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# Pandas DataFrame | corrwith method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
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Pandas DataFrame.corrwith(~) computes the pairwise correlation between the columns or rows of the source DataFrame and the given Series or DataFrame.

WARNING

corrwith(~) will only compute the correlation of columns or rows where the column labels or row labels align. Otherwise, a column or row filled with NaN will be returned.

Note that the unbiased estimator of the correlation is computed:

$$\mathrm{cov}(\mathbf{x},\mathbf{y})=\frac{1}{N-1}\sum_{i=0}^{N-1}\left[\left(x_i-\bar{x}\right)(y_i-\bar{y})\right]$$

# Parameters

1. other | Series or DataFrame

The Series or DataFrame with which to compute the correlation.

2. axis | int or string | optional

Whether to compute the correlation of rows or columns:

Axis

Description

Compute the correlation between columns.

0 or "index"

Compute the correlation between rows.

1 or "columns"

By default, axis=0.

3. drop | boolean | optional

Whether or not to remove rows or columns that are not present in both the source DataFrame and other. By default, drop=False.

4. 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.

# Return Value

A Series holding the pairwise correlation between the columns or rows of the source DataFrame and other.

# Examples

## Basic usage

Consider the following DataFrames:

 df = pd.DataFrame({"A":[2,4,6], "B":[3,4,5]})df_other = pd.DataFrame({"A":[6,2,3],"C":[1,2,3]}) A B | A C0 2 3 | 0 6 11 4 4 | 1 2 22 6 5 | 2 3 3 

Computing the correlation of df and df_other:

 df.corrwith(df_other) A -0.720577B NaNC NaNdtype: float64 

Notice how only the correlation for the pair of column A, which existed in both DataFrames, was computed.

## Specifying drop

To remove row or column labels that do not match up, set drop=True:

 df.corrwith(df_other, drop=True) A -0.720577dtype: float64