To calculate mean in pandas, we use the dataframe’s **mean() **method. In statistics, the mean or average is the sum of the all the values in a set divided by the total number of items in the set.

## Examples –

Let’s create a dataset to work with.

```
import pandas as pd
data = {'Apple':[89, 89, 90, 110, 125, 84, 131, 123, 123, 140, 145, 145],
'Orange': [46, 46, 50, 65, 63, 48, 110, 120, 60, 42, 47, 62],
'Banana': [26, 30, 30, 25, 38, 22, 22, 36, 20, 27, 23, 34 ],
'Mango': [80, 80, 90, 125, 130, 150, 140, 140, 135, 135, 80, 90]}
index = ['Jan','Feb','Mar','Apr','May','June','Jul','Aug','Sep','Oct','Nov','Dec']
df = pd.DataFrame(data, index=index)
df
```

### 1 . Calculate the Mean of the Columns –

By default, the **mean()** method calculate the mean of the columns.

You can calculate the mean of a single column like this –

`df['Apple'].mean()`

```
#output
116.16666666666667
```

Or you can calculate the mean of multiple numeric columns at once like this.

`df.mean()`

```
#output
Apple 116.166667
Orange 63.250000
Banana 27.750000
Mango 114.583333
dtype: float64
```

### 2 . Calculate the Mean of the rows –

To calculate the mean of each rows, you need to set the **axis** parameter to** axis=1 or columns**.

`df.mean(axis=1)`

```
#output
Jan 60.25
Feb 61.25
Mar 65.00
Apr 81.25
May 89.00
June 76.00
Jul 100.75
Aug 104.75
Sep 84.50
Oct 86.00
Nov 73.75
Dec 82.75
dtype: float64
```