{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Visualization\n", "In this tutorial we show how Python and its graphics libraries can be used to create the two most common types of distributional plots: histograms and boxplots.\n", "\n", "## Preliminaries\n", "I include the data import and library import commands at the start of each lesson so that the lessons are self-contained.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "bank = pd.read_csv('Data/Bank.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Basic descriptive statistics\n", "Pandas provides basic descriptive statistic functions as methods of the Series object. Recall that each DataFrame object consists of multiple Series (columns). Thus, the average salary for bank employees can be found as: " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "39.921923076923086" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bank['Salary'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, using a variable to save some typing:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(26.7, 39.921923076923086, 37.0, 97.0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sal = bank['Salary']\n", "sal.min(), sal.mean(), sal.median(), sal.max() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, recall, we can get statistical summary of all numerical columns using the `describe()` method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EmployeeEducLevJobGradeYrHiredYrBornYrsPriorSalary
count208.000000208.000000208.000000208.000000208.000000208.000000208.000000
mean104.5000003.1586542.75961585.32692354.6057692.37500039.921923
std60.1885921.4674641.5665296.98783210.3189883.13523711.256154
min1.0000001.0000001.00000056.00000030.0000000.00000026.700000
25%52.7500002.0000001.00000082.00000047.7500000.00000033.000000
50%104.5000003.0000003.00000087.00000056.5000001.00000037.000000
75%156.2500005.0000004.00000090.00000063.0000004.00000044.000000
max208.0000005.0000006.00000093.00000073.00000018.00000097.000000
\n", "
" ], "text/plain": [ " Employee EducLev JobGrade YrHired YrBorn YrsPrior \\\n", "count 208.000000 208.000000 208.000000 208.000000 208.000000 208.000000 \n", "mean 104.500000 3.158654 2.759615 85.326923 54.605769 2.375000 \n", "std 60.188592 1.467464 1.566529 6.987832 10.318988 3.135237 \n", "min 1.000000 1.000000 1.000000 56.000000 30.000000 0.000000 \n", "25% 52.750000 2.000000 1.000000 82.000000 47.750000 0.000000 \n", "50% 104.500000 3.000000 3.000000 87.000000 56.500000 1.000000 \n", "75% 156.250000 5.000000 4.000000 90.000000 63.000000 4.000000 \n", "max 208.000000 5.000000 6.000000 93.000000 73.000000 18.000000 \n", "\n", " Salary \n", "count 208.000000 \n", "mean 39.921923 \n", "std 11.256154 \n", "min 26.700000 \n", "25% 33.000000 \n", "50% 37.000000 \n", "75% 44.000000 \n", "max 97.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bank.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histograms in Seaborn\n", "Two graphics libraries are in common use in Python: Matplotlib and Seaborn. Seaborn is an extension of Matplotlib that addresses a few specific graphics challenges, including histograms and boxplots. As such, we will restrict our attention here to Seaborn." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the library\n", "As before, we must load a library before we can use it. Seaborn is typically aliased as `sns`, but this is just a convention." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a histogram\n", "Histograms are created in Seaborn using the `histplot()` (histogram plot) method. The syntax of Seaborn is closer to R than Python. For example, the plot is called on a Seaborn library object (`sns`) and passed a data frame as an argument." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(x=bank['Salary'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few things to notice about this output\n", "+ The `histplot()` method returns an AxesSubplot value. Since we don't need this (or even know what it is), we can clean-up our output in ending each Seaborn (or Matplotlib) call with a semicolon.\n", "+ Seaborn guesses at a good number of bins. It appears to be more than the default in R. But recall that the point of a histogram is to get a rough sense of the shape of the distribution of the variable. We can certainly change the number of bins (to say 10 or 12), but it is not critical.\n", "\n", "We can pass some arguments to the method to get a more elaborate histogram. Turning on the kernel density estimate (`kde=True`) gives us a smoothed \"kernel density\" line, like in SAS EG." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(x=bank['Salary'], bins=10, kde=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, it is possible to change colors, and so on. I have split the more detailed method call below over multiple lines, which is more readable and more with keeping with R-style coding." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(x=bank['Salary'], \n", " bins=10, kde=False,\n", " stat=\"probability\",\n", " color='green' \n", " );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a boplot\n", "Creating a boxplot in Seaborn is very simple:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=bank['Salary']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you prefer a vertical orientation, you can plot your data as the `y` variable instead of the `x` variable, as done above. Also, notice that Seaborn does not provide an indicator of the mean by default. Obviously, skewed data such as this pulls the mean away from the median. I like to eyeball the difference between the two measures." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": "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