def corr(self, method: str='pearson') -> 'DataFrame':
    "\n        Compute pairwise correlation of columns, excluding NA/null values.\n\n        Parameters\n        ----------\n        method : {'pearson', 'spearman'}\n            * pearson : standard correlation coefficient\n            * spearman : Spearman rank correlation\n\n        Returns\n        -------\n        y : DataFrame\n\n        See Also\n        --------\n        Series.corr\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],\n        ...                   columns=['dogs', 'cats'])\n        >>> df.corr('pearson')\n                  dogs      cats\n        dogs  1.000000 -0.851064\n        cats -0.851064  1.000000\n\n        >>> df.corr('spearman')\n                  dogs      cats\n        dogs  1.000000 -0.948683\n        cats -0.948683  1.000000\n\n        Notes\n        -----\n        There are behavior differences between pandas-on-Spark and pandas.\n\n        * the `method` argument only accepts 'pearson', 'spearman'\n        * the data should not contain NaNs. pandas-on-Spark will return an error.\n        * pandas-on-Spark doesn't support the following argument(s).\n\n          * `min_periods` argument is not supported\n        "
    return cast(DataFrame, ps.from_pandas(corr(self, method)))
Compute pairwise correlation of columns, excluding NA/null values.

Parameters
----------
method : {'pearson', 'spearman'}
    * pearson : standard correlation coefficient
    * spearman : Spearman rank correlation

Returns
-------
y : DataFrame

See Also
--------
Series.corr

Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr('pearson')
          dogs      cats
dogs  1.000000 -0.851064
cats -0.851064  1.000000

>>> df.corr('spearman')
          dogs      cats
dogs  1.000000 -0.948683
cats -0.948683  1.000000

Notes
-----
There are behavior differences between pandas-on-Spark and pandas.

* the `method` argument only accepts 'pearson', 'spearman'
* the data should not contain NaNs. pandas-on-Spark will return an error.
* pandas-on-Spark doesn't support the following argument(s).

  * `min_periods` argument is not supported
def corr(self, method: str='pearson') -> 'DataFrame':
    "\n        Compute pairwise correlation of columns, excluding NA/null values.\n\n        Parameters\n        ----------\n        method : {'pearson', 'spearman'}\n            * pearson : standard correlation coefficient\n            * spearman : Spearman rank correlation\n\n        Returns\n        -------\n        y : DataFrame\n\n        See Also\n        --------\n        Series.corr\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],\n        ...                   columns=['dogs', 'cats'])\n        >>> df.corr('pearson')\n                  dogs      cats\n        dogs  1.000000 -0.851064\n        cats -0.851064  1.000000\n\n        >>> df.corr('spearman')\n                  dogs      cats\n        dogs  1.000000 -0.948683\n        cats -0.948683  1.000000\n\n        Notes\n        -----\n        There are behavior differences between pandas-on-Spark and pandas.\n\n        * the `method` argument only accepts 'pearson', 'spearman'\n        * the data should not contain NaNs. pandas-on-Spark will return an error.\n        * pandas-on-Spark doesn't support the following argument(s).\n\n          * `min_periods` argument is not supported\n        "
    return cast(DataFrame, ps.from_pandas(corr(self, method)))