Question

我想基于熊猫的其他列来填充缺失值。这是我的桌子：

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如果性别是男性，我要填写已婚字段的缺失值->已婚是，否则已婚是：

Gender     Married
Male       Yes
Male       Yes
Female     No
Female     No
Male       NaN
Female     NaN

但是它失败了，我尝试了很多方法，但没有得到期望的结果。希望收到大家的来信。

Answer 1

我相信您仅在过滤的行中需要map和dictionary：

mask = df['Married'].isnull()
df.loc[mask, 'Married'] = df.loc[mask, 'Gender'].map({'Male':'Yes', 'Female':'No'})
print (df)
   Gender Married
0    Male     Yes
1    Male     Yes
2  Female      No
3  Female      No
4    Male     Yes
5  Female      No

使用numpy.where的另一种解决方案：

mask = df['Married'].isnull()
df.loc[mask, 'Married'] = np.where(df.loc[mask, 'Gender']  == 'Male', 'Yes','No')
print (df)
   Gender Married
0    Male     Yes
1    Male     Yes
2  Female      No
3  Female      No
4    Male     Yes
5  Female      No

将fillna映射为Series的另一种解决方案：

df['Married'] = df['Married'].fillna(df['Gender'].map({'Male':'Yes', 'Female':'No'}))

根据其他列熊猫填充缺失值

1 个答案: