pandas.DataFrame.sort_values   DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)[source]
 
Sort by the values along either axis.  Parameters 
 
by:str or list of str


Name or list of names to sort by.  if axis is 0 or ‘index’ then by may contain index levels and/or column labels. if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.   
axis:{0 or ‘index’, 1 or ‘columns’}, 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. mergesort and 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.  New in version 1.0.0.   
key:callable, optional


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.  New in version 1.1.0.     Returns 
 DataFrame or None

DataFrame with sorted values or None if inplace=True.      See also  DataFrame.sort_index

Sort a DataFrame by the index.  Series.sort_values

Similar method for a Series.    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 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 
>>> 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 
>>> 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 
>>> 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 
>>> 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 <https://github.com/SethMMorton/natsort> package. 
>>> 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
>>> 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