On Tuesday, 10 September 2019 12:56:36 UTC+10, Sayth Renshaw wrote: > On Friday, 6 September 2019 07:52:56 UTC+10, Piet van Oostrum wrote: > > Piet van Oostrum <> writes: > > > > > That would select ROWS 0,1,5,6,7, not columns. > > > To select columns 0,1,5,6,7, use two-dimensional indexes > > > > > > df1 = df.iloc[:, [0,1,5,6,7]] > > > > > > : selects all rows. > > > > And that also solves your original problem. > > > > This statement: > > > > df1['Difference'] = df1.loc['Current Team'].str.lower().str.strip() == > > df1.loc['New Team'].str.lower().str.strip() > > > > should not use .loc, because then you are selecting rows, not columns, but: > > > > df1['Difference'] = df1['Current Team'].str.lower().str.strip() == df1['New > > Team'].str.lower().str.strip() > > -- > > Piet van Oostrum <> > > WWW: http://piet.vanoostrum.org/ > > PGP key: [8DAE142BE17999C4] > > That actually creates another error. > > 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 > > See the caveats in the documentation: > http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy > > So tried this > df['c'] = df.apply(lambda df1: df1['Current Team'].str.lower().str.strip() == > df1['New Team'].str.lower().str.strip(), axis=1) > > Based on this SO answer https://stackoverflow.com/a/46570641 > > Thoughts? > > Sayth
This works on an individual row df2 = df1.loc[(df1['Current Team'] == df1['New Team']),'D'] = 'Wow' But how do I apply it to the whole new column and return the new dataset? Trying to use lambda but it cannot contain assigment df2 = df1.apply(lambda df1: [ (df1['Current Team'] == df1['New Team']) ]['D'] = 'succeed') df2 Confused Sayth -- https://mail.python.org/mailman/listinfo/python-list
