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marc-jonesmarc-jones
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Update documentation for sort_values and natural sorting
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‎pandas/core/frame.py

Lines changed: 40 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -7173,35 +7173,54 @@ def sort_values(
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`natural sorting <https://en.wikipedia.org/wiki/Natural_sort_order>`__.
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This can be done using
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``natsort`` `package <https://github.com/SethMMorton/natsort>`__,
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which provides sorted indices according
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to their natural order, as shown below:
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which provides a function to generate a key
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to sort data in their natural order:
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>>> df = pd.DataFrame(
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... {
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... "time": ["0hr", "128hr", "72hr", "48hr", "96hr"],
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... "value": [10, 20, 30, 40, 50],
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... "hours": ["0hr", "128hr", "0hr", "64hr", "64hr", "128hr"],
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... "mins": [
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... "10mins",
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... "40mins",
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... "40mins",
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... "40mins",
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... "10mins",
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... "10mins",
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... ],
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... "value": [10, 20, 30, 40, 50, 60],
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... }
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... )
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>>> df
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time value
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0 0hr 10
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1 128hr 20
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2 72hr 30
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3 48hr 40
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4 96hr 50
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>>> from natsort import index_natsorted
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>>> index_natsorted(df["time"])
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[0, 3, 2, 4, 1]
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hours mins value
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0 0hr 10mins 10
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1 128hr 40mins 20
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2 0hr 40mins 30
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3 64hr 40mins 40
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4 64hr 10mins 50
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5 128hr 10mins 60
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>>> from natsort import natsort_keygen
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>>> natsort_keygen()(df["hours"])
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(('', 0, 'hr'), ('', 128, 'hr'), ('', 72, 'hr'), ('', 48, 'hr'), ('', 96, 'hr'))
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>>> natsort_keygen()(df["mins"])
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(
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('', 10, 'mins'),
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('', 40, 'mins'),
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('', 40, 'mins'),
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('', 40, 'mins'),
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('', 10, 'mins'),
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('', 10, 'mins'),
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)
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>>> df.sort_values(
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... by="time",
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... key=lambda x: np.argsort(index_natsorted(x)),
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... by=["hours", "mins"],
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... key=natsort_keygen(),
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... )
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time value
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0 0hr 10
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3 48hr 40
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2 72hr 30
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4 96hr 50
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1 128hr 20
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hours mins value
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0 0hr 10mins 10
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2 0hr 40mins 30
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4 64hr 10mins 50
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3 64hr 40mins 40
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5 128hr 10mins 60
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1 128hr 40mins 20
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"""
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inplace = validate_bool_kwarg(inplace, "inplace")
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axis = self._get_axis_number(axis)

‎pandas/core/generic.py

Lines changed: 38 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -5004,27 +5004,49 @@ def sort_values(
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>>> df = pd.DataFrame(
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... {
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... "time": ["0hr", "128hr", "72hr", "48hr", "96hr"],
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... "value": [10, 20, 30, 40, 50],
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... "hours": ["0hr", "128hr", "0hr", "64hr", "64hr", "128hr"],
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... "mins": [
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... "10mins",
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... "40mins",
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... "40mins",
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... "40mins",
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... "10mins",
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... "10mins",
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... ],
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... "value": [10, 20, 30, 40, 50, 60],
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... }
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... )
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>>> df
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time value
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0 0hr 10
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1 128hr 20
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2 72hr 30
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3 48hr 40
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4 96hr 50
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>>> from natsort import index_natsorted
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hours mins value
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0 0hr 10mins 10
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1 128hr 40mins 20
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2 0hr 40mins 30
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3 64hr 40mins 40
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4 64hr 10mins 50
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5 128hr 10mins 60
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>>> from natsort import natsort_keygen
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>>> natsort_keygen()(df["hours"])
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(('', 0, 'hr'), ('', 128, 'hr'), ('', 72, 'hr'), ('', 48, 'hr'), ('', 96, 'hr'))
5030+
>>> natsort_keygen()(df["mins"])
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(
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('', 10, 'mins'),
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('', 40, 'mins'),
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('', 40, 'mins'),
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('', 40, 'mins'),
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('', 10, 'mins'),
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('', 10, 'mins'),
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)
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>>> df.sort_values(
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... by="time", key=lambda x: np.argsort(index_natsorted(df["time"]))
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... by=["hours", "mins"],
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... key=natsort_keygen(),
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... )
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time value
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0 0hr 10
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3 48hr 40
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2 72hr 30
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4 96hr 50
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1 128hr 20
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hours mins value
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0 0hr 10mins 10
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2 0hr 40mins 30
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4 64hr 10mins 50
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3 64hr 40mins 40
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5 128hr 10mins 60
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1 128hr 40mins 20
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"""
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raise AbstractMethodError(self)
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