How to Create a Countplot in Seaborn with Python

In this article, we show how to create a countplot in seaborn with Python.

Seaborn is a module in Python that is built on top of matplotlib that is designed for statistical plotting.

Seaborn can create all types of statistical plotting graphs.

One of the plots that seaborn can create is a countplot.

A countplot is kind of likea histogram or a bar graph for some categorical area. It simply shows the number of occurrences of an item based on a certain type of category.

So in the following code below, we show how to create a countplot based on a category.

import seaborn as sns %matplotlib inline tips=sns.load_dataset('tips') tips Out[9]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 5 25.29 4.71 Male No Sun Dinner 4 6 8.77 2.00 Male No Sun Dinner 2 7 26.88 3.12 Male No Sun Dinner 4 8 15.04 1.96 Male No Sun Dinner 2 9 14.78 3.23 Male No Sun Dinner 2 10 10.27 1.71 Male No Sun Dinner 2 11 35.26 5.00 Female No Sun Dinner 4 12 15.42 1.57 Male No Sun Dinner 2 13 18.43 3.00 Male No Sun Dinner 4 14 14.83 3.02 Female No Sun Dinner 2 15 21.58 3.92 Male No Sun Dinner 2 16 10.33 1.67 Female No Sun Dinner 3 17 16.29 3.71 Male No Sun Dinner 3 18 16.97 3.50 Female No Sun Dinner 3 19 20.65 3.35 Male No Sat Dinner 3 20 17.92 4.08 Male No Sat Dinner 2 21 20.29 2.75 Female No Sat Dinner 2 22 15.77 2.23 Female No Sat Dinner 2 23 39.42 7.58 Male No Sat Dinner 4 24 19.82 3.18 Male No Sat Dinner 2 25 17.81 2.34 Male No Sat Dinner 4 26 13.37 2.00 Male No Sat Dinner 2 27 12.69 2.00 Male No Sat Dinner 2 28 21.70 4.30 Male No Sat Dinner 2 29 19.65 3.00 Female No Sat Dinner 2 .. ... ... ... ... ... ... ... 214 28.17 6.50 Female Yes Sat Dinner 3 215 12.90 1.10 Female Yes Sat Dinner 2 216 28.15 3.00 Male Yes Sat Dinner 5 217 11.59 1.50 Male Yes Sat Dinner 2 218 7.74 1.44 Male Yes Sat Dinner 2 219 30.14 3.09 Female Yes Sat Dinner 4 220 12.16 2.20 Male Yes Fri Lunch 2 221 13.42 3.48 Female Yes Fri Lunch 2 222 8.58 1.92 Male Yes Fri Lunch 1 223 15.98 3.00 Female No Fri Lunch 3 224 13.42 1.58 Male Yes Fri Lunch 2 225 16.27 2.50 Female Yes Fri Lunch 2 226 10.09 2.00 Female Yes Fri Lunch 2 227 20.45 3.00 Male No Sat Dinner 4 228 13.28 2.72 Male No Sat Dinner 2 229 22.12 2.88 Female Yes Sat Dinner 2 230 24.01 2.00 Male Yes Sat Dinner 4 231 15.69 3.00 Male Yes Sat Dinner 3 232 11.61 3.39 Male No Sat Dinner 2 233 10.77 1.47 Male No Sat Dinner 2 234 15.53 3.00 Male Yes Sat Dinner 2 235 10.07 1.25 Male No Sat Dinner 2 236 12.60 1.00 Male Yes Sat Dinner 2 237 32.83 1.17 Male Yes Sat Dinner 2 238 35.83 4.67 Female No Sat Dinner 3 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] sns.countplot(x='day', data=tips) Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x1ad8adf7588>

By convention, we import seaborn as sns.

In order to see the graph of code within the editor, we put in the statement, %matplotlib inline

You put this statement in if you are using an editor such as jupyter notebooks so that you can see the graph output in the editor.

Seaborn already has built-in data sets.

One data set that can be used is tips.

We import this dataset with the line, tips=sns.load_dataset('tips')

We then output the contents of tips, and you can see that it is a data set composed of columns.

The columns are total_bill, tip, sex, smoker, day, time, and size.

There are 244 rows of data we are plotting off of.

Countplots are plots that graph a certain column to show the number of times an item occurs in a data set. And the column represents a certain category.

In our example, we plot a countplot of the occurrences of days that appears in the tips data set.

Remember that there are 244 items of data from the tips data set.

The countplot shows the occurrences of the days of the week that are represented in the days column of the tips data set.

The countplot is shown below.

Thus, we can see the occurrences of the days.

Friday occurred with the greatest frequency, just about 90 times.

Saturday had the next greatest frequency of occurrences at just about 75.

Thursday then occurs the most at about 62.

Then Friday at about 18.

This is the use of a countplot in seaborn.

It's very much like a histogram or a bar graph, plotting the number of occurrences of an item in a data set.

And this is how to create a countplot in seaborn with Python.

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