# pandas.DataFrame.iloc

`DataFrame.``iloc`

  Purely integer-location based indexing for selection by position.

  `.iloc[]` is primarily integer position based (from `0` to `length-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 `callable` function 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.

  `.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

  `DataFrame.iat`
    Fast integer location scalar accessor.
  `DataFrame.loc`
    Purely label-location based indexer for selection by label.
  `Series.iloc`
    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.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  b  c  d
      0  1  2  3  4
      >>> type(df.iloc[[0]])
      <class 'pandas.core.frame.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]
      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

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