# 5 Best Ways to Calculate the Mean of Numeric Columns in a DataFrame Using pandas

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π‘ Problem Formulation: When working with data in Python, the pandas library is a powerful tool for data manipulation. Users often need to calculate the mean of numerical columns in a DataFrame for statistical analysis or data normalization. Let’s say you have a DataFrame containing sales data with several numeric columns, and your goal is to find the average value in each of these columns. This article will guide you through different methods to achieve this efficiently.

## Method 1: Using `df.mean()` Function

The `df.mean()` function in pandas is the most straightforward way to compute the mean of all numeric columns in a DataFrame. It automatically disregards non-numeric columns and returns a Series containing the mean values indexed by the column names.

Here’s an example:

```import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': ['a', 'b', 'c']
})

# Calculate the mean of numeric columns
mean_values = df.mean()
print(mean_values)```

Output:

```A    2.0
B    5.0
dtype: float64```

This code snippet creates a DataFrame with two numeric columns, ‘A’ and ‘B’, and one non-numeric column, ‘C’. The `df.mean()` method computes the mean of the numeric columns, skipping the non-numeric ones, resulting in a Series with the mean values.

## Method 2: Selecting Specific Columns

If you want to compute the mean of specific numeric columns, you can select those columns first using DataFrame indexing and then apply the `mean()` method.

Here’s an example:

```import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({
'Sales': [100, 200, 300],
'Profit': [50, 80, 120],
'Region': ['East', 'West', 'South']
})

# Specify the columns you want to compute the mean for
selected_columns = ['Sales', 'Profit']
mean_values = df[selected_columns].mean()
print(mean_values)```

Output:

```Sales     200.0
Profit     83.333333
dtype: float64```

This code selects only the ‘Sales’ and ‘Profit’ columns and calculates the mean of these columns specifically. This method is helpful when you want to exclude certain numeric columns from the mean calculation.

## Method 3: Using `agg()` Function for Multiple Statistics

The `agg()` function in pandas allows you to perform multiple aggregation operations on your DataFrame columns. If you need the mean along with other statistics, this is a flexible method to apply several functions at once.

Here’s an example:

```import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({
'Price': [10, 20, 15],
'Quantity': [100, 150, 200]
})

# Use agg() to get the mean and other statistics
statistics = df.agg(['mean', 'sum', 'min'])
print(statistics)```

Output:

```          Price  Quantity
mean   15.000000     150.0
sum    45.000000     450.0
min    10.000000     100.0```

This code computes not only the mean but also the sum and the minimum values for the ‘Price’ and ‘Quantity’ columns. The `agg()` function is applied to the entire DataFrame and results in a new DataFrame with the calculated statistics.

## Method 4: Skip NaN Values with `skipna` Parameter

The mean calculation in pandas can be affected by NaN (Not a Number) values. Using the `skipna` parameter, you can control whether to include or exclude NaN values in the mean calculation.

Here’s an example:

```import pandas as pd
import numpy as np

# Create a simple DataFrame with NaN values
df = pd.DataFrame({
'A': [1, np.nan, 3],
'B': [4, 5, np.nan]
})

# Calculate the mean, skipping NaN values
mean_values = df.mean(skipna=True)
print(mean_values)```

Output:

```A    2.0
B    4.5
dtype: float64```

This example shows a DataFrame with NaN values included. The `mean()` method skips these values (which is the default behavior) to calculate the mean of each numeric column.

## Bonus One-Liner Method 5: Mean Calculation with Lambda

Applying a lambda function to calculate the mean can be useful for quick inline operations or for applying a mean calculation with additional logic across columns.

Here’s an example:

```import pandas as pd

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

# Calculate the mean using a lambda function
mean_values = df.apply(lambda x: x.mean() if x.dtype != 'object' else x)
print(mean_values)```

Output:

```A    2.0
B    5.0
dtype: float64```

This one-liner lambda function checks the datatype of each column and calculates the mean only for numeric columns, ignoring non-numeric ones. It’s a concise and flexible way to implement conditional logic.

## Summary/Discussion

• Method 1: Using `df.mean()` Function. It is simple and automatic, best for quick calculations without specific column selection. However, it includes all numeric columns by default.
• Method 2: Selecting Specific Columns. Best for when you need control over which columns to average. It offers precision but requires manual column selection.
• Method 3: Using `agg()` Function for Multiple Statistics. Ideal for computing various statistics in one go. It’s flexible but may be overkill for just calculating means.
• Method 4: Skip NaN Values with `skipna` Parameter. Useful when dealing with incomplete data. It handles NaN values neatly, but the setup might be slightly more complex than a straightforward mean.
• Method 5: Mean Calculation with Lambda. Provides inline, concise calculations with custom logic. While versatile, it may be less readable for those unfamiliar with lambda functions.