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Pandas DataFrame | corr method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.corr(~) method computes pair-wise correlation of the columns in the source DataFrame.

NOTE

All NaN values are ignored.

Parameters

1. method | string or callable | optional

The type of correlation coefficient to compute:

Value

Description

"pearson"

Compute the standard correlation coefficient.

"kendall"

Compute the Kendall Tau correlation coefficient.

"spearman"

Compute the Spearman rank correlation.

callable

A function that takes in as argument two 1D Numpy arrays and returns a single float. The matrix that is returned will always be symmetric and have 1 filled along the main diagonal.

By default, method="pearson".

2. min_periodslink | int | optional

The minimum number of non-NaN values required to compute the correlation.

Return Value

A DataFrame that represents the correlation matrix of the values in the source DataFrame.

Examples

Basic usage

Consider the following DataFrame:

df = pd.DataFrame({"A":[8,5,2,1],"B":[3,4,5,9]})
df
   A  B
0  8  3
1  5  4
2  2  5
3  1  9

To compute the "pearson" correlation of two columns:

df.corr()
   A          B
A  1.000000   -0.841685
B  -0.841685  1.000000

We get the result that columns A and B have a correlation of -0.84.

Specifying min_periods

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,np.NaN,4],"B":[5,6,np.NaN]})
df
   A    B
0  3.0  5.0
1  NaN  6.0
2  4.0  NaN

Setting min_periods=3 yields:

df.corr(min_periods=3)
   A    B
A  NaN  NaN
B  NaN  NaN

Here, the reason why we get all NaN is that, the method ignores NaN and so each column only has 2 values. Since we've set the minimum threshold to compute the correlation to be 3, we end up with a DataFrame filled with NaN.

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Published by Isshin Inada
Edited by 0 others
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