如何分组并将操作聚合到多列？

Question

我正在尝试为基于两列的数据框中的行创建均值，但出现以下错误：

TypeError: 'numpy.float64' object is not callable

数据框：

       date               origin  positive_score  neutral_score  negativity_score  compound_score
 2020-09-19            the verge           0.130          0.846             0.024          0.9833
 2020-09-19            the verge           0.130          0.846             0.024          0.9833
 2020-09-19                 fool           0.075          0.869             0.056          0.8560
 2020-09-19        seeking_alpha           0.067          0.918             0.015          0.9983
 2020-09-19        seeking_alpha           0.171          0.791             0.038          0.7506
 2020-09-19        seeking_alpha           0.095          0.814             0.091          0.9187
 2020-09-19        seeking_alpha           0.113          0.801             0.086          0.9890
 2020-09-19        seeking_alpha           0.094          0.869             0.038          0.9997
 2020-09-19  wall street journal           0.000          1.000             0.000          0.0000
 2020-09-19        seeking_alpha           0.179          0.779             0.042          0.9997
 2020-09-19        seeking_alpha           0.178          0.704             0.117          0.7360

我的代码：

    def mean_indicators(cls, df: pd.DataFrame):
        df_with_mean = df.groupby([DATE, ORIGIN], as_index=False).agg({POSITIVE_SCORE: df[POSITIVE_SCORE].mean(),
                                                                       NEGATIVE_SCORE: df[NEGATIVE_SCORE].mean(),
                                                                       NEUTRAL_SCORE: df[NEUTRAL_SCORE].mean(),
                                                                       COMPOUND_SCORE: df[COMPOUND_SCORE].mean()
                                                                       })
        return df_with_mean

Answer 1

我认为这应该可以满足您的要求：

def mean_indicators(cls, df: pd.DataFrame):
    df_with_mean = df.groupby([DATE, ORIGIN], as_index=False).agg(
    {POSITIVE_SCORE: "mean",
     NEGATIVE_SCORE: "mean",
     NEUTRAL_SCORE: "mean",
     COMPOUND_SCORE: "mean",
})
    return df_with_mean

您也可以使用命名聚合语法，如 here

Answer 2

该错误是错误聚合操作的结果。
- {POSITIVE_SCORE: df[POSITIVE_SCORE].mean() 不正确。
- {'positive_score': 'mean'} 正确
由于您要对所有 non-grouped 数值列求平均值，因此不需要该函数。
对整个数据帧使用 pandas.core.groupby.GroupBy.mean 一次操作。
使用pandas.core.groupby.DataFrameGroupBy.aggregate聚合不同的操作。
- Applying multiple functions at once

# just groupby and mean
df_mean = df.groupby(['date', 'origin'], as_index=False).mean()

# display(df_mean())
       date               origin  positive_score  neutral_score  negativity_score  compound_score
 2020-09-19                 fool        0.075000       0.869000             0.056        0.856000
 2020-09-19        seeking_alpha        0.128143       0.810857             0.061        0.913143
 2020-09-19            the verge        0.130000       0.846000             0.024        0.983300
 2020-09-19  wall street journal        0.000000       1.000000             0.000        0.000000

如何分组并将操作聚合到多列？

How to groupby and aggregate an operation to multiple columns?

python

aggregate

mean

pandas

pandas-groupby