The term  refers to the analysis of one variable. You can remember this because the prefix “uni” means “one.”

There are three common ways to perform univariate analysis on one variable:

1. Summary statistics – Measures the center and spread of values.

2. Frequency table – Describes how often different values occur.

3. Charts – Used to visualize the distribution of values.

This tutorial provides an example of how to perform univariate analysis with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [1, 1, 2, 3.5, 4, 4, 4, 5, 5, 6.5, 7, 7.4, 8, 13, 14.2],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4, 6, 8, 8, 9, 3, 2, 6],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12, 6, 6, 7, 8, 7, 9, 15]})

#view first five rows of DataFrame
df.head()

	points	assists	rebounds
0	1.0	5	11
1	1.0	7	8
2	2.0	7	10
3	3.5	9	6
4	4.0	12	6

1. Calculate Summary Statistics

We can use the following syntax to calculate various summary statistics for the ‘points’ variable in the DataFrame:

#calculate mean of 'points'
df['points'].mean()

5.706666666666667

#calculate median of 'points' 
df['points'].median() 

5.0

#calculate standard deviation of 'points'
df['points'].std() 

3.858287308169384

2. Create Frequency Table

We can use the following syntax to create a frequency table for the ‘points’ variable:

#create frequency table for 'points'
df['points'].value_counts()

4.0     3
1.0     2
5.0     2
2.0     1
3.5     1
6.5     1
7.0     1
7.4     1
8.0     1
13.0    1
14.2    1
Name: points, dtype: int64

This tells us that:

• The value 4 occurs 3 times
• The value 1 occurs 2 times
• The value 5 occurs 2 times
• The value 2 occurs 1 time

And so on.

Related:

3. Create Charts

We can use the following syntax to create a for the ‘points’ variable:

import matplotlib.pyplotas plt

df.boxplot(column=['points'], grid=False, color='black')

Related:

We can use the following syntax to create a histogram for the ‘points’ variable:

import matplotlib.pyplotas plt

df.hist(column='points', grid=False, edgecolor='black')

Related:

We can use the following syntax to create a for the ‘points’ variable:

import seaborn as sns

sns.kdeplot(df['points'])

Related:

Each of these charts give us a unique way to visualize the distribution of values for the ‘points’ variable.