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Sorting dataframe by columns which contains nan values. DataFrame has "sort_values()" method can take an another parameter called "na_position".Using this parameter the rows containing nan values can be pushed to either top or bottom Creating a new dataframe with dictionary  # importing pandas import pandas as pd import numpy as np # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], "Rank": [1, 5, 3], "Jumping_height": [20, 10, np.NaN], } # creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) # printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound Rank Jumping_height 0 Cat 15 Meow 1 20.0 1 Dog 12 Woof 5 10.0 2 Cow 10 Mooo ...

Sorting pandas dataframe by single or multiple columns. DataFrame has "sort_values()" method which can be used to sort the dataframe based single or multiple columns , control sorting flow and choose ascending or descending order. Creating a new dataframe with dictionary  # importing pandas import pandas as pd import numpy as np # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], "Rank": [1, 5, 3], "Jumping_height": [20, 10, np.NaN], } # creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) # printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound Rank Jumping_height 0 Cat 15 Meow 1 20.0 1 Dog 12 Woof 5 10.0 2 Cow 10 Mooo 3 ...