Python 熊猫：如果满足 3 列中的条件，则更新值
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pandas : update value if condition in 3 columns are met
提问by Eduardo Oliveira
I have a dataframe like this:
我有一个这样的数据框：
In[1]: df
Out[1]:
A B C D
1 blue red square NaN
2 orange yellow circle NaN
3 black grey circle NaN
and I want to update column D when it meets 3 conditions. Ex:
我想在满足 3 个条件时更新列 D。前任：
df.ix[ np.logical_and(df.A=='blue', df.B=='red', df.C=='square'), ['D'] ] = 'succeed'
It works for the first two conditions, but it doesn't work for the third, thus:
它适用于前两个条件，但不适用于第三个条件，因此：
df.ix[ np.logical_and(df.A=='blue', df.B=='red', df.C=='triangle'), ['D'] ] = 'succeed'
has exactly the same result:
有完全相同的结果：
In[1]: df
Out[1]:
A B C D
1 blue red square succeed
2 orange yellow circle NaN
3 black grey circle NaN
回答by Tim
You could try this instead:
你可以试试这个：
df[ (df.A=='blue') & (df.B=='red') & (df.C=='square') ]['D'] = 'succeed'
回答by waitingkuo
The third parameter of logical_andis to assign the array used to store the result.
logical_and的第三个参数是分配用于存储结果的数组。
Currently, the method @TimRich provided might be the best. In pandas 0.13 (in development), there's a new experimental querymethod. Try it!
目前，@TimRich 提供的方法可能是最好的。在 pandas 0.13（开发中）中，有一种新的实验性查询方法。尝试一下！
回答by Praveen
Using:
使用：
df[ (df.A=='blue') & (df.B=='red') & (df.C=='square') ]['D'] = 'succeed'
gives the warning:
给出警告：
/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
A better way of achieving this seems to be:
实现这一目标的更好方法似乎是：
df.loc[(df['A'] == 'blue') & (df['B'] == 'red') & (df['C'] == 'square'),'D'] = 'M5'
回答by theSanjeev
You could try:
你可以试试：
df['D'] = np.where((df.A=='blue') & (df.B=='red') & (df.C=='square'), 'succeed')
This answer might provide a detailed answer to the your question: Update row values where certain condition is met in pandas
此答案可能会为您的问题提供详细的答案： 在熊猫中满足特定条件时更新行值
回答by Alex Schwab
This format might have been implied in the new answers, but the following bit actually worked for me.
新答案中可能暗示了这种格式，但以下内容实际上对我有用。
df['D'].loc[(df['A'] == 'blue') & (df['B'] == 'red') & (df['C'] == 'square')] = 'succeed'
df['D'].loc[(df['A'] == 'blue') & (df['B'] == 'red') & (df['C'] == 'square')] = 'succeed'
