在 Pandas 数据框中的每一列上应用函数
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Apply function on each column in a pandas dataframe
提问by Night Walker
How I can write following function in more pandas way:
我如何以更多的Pandas方式编写以下函数：
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.
感谢帮助。
采纳答案by EdChum
It seems to me that the iteration over the columns is unnecessary:
在我看来，对列的迭代是不必要的：
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_outliersstill returns a df
假设remove_outliers仍然返回 df ，以上应该足够了
EDIT
编辑
I think the following should work:
我认为以下应该有效：
def calculate_df_columns_mean(self, df):
return df.apply(lambda x: remove_outliers(x.tolist()).mean()
回答by Nick Bull
Use dataFrame.apply(func, axis=0):
使用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)
