Sort by the values along either axis.
Name or list of names to sort by.
if axis is 0 or âindexâ then by may contain index levels and/or column labels.
if axis is 1 or âcolumnsâ then 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. mergesort and 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 first; last puts NaNs at the end.
If True, the resulting axis will be labeled 0, 1, â¦, n - 1.
Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this 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 by independently. The values in the returned Series will be used as the keys for sorting.
DataFrame with sorted values or None if inplace=True.
Examples
>>> 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 a single column
In this case, we are sorting the rows according to values in col1:
>>> 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
You can also provide multiple columns to by argument, as shown below. In this example, the rows are first sorted according to col1, and then the rows that have an identical value in col1 are sorted according to col2.
>>> 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 in a descending order
The sort order can be reversed using ascending argument, as shown below:
>>> 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
Placing any NA first
Note that in the above example, the rows that contain an NA value in their col1 are placed at the end of the dataframe. This behavior can be modified via na_position argument, as shown below:
>>> 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
Customized sort order
The key argument allows for a further customization of sorting behaviour. For example, you may want to ignore the letterâs case when sorting strings:
>>> 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
Another typical example is natural sorting. This can be done using natsort package, which provides sorted indices according to their natural order, as shown below:
>>> 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
>>> from natsort import index_natsorted
>>> index_natsorted(df["time"])
[0, 3, 2, 4, 1]
>>> df.sort_values(
... by="time",
... key=lambda x: np.argsort(index_natsorted(x)),
... )
time value
0 0hr 10
3 48hr 40
2 72hr 30
4 96hr 50
1 128hr 20
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