在pandas数据帧中的每列上应用函数

Question

How I can write following function in more pandas way:

     def calculate_df_columns_mean(self, df):
        means = {}
        for column in df.columns.columns.tolist():
            cleaned_data = self.remove_outliers(df[column].tolist())
            means[column] = np.mean(cleaned_data)
        return means

Thanks for help.

Answer 1

It seems to me that the iteration over the columns is unnecessary:

def calculate_df_columns_mean(self, df):
    cleaned_data = self.remove_outliers(df[column].tolist())
    return cleaned_data.mean()

the above should be enough assuming that remove_outliers still returns a df

EDIT

I think the following should work:

def calculate_df_columns_mean(self, df):
    return df.apply(lambda x: remove_outliers(x.tolist()).mean()

Answer 2

Use dataFrame.apply(func, axis=0) :

# axis=0 means apply to columns; axis=1 to rows
df.apply(numpy.sum, axis=0) # equiv to df.sum(0)