# Pandas DataFrame.mean()

The mean() function is used to return the mean of the values for the requested axis. If we apply this method on a **Series object** , then it returns a **scalar value** , which is the mean value of all the observations in the dataframe.

If we apply this method on a DataFrame object, then it returns a Series object which contains mean of values over the specified axis.

### Syntax

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

### Parameters

**axis:**{index (0), columns (1)}.

This refers to the axis for a function that is to be applied.**skipna:**It excludes all the null values when computing result.**level:**It counts along with a particular level and collapsing into a Series if the axis is a MultiIndex (hierarchical),**numeric_only:**It includes only int, float, boolean columns. If None, it will attempt to use everything, then use only numeric data. Not implemented for Series.

### Returns

It returns the mean of the Series or DataFrame if the level is specified.

### Example

`# importing pandas as pd import pandas as pd # Creating the dataframe info = pd.DataFrame({"A":[8, 2, 7, 12, 6], "B":[26, 19, 7, 5, 9], "C":[10, 11, 15, 4, 3], "D":[16, 24, 14, 22, 1]}) # Print the dataframe info # If axis = 0 is not specified, then # by default method return the mean over # the index axis info.mean(axis = 0)`

**Output**

```
A 7.0
B 13.2
C 8.6
D 15.4
dtype: float64
```

**Example2**

`# importing pandas as pd import pandas as pd # Creating the dataframe info = pd.DataFrame({"A":[5, 2, 6, 4, None], "B":[12, 19, None, 8, 21], "C":[15, 26, 11, None, 3], "D":[14, 17, 29, 16, 23]}) # while finding mean, it skip null values info.mean(axis = 1, skipna = True)`

**Output**

```
0 11.500000
1 16.000000
2 15.333333
3 9.333333
4 15.666667
dtype: float64
```