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DOC: Using np.histogram_bin_edges with pd.cut #59281

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Jul 22, 2024
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DOC: Using np.histogram_bin_edges with pd.cut #59281

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update documentation
chaarvii Jul 19, 2024
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Update pandas/core/reshape/tile.py
chaarvii Jul 20, 2024
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Merge branch 'main' into pd-cut-doc
chaarvii Jul 20, 2024
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Update pandas/core/reshape/tile.py
chaarvii Jul 22, 2024
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Merge branch 'main' into pd-cut-doc
chaarvii Jul 22, 2024
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15 changes: 15 additions & 0 deletions pandas/core/reshape/tile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,12 +142,17 @@ def cut(
fixed set of values.
Series : One-dimensional array with axis labels (including time series).
IntervalIndex : Immutable Index implementing an ordered, sliceable set.
numpy.histogram_bin_edges: Function to calculate only the edges of the bins
used by the histogram function.

Notes
-----
Any NA values will be NA in the result. Out of bounds values will be NA in
the resulting Series or Categorical object.

``numpy.histogram_bin_edges`` can be used along with cut to calculate bins according
to some predefined methods.

Reference :ref:`the user guide <reshaping.tile.cut>` for more examples.

Examples
Expand Down Expand Up @@ -239,6 +244,16 @@ def cut(
>>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
[NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]

Using np.histogram_bin_edges with cut

>>> pd.cut(
... np.array([1, 7, 5, 4]),
... bins=np.histogram_bin_edges(np.array([1, 7, 5, 4]), bins="auto"),
... )
... # doctest: +ELLIPSIS
[NaN, (5.0, 7.0], (3.0, 5.0], (3.0, 5.0]]
Categories (3, interval[float64, right]): [(1.0, 3.0] < (3.0, 5.0] < (5.0, 7.0]]
"""
# NOTE: this binning code is changed a bit from histogram for var(x) == 0

Expand Down