# How to Calculate Standard Deviation in Python Pandas ?

The std() method in pandas calculates the sample standard deviation over requested axis. In statistics standard deviation is the average amount of variability in your data set. It tells you on average how far each score lies from the mean.

## 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 standard deviation of a column –

You can calculate the standard deviation of a single column like this

``df['Apple'].std()``
``````#output
23.072349974229617``````

or you can calculate the standard deviation for all the columns like this

``df.std()``
``````#output
Apple     23.072350
Orange    25.477709
Banana     5.894913
Mango     27.835420
dtype: float64``````

### 2 . Calculate the standard deviation of a row –

To calculate the standard deviation of a row, we need to set the axis parameter to axis=1 or columns.

``df.std(axis=1)``
``````Jan     29.398129
Feb     27.873225
Mar     30.000000
Apr     45.345893
May     45.658150
June    55.497748
Jul     53.983794
Aug     46.671726
Sep     54.138711
Oct     59.816386
Nov     52.936282
Dec     47.380552
dtype: float64``````

### 3 . Change degrees of freedom –

You can change the degrees of freedom using the ddof parameter. By default it is normalized by N-1. To normalize by N, we need to set the ddof=0.

``df.std(ddof=0)``
``````Apple     22.090093
Orange    24.393049
Banana     5.643950
Mango     26.650386
dtype: float64``````

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