Pandas DataFrame mean() method calculates the mean (average) of the elements in each column (by default) or along the rows. You can specify the axis along which you want to compute the mean.
To find the mean of DataFrame, you can use the “DataFrame.mean()” function. If the mean() method is applied to a series object, it returns the scalar value, which is the mean value of all the values in the DataFrame. If the mean() method is applied to a DataFrame object, and it returns the series object that contains the mean of the values over the specified axis.
Syntax
DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters
axis{index (0), columns (1)}
Axis for the method to be applied.
skipna: bool, default True
Exclude NA/None values when computing the result.
level: int or level name, default None
If the axis is the MultiIndex, count along with a specific level, collapsing into the Series.
numeric_only: bool, default None
Include only float, int, and boolean columns. If the values are None, I will attempt to use everything, then use only numeric data. However, I have not implemented it for Series.
**kwargs
Additional keyword arguments are to be passed to the function.
Return Value
It returns Series or DataFrame (if level specified).
Example
import pandas as pd
data = {
"A": [1, 2, 3],
"B": [4, 5, 6],
"C": [7, 8, 9]
}
df = pd.DataFrame(data)
column_mean = df.mean()
print("Mean along columns:")
print(column_mean)
row_mean = df.mean(axis=1)
print("\nMean along rows:")
print(row_mean)
Output
Mean along columns:
A 2.0
B 5.0
C 8.0
dtype: float64
Mean along rows:
0 4.0
1 5.0
2 6.0
dtype: float64
In this code, we created a sample DataFrame and used the mean() method to calculate the mean along the columns (default) and the rows (by setting axis=1).
