Sort Pandas DataFrame with Examples

Dataframes values can be sorted by index and values . You can use the pandas dataframe sort_values() function to sort a dataframe.

sort_values(by, axis=0, ascending=True,na_position='first', kind='quicksort')

The sort_values() method allows the flexibility to sort a dataframe by one or more columns, ascending or descending, choose the sorting algorithm, treat NaNs during sorting, using a custom key for sorting, etc.

df.sort_values(by=["Name"])

Above code sorting by "Name" column in default ascending order.

Lets' create a DataFrame...

import pandas as pd import numpy as np df = pd.DataFrame() df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben'] df['TotalMarks'] = [82, 38, 63,22,55,40] df['Grade'] = ['A', 'E', 'B','E','C','D'] df['Promoted'] = [True, False,True,False,True,True] df

Name TotalMarks Grade Promoted 0 John 82 A True 1 Doe 38 E False 2 Bill 63 B True 3 Jim 22 E False 4 Harry 55 C True 5 Ben 40 D True

Sort by single column

df.sort_values(by=["Name"])

Name TotalMarks Grade Promoted 5 Ben 40 D True 2 Bill 63 B True 1 Doe 38 E False 4 Harry 55 C True 3 Jim 22 E False 0 John 82 A True

Sort by two columns

df.sort_values(by=["TotalMarks","Name"])

Sort by column in descending order

By default DataFrame is sorted by ascending order, if you want to sort in descending order you have to set the ascending=False inside the sort_values() method.

df.sort_values(by=["TotalMarks"],ascending=False)

Name TotalMarks Grade Promoted 0 John 82 A True 2 Bill 63 B True 4 Harry 55 C True 5 Ben 40 D True 1 Doe 38 E False 3 Jim 22 E False

Sort by missing value

df = pd.DataFrame({'x':[1.0, np.NaN, 3.0, 4.0]}) df

x 0 1.0 1 NaN 2 3.0 3 4.0

Sort by missing value first/last

df.sort_values(by=["x"],na_position='first')

x 1 NaN 0 1.0 2 3.0 3 4.0

Also, you can sort by missing value last using the following method.

df.sort_values(by=["x"],na_position='last')

x 0 1.0 2 3.0 3 4.0 1 NaN

Next : Pandas DataFrame: query() function

Related Topics