Uncover the Power of Pandas’ Mean Method

Understanding the Syntax

The mean method’s syntax is straightforward: mean(). However, it can take several optional arguments to customize its behavior:

• axis: specifies the axis along which the mean will be computed
• skipna: determines whether to include or exclude missing values
• level: computes the mean at a particular level
• numeric_only: specifies whether to include only numeric columns in the computation or not

Computing Means Along Different Axes

By default, the mean method computes the mean for each column. But what if you want to calculate the mean across each row? Simply pass axis=1 as an argument.

import pandas as pd

# create a sample dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# compute the mean along each row
print(df.mean(axis=1))

You can also pass axis=0 to compute the mean of each column.

print(df.mean(axis=0))

Calculating the Mean of a Specific Column

Need to compute the average of a specific column? No problem! Just use the column name with the mean method, like this:

print(df['A'].mean())

This will give you the average value of the column.

The Importance of numeric_only

When dealing with datasets containing non-numeric columns, the numeric_only argument is crucial. By setting it to True, the mean method will only compute the mean for numeric columns, ignoring the rest.

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})

print(df.mean(numeric_only=True))

Set it to False, and it will attempt to compute the mean for all columns, which may raise a TypeError.

Handling Missing Values with skipna

Missing values can greatly impact your calculations. The skipna argument allows you to decide whether to include or exclude these values.

df = pd.DataFrame({'A': [1, 2, None], 'B': [4, 5, 6]})

print(df.mean(skipna=True))  # exclude missing values
print(df.mean(skipna=False))  # include missing values

When set to True, the mean method will compute the average without considering missing values. Set it to False, and it will include them, potentially resulting in NaN values.