Python | Pandas Series.corr ()

Python Methods and Functions

`Series.corr() ` Pandas `Series.corr() ` calculates correlation with others ` Series.corr () `, excluding missing values.

Syntax: Series.corr (other, method = 'pearson' , min_periods = None)

Parameter:
other: Series
method: {' pearson ',' kendall ',' spearman '} or callable
min_periods: Minimum number of observations needed to have a valid result

Returns: correlation: float

Example # 1: Use ` Series.corr () ` to find the correlation of a given series object with another.

 ` # import pandas as pd ` ` import ` ` pandas as pd `    ` # Create the first episode ` ` sr1 ` ` = ` ` pd.Series ([` ` 80 ` `, ` ` 25 ` `, ` ` 3 ` `, ` ` 25 ` `, ` ` 24 ` `, ` ` 6 ` `]) `   ` # Create second episode ` ` sr2 ` ` = ` ` pd.Series ([` ` 34 ` `, ` ` 5 ` `, ` ` 13 ` `, ` ` 32 ` `, ` < code class = "value"> 4 `, ` ` 15 ` `]) `   ` # Create Index ` ` index_ ` ` = ` ` [` ` 'Coca Cola' ` `, ` ` 'Sprite' ` `, ` `' Coke' ` `, ` ` 'Fanta' ` `, ` ` 'Dew' ` `, ` `' ThumbsUp' ` `] `   ` # set first index ` ` sr1.index ` ` = ` ` index_ `   ` # set the second index ` ` sr2.index ` ` = ` ` index_ `   ` # Print first episode ` ` print ` ` (sr1) ` ` `  ` # Print the second series ` ` print ` ` ( sr2) `

Output:

We will now use ` Series.corr () ` to find the correlation between the master data of this series object and others.

 ` # find correlation ` ` result ` ` = ` ` sr1.corr (sr2) ` ` `  ` # Print result ` ` print ` ` (result) `

Output:

As we can see from the output, ` Series.corr () ` successfully returned the correlation between the underlying data of the objects in this series.

Example # 2 : Use ` Series.corr () ` to find the correlation of a given series object with another. The series object contains some missing values.

 ` # import pandas as pd ` ` import ` ` pandas as pd `   ` # Create first episode ` ` sr1 ` ` = ` ` pd.Series ([` ` 51 ` `, ` ` 10 ` `, ` ` 24 ` `, ` ` 18 ` `, ` ` None ` `, ` ` 84 ` `, ` ` 12 ` `, ` ` 10 ` `, ` ` 5 , 24 , 2 ]) ``   # Create the second episode sr2 = pd .Series ([ 11 , 21 , 8 , 18 , 65 , 18 , 32 , 10 , 5 , 32 , None ])   # Create Index index_ = pd.date_range ( '2010 -10-09' , periods = 11 , freq = 'M' )   # set first index sr1.index = index_   # set second index sr2.index = index_   # Printout Attach first series print (sr1)   # Print the second batch print (sr2) `

Output:

We will now use ` Series.corr () ` to find the correlation between the master data of a given series object and others.

 ` # find correlation ` ` result ` ` = ` ` sr1.corr (sr2) `   ` # Print result ` ` print ` ` (result) `

Output:

As we can see from the output, ` Series.corr () ` successfully returned a correlation between the underlying data of the objects in this series. Missing values ​​are ignored when calculating the correlation between objects.