Selecting multiple columns in a Pandas dataframe Selecting multiple columns in a Pandas dataframe python python

Selecting multiple columns in a Pandas dataframe


The column names (which are strings) cannot be sliced in the manner you tried.

Here you have a couple of options. If you know from context which variables you want to slice out, you can just return a view of only those columns by passing a list into the __getitem__ syntax (the []'s).

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] # Remember that Python does not slice inclusive of the ending index.

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() method 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 look out for this.

df1 = df.iloc[0, 0:2].copy() # To avoid the case where changing df1 also changes df

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.


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.


A demo on a randomly generated DataFrame:

import pandas as pdimport numpy as npnp.random.seed(5)df = pd.DataFrame(np.random.randint(100, size=(100, 6)),                  columns=list('ABCDEF'),                  index=['R{}'.format(i) for i in range(100)])df.head()Out:     A   B   C   D   E   FR0  99  78  61  16  73   8R1  62  27  30  80   7  76R2  15  53  80  27  44  77R3  75  65  47  30  84  86R4  18   9  41  62   1  82

To get the columns from C to E (note that unlike integer slicing, 'E' is included in the columns):

df.loc[:, 'C':'E']Out:      C   D   ER0   61  16  73R1   30  80   7R2   80  27  44R3   47  30  84R4   41  62   1R5    5  58   0...

The same works for selecting rows based on labels. Get the rows 'R6' to 'R10' from those columns:

df.loc['R6':'R10', 'C':'E']Out:      C   D   ER6   51  27  31R7   83  19  18R8   11  67  65R9   78  27  29R10   7  16  94

.loc also accepts a Boolean array so you can select the columns whose corresponding entry in the array is True. For example, df.columns.isin(list('BCD')) returns array([False, True, True, True, False, False], dtype=bool) - True if the column name is in the list ['B', 'C', 'D']; False, otherwise.

df.loc[:, df.columns.isin(list('BCD'))]Out:      B   C   DR0   78  61  16R1   27  30  80R2   53  80  27R3   65  47  30R4    9  41  62R5   78   5  58...


Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in thethird and fourth columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is zero-offset! The "third" entry is at slot two.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural, because it uses the vanilla one-dimensional Python list indexing/slicing syntax.

Warning: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, an Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of its elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.


matomo