMTPBIkiRJ+rUkK4GnAxuBA6pqOzRJIGD/KZ6zOsmmJJvGxsYWK1RJUg8TPJIkSZIASPJI4LPAm6rqzn6fV1Xrqmq0qkZHRkYWLkBJ0pRM8EiSJEkiycNpkjufrKqL2+LbkxzY7j8Q2NFVfJKk6ZngkSRJkpa4JAHOAzZX1Qd6dl0KrGofrwIuWezYJEn9WdZ1AJIkSZI69xzgVcC3k1zXlr0LWAtcmOR04Gbg5R3FJ0magQkeSZIkaYmrqq8CmWL3cYsZiyRpdpyiJUmSJEmSNOSGZgTPyjUbug5BkiRJkiRpIDmCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkIWeCR5IkSZIkaciZ4JEkSZIkSRpyJngkSZIkSZKGnAkeSZIkSZKkITenBE+S45N8L8mWJGvmKyhJ0uCy75ekpcV+X5KGw6wTPEl2Af4KeDFwOHBaksPnKzBJ0uCx75ekpcV+X5KGx1xG8BwFbKmqm6rqbuDTwEnzE5YkaUDZ90vS0mK/L0lDYtkcnrscuKVnexvwzImVkqwGVrebP0vyvTkcczr7AT9coNeeK2ObHWObnUGNbVDjgg5jy1kzVpkptsfMWzD9mbHvX8R+fyaD/H9ukNhO/bGd+mM79SFnzbmdFrPvH7Rr/un4/68/tlN/bKf+2E59mmPf31e/P5cETyYpqwcVVK0D1s3hOP0Fk2yqqtGFPs5sGNvsGNvsDGpsgxoXGNtOmrHvX6x+fyYD2HYDyXbqj+3UH9upP0PWTgN1zT+dIWvXzthO/bGd+mM79W8x2mouU7S2AQf3bK8Abp1bOJKkAWffL0lLi/2+JA2JuSR4/hE4LMmhSXYFTgUunZ+wJEkDyr5fkpYW+31JGhKznqJVVfckeT3wRWAX4Pyqun7eItt5nU8HmIaxzY6xzc6gxjaocYGx9W0A+/7pDFTbDTDbqT+2U39sp/4MTTvZ7z8k2U79sZ36Yzv1b+GXrql60BRaSZIkSZIkDZG5TNGSJEmSJEnSADDBI0mSJEmSNOSGMsGT5OAkVybZnOT6JGe05fsmuTzJje3vfQYkrvck+UGS69qflyxmXG0Muyf5epJvtrH9cVt+aJKNbZt9pl08b1Bi+1iS7/e02xGLHVtPjLsk+UaSz7fbnbfbNLENRLsl2Zrk220Mm9qyTt+jM8TW+fu0jWPvJBcl+W7blzxrUNptUA3qOWHQDPJ5YBANcr8/KAa5nx8k9usLw76/P/b9O8e+f2b2/f3pqu8fygQPcA/w1qp6EnA08LokhwNrgCuq6jDginZ7EOICOLuqjmh/LlvkuADuAo6tqqcBRwDHJzkaOKuN7TDgx8DpAxQbwNt72u26DmIbdwawuWd7ENpt3MTYYHDa7fltDKPtdtfv0V4TY4Pu36cAHwS+UFVPBJ5G87cdpHYbRIN6Thg0g3weGESD3O8PkkHu5weF/frCsO/vj33/zrHv7499/8w66fuHMsFTVdur6tr28U9pGms5cBKwvq22Hjh5QOLqXDV+1m4+vP0p4FjgorZ80dtshtgGQpIVwAnAue12GIB2myy2IdDpe3TQJXkU8DzgPICquruq7sB2m9agnhMGzSCfBwbNIPf7Q8D3XQ/79YVj398f+/7+2ffPie+7Hl32/UOZ4OmVZCXwdGAjcEBVbYem0wf2H5C4AF6f5FtJzu9qyFo75PA6YAdwOfDPwB1VdU9bZRsdJaQmxlZV4+32vrbdzk6yWxexAecA7wDua7cfzYC0Gw+ObdwgtFsBX0pyTZLVbdmgvEcniw26f58+FhgDPtoODz43yZ4MTrsNvEE9JwyKQT4PDJhB7vcHySD384PCfn0R2PdPz76/b/b9/bHvn1lnff9QJ3iSPBL4LPCmqrqz63jGTRLXh4HfpBkWuR34iy7iqqp7q+oIYAVwFPCkyaotblTtQSfEluQpwJnAE4FnAPsC71zsuJKcCOyoqmt6iyepuujtNkVsMADt1npOVR0JvJhm2PTzOopjMpPFNgjv02XAkcCHq+rpwM9xiGvfBvWcMEgG+TwwKAa53x9Ag9zPDwr79QVm3z8z+/6Z2ffvFPv+mXXW9w9tgifJw2k6809W1cVt8e1JDmz3H0iTpe48rqq6ve1Y7wM+QtOxdqYdHnYVzXzlvZMsa3etAG7tKi54QGzHt0Nvq6ruAj5KN+32HOBlSbYCn6YZpnkOg9FuD4otyScGpN2oqlvb3zuAz7VxdP4enSq2AXmfbgO29Yxgu4jm5DAQ7TbIBvWcMKgG+TwwAAa53x8og9zPDxD79QVk379z7PunZd/fJ/v+vnTW9w9lgqedD3kesLmqPtCz61JgVft4FXDJIMQ1/kds/QfgO4sZVxvDSJK928ePAF5AM1f5SuCUttqit9k0sX235z9/aOYnLnq7VdWZVbWiqlYCpwJfrqpXMADtNkVsrxyEdkuyZ5K9xh8Dv93G0el7dLrYBuF9WlW3AbckeUJbdBxwAwPQboNsUM8Jg2aQzwODZJD7/UEyyP38ILFfXzj2/f2x7++PfX9/7Pv702Xfn6rhG2WW5LnAPwDf5v45ku+imXd7IXAIcDPw8qr60QDEdRrNtI8CtgK/Pz73bhFjeyrNQk670CT2Lqyq9yZ5LE2Wel/gG8Ar25EfgxDbl4ERmuGR1wGv7VkkbtElOQZ4W1WdOAjtNk1snbdb2z6fazeXAZ+qqvcleTQdvkdniO1v6Ph92sZ3BM3ifrsCNwGvoX1f0GG7DbJBPScMmkE+DwyqQe73uzbI/fygsV9fGPb9/bHv33n2/VOz7+9fV33/UCZ4JEmSJEmSdL+hnKIlSZIkSZKk+5ngkSRJkiRJGnImeCRJkiRJkoacCR5JkiRJkqQhZ4JHkiRJkiRpyJngkSRJkiRJGnImeCRJkiRJkobc/w9XqiUpO8eAHQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# allow each y-axis scale to be independent of other plots\n",
"\n",
"fig = plt.figure(figsize = (16,4))\n",
"sub1 = plt.subplot(1,3,1)\n",
"sub1.set_title('Human Resources')\n",
"sub1.hist(empl_data[empl_data['Department']=='Human Resources']['Age'], bins=10)\n",
"sub2 = plt.subplot(1,3,2)\n",
"sub2.set_title('Sales')\n",
"sub2.hist(empl_data[empl_data['Department']=='Sales']['Age'], bins=10)\n",
"sub3 = plt.subplot(1,3,3)\n",
"sub3.set_title('Research & Development')\n",
"sub3.hist(empl_data[empl_data['Department']=='Research & Development']['Age'], bins=10)\n",
"\n",
"# tight_layout adjusts the scale and layout\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Un5JkY7vvBUsfsSRpUCu6DkCSJEnSkvko8AHgY5MFSZ4LHAU8paruTLJbW74/cBzwJGBP4O+SPK6q7lnyqCVJszLBI0mSJC0TVfWVJPtMKf6vwOlVdWdbZ0tbfhTwqbb8B0k2AgcDX1uicLVEclq6DkHSAnCKliRJkrS8PQ7490nWJfn7JE9ry1cCN/fU29SWPUCSNUnWJ1k/MTGxyOFKkvoxwSNJkiQtbyuAnYFDgLcA5ycJ0G9YR/V7gapaW1XjVTU+Nja2eJFKkqZlgkeSJEla3jYBF1XjG8C9wK5t+V499VYBt3QQnyRpACZ4JEmSpOXtb4HDAJI8DtgW+BFwCXBckocl2RfYD/hGZ1FKkmbkIsuSJEnSMpHkPOBQYNckm4BTgXOAc9pbp98FrK6qAq5Lcj7wXeBu4DXeQUuShpcJHkmSJGmZqKrjp9n18mnqvwt41+JFJElaKLNO0UpyTpItbUZ/smyXJJclub79vfPihilJkiRJkqTpDLIGz0eBI6aUnQxcXlX7AZe325IkSZIkSerArAmeqvoK8OMpxUcB57aPzwWOXuC4JEmSJEmSNKC53kVr96raDND+3m26iknWJFmfZP3ExMQcDydJkiRJkqTpLPpt0qtqbVWNV9X42NjYYh9OkiRJkiRp2Zlrgue2JHsAtL+3LFxIkiRJkiRJ2hpzTfBcAqxuH68GLl6YcCRJkiRJkrS1BrlN+nnA14DHJ9mU5ATgdOD5Sa4Hnt9uS5IkSZIkqQMrZqtQVcdPs+vwBY5FkiRJkpalnJauQ5A04hZ9kWVJkiRJkiQtrllH8EiSJEmS9GDT5aipOrU6O7YevBzBI0mSJEmSNOJM8EiSJEmSJI04EzySJEmSJEkjzgSPJOkBkrwhyXVJvpPkvCTbJdk3ybok1yf5dJJtu45TkiRJUsMEjyTpfpKsBF4HjFfVk4FtgOOAdwPvq6r9gJ8AJ3QXpSRJkqReJngkSf2sAB6eZAWwPbAZOAy4sN1/LnB0R7FJkiRJmsIEjyTpfqrqh8B7gJtoEjs/Ba4Cbq+qu9tqm4CV3UQoSZqrJOck2ZLkO332vTlJJdm13U6SM5NsTPKtJActfcSSpEGZ4JEk3U+SnYGjgH2BPYEdgBf2qVrTPH9NkvVJ1k9MTCxeoJKkufgocMTUwiR7Ac+nSe5PeiGwX/uzBvjQEsQnSZojEzySpKmeB/ygqiaq6tfARcAzgZ3aKVsAq4Bb+j25qtZW1XhVjY+NjS1NxJKkgVTVV4Af99n1PuCt3D95fxTwsWp8neY8sMcShClJmgMTPJKkqW4CDkmyfZIAhwPfBa4AjmnrrAYu7ig+SdICSvJS4IdV9c0pu1YCN/dsOz1XkoaYCR5J0v1U1TqaxZSvBr5Nc65YC7wNeGOSjcCjgLM7C1KStCCSbA/8MfAn/Xb3KXN6riQNqRWzV5EkLTdVdSpw6pTiG4CDOwhHkrR4fptmzbVvNoM2WQVcneRgmhE7e/XUnXF6Ls2XAYyPj/dNAkmSFpcJnhFw4pnXdB3CsrNc2/wDrzuw6xAkSdISqqpvA7tNbie5ERivqh8luQQ4McmngKcDP62qzd1EKkmajVO0JEmSpGUiyXnA14DHJ9mU5IQZql9KM3pzI/Bh4I+WIERJ0hw5gkeSJElaJqrq+Fn279PzuIDXLHZMkqSF4QgeSZIkSZKkEWeCR5IkSZIkacSZ4JEkSZIkSRpxrsEjSZKGwgXNLZo7cWx5V2dJkjTaHMEjSZIkSZI04kzwSJIkSZIkjTgTPJIkSZIkSSPOBI8kSZIkSdKIM8EjSZIkSZI04kzwSJIkSZIkjTgTPJIkSZIkSSPOBI8kSZIkSdKIM8EjSZIkSZI04kzwSJIkSZIkjbh5JXiSvCHJdUm+k+S8JNstVGCSJEmSJEkazJwTPElWAq8DxqvqycA2wHELFZgkSZIkSZIGM98pWiuAhydZAWwP3DL/kCRJkiRJkrQ15pzgqaofAu8BbgI2Az+tqi9NrZdkTZL1SdZPTEzMPVJJkiRJkiT1NZ8pWjsDRwH7AnsCOyR5+dR6VbW2qsaranxsbGzukUqSJEmalyTnJNmS5Ds9ZX+R5HtJvpXks0l26tl3SpKNSb6f5AXdRC1JGsR8pmg9D/hBVU1U1a+Bi4BnLkxYkiRJkhbBR4EjppRdBjy5qp4C/BNwCkCS/WnW2HxS+5y/SrLN0oUqSdoa80nw3AQckmT7JAEOBzYsTFiSJEmSFlpVfQX48ZSyL1XV3e3m14FV7eOjgE9V1Z1V9QNgI3DwkgUrSdoq81mDZx1wIXA18O32tdYuUFySJEmSlt7vA/9f+3glcHPPvk1t2QO47qYkdW9ed9GqqlOr6glV9eSqekVV3blQgUmSJElaOkn+GLgb+MRkUZ9q1e+5rrspSd1b0XUAkiRJkrqVZDVwJHB4VU0mcTYBe/VUWwXcstSxSZIGM68RPJIkSZJGW5IjgLcBL62qf+vZdQlwXJKHJdkX2A/4RhcxSpJm5wgeSZIkaZlIch5wKLBrkk3AqTR3zXoYcFlz7xS+XlWvrqrrkpwPfJdm6tZrquqebiKXJM3GBI8kSZK0TFTV8X2Kz56h/ruAdy1eRJKkhWKCR5KkIXNB+q1rKkmSJE3PNXgkSZIkSZJGnAkeSZIkSZKkEWeCR5IkSZIkacSZ4JEkSZIkSRpxJngkSZIkSZJGnAkeSZIkSZKkEWeCR5IkSZIkacSZ4JEkPUCSnZJcmOR7STYkeUaSXZJcluT69vfOXccpSZIkqWGCR5LUz/uBL1TVE4CnAhuAk4HLq2o/4PJ2W5IkSdIQMMEjSbqfJI8EngOcDVBVd1XV7cBRwLlttXOBo7uJUJIkSdJUJngkSVM9BpgAPpLkmiRnJdkB2L2qNgO0v3fr9+Qka5KsT7J+YmJi6aKWJEmSljETPJKkqVYABwEfqqoDgV+wFdOxqmptVY1X1fjY2NhixShJkiSphwkeSdJUm4BNVbWu3b6QJuFzW5I9ANrfWzqKT5IkSdIUK7oOQJI0XKrq1iQ3J3l8VX0fOBz4bvuzGji9/X1xh2FKC+qCpNPjH1vV6fElSdLoM8EjSerntcAnkmwL3AC8imbU5/lJTgBuAo7tMD5JkiRJPUzwSJIeoKquBcb77Dp8qWORJEmSNDvX4JEkSZKWiSTnJNmS5Ds9ZbskuSzJ9e3vndvyJDkzycYk30pyUHeRS5JmY4JHkiRJWj4+Chwxpexk4PKq2g+4nPvunPhCYL/2Zw3woSWKUZI0ByZ4JEmSpGWiqr4C/HhK8VHAue3jc4Gje8o/Vo2vAztN3k1RkjR8TPBIkiRJy9vuVbUZoP29W1u+Eri5p96mtuwBkqxJsj7J+omJiUUNVpLUnwkeSZIkSf2kT1n1q1hVa6tqvKrGx8bGFjksSVI/JngkSZKk5e22yalX7e8tbfkmYK+eequAW5Y4NknSgEzwSJIkScvbJcDq9vFq4OKe8v/S3k3rEOCnk1O5JEnDZ0XXAUiSJElaGknOAw4Fdk2yCTgVOB04P8kJwE3AsW31S4EXARuBfwNeteQBS5IGZoJHkiRJWiaq6vhpdh3ep24Br1nciCRJC8UpWpIkSZIkSSNuXgmeJDsluTDJ95JsSPKMhQpMkiRJkiRJg5nvFK33A1+oqmOSbAtsvwAxSZIkSZIkaSvMOcGT5JHAc4BXAlTVXcBdCxOWJEmSJEmSBjWfKVqPASaAjyS5JslZSXZYoLgkSZIkSZI0oPlM0VoBHAS8tqrWJXk/cDLw33srJVkDrAHYe++953G47px45jVdhyBJkiRJkjSt+Yzg2QRsqqp17faFNAmf+6mqtVU1XlXjY2Nj8zicJEmSJEmS+plzgqeqbgVuTvL4tuhw4LsLEpUkSZIkSZIGNt+7aL0W+ER7B60bgFfNPyRJkiRJkiRtjXkleKrqWmB8gWKRJEmSJEnSHMxnDR5JkiRJkiQNARM8kiRJkiRJI84EjyRJkiRJ0ogzwSNJkiRJkjTiTPBIkiRJkiSNOBM8kiRJkiRJI84EjyRJkiRJ0ogzwSNJkiSJJG9Icl2S7yQ5L8l2SfZNsi7J9Uk+nWTbruOUJPVngkeSJEla5pKsBF4HjFfVk4FtgOOAdwPvq6r9gJ8AJ3QXpSRpJiZ4JEmSJAGsAB6eZAWwPbAZOAy4sN1/LnB0R7FJkmZhgkeSJEla5qrqh8B7gJtoEjs/Ba4Cbq+qu9tqm4CV3UQoSZqNCR5JkiRpmUuyM3AUsC+wJ7AD8MI+VWua569Jsj7J+omJicULVJI0rRVdByBJkiSpc88DflBVEwBJLgKeCeyUZEU7imcVcEu/J1fVWmAtwPj4eN8kkKT75LR0duw61bfog5UjeCRJkiTdBBySZPskAQ4HvgtcARzT1lkNXNxRfJKkWZjgkSRJkpa5qlpHs5jy1cC3aT4nrAXeBrwxyUbgUcDZnQUpSZqRU7QkSZI6dkG6G6p/bDlUX42qOhU4dUrxDcDBHYQjSdpKjuCRJEmSJEkacSZ4JEmSJEmSRpwJHkmSJEmSpBFngkeS1FeSbZJck+Rz7fa+SdYluT7Jp5Ns23WMkiRJkhomeCRJ0zkJ2NCz/W7gfVW1H/AT4IROopIkSZL0ACZ4JEkPkGQV8GLgrHY7wGE0t9AFOBc4upvoJEmSJE1lgkeS1M8ZwFuBe9vtRwG3V9Xd7fYmYGW/JyZZk2R9kvUTExOLH6kkSZIkEzySpPtLciSwpaqu6i3uU7X6Pb+q1lbVeFWNj42NLUqMkiRJku5vRdcBSJKGzrOAlyZ5EbAd8EiaET07JVnRjuJZBdzSYYySJEmSejiCR5J0P1V1SlWtqqp9gOOAL1fVy4ArgGPaaquBizsKUZIkSdIUJngkSYN6G/DGJBtp1uQ5u+N4JEmSJLWcoiVJmlZVXQlc2T6+ATi4y3gkSZIk9ecIHkmSJEmSpBFngkeSJEmSJGnEOUVLkiRJkoCclq5DkBZdl//P69Tq7NjLgSN4JEmSJEmSRty8EzxJtklyTZLPLURAkiRJkiRJ2joLMYLnJGDDAryOJEmSJEmS5mBeCZ4kq4AXA2ctTDiSJEmSupBkpyQXJvlekg1JnpFklySXJbm+/b1z13FKkvqb7yLLZwBvBXacrkKSNcAagL333nueh5OkhXfimdd0duwPvO7Azo4tSdIU7we+UFXHJNkW2B54O3B5VZ2e5GTgZOBtXQYpSepvzgmeJEcCW6rqqiSHTlevqtYCawHGx8ddMluSNBIuiHdSkbR8JHkk8BzglQBVdRdwV5KjgEPbaucCV2KCR5KG0nymaD0LeGmSG4FPAYcl+fiCRCVJkiRpKT0GmAA+0t5A5awkOwC7V9VmgPb3bv2enGRNkvVJ1k9MTCxd1JKk35hzgqeqTqmqVVW1D3Ac8OWqevmCRSZJkiRpqawADgI+VFUHAr+gmY41kKpaW1XjVTU+Nja2WDFKkmawEHfRkiRJkjTaNgGbqmpdu30hTcLntiR7ALS/t3QUnyRpFguS4KmqK6vqyIV4LUmSJElLq6puBW5O8vi26HDgu8AlwOq2bDVwcQfhSZIGMN+7aEmSJEl6cHgt8In2Dlo3AK+i+UL4/CQnADcBx3YYnyRpBiZ4JEmSJFFV1wLjfXYdvtSxSJK2nmvwSJIkSZIkjTgTPJIkSZIkSSPOKVqSJEmSJGnR5bR0duw6tTo79lJxBI8kSZIkSdKIM8EjSZIkSZI04kzwSJIkSZIkjTgTPJIkSZIkSSPOBI8kSZIkSdKIM8EjSZIkSZI04kzwSJIkSZIkjTgTPJIkSZIkSSPOBI8kSZIkSdKIW9F1AJIEcOKZ13QdgiRJkiSNLEfwSJIkSZIkjThH8EiSJC1jFySdHfvYqs6OLUnSg40jeCRJkiRJkkacCR5JkiRJkqQRZ4JHkiRJEgBJtklyTZLPtdv7JlmX5Pokn06ybdcxSpL6M8EjSZIkadJJwIae7XcD76uq/YCfACd0EpUkaVYmeCRJkiSRZBXwYuCsdjvAYcCFbZVzgaO7iU6SNBsTPJIkSZIAzgDeCtzbbj8KuL2q7m63NwEr+z0xyZok65Osn5iYWPxIJUkPYIJHkiRJWuaSHAlsqaqreov7VO2Wjn5AAAAL2UlEQVR7b/uqWltV41U1PjY2tigxSpJmtqLrACRJkiR17lnAS5O8CNgOeCTNiJ6dkqxoR/GsAm5ZzCByWr+ckiRpEI7gkSTdT5K9klyRZEOS65Kc1JbvkuSy9k4qlyXZuetYJUkLo6pOqapVVbUPcBzw5ap6GXAFcExbbTVwcUchSpJmYYJHkjTV3cCbquqJwCHAa5LsD5wMXN7eSeXydluS9OD2NuCNSTbSrMlzdsfxSJKm4RQtSdL9VNVmYHP7+GdJNtAsqnkUcGhb7VzgSpoLf0nSg0hVXUnTx1NVNwAHdxmPJGkwJngkSdNKsg9wILAO2L1N/lBVm5PsNs1z1gBrAPbee++lCVTSSLog3a23cmz1XStYkqSR5RQtSVJfSR4BfAZ4fVXdMejzvJOKJEmStPRM8EiSHiDJQ2mSO5+oqova4tuS7NHu3wPY0lV8kiRJku5vzgme6e6yIkkabUlCs4jmhqp6b8+uS2juoALeSUWSJEkaKvNZg2fyLitXJ9kRuCrJZVX13QWKTZLUjWcBrwC+neTatuztwOnA+UlOAG4Cju0oPkmSJElTzDnBM8NdVkzwSNIIq6qvAtOtfHr4UsYiSZIkaTALchetKXdZmbrPu6lII+LEM6/pOgRJkiRJWnA5rbs7NwLUqYt/98Z5J3hmu8tKVa0F1gKMj497P0pJ0sC6vIWyJEmSNErmdRetae6yIkmSJEmSpCU0n7toTXeXFUmSJEmSJC2h+YzgmbzLymFJrm1/XrRAcUmSJEmSJGlA87mL1kx3WZEkSZIkSdISmdcaPJIkSZIkSeqeCR5JkiRJkqQRZ4JHkiRJkiRpxJngkSRJkiRJGnFzXmRZkiRJGlUXpLt7hRxb1dmxJUkPXo7gkSRJkpa5JHsluSLJhiTXJTmpLd8lyWVJrm9/79x1rJKk/kzwSJIkSbobeFNVPRE4BHhNkv2Bk4HLq2o/4PJ2W5I0hEzwSJIkSctcVW2uqqvbxz8DNgArgaOAc9tq5wJHdxOhJGk2JngkSZIk/UaSfYADgXXA7lW1GZokELDbNM9Zk2R9kvUTExNLFaokqYcJHkmSJEkAJHkE8Bng9VV1x6DPq6q1VTVeVeNjY2OLF6AkaVomeCRJkiSR5KE0yZ1PVNVFbfFtSfZo9+8BbOkqPknSzEzwSJIkSctckgBnAxuq6r09uy4BVrePVwMXL3VskqTBrOg6AEmSJEmdexbwCuDbSa5ty94OnA6cn+QE4Cbg2I7ikyTNwgSPJEmStMxV1VeBTLP78KWMRZI0N07RkiRJkiRJGnEjM4LnxDOv6ToESZIkSZKkoeQIHkmSJEmSpBFngkeSJEmSJGnEmeCRJEmSJEkacSZ4JEmSJEmSRpwJHkmSJEmSpBFngkeSJEmSJGnEmeCRJEmSJEkacSZ4JEmSJEmSRpwJHkmSJEmSpBFngkeSJEmSJGnEmeCRJEmSJEkacSZ4JEmSJEmSRpwJHkmSJEmSpBFngkeSJEmSJGnEmeCRJEmSJEkacSZ4JEmSJEmSRty8EjxJjkjy/SQbk5y8UEFJkoaXfb8kLS/2+5I0Guac4EmyDfBB4IXA/sDxSfZfqMAkScPHvl+Slhf7fUkaHfMZwXMwsLGqbqiqu4BPAUctTFiSpCFl3y9Jy4v9viSNiBXzeO5K4Oae7U3A06dWSrIGWNNu/jzJ9+dxzJnsCvxokV57voxtboxtboY1tmGNCzqM7YMnzVplttgevWDBDGbWvn8J+/3ZDPP/uWFiOw3GdhqM7TSIZL7ttJR9/7Bd88/E/3+DsZ0GYzsNxnYaUN4xr75/oH5/Pgme9CmrBxRUrQXWzuM4gwWTrK+q8cU+zlwY29wY29wMa2zDGhcY21aate9fqn5/NkPYdkPJdhqM7TQY22kwI9ZOQ3XNP5MRa9fO2E6DsZ0GYzsNbinaaj5TtDYBe/VsrwJumV84kqQhZ98vScuL/b4kjYj5JHj+Edgvyb5JtgWOAy5ZmLAkSUPKvl+Slhf7fUkaEXOeolVVdyc5EfgisA1wTlVdt2CRbb3OpwPMwNjmxtjmZlhjG9a4wNgGNoR9/0yGqu2GmO00GNtpMLbTYEamnez3H5Rsp8HYToOxnQa3+EvXVD1gCq0kSZIkSZJGyHymaEmSJEmSJGkImOCRJEmSJEkacSOZ4EmyV5IrkmxIcl2Sk9ryXZJcluT69vfOQxLXO5L8MMm17c+LljKuNobtknwjyTfb2E5ry/dNsq5ts0+3i+cNS2wfTfKDnnY7YKlj64lxmyTXJPlcu915u80Q21C0W5Ibk3y7jWF9W9bpe3SW2Dp/n7Zx7JTkwiTfa/uSZwxLuw2rYT0nDJthPg8Mo2Hu94fFMPfzw8R+fXHY9w/Gvn/r2PfPzr5/MF31/SOZ4AHuBt5UVU8EDgFek2R/4GTg8qraD7i83R6GuADeV1UHtD+XLnFcAHcCh1XVU4EDgCOSHAK8u41tP+AnwAlDFBvAW3ra7doOYpt0ErChZ3sY2m3S1NhgeNrtuW0M4+121+/RXlNjg+7fpwDvB75QVU8Ankrztx2mdhtGw3pOGDbDfB4YRsPc7w+TYe7nh4X9+uKw7x+Mff/Wse8fjH3/7Drp+0cywVNVm6vq6vbxz2gaayVwFHBuW+1c4Oghiatz1fh5u/nQ9qeAw4AL2/Ilb7NZYhsKSVYBLwbOarfDELRbv9hGQKfv0WGX5JHAc4CzAarqrqq6HdttRsN6Thg2w3weGDbD3O+PAN93PezXF499/2Ds+wdn3z8vvu96dNn3j2SCp1eSfYADgXXA7lW1GZpOH9htSOICODHJt5Kc09WQtXbI4bXAFuAy4J+B26vq7rbKJjpKSE2Nraom2+1dbbu9L8nDuogNOAN4K3Bvu/0ohqTdeGBsk4ah3Qr4UpKrkqxpy4blPdovNuj+ffoYYAL4SDs8+KwkOzA87Tb0hvWcMCyG+TwwZIa53x8mw9zPDwv79SVg3z8z+/6B2fcPxr5/dp31/SOd4EnyCOAzwOur6o6u45nUJ64PAb9NMyxyM/CXXcRVVfdU1QHAKuBg4In9qi1tVO1Bp8SW5MnAKcATgKcBuwBvW+q4khwJbKmqq3qL+1Rd8nabJjYYgnZrPauqDgJeSDNs+jkdxdFPv9iG4X26AjgI+FBVHQj8Aoe4DmxYzwnDZJjPA8NimPv9ITTM/fywsF9fZPb9s7Pvn519/1ax759dZ33/yCZ4kjyUpjP/RFVd1BbflmSPdv8eNFnqzuOqqtvajvVe4MM0HWtn2uFhV9LMV94pyYp21yrglq7igvvFdkQ79Laq6k7gI3TTbs8CXprkRuBTNMM0z2A42u0BsSX5+JC0G1V1S/t7C/DZNo7O36PTxTYk79NNwKaeEWwX0pwchqLdhtmwnhOG1TCfB4bAMPf7Q2WY+/khYr++iOz7t459/4zs+wdk3z+Qzvr+kUzwtPMhzwY2VNV7e3ZdAqxuH68GLh6GuCb/iK3/AHxnKeNqYxhLslP7+OHA82jmKl8BHNNWW/I2myG27/X85w/N/MQlb7eqOqWqVlXVPsBxwJer6mUMQbtNE9vLh6HdkuyQZMfJx8DvtnF0+h6dKbZheJ9W1a3AzUke3xYdDnyXIWi3YTas54RhM8zngWEyzP3+MBnmfn6Y2K8vHvv+wdj3D8a+fzD2/YPpsu9P1eiNMkvybOAfgG9z3xzJt9PMuz0f2Bu4CTi2qn48BHEdTzPto4AbgT+cnHu3hLE9hWYhp21oEnvnV9U7kzyGJku9C3AN8PJ25McwxPZlYIxmeOS1wKt7FolbckkOBd5cVUcOQ7vNEFvn7da2z2fbzRXAJ6vqXUkeRYfv0Vli+xs6fp+28R1As7jftsANwKto3xd02G7DbFjPCcNmmM8Dw2qY+/2uDXM/P2zs1xeHff9g7Pu3nn3/9Oz7B9dV3z+SCR5JkiRJkiTdZySnaEmSJEmSJOk+JngkSZIkSZJGnAkeSZIkSZKkEWeCR5IkSZIkacSZ4JEkSZIkSRpxJngkSZIkSZJGnAkeSZIkSZKkEff/A664Iou4n5HgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# add color\n",
"\n",
"fig = plt.figure(figsize = (16,4))\n",
"sub1 = plt.subplot(1,3,1)\n",
"sub1.set_title('Human Resources')\n",
"sub1.hist(empl_data[empl_data['Department']=='Human Resources']['Age'], bins=10, color='#6b91ce')\n",
"sub2 = plt.subplot(1,3,2)\n",
"sub2.set_title('Sales')\n",
"sub2.hist(empl_data[empl_data['Department']=='Sales']['Age'], bins=10, color='#AA0000')\n",
"sub3 = plt.subplot(1,3,3)\n",
"sub3.set_title('Research & Development')\n",
"sub3.hist(empl_data[empl_data['Department']=='Research & Development']['Age'], bins=10, color='green')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>Subplots are very powerful, and can be completely controlled independent of one another. You can place any visualization within each subplot.</p>"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (16,4))\n",
"sub1 = plt.subplot(1,3,1)\n",
"sub1.set_title('Histogram')\n",
"sub1.hist(empl_data[empl_data['Department']=='Sales']['Age'], bins=10, color='#6b91ce')\n",
"sub2 = plt.subplot(1,3,2)\n",
"sub2.set_title('Violinplot')\n",
"sub2.violinplot(empl_data[empl_data['Department']=='Sales']['Age'])\n",
"sub3 = plt.subplot(1,3,3)\n",
"sub3.set_title('Boxplot')\n",
"sub3.boxplot(empl_data[empl_data['Department']=='Sales']['Age'])\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Findings</h3>\n",
"<p>Having explored Age, we learned:\n",
"<ul>\n",
" <li>The average age is roughly 37 years.</li>\n",
" <li>This average does not change much across the 3 departments.</li>\n",
" <li>The median age does show more variance than the average.</li>\n",
" <li>HR was the lone department where the employee count of older employees increaesed at the higher end of the age range (50-60 years).</li>\n",
" </ul>\n",
" </p> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Matplotlib summary</h3>\n",
"<p>Matplotlib is extremely powerful. It is the underlying functionality of much visualization in Python. While very powerful, it's also verbose. This allows for a lot of control, but requires a lot of code.</p>\n",
"<p>Let's move on to some other packages that build on top of Matplotlib, and apply sensible defaults that can reduce code and make EDA more efficient. Having to not think much about the code and more about the data and problem at hand is often preferable and keeps analysts in their flow.</p>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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