{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Statistical Thinking and Visualization\n", "\n", "\n", "\n", "The dataset we will be using is from a restaurant and looks like:\n", "\n", "id\t| total_bill\t| tip\t| sex\t| smoker\t| day\t| time\t| size\n", ":---: | :---:| :---: | :---: | :---: | :---: | :---: | :---: \n", "0\t| 16.99 | 1.01\t| Female\t| No\t| Sun\t| Dinner\t| 2\n", "1\t| 10.34 | 1.66\t| Male\t| No\t| Sun\t| Dinner |\t3\n", "2\t| 21.01 | 3.50\t| Male\t| No\t| Sun\t| Dinner |\t3\n", "3\t| 23.68 |\t3.31\t| Male\t| No\t| Sun\t| Dinner | 2\t\n", "\n", "Before you move on, take a minute to look at that table. Seriously. Take 10 seconds out of your life to look at the table above.\n", "\n", "Okay.\n", "\n", "First, Let's load the dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
525.294.71MaleNoSunDinner4
68.772.00MaleNoSunDinner2
726.883.12MaleNoSunDinner4
815.041.96MaleNoSunDinner2
914.783.23MaleNoSunDinner2
1010.271.71MaleNoSunDinner2
1135.265.00FemaleNoSunDinner4
1215.421.57MaleNoSunDinner2
1318.433.00MaleNoSunDinner4
1414.833.02FemaleNoSunDinner2
1521.583.92MaleNoSunDinner2
1610.331.67FemaleNoSunDinner3
1716.293.71MaleNoSunDinner3
1816.973.50FemaleNoSunDinner3
1920.653.35MaleNoSatDinner3
2017.924.08MaleNoSatDinner2
2120.292.75FemaleNoSatDinner2
2215.772.23FemaleNoSatDinner2
2339.427.58MaleNoSatDinner4
2419.823.18MaleNoSatDinner2
2517.812.34MaleNoSatDinner4
2613.372.00MaleNoSatDinner2
2712.692.00MaleNoSatDinner2
2821.704.30MaleNoSatDinner2
2919.653.00FemaleNoSatDinner2
........................
21428.176.50FemaleYesSatDinner3
21512.901.10FemaleYesSatDinner2
21628.153.00MaleYesSatDinner5
21711.591.50MaleYesSatDinner2
2187.741.44MaleYesSatDinner2
21930.143.09FemaleYesSatDinner4
22012.162.20MaleYesFriLunch2
22113.423.48FemaleYesFriLunch2
2228.581.92MaleYesFriLunch1
22315.983.00FemaleNoFriLunch3
22413.421.58MaleYesFriLunch2
22516.272.50FemaleYesFriLunch2
22610.092.00FemaleYesFriLunch2
22720.453.00MaleNoSatDinner4
22813.282.72MaleNoSatDinner2
22922.122.88FemaleYesSatDinner2
23024.012.00MaleYesSatDinner4
23115.693.00MaleYesSatDinner3
23211.613.39MaleNoSatDinner2
23310.771.47MaleNoSatDinner2
23415.533.00MaleYesSatDinner2
23510.071.25MaleNoSatDinner2
23612.601.00MaleYesSatDinner2
23732.831.17MaleYesSatDinner2
23835.834.67FemaleNoSatDinner3
23929.035.92MaleNoSatDinner3
24027.182.00FemaleYesSatDinner2
24122.672.00MaleYesSatDinner2
24217.821.75MaleNoSatDinner2
24318.783.00FemaleNoThurDinner2
\n", "

244 rows × 7 columns

\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n", "5 25.29 4.71 Male No Sun Dinner 4\n", "6 8.77 2.00 Male No Sun Dinner 2\n", "7 26.88 3.12 Male No Sun Dinner 4\n", "8 15.04 1.96 Male No Sun Dinner 2\n", "9 14.78 3.23 Male No Sun Dinner 2\n", "10 10.27 1.71 Male No Sun Dinner 2\n", "11 35.26 5.00 Female No Sun Dinner 4\n", "12 15.42 1.57 Male No Sun Dinner 2\n", "13 18.43 3.00 Male No Sun Dinner 4\n", "14 14.83 3.02 Female No Sun Dinner 2\n", "15 21.58 3.92 Male No Sun Dinner 2\n", "16 10.33 1.67 Female No Sun Dinner 3\n", "17 16.29 3.71 Male No Sun Dinner 3\n", "18 16.97 3.50 Female No Sun Dinner 3\n", "19 20.65 3.35 Male No Sat Dinner 3\n", "20 17.92 4.08 Male No Sat Dinner 2\n", "21 20.29 2.75 Female No Sat Dinner 2\n", "22 15.77 2.23 Female No Sat Dinner 2\n", "23 39.42 7.58 Male No Sat Dinner 4\n", "24 19.82 3.18 Male No Sat Dinner 2\n", "25 17.81 2.34 Male No Sat Dinner 4\n", "26 13.37 2.00 Male No Sat Dinner 2\n", "27 12.69 2.00 Male No Sat Dinner 2\n", "28 21.70 4.30 Male No Sat Dinner 2\n", "29 19.65 3.00 Female No Sat Dinner 2\n", ".. ... ... ... ... ... ... ...\n", "214 28.17 6.50 Female Yes Sat Dinner 3\n", "215 12.90 1.10 Female Yes Sat Dinner 2\n", "216 28.15 3.00 Male Yes Sat Dinner 5\n", "217 11.59 1.50 Male Yes Sat Dinner 2\n", "218 7.74 1.44 Male Yes Sat Dinner 2\n", "219 30.14 3.09 Female Yes Sat Dinner 4\n", "220 12.16 2.20 Male Yes Fri Lunch 2\n", "221 13.42 3.48 Female Yes Fri Lunch 2\n", "222 8.58 1.92 Male Yes Fri Lunch 1\n", "223 15.98 3.00 Female No Fri Lunch 3\n", "224 13.42 1.58 Male Yes Fri Lunch 2\n", "225 16.27 2.50 Female Yes Fri Lunch 2\n", "226 10.09 2.00 Female Yes Fri Lunch 2\n", "227 20.45 3.00 Male No Sat Dinner 4\n", "228 13.28 2.72 Male No Sat Dinner 2\n", "229 22.12 2.88 Female Yes Sat Dinner 2\n", "230 24.01 2.00 Male Yes Sat Dinner 4\n", "231 15.69 3.00 Male Yes Sat Dinner 3\n", "232 11.61 3.39 Male No Sat Dinner 2\n", "233 10.77 1.47 Male No Sat Dinner 2\n", "234 15.53 3.00 Male Yes Sat Dinner 2\n", "235 10.07 1.25 Male No Sat Dinner 2\n", "236 12.60 1.00 Male Yes Sat Dinner 2\n", "237 32.83 1.17 Male Yes Sat Dinner 2\n", "238 35.83 4.67 Female No Sat Dinner 3\n", "239 29.03 5.92 Male No Sat Dinner 3\n", "240 27.18 2.00 Female Yes Sat Dinner 2\n", "241 22.67 2.00 Male Yes Sat Dinner 2\n", "242 17.82 1.75 Male No Sat Dinner 2\n", "243 18.78 3.00 Female No Thur Dinner 2\n", "\n", "[244 rows x 7 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "tipsFile = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv'\n", "tips = pd.read_csv(tipsFile)\n", "tips\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've created a Pandas dataframe from a file before, but I haven't really explained it. The Pandas function that loads a file is:\n", "\n", " pd.read_csv(filename)\n", " \n", "So for example:\n", "\n", " tips = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv')\n", " \n", "\n", "will read the file tips.csv and create a pandas dataframe we call `tips`.\n", "\n", "As the name suggests `read_csv` reads a csv file. **csv** stands for comma separated values. If you click on [this link to the tips.csv file](https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv), you will see that the file looks like:\n", "\n", " \"total_bill\",\"tip\",\"sex\",\"smoker\",\"day\",\"time\",\"size\"\n", " 16.99,1.01,\"Female\",\"No\",\"Sun\",\"Dinner\",2\n", " 10.34,1.66,\"Male\",\"No\",\"Sun\",\"Dinner\",3\n", " 21.01,3.5,\"Male\",\"No\",\"Sun\",\"Dinner\",3\n", " 23.68,3.31,\"Male\",\"No\",\"Sun\",\"Dinner\",2\n", " \n", "CSV files are a very common format and every spreadsheet program from Microsoft Excel to Google Sheets can read and write them. Plus, they are one of the standard file formats for data science. \n", "\n", "The pandas function `read_csv` can read csv files from the web, as we just did, and it can also read files directly from your computer. So if you have your own csv file -- say it's named `biology_data.csv` and its in the same folder as your Python Notebook file, you can load it into a dataframe called `bio` by executing:\n", "\n", " bio = pd.read_csv('biology_data.csv')\n", "\n", "# Some plot examples\n", "\n", "Now that we have a data set we can review our knowledge of making plots and charts.\n", "\n", "\n", "## Scatter Plot\n", "Let's do a Scatter Plot of the total bill :" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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4JvHwc08MTNxEcUjKwhMUGiUmSX7uiYGJmygOeRaeEKLmhSeURIlJkp97YmDi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\nunQpampq8Oabb+LNN98EALz11lswmUz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "plt.scatter(tips['total_bill'], tips['tip'])\n", "plt.title('Tips in Relation To The Total Bill')\n", "plt.ylabel('Tip ($)')\n", "plt.xlabel('Total Bill ($)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Plot\n", "Let's still use total bill but plot it as a bar plot using the default binning:\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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sbDU2Nlo5NAAAHY5lof3SSy8pMzNTtbW1kqScnBylpaUpPz9fbrdbhYWFVg0N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\nhDYAADZBaAMAYBOENgAANvF/PTcpTh9RUdYAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "_ = plt.hist(tips['total_bill'])\n", "_ = plt.title('Histogram of Total Bill')\n", "_ = plt.xlabel('total bill')\n", "_ = plt.ylabel('number of customers')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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Aarh8I9orr7zi8snjx4/XokWLruhAAADgwjg8DgCAIVzuaY8fP/6Ct1uWpZycHFsGAgAA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\nYAiiDQCAIYg2AACGINoAABji/wOnvnd/++VV8QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.barplot(x=\"day\", y=\"total_bill\", data=tips)\n", "plt.title('Mean Total Bill')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows the average bill for each of the days. So, for example, the average bill on sunday was around $21.00. The whiskers indicate uncertainty. Basically, if this was just a sample of all customers, then we are 95% certain that the average bill will be within the whisker. \n", "\n", "#### The sum of all the bills\n", "Suppose we are interested in the total of the bills for each day (instead of the average or mean). For that we can use the `estimator=sum` argument (The `ci` argument gets rid of the whiskers): " ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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ajAoAQINWqwXvcDiUkZFx1fq1a9detS4hIUEJCQm1EQsAAONwoRsAAAxEwQMA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\nABiIggcAwEAUPAAABqLgAQAw0P8D0XMumab+FwwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "ax = sns.barplot(x=\"day\", y=\"total_bill\", data=tips, estimator=sum, palette=\"Blues_d\", ci=None)\n", "plt.title('Total Sales Per Day')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " So we have the most sales on Saturday." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slightly fancier bar plots\n", "Do women or men spend more?" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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DBg20fft25efnS7pyI5RffvlFqampCgoKcnH1QPnF4XGgHAoKCtLe\nvXvVq1cv1ahRQ3feeacuXbqkgwcPKjg4WB4eHmrUqJHS0tIK7JOens69pgEX4upxAEUyDEOXL1/W\noEGD9OKLL6pp06auLgkotzg8DqBI586d09///ne1aNGCwAZcjJk2AAAmwUwbAACTILQBADAJQhsA\nAJMgtAEAMAlCGwAAkyC0AQAwif8f+HckoZ7utwEAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.barplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips)\n", "plt.title('Mean Total Bill By Day and Sex')\n", "plt.margins(0.2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the average bill of a man is greater than the average bill of a woman. Suppose we are interested in whether there are more women or men on the various days. For that we switch to a countplot:\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(style=\"darkgrid\")\n", "plt.title('Number of Customers Per Day')\n", "ax = sns.countplot(x=\"day\", hue=\"sex\", data=tips)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow. That is pretty interesting. There are more women customers then men customers on Thursdays. And it seems like Friday is our slowest day.\n", "\n", "There are more examples of bar plots on the [Seaborn site](https://seaborn.pydata.org/generated/seaborn.barplot.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bee Swarm\n", "\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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M+iR5swuu1hEZGUlZWZl7u7a2lvDw8B4tSniv0tq2rm01XduEEJcuWB/EtLiJ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\nXiovJgane9QYB2gytmB3eFZYs5+3jRhbZKQtLlpGQhA7zknxUikVF+x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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.swarmplot(x='sex', y='total_bill', data= tips)\n", "plt.title('Total Bill By Sex')\n", "plt.xlabel('sex')\n", "plt.ylabel('total bill')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Violin" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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Dhw/7okThRcXFxcAwQjuo/9+8vLxc/l2FGIGioiL0QUaMIcPfjnMw1vhgWs7Uk5OTQ1pa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\nbmf8+PFERUV5q3y5Q5owRERExEfoRjQREREfodAWERHxEQp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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.violinplot(x='sex', y='total_bill', data= tips, palette='pastel')\n", "plt.title('Total Bill By Sex')\n", "plt.xlabel('sex')\n", "plt.ylabel('total bill')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ECDF\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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V7VBtx20/B6TqwbLxtLoBxL0eu81fe+01SVJGRoYWLVqkc845RxZL27aKFRUV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\n+9qh/NNsfXrtM4ZmhbQc3cn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "x = np.sort(tips['total_bill'])\n", "y = np.arange(1, len(x)+ 1) / len(x)\n", "plt.plot(x, y, marker='.', linestyle='none')\n", "plt.title('ECDF Plot of Total Bill')\n", "plt.xlabel('total bill')\n", "plt.ylabel('ECDF')\n", "plt.margins(0.02) # keeps data off plot edges\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Box Plot" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='sex', y='total_bill', data=tips)\n", "plt.title('Total Bill by Sex')\n", "plt.xlabel('sex')\n", "plt.ylabel('Total Bill')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## the small task\n", "\n", "Are you curious about any other parts of this data? If so, explore it with a few (3-4) other plots. (500xp)\n", "\n", "# Your Task\n", "\n", "I would like you to create a new Python Notebook from scratch for this task. You can work together in groups of 2-3 people. \n", "\n", "**The data** is in the file https://raw.githubusercontent.com/zacharski/data101/master/umw.csv\n", "\n", "This is a free form lab. There are two options:\n", "\n", "1. Demo to either Pratima or me - 4000xp max.\n", "2. Do a 5 minute presentation to that class. - 8000xp max.\n", "\n", "For both options you should explore your data and decide on a topic. For example,\n", "\n", "* You are given a 5 minute slot to present this data to high school seniors during one of UMW's open houses. What would you present?\n", "* For the last few years the computer science department had a goal to increase the number of women who major in computer science. Has the department been effective? How does it compare to other departments?\n", "* What are popular majors for women? For men? What are the fastest growing majors?\n", "\n", "\n", "## Requirements and suggestions\n", "\n", "1. The majority of your argument should be based on talking and visualization (plots). Some supporting descriptive statistics might also be useful. \n", "2. More points will be given if you explore the [Seaborn document](https://seaborn.pydata.org/index.html) and find plotting options that help your visualization.\n", "3. This is an exercise in thinking what type of plot (for ex., bar, scatter, etc) would be most effective and also practice in basing an argument or presentation on data.\n", "4. If you have a great idea of something you want to do, but are stuck implementing it, ask Pratima or me. We can help with information on how to do things in Pandas and with plotting (or can help you search Google).\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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 }