python - 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'