Want to calculate the variance of a column in your Pandas DataFrame?
In case you’ve attended your last statistics course a few years ago, let’s quickly recap the definition of variance: it’s the average squared deviation of the list elements from the average value.
You can calculate the variance of a Pandas DataFrame by using the pd.var() function that calculates the variance along all columns. You can then get the column you’re interested in after the computation.
import pandas as pd
# Create your Pandas DataFrame
d = {'username': ['Alice', 'Bob', 'Carl'],
'age': [18, 22, 43],
'income': [100000, 98000, 111000]}
df = pd.DataFrame(d)
print(df)Your DataFrame looks like this:
| username | age | income | |
| 0 | Alice | 18 | 100000 |
| 1 | Bob | 22 | 98000 |
| 2 | Carl | 43 | 111000 |
Here’s how you can calculate the variance of all columns:
print(df.var())
The output is the variance of all columns:
age 1.803333e+02 income 4.900000e+07 dtype: float64
To get the variance of an individual column, access it using simple indexing:
print(df.var()['age']) # 180.33333333333334
Together, the code looks as follows. Use the interactive shell to play with it!
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