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Python Pandas DataFrame.mean() function calculates the mean of the values ​​of the DataFrame object over the specified axis.

pandas.DataFrame.mean()grammar

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

parameter

Return Value

If not specified level, returns the mean over the requested axis Series, otherwise returns the average DataFrame.

Example Code: DataFrame.mean()Method to find the mean along the column axis

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 2, 3],
                   'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)

means=df.mean()
print("Means of Each Column:")
print(means)

Output:

DataFrame:
   X  Y
0  1  4
1  2  3
2  2  8
3  3  4
Means of Each Column:
X    2.00
Y    4.75
dtype: float64

Computes the average of two columns, and , and returns a object containing the average for each Xcolumn .YSeries

In Pandas, if we want to find the mean of a column in a DataFrame, we just call mean()the mean function on that column.

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 2, 3],
                   'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)

means=df["X"].mean()
print("Mean of Column X:")
print(means)

Output:

DataFrame:
   X  Y
0  1  4
1  2  3
2  2  8
3  3  4
Mean of Column X:
2.0

It just gives the average of the values DataFrame​​in Xthe columns.

Example Code: DataFrame.mean()Method to find the mean along the row axis

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 2, 3],
                   'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)

means=df.mean(axis=1)
print("Mean of Rows:")
print(means)

Output:

DataFrame:
   X  Y
0  1  4
1  2  3
2  2  8
3  3  4
Mean of Rows:
0    2.5
1    2.5
2    5.0
3    3.5
dtype: float64

It calculates the average of all rows and returns a Seriesobject containing the average for each row.

In Pandas, if we want to find DataFramethe mean of a row in , we just call mean()the function to calculate the mean of that row.

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 2, 3],
                   'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)

mean=df.iloc[[0]].mean(axis=1)
print("Mean of 1st Row:")
print(mean)

Output:

DataFrame:
   X  Y
0  1  4
1  2  3
2  2  8
3  3  4
Mean of 1st Row:
0    2.5
dtype: float64

It only gives DataFramethe average of the first row of values ​​in .

We use ilocthe method to select a row based on its index.

Example code: DataFrame.mean()Method ignores NaNvalue to find average

We use skipnathe default value of the parameter, which is , skipna=Trueto find DataFramethe mean of along the specified axis, ignoring NaNthe value of .

import pandas as pd

df = pd.DataFrame({'X': [1, 2, None, 3],
                   'Y': [4, 3, None, 4]})
print("DataFrame:")
print(df)

means=df.mean(skipna=True)
print("Mean of Columns")
print(means)

Output:

DataFrame:
     X    Y
0  1.0  4.0
1  2.0  3.0
2  NaN  NaN
3  3.0  4.0
Mean of Columns
X    2.000000
Y    3.666667
dtype: float64

If we set skipna=True, it will ignore the in DataFrame NaN. It allows us to calculate DataFramethe mean of along the column axis, ignoring NaNthe values.

import pandas as pd

df = pd.DataFrame({'X': [1, 2, None, 3],
                   'Y': [4, 3, 3, 4]})
print("DataFrame:")
print(df)
means=df.mean(skipna=False)
print("Mean of Columns")
print(means)

Output:

DataFrame:
     X  Y
0  1.0  4
1  2.0  3
2  NaN  3
3  3.0  4
Mean of Columns
X    NaN
Y    3.5
dtype: float64

Here, we get the value Xof the mean of column NaNsince Xthere are values ​​in column NaN.