Pandas Standard Deviation

by Online Tutorials Library July 14, 2022

Pandas Series.std()

The Pandas std() is defined as a function for calculating the standard deviation of the given set of numbers, DataFrame, column, and rows. In respect to calculate the standard deviation, we need to import the package named “statistics” for the calculation of median.

The standard deviation is normalized by N-1 by default and can be changed using the ddof argument.

Syntax:

  Series.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)  

Parameters:

axis: {index (0), columns (1)}
skipna: It excludes all the NA/null values. If NA is present in an entire row/column, the result will be NA.
level: It counts along with a particular level, and collapsing into a scalar if the axis is a MultiIndex (hierarchical).
ddof: Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of elements.
numeric_only: boolean, default value None
It includes only float, int, boolean columns. If it is None, it will attempt to use everything, so use only numeric data.
It is not implemented for a Series.

Returns:

It returns Series or DataFrame if the level is specified.

Example1:

  import pandas as pd  # calculate standard deviation  import numpy as np   print(np.std([4,7,2,1,6,3]))  print(np.std([6,9,15,2,-17,15,4]))  

Output

2.1147629234082532  10.077252622027656

Example2:

  import pandas as pd  import numpy as np     #Create a DataFrame  info = {      ‘Name’:[‘Parker’,’Smith’,’John’,’William’],     ‘sub1_Marks’:[52,38,42,37],     ‘sub2_Marks’:[41,35,29,36]}   data = pd.DataFrame(info)  data  # standard deviation of the dataframe  data.std()  

Output

sub1_Marks    6.849574  sub2_Marks    4.924429  dtype: float64

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