Mean of the values for the requested axis in Pandas

The mean, often referred to as the average, is a central measure of location for a dataset. It represents the arithmetic average of the numbers and is calculated as the sum of the values divided by the number of values. Pandas provides a simple and efficient way to compute the mean for a Series or along a particular axis of a DataFrame.

Here's a tutorial on how to compute the mean using pandas:

1. Setup:

Make sure you have pandas installed:

pip install pandas

2. Import Necessary Libraries:

import pandas as pd

3. Create a Sample DataFrame:

data = {
    'A': [1, 2, 3, 4, 5],
    'B': [5, 6, 7, 8, 9],
    'C': [9, 8, 7, 6, 5]
}
df = pd.DataFrame(data)
print(df)

4. Compute Mean for a Series:

You can compute the mean for a particular column (Series) like this:

mean_A = df['A'].mean()
print(f"Mean of Column 'A': {mean_A}")

5. Compute Mean for a DataFrame:

To compute the mean for all columns in a DataFrame:

mean_all = df.mean()
print("Mean for each column:")
print(mean_all)

6. Compute Mean Along a Specific Axis:

By default, the mean is computed column-wise (along axis 0). If you want to compute the mean row-wise (along axis 1):

mean_rows = df.mean(axis=1)
print("Mean for each row:")
print(mean_rows)

Explanation:

Here's a brief explanation of how the mean is calculated:

For a set of values:

x1,x2,…,xn

The mean is:

mean=n1∑i=1nxi

So, it's the sum of all values divided by the number of values.

Summary:

Computing the mean of datasets is a fundamental operation in data analysis. With pandas' built-in .mean() method, you can easily calculate the mean of data in a Series or DataFrame. The method is both flexible and powerful, allowing you to specify the axis along which the mean is computed and even to skip missing or NaN values, which is the default behavior.

Calculate mean in Pandas DataFrame:
- The mean is the average value of all elements in the DataFrame.
```
mean_value = df.mean().mean()
```
Mean along a specific axis in Pandas:
- Calculate the mean along a specific axis (rows or columns).
```
row_mean = df.mean(axis=1)
column_mean = df.mean(axis=0)
```
Pandas DataFrame mean by column:
- Compute the mean for each column in the DataFrame.
```
column_mean = df.mean()
```
Compute row-wise mean in Pandas:
- Calculate the mean for each row in the DataFrame.
```
row_mean = df.mean(axis=1)
```
Using mean() to calculate average in Pandas:
- Directly apply the .mean() function for average calculation.
```
mean_value = df.mean()
```
Aggregating mean by group in Pandas:
- Aggregate mean values based on a grouping variable.
```
grouped_mean = df.groupby('Group_Column')['Value_Column'].mean()
```
Axis-wise mean calculation in Pandas:
- Calculate the mean along rows or columns.
```
row_mean = df.mean(axis=1)
column_mean = df.mean(axis=0)
```
Calculate mean excluding NaN values in Pandas:
- Compute mean while excluding NaN (missing) values.
```
mean_value_without_nan = df.mean(skipna=True)
```
Custom mean function in Pandas DataFrame:
- Implement a custom mean function for specific requirements.
```
def custom_mean(data):
    return data.sum() / data.count()

mean_value = df.apply(custom_mean)
```