pandas.DataFrame.iloc — pandas 3.0.0.dev0+2097.gcdc5b7418e documentation (original) (raw)
property DataFrame.iloc[source]#
Purely integer-location based indexing for selection by position.
Changed in version 3.0: Callables which return a tuple are deprecated as input.
.iloc[] is primarily integer position based (from 0 tolength-1 of the axis), but may also be used with a boolean array.
Allowed inputs are:
- An integer, e.g.
5. - A list or array of integers, e.g.
[4, 3, 0]. - A slice object with ints, e.g.
1:7. - A boolean array.
- A
callablefunction with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value. - A tuple of row and column indexes. The tuple elements consist of one of the above inputs, e.g.
(0, 1).
.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
See more at Selection by Position.
See also
Fast integer location scalar accessor.
Purely label-location based indexer for selection by label.
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 = pd.DataFrame(mydict) df a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000
Indexing just the rows
With a scalar integer.
type(df.iloc[0]) <class 'pandas.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 b c d 0 1 2 3 4 type(df.iloc[[0]]) <class 'pandas.DataFrame'>
df.iloc[[0, 1]] a b c d 0 1 2 3 4 1 100 200 300 400
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
With a boolean mask the same length as the index.
df.iloc[[True, False, True]] a b c d 0 1 2 3 4 2 1000 2000 3000 4000
With a callable, useful in method chains. The x passed to the lambda is the DataFrame being sliced. This selects the rows whose index label even.
df.iloc[lambda x: x.index % 2 == 0] a b c d 0 1 2 3 4 2 1000 2000 3000 4000
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)
With lists of integers.
df.iloc[[0, 2], [1, 3]] b d 0 2 4 2 2000 4000
With slice objects.
df.iloc[1:3, 0:3] a b c 1 100 200 300 2 1000 2000 3000
With a boolean array whose length matches the columns.
df.iloc[:, [True, False, True, False]] a c 0 1 3 1 100 300 2 1000 3000
With a callable function that expects the Series or DataFrame.
df.iloc[:, lambda df: [0, 2]] a c 0 1 3 1 100 300 2 1000 3000