pandas 根据另一列的值从一列复制值
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Copy value from one column based on the value of another column
提问by Nicholas Tulach
I'm trying to fill values in one column from two other columns based on the values in a fourth column.
我正在尝试根据第四列中的值填充其他两列中的一列中的值。
I have a pandas dataframe with four columns: A, B, C, D
我有一个包含四列的 Pandas 数据框：A、B、C、D
df_copy = df.copy()
for i, row in df.iterrows():
if 'Test' in row.D:
df_copy.loc[i, 'A'] = row.B
elif 'Other' in row.D:
df_copy.loc[i, 'A'] = row.C
This works, but is very slow. Is there a more efficient way?
这有效，但速度很慢。有没有更有效的方法？
回答by joris
You can use 'boolean indexing' for this instead of iterating over all rows:
您可以为此使用“布尔索引”，而不是遍历所有行：
df_copy.loc[df['D']=='Test', 'A'] = df['B']
df_copy.loc[df['D']=='Other', 'A'] = df['C']
If you know that column D only consists of these two values, it can even shorter:
如果您知道 D 列仅包含这两个值，则它甚至可以更短：
df_copy['A'] = df['B']
df_copy.loc[df['D']=='Other', 'A'] = df['C']
If you want to have the same as the inoperator to test if that substring is in the column, you can do:
如果你想和in操作符一样来测试该子串是否在列中，你可以这样做：
df['D'].str.contains('Other')
to become the boolean values instead of the df['D']=='Other'
成为布尔值而不是 df['D']=='Other'
