cudf.core.groupby.groupby.DataFrameGroupBy.corr#

DataFrameGroupBy.corr(method='pearson', min_periods=1, numeric_only: bool = False)[source]#

计算列的成对相关性，排除NA/空值。

Parameters:

method: {“pearson”, “kendall”, “spearman”} or callable,

默认值为“pearson”。目前仅支持皮尔逊相关系数。

min_periods: int, optional

每对列所需的最小观测数以获得有效结果。

Returns:

DataFrame

相关矩阵。

示例

>>> import cudf
>>> gdf = cudf.DataFrame({
...             "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
...             "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
...             "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
...             "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1]})
>>> gdf
   id  val1  val2  val3
0  a     5     4     4
1  a     4     5     5
2  a     6     6     6
3  b     4     1     1
4  b     8     2     2
5  b     7     9     9
6  c     4     8     8
7  c     5     5     5
8  c     2     1     1
>>> gdf.groupby("id").corr(method="pearson")
            val1      val2      val3
id
a   val1  1.000000  0.500000  0.500000
    val2  0.500000  1.000000  1.000000
    val3  0.500000  1.000000  1.000000
b   val1  1.000000  0.385727  0.385727
    val2  0.385727  1.000000  1.000000
    val3  0.385727  1.000000  1.000000
c   val1  1.000000  0.714575  0.714575
    val2  0.714575  1.000000  1.000000
    val3  0.714575  1.000000  1.000000