5 Best Ways to Calculate Row Mean in Python DataFrames

💡 Problem Formulation: When working with data in Python, you may often encounter the need to compute the average value of each row in a DataFrame. This is a common operation in data analysis, where you’re interested in summarizing rows of numerical data. For instance, if you have a DataFrame representing a class of students with their marks in several subjects, you may want to find the mean score of each student across all subjects. This article provides various methods to calculate the row mean, accommodating different scenarios and requirements.

Method 1: Using mean() Function with axis=1

This method involves the pandas library’s mean() function, which calculates the mean of a DataFrame’s rows when axis=1 is specified. This approach is straightforward and efficient for computing means across rows in DataFrames with numerical values.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Math': [88, 92, 76],
    'Science': [94, 87, 90],
    'History': [78, 85, 80]
})

# Compute the row mean
row_mean = df.mean(axis=1)
print(row_mean)

Output:

0    86.666667
1    88.000000
2    82.000000
dtype: float64

This snippet creates a DataFrame with three subjects and calculates the mean score for each student (each row) across all subjects. The mean() function with axis=1 computes the average across the horizontal axis, effectively giving us the row mean. These values correspond to the means of the first, second, and third rows, respectively.

Method 2: Apply with a Lambda Function

Using the apply() method with a lambda function can provide greater flexibility. It allows not only the computation of means but also the possibility to include custom operations or conditions within the row mean calculation process.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Math': [88, None, 76],
    'Science': [94, 87, 90],
    'English': [82, 85, None]
})

# Compute the row mean, excluding NaN values
row_mean = df.apply(lambda x: x.mean(), axis=1)
print(row_mean)

Output:

0    88.000000
1    86.000000
2    83.000000
dtype: float64

This code defines a DataFrame with grades, including some missing values. The apply() method with a lambda function is used to calculate the row mean while ignoring these NaNs. The mean() function within the lambda automatically skips over NaNs, providing an average only of the non-missing values.

Method 3: Using mean() After Filtering Columns

If you want to calculate the mean of specific columns for each row, you can first filter the DataFrame to include only the relevant columns, and then use the mean() function as before. This is useful when you have a mix of numerical and non-numerical columns, but only want to consider certain numerical ones.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Math': [88, 92, 76],
    'Science': [94, 87, 90],
    'Student_Name': ['Alice', 'Bob', 'Charlie']
})

# Filter numerical columns
numerical_df = df[['Math', 'Science']]

# Compute the row mean of the filtered DataFrame
row_mean = numerical_df.mean(axis=1)
print(row_mean)

Output:

0    91.0
1    89.5
2    83.0
dtype: float64

The DataFrame includes a mix of numerical scores and a non-numerical ‘Student_Name’ column. To calculate the row means of the scores, we first select only the ‘Math’ and ‘Science’ columns and then apply the mean() function with axis=1 to the resulting DataFrame.

Method 4: With numpy for Multi-Dimensional Arrays

For those already utilizing numpy, or when working with larger, multi-dimensional data, you can convert the DataFrame into a numpy array and then compute the mean across the specified axis. Numpy’s computational performance is well-suited for large datasets.

Here’s an example:

import pandas as pd
import numpy as np

# Create a DataFrame
df = pd.DataFrame({
    'Math': [88, 92, 76],
    'Science': [94, 87, 90],
    'History': [78, 85, 80]
})

# Compute the row mean using numpy
row_mean = np.mean(df.values, axis=1)
print(row_mean)

Output:

[86.666667 88.       82.      ]

This example uses numpy to calculate the row mean of the DataFrame scores. By converting the DataFrame to a numpy array using df.values, we can then apply np.mean and specify axis=1 to compute the row means.

Bonus One-Liner Method 5: Sum and Divide

For simple DataFrames with only numerical data, you can compute the row mean by summing each row with sum(axis=1) and dividing by the number of columns. It’s a quick, manual method suitable for straightforward cases.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Math': [88, 92, 76],
    'Science': [94, 87, 90],
    'History': [78, 85, 80]
})

# Compute the row mean by summing and dividing by the number of columns
row_mean = df.sum(axis=1) / len(df.columns)
print(row_mean)

Output:

0    86.666667
1    88.000000
2    82.000000
dtype: float64

By summing up each row and dividing by the number of columns, we have manually calculated the mean for each row. This method is simple, but less flexible compared to other methods that handle missing values or non-numerical data.

Summary/Discussion

• Method 1: mean() Function with axis=1. Straightforward and efficient for numerical DataFrames. Does not directly handle non-numerical data.
• Method 2: Apply with Lambda Function. Flexible and allows for custom operations or conditions. Slightly more complex syntax.
• Method 3: mean() After Filtering Columns. Useful for calculating means on selected columns. Requires extra step to filter DataFrame.
• Method 4: Using numpy for Multi-Dimensional Arrays. Optimal for large datasets and those who are already using numpy. Adds dependency on numpy.
• Bonus Method 5: Sum and Divide. Quick one-liner for simple cases. Lacks robustness in handling non-numeric data and missing values.