pandas 1.4.2

ParametersReturns
sort_values(self, axis=0, ascending=True, inplace: 'bool_t' = False, kind: 'str' = 'quicksort', na_position: 'str' = 'last', ignore_index: 'bool_t' = False, key: 'ValueKeyFunc' = None)

Parameters

axis : %(axes_single_arg)s, default 0

Axis to be sorted.

ascending : bool or list of bool, default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

inplace : bool, default False

If True, perform operation in-place.

kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'

Choice of sorting algorithm. See also numpy.sort for more information. :None:None:`mergesort` and :None:None:`stable` are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.

na_position : {'first', 'last'}, default 'last'

Puts NaNs at the beginning if first ; last puts NaNs at the end.

ignore_index : bool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

versionadded
key : callable, optional

Apply the key function to the values before sorting. This is similar to the :None:None:`key` argument in the builtin sorted function, with the notable difference that this :None:None:`key` function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in :None:None:`by` independently.

versionadded

Returns

DataFrame or None

DataFrame with sorted values or None if inplace=True .

Sort by the values along either axis.

See Also

DataFrame.sort_index

Sort a DataFrame by the index.

Series.sort_values

Similar method for a Series.

Examples

This example is valid syntax, but we were not able to check execution
>>> df = pd.DataFrame({
...  'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...  'col2': [2, 1, 9, 8, 7, 4],
...  'col3': [0, 1, 9, 4, 2, 3],
...  'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
... df col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F

Sort by col1

This example is valid syntax, but we were not able to check execution
>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort by multiple columns

This example is valid syntax, but we were not able to check execution
>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
1    A     1     1    B
0    A     2     0    a
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort Descending

This example is valid syntax, but we were not able to check execution
>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B
3  NaN     8     4    D

Putting NAs first

This example is valid syntax, but we were not able to check execution
>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B

Sorting with a key function

This example is valid syntax, but we were not able to check execution
>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Natural sort with the key argument, using the natsort package.

This example is valid syntax, but we were not able to check execution
>>> df = pd.DataFrame({
...  "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...  "value": [10, 20, 30, 40, 50]
... })
... df time value 0 0hr 10 1 128hr 20 2 72hr 30 3 48hr 40 4 96hr 50
This example is valid syntax, but we were not able to check execution
>>> from natsort import index_natsorted
... df.sort_values(
...  by="time",
...  key=lambda x: np.argsort(index_natsorted(df["time"]))
... ) time value 0 0hr 10 3 48hr 40 2 72hr 30 4 96hr 50 1 128hr 20
See :

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File: /pandas/core/generic.py#4510
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