Pandas DataFrame mean() method – Explained with examples

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When working with data in Python, the Pandas library is a powerful tool that makes data manipulation and analysis straightforward. One of the many useful methods provided by Pandas is the mean() method, which is used to calculate the mean (average) of the values in a DataFrame. In this blog post, we’ll dive into the mean() method, exploring its functionality, usage, and practical examples.

What is the Pandas DataFrame mean() Method?

The mean() method in Pandas is used to compute the mean value of a DataFrame’s numeric columns. By default, it calculates the mean for each column, but it can also be applied to rows. This method is handy for summarizing and understanding your data at a glance.

Syntax of mean()

The syntax of the mean() method is straightforward:

Python
DataFrame.mean(axis=None, skipna=True, level=None, numeric_only=None, **kwargs)

Here’s a breakdown of the parameters:

  • axis: {index (0), columns (1)}, default 0
    • The axis along which to compute the means.
    • 0 or index for column-wise,
    • 1 or columns for row-wise.

  • skipna: bool, default True
    • Exclude NA/null values when computing the result.

  • level: int or level name, default None
    • If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only: bool, default None
    • Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.

  • kwargs: Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy.

Examples of Using the mean() Method

Let’s look at some examples to see how the mean() method works in practice.

Example 1: Basic Usage
Python
import pandas as pd

# Create a simple DataFrame
data = {
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12]
}
df = pd.DataFrame(data)

# Calculate the mean for each column
column_means = df.mean()
print(column_means)

Output:

Bash
A    2.5
B    6.5
C   10.5
dtype: float64

In this example, the mean() method calculates the mean of each column. The result is a Series with the mean values.

Example 2: Mean Across Rows
Python
# Calculate the mean for each row
row_means = df.mean(axis=1)
print(row_means)

Output:

Bash
0    5.0
1    6.0
2    7.0
3    8.0
dtype: float64

Here, by setting axis=1, the mean() method calculates the mean for each row instead of each column.

Example 3: Handling Missing Values
Python
# Create a DataFrame with missing values
data_with_nan = {
    'A': [1, 2, None, 4],
    'B': [5, None, 7, 8],
    'C': [9, 10, 11, None]
}
df_with_nan = pd.DataFrame(data_with_nan)

# Calculate the mean, skipping NaN values
mean_skipna = df_with_nan.mean()
print(mean_skipna)

# Calculate the mean, including NaN values
mean_includena = df_with_nan.mean(skipna=False)
print(mean_includena)

Output:

Bash
A     2.333333
B     6.666667
C    10.000000
dtype: float64

A    NaN
B    NaN
C    NaN
dtype: float64

In the first calculation, skipna=True (default), so the method skips NaN values and calculates the mean of the remaining values. In the second calculation, skipna=False, resulting in NaN values for columns that contain any NaN values.

Example 4: Mean for Specific Data Types
Python
# Create a DataFrame with mixed data types
mixed_data = {
    'A': [1, 2, 3, 4],
    'B': [5.5, 6.5, 7.5, 8.5],
    'C': ['x', 'y', 'z', 'w']
}
df_mixed = pd.DataFrame(mixed_data)

# Calculate the mean for numeric columns only
mean_numeric = df_mixed.mean(numeric_only=True)
print(mean_numeric)

Output:

Bash
A    2.5
B    7.0
dtype: float64

Here, the mean() method ignores the non-numeric column ‘C’ and calculates the mean for the numeric columns only.

Conclusion

The Pandas DataFrame mean() method is a powerful and flexible tool for quickly calculating the mean values of your data. Whether you’re working with simple datasets or more complex ones with missing values and mixed data types, understanding how to leverage the mean() method can help you gain valuable insights and perform essential statistical analysis with ease.

By mastering the use of this method, you can streamline your data analysis workflow and focus on extracting meaningful patterns and trends from your data. Happy coding!

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