Using Pandas for Descriptive Statistics in Python

One of the beautiful things about Python is the ease with which you can generate useful information from a given data set. In this example, we’ll use Pandas to generate some high-level descriptive statistics.

Importing Numpy and Pandas

Let’s import Pandas and assign it the alias pd as is convention.

import pandas as pd

Once these are imported, we can generate a simple dataframe that we can later use for analysis. First we’ll create a dictionary:

my_dict = { 
     'name' : ["Kevin", "George", "Jane", "Mel", "Thomas","Erica", "Lisa"],
     'age' : [20,27, 35, 55, 18, 21, 35],
     'salary': [30000, 45000, 55000, 78000, 28000, 32000, 70000],
     'gender': ['m', 'm', 'f', 'f', 'm', 'f', 'f']
}

Check out some other Python tutorials on datagy, including our complete guide to styling Pandas and our comprehensive overview of Pivot Tables in Pandas!

Using Pandas, we can turn this regular Python dictionary into a dataframe, using the DataFrame object.

data = pd.DataFrame(my_dict)

To make sure that the conversion happened correctly, we can use the .head function to print out some data.

print(data.head())

This prints out the first five records of our data, which are printed below:

	name	age	salary	gender
0	Kevin	20	30000	m
1	George	27	45000	m
2	Jane	35	55000	f
3	Mel	55	78000	f
4	Thomas	18	28000	m

Generating Simple Descriptive Statistics with Pandas

While Pandas provides functions to return descriptive statistics individually on the median, the max, and the min (among others), we can use the .describe function to easily print out key descriptive statistics.

print(data.describe())

This will return the output below!

Note that descriptive statistics are only displayed for numeric data types.

	age	salary
count	7.000000	7.000000
mean	30.142857	48285.714286
std	12.966991	20089.087301
min	18.000000	28000.000000
25%	20.500000	31000.000000
50%	27.000000	45000.000000
75%	35.000000	62500.000000
max	55.000000	78000.000000

Pandas makes it very easily to generate simple descriptive statistics of whatever dataset that you are working with.

You can learn more about the describe function by checking out the official documentation here.