Hwo to select multiple columns in a Pandas dataframe ?

As of version 0.11.0, columns can be sliced in the manner you tried using the .loc indexer:

df.loc[:, 'C':'E']

is equivalent to



df[['C', 'D', 'E']]  # or df.loc[:, ['C', 'D', 'E']]

and returns columns C through E.

3 years ago

df1 = df[['a', 'b']]

Alternatively, if it matters to index them numerically and not by their name (say your code should automatically do this without knowing the names of the first two columns) then you can do this instead:

df1 = df.iloc[:, 0:2]

Additionally, you should familiarize yourself with the idea of a view into a Pandas object vs. a copy of that object. The first of the above methods will return a new copy in memory of the desired sub-object (the desired slices).

Sometimes, however, there are indexing conventions in Pandas that don't do this and instead give you a new variable that just refers to the same chunk of memory as the sub-object or slice in the original object. This will happen with the second way of indexing, so you can modify it with the copy() function to get a regular copy. When this happens, changing what you think is the sliced object can sometimes alter the original object. Always good to be on the lookout for this.

df1 = df.iloc[0, 0:2].copy()

To use iloc, you need to know the column positions (or indices). As the column positions may change, instead of hard-coding indices, you can use iloc along with get_loc function of columns method of dataframe object to obtain column indices.

{df.columns.get_loc(c): c for idx, c in enumerate(df.columns)}
Now you can use this dictionary to access columns through names and using iloc.

3 years ago