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# Pandas DataFrame | corr method

schedule Aug 12, 2023
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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_periods`link | `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  B0  8  31  5  42  2  53  1  9 ```

To compute the `"pearson"` correlation of two columns:

``` df.corr()    A          BA  1.000000   -0.841685B  -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    B0  3.0  5.01  NaN  6.02  4.0  NaN ```

Setting `min_periods=3` yields:

``` df.corr(min_periods=3)    A    BA  NaN  NaNB  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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