Python Pandas Series std() Function

Pandas Series std() Function:

The std() function of the Pandas Series returns the sample standard deviation of the values along the chosen axis.

Syntax:

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

Parameters

axis: This is optional. It indicates 0 or ‘index’. This is the axis on which the function will be applied.

skipna: This is optional. When computing the result, specify True to exclude NA/null values. The default value is True.

level: This is optional. It indicates the level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. The level name is specified by str.

ddof: This is optional. This indicates the Delta Degrees of Freedom. In computations, the divisor used is N – ddof, where N indicates the number of elements.

numeric_only: This is optional. Pass True to include just float, int, or boolean data. False by default

Return Value:

If a level is given, it returns a scalar or a series. The sample standard deviation of the values along the given axis is returned by the std() function of the Pandas Series.

Pandas Series std() Function in Python:

Example1

Approach:

Import pandas module using the import keyword.
Import numpy module using the import keyword.
Give the category(level) values as arguments list to from_arrays() functions
Pass some random list, index values from the above and name as Numbers as the arguments to the Series() function of the pandas module to create a series.
Store it in a variable.
Print the above-given series
Printing the sample standard deviation of all elements in the given series using the std() function
Printing the sample standard deviation of each level of the series using level=’DataType’
Printing the sample standard deviation of each level of the series using level=0.
The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Import numpy module using the import keyword.
import numpy as np
# Give the category(level) values as arguments list to from_arrays() functions
gvn_indx = pd.MultiIndex.from_arrays([
    ['positive', 'negative', 'positive', 
     'positive', 'negative', 'negative']],
    names=['DataType'])
# Pass some random list, index values from the above and name as Numbers
# as the arguments to the Series() function of the pandas module to create a series.
# Store it in a variable.
gvn_series = pd.Series([12, 3, 4, 5, 1, 2], 
              name='Numbers', index=gvn_indx)
# Print the above given series
print("The given series is:")
print(gvn_series)
print()

# Printing the sample standard deviation of all elements in the given series 
# using the std() function
print("The sample standard deviation of all elements in the given series:")
print(gvn_series.std())
print()
# Printing the sample standard deviation of each level of the series using level='DataType'
print("The sample standard deviation of all level values using level='DataType':")
print(gvn_series.std(level='DataType'))
print()
# Printing the sample standard deviation of each level of the series using level=0
print("The sample standard deviation of all level values using level=0:")
print(gvn_series.std(level=0))

Output:

The given series is:
DataType
positive    12
negative     3
positive     4
positive     5
negative     1
negative     2
Name: Numbers, dtype: int64

The sample standard deviation of all elements in the given series:
3.9370039370059056

The sample standard deviation of all level values using level='DataType':
DataType
positive    4.358899
negative    1.000000
Name: Numbers, dtype: float64

The sample standard deviation of all level values using level=0:
DataType
positive    4.358899
negative    1.000000
Name: Numbers, dtype: float64

Example2

Here, the std() function is used on a specific series/column in a DataFrame.

Approach:

Import pandas module using the import keyword.
Pass some random key-value pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe.
Store it in a variable.
Print the given dataframe.
Apply std() function on the student_marks column of the dataframe to get the sample standard deviation of all the values of the student_marks column and print the result.
The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Pass some random key-value pair(dictionary), index list as arguments to the 
# DataFrame() function of the pandas module to create a dataframe
# Store it in a variable.
data_frme = pd.DataFrame({
  "student_rollno": [1, 2, 3, 4],
  "student_marks": [80, 35, 25, 90]},
  index= ["virat", "nick" , "jessy", "sindhu"]
)
# Print the given dataframe
print("The given Dataframe:")
print(data_frme)
print()
# Apply std() function on the student_marks column of the dataframe to
# get the sample standard deviation of all the values of the of the student_marks
# column and print the result.
print("The sample standard deviation of student_marks column of the dataframe:")
print(data_frme["student_marks"].std())

Output:

The given Dataframe:
        student_rollno  student_marks
virat                1             80
nick                 2             35
jessy                3             25
sindhu               4             90

The sample standard deviation of student_marks column of the dataframe:
32.274861218395145

Pandas Series std() Function in Python:

Related