pandas : update value if condition in 3 columns are met

Question

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 

Answer 1

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'