Pandas Fillna 多列与每列众数

使用人口普查数据时，我想将两列（“workclass”和“native-country”）中的 NaN 替换为这两列各自的模式。我可以轻松获得模式：

mode = df.filter(["workclass", "native-country"]).mode()

它返回一个数据帧：

  workclass native-country
0   Private  United-States

然而，

df.filter(["workclass", "native-country"]).fillna(mode)

does not将每列中的 NaN 替换为任何内容，更不用说与该列对应的众数了。有一个顺利的方法来做到这一点吗？

如果您想用mode在某些列中是一个数据框df，你可以只fillna http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html by Series由选择创建，按位置选择iloc http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.html:

cols = ["workclass", "native-country"]
df[cols]=df[cols].fillna(df.mode().iloc[0])

Or:

df[cols]=df[cols].fillna(mode.iloc[0])

您的解决方案：

df[cols]=df.filter(cols).fillna(mode.iloc[0])

Sample:

df = pd.DataFrame({'workclass':['Private','Private',np.nan, 'another', np.nan],
                   'native-country':['United-States',np.nan,'Canada',np.nan,'United-States'],
                   'col':[2,3,7,8,9]})

print (df)
   col native-country workclass
0    2  United-States   Private
1    3            NaN   Private
2    7         Canada       NaN
3    8            NaN   another
4    9  United-States       NaN

mode = df.filter(["workclass", "native-country"]).mode()
print (mode)
  workclass native-country
0   Private  United-States

cols = ["workclass", "native-country"]
df[cols]=df[cols].fillna(df.mode().iloc[0])
print (df)
   col native-country workclass
0    2  United-States   Private
1    3  United-States   Private
2    7         Canada   Private
3    8  United-States   another
4    9  United-States   Private