bigframes.pandas.Series.iloc

property Series.iloc: IlocSeriesIndexer

Purely integer-location based indexing for selection by position.

Examples:

>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
...               {'a': 100, 'b': 200, 'c': 300, 'd': 400},
...               {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
>>> df = bpd.DataFrame(mydict)
>>> df
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

[3 rows x 4 columns]

Indexing just the rows

With a scalar integer.

>>> type(df.iloc[0])
<class 'pandas.core.series.Series'>

>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: Int64

With a list of integers.

>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: Int64

>>> type(df.iloc[[0]])
<class 'bigframes.dataframe.DataFrame'>

>>> df.iloc[[0, 1]]
    a    b    c    d
0    1    2    3    4
1  100  200  300  400

[2 rows x 4 columns]

With a slice object.

>>> df.iloc[:3]
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

[3 rows x 4 columns]

Indexing both axes

You can mix the indexer types for the index and columns. Use : to select the entire axis.

With scalar integers.

>>> df.iloc[0, 1]
np.int64(2)

Returns:

Purely integer-location Indexers.

Return type:

bigframes.core.indexers.IlocSeriesIndexer