cudf.DataFrame.set_index

DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Return a new DataFrame with a new index

Parameters:

keysIndex, Series-convertible, label-like, or list

Index : the new index. Series-convertible : values for the new index. Label-like : Label of column to be used as index. List : List of items from above.

dropboolean, default True

Whether to drop corresponding column for str index argument

appendboolean, default True

Whether to append columns to the existing index, resulting in a MultiIndex.

inplaceboolean, default False

Modify the DataFrame in place (do not create a new object).

verify_integrityboolean, default False

Check for duplicates in the new index.

Examples

>>> df = cudf.DataFrame({
...     "a": [1, 2, 3, 4, 5],
...     "b": ["a", "b", "c", "d","e"],
...     "c": [1.0, 2.0, 3.0, 4.0, 5.0]
... })
>>> df
   a  b    c
0  1  a  1.0
1  2  b  2.0
2  3  c  3.0
3  4  d  4.0
4  5  e  5.0

Set the index to become the ‘b’ column:

>>> df.set_index('b')
   a    c
b
a  1  1.0
b  2  2.0
c  3  3.0
d  4  4.0
e  5  5.0

Create a MultiIndex using columns ‘a’ and ‘b’:

>>> df.set_index(["a", "b"])
       c
a b
1 a  1.0
2 b  2.0
3 c  3.0
4 d  4.0
5 e  5.0

Set new Index instance as index:

>>> df.set_index(cudf.RangeIndex(10, 15))
    a  b    c
10  1  a  1.0
11  2  b  2.0
12  3  c  3.0
13  4  d  4.0
14  5  e  5.0

Setting append=True will combine current index with column a:

>>> df.set_index("a", append=True)
     b    c
  a
0 1  a  1.0
1 2  b  2.0
2 3  c  3.0
3 4  d  4.0
4 5  e  5.0

set_index supports inplace parameter too:

>>> df.set_index("a", inplace=True)
>>> df
   b    c
a
1  a  1.0
2  b  2.0
3  c  3.0
4  d  4.0
5  e  5.0