sort_values(self, by, axis: 'Axis' = 0, ascending=True, inplace: 'bool' = False, kind: 'str' = 'quicksort', na_position: 'str' = 'last', ignore_index: 'bool' = False, key: 'ValueKeyFunc' = None)
Name or list of names to sort by.
if :None:None:`axis` is 0 or :None:None:`'index'` then :None:None:`by` may contain index levels and/or column labels.
if :None:None:`axis` is 1 or :None:None:`'columns'` then :None:None:`by` may contain column levels and/or index labels.
Axis to be sorted.
Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
If True, perform operation in-place.
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.
Puts NaNs at the beginning if :None:None:`first`; :None:None:`last` puts NaNs at the end.
If True, the resulting axis will be labeled 0, 1, …, n - 1.
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.
DataFrame with sorted values or None if inplace=True
.
Sort by the values along either axis.
DataFrame.sort_index
Sort a DataFrame by the index.
Series.sort_values
Similar method for a Series.
>>> 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({This example is valid syntax, but we were not able to check execution
... "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
>>> from natsort import index_natsortedSee :
... 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
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.nsmallest
pandas.core.indexes.base.Index.sort_values
pandas.core.series.Series.sort_index
pandas.core.series.Series.sort_values
pandas.core.frame.DataFrame.nlargest
pandas.core.frame.DataFrame.sort_index
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