Javatpoint Logo
Javatpoint Logo

Pandas DataFrame.sort()

We can efficiently perform sorting in the DataFrame through different kinds:

  • By label
  • By Actual value

Before explaining these two kinds of sorting, first we have to take the dataset for demonstration:

Output

         col2          col1
1      -0.456763     -0.931156
3       0.242766     -0.793590
7       1.133803      0.454363
2      -0.843520     -0.938268
4      -0.018571     -0.315972
5      -1.951544     -1.300100
9      -0.711499      0.031491
8       1.648080      0.695637
0       2.576250     -0.625171
6      -0.301717      0.879970

In the above DataFrame, the labels and the values are unsorted. So, let's see how it can be sorted:

  • By label

The DataFrame can be sorted by using the sort_index() method. It can be done by passing the axis arguments and the order of sorting. The sorting is done on row labels in ascending order by default.

Example

Output

       col4          col3
0     0.698346      1.897573
1     1.247655     -1.208908
2    -0.469820     -0.546918
3    -0.793445      0.362020
4    -1.184855     -1.596489
5     1.500156      -0.397635
6    -1.239635      -0.255545
7     1.110986      -0.681728
8    -1.797474       0.108840
9     0.063048       1.512421
  • Order of Sorting

The order of sorting can be controlled by passing the Boolean value to the ascending parameter.

Example:

Output

        col4          col5
1      0.664336     -1.846533
4     -0.456203     -1.255311
7      0.537063     -0.774384
2     -1.937455      0.257315
5      0.331764     -0.741020
3     -0.082334      0.304390
0     -0.983810     -0.711582
8      0.208479     -1.234640
9      0.656063      0.122720
6      0.347990     -0.410401
  • Sort the Columns:

We can sort the columns labels by passing the axis argument respected to its values 0 or 1. By default, the axis=0, it sort by row.

Example:

Output

       col4          col7
1    -0.509367     -1.609514
4    -0.516731      0.397375
8    -0.201157     -0.009864
2     1.440567       1.058436
0     0.955486      -0.009777
6    -1.211133       0.415147
7     0.095644       0.531727
5    -0.881241      -0.871342
3     0.206327       -1.154724
9     1.418127        0.146788

By Actual Value

It is another kind through which sorting can be performed in the DataFrame. Like index sorting, sort_values() is a method for sorting by the values.

It also provides a feature in which we can specify the column name of the DataFrame with which values are to be sorted. It is done by passing the 'by' argument.

Example:

Output

     col1    col2
2     8       4
0     7       8
3     3       9
1     1       12

In the above output, observe that the values are sorted in col2 only, and the respective col1 value and row index will alter along with col2. Thus, they look unsorted.

Parameters

  • columns: Before Sorting, you have to pass an object or the column names.
  • ascending: A Boolean value is passed that is responsible for sorting in the ascending order. Its default value is True.
  • axis: 0 or index; 1 or 'columns'. The default value is 0. It decides whether you sort by index or columns.
  • inplace: A Boolean value is passed. The default value is false. It will modify any other views on this object and does not create a new instance while sorting the DataFrame.
  • kind: 'heapsort', 'mergesort', 'quicksort'. It is an optional parameter that is to be applied only when you sort a single column or labels.
  • na_position: 'first', 'last'. The 'first' puts NaNs at the beginning, while the 'last' puts NaNs at the end. Default option last.

Next TopicDataFrame.sum()




Youtube For Videos Join Our Youtube Channel: Join Now

Help Others, Please Share

facebook twitter pinterest

Learn Latest Tutorials


Preparation


Trending Technologies


B.Tech / MCA