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:

bystr or list of str

Name or list of names to sort by.

• ifaxis is 0 or‘index’ thenby may contain indexlevels and/or column labels.

• ifaxis is 1 or‘columns’ thenby may contain columnlevels and/or index labels.

axis“{0 or ‘index’, 1 or ‘columns’}”, default 0

Axis to be sorted.

ascendingbool or list of bool, default True

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

inplacebool, default False

If True, perform operation in-place.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’

Choice of sorting algorithm. See alsonumpy.sort() for moreinformation.mergesort andstable are the only stable algorithms. ForDataFrames, this option is only applied when sorting on a singlecolumn or label.

na_position{‘first’, ‘last’}, default ‘last’

Puts NaNs at the beginning iffirst;last puts NaNs at theend.

ignore_indexbool, default False

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

keycallable, optional

Apply the key function to the valuesbefore sorting. This is similar to thekey argument in thebuiltinsorted() function, with the notable difference thatthiskey function should bevectorized. It should expect aSeries and return a Series with the same shape as the input.It will be applied to each column inby independently.

Returns:

DataFrame or None

DataFrame with sorted values or None ifinplace=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 col40    A     2     0    a1    A     1     1    B2    B     9     9    c3  NaN     8     4    D4    D     7     2    e5    C     4     3    F

Sort by col1

>>>df.sort_values(by=['col1'])  col1  col2  col3 col40    A     2     0    a1    A     1     1    B2    B     9     9    c5    C     4     3    F4    D     7     2    e3  NaN     8     4    D

Sort by multiple columns

>>>df.sort_values(by=['col1','col2'])  col1  col2  col3 col41    A     1     1    B0    A     2     0    a2    B     9     9    c5    C     4     3    F4    D     7     2    e3  NaN     8     4    D

Sort Descending

>>>df.sort_values(by='col1',ascending=False)  col1  col2  col3 col44    D     7     2    e5    C     4     3    F2    B     9     9    c0    A     2     0    a1    A     1     1    B3  NaN     8     4    D

Putting NAs first

>>>df.sort_values(by='col1',ascending=False,na_position='first')  col1  col2  col3 col43  NaN     8     4    D4    D     7     2    e5    C     4     3    F2    B     9     9    c0    A     2     0    a1    A     1     1    B

Sorting with a key function

>>>df.sort_values(by='col4',key=lambdacol:col.str.lower())   col1  col2  col3 col40    A     2     0    a1    A     1     1    B2    B     9     9    c3  NaN     8     4    D4    D     7     2    e5    C     4     3    F

Natural sort with the key argument,using thenatsort <https://github.com/SethMMorton/natsort> package.

>>>df=pd.DataFrame({..."time":['0hr','128hr','72hr','48hr','96hr'],..."value":[10,20,30,40,50]...})>>>df    time  value0    0hr     101  128hr     202   72hr     303   48hr     404   96hr     50>>>fromnatsortimportindex_natsorted>>>df.sort_values(...by="time",...key=lambdax:np.argsort(index_natsorted(df["time"]))...)    time  value0    0hr     103   48hr     402   72hr     304   96hr     501  128hr     20