pandas : update value if condition in 3 columns are met
PythonPandasPython Problem Overview
I have a dataframe like this:
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:
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:
df.ix[ np.logical_and(df.A=='blue', df.B=='red', df.C=='triangle'), ['D'] ] = 'succeed'
has exactly the same result:
In[1]: df
Out[1]:
A B C D
1 blue red square succeed
2 orange yellow circle NaN
3 black grey circle NaN
Python Solutions
Solution 1 - Python
Using:
df[ (df.A=='blue') & (df.B=='red') & (df.C=='square') ]['D'] = 'succeed'
gives the warning:
/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:
df.loc[(df['A'] == 'blue') & (df['B'] == 'red') & (df['C'] == 'square'),'D'] = 'M5'
Solution 2 - Python
You could try this instead:
df[ (df.A=='blue') & (df.B=='red') & (df.C=='square') ]['D'] = 'succeed'
Solution 3 - Python
You could try:
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
Solution 4 - Python
This format might have been implied in the new answers, but the following bit actually worked for me.
df['D'].loc[(df['A'] == 'blue') & (df['B'] == 'red') & (df['C'] == 'square')] = 'succeed'
Solution 5 - Python
The third parameter of logical_and is to assign the array used to store the result.
Currently, the method @TimRich provided might be the best. In pandas 0.13 (in development), there's a new experimental query method. Try it!