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DOC: Enforce Numpy Docstring Validation for pandas.IntervalIndex.mid #58657

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Merged
merged 3 commits into from
May 9, 2024
Merged

DOC: Enforce Numpy Docstring Validation for pandas.IntervalIndex.mid #58657

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38bff9b
DOC: add GL08 for pandas.IntervalIndex.mid
tuhinsharma121 May 9, 2024
b13dfe4
DOC: remove GL08 for pandas.IntervalIndex.mid
tuhinsharma121 May 9, 2024
eb72d24
ENH: fix return type in examples section
tuhinsharma121 May 9, 2024
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1 change: 0 additions & 1 deletion ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
-i "pandas.IntervalIndex.is_non_overlapping_monotonic SA01" \
-i "pandas.IntervalIndex.left GL08" \
-i "pandas.IntervalIndex.length GL08" \
-i "pandas.IntervalIndex.mid GL08" \
-i "pandas.IntervalIndex.set_closed RT03,SA01" \
-i "pandas.IntervalIndex.to_tuples RT03,SA01" \
-i "pandas.MultiIndex PR01" \
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37 changes: 37 additions & 0 deletions pandas/core/indexes/interval.py
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Expand Up @@ -869,6 +869,43 @@ def right(self) -> Index:

@cache_readonly
def mid(self) -> Index:
"""
Return the midpoint of each interval in the IntervalIndex as an Index.

Each midpoint is calculated as the average of the left and right bounds
of each interval. The midpoints are returned as a pandas Index object.

Returns
-------
pandas.Index
An Index containing the midpoints of each interval.

See Also
--------
IntervalIndex.left : Return the left bounds of the intervals
in the IntervalIndex.
IntervalIndex.right : Return the right bounds of the intervals
in the IntervalIndex.
IntervalIndex.length : Return the length of the intervals in
the IntervalIndex.

Notes
-----
The midpoint is the average of the interval bounds, potentially resulting
in a floating-point number even if bounds are integers. The returned Index
will have a dtype that accurately holds the midpoints. This computation is
the same regardless of whether intervals are open or closed.

Examples
--------
>>> iv_idx = pd.IntervalIndex.from_arrays([1, 2, 3], [4, 5, 6])
>>> iv_idx.mid
Float64Index([2.5, 3.5, 4.5], dtype='float64')
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Shouldn't this be a regular Index?

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Yes. I wonder how the test passed. I see similar issue with the right method as well. Fixing both of them.

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Fixed now.

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updated the PR description.


>>> iv_idx = pd.IntervalIndex.from_tuples([(1, 4), (2, 5), (3, 6)])
>>> iv_idx.mid
Float64Index([2.5, 3.5, 4.5], dtype='float64')
"""
return Index(self._data.mid, copy=False)

@property
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