This tutorial shows two methods to reverse row order based on the dataframe index. The following shows the key syntax code.
Method 1:
df_name[::-1.]
Method 2:
df_name.loc[::-1.]
Example 1 for Method 1
We can reverse row order in Python Pandas dataframes using car_data[::-1].
import pandas as pd
# create a new dataframe
car_data = {'Unique_Number': ['12', '89','63','43'],
'Brand': ['Tesla', 'Tesla','Tesla','Ford'],
'Location': ['CA', 'CA','NY','MA'],
'Year':['2019','2018','2020','2019']}
car_data=pd.DataFrame(data=car_data)
# print out the dataframe
print('Original Dataframe: \n',car_data)
# reverse the order of the dataframe
car_data_reversed=car_data[::-1]
# print out the reversed dataframe
print('Reversed Dataframe: \n',car_data_reversed)Below shows the original dataframe and the reversed dadaframe based on the index.
Original Dataframe: Unique_Number Brand Location Year 0 12 Tesla CA 2019 1 89 Tesla CA 2018 2 63 Tesla NY 2020 3 43 Ford MA 2019 Reversed Dataframe: Unique_Number Brand Location Year 3 43 Ford MA 2019 2 63 Tesla NY 2020 1 89 Tesla CA 2018 0 12 Tesla CA 2019
Example 2 for method 2
We can also use car_data.loc[::-1] to reverse the row order and the following is the Python code example.
import pandas as pd
# create a new dataframe
car_data = {'Unique_Number': ['12', '89','63','43'],
'Brand': ['Tesla', 'Tesla','Tesla','Ford'],
'Location': ['CA', 'CA','NY','MA'],
'Year':['2019','2018','2020','2019']}
car_data=pd.DataFrame(data=car_data)
# print out the dataframe
print('Original Dataframe: \n',car_data)
# reverse the order of the dataframe
car_data_reversed=car_data.loc[::-1]
# print out the reversed dataframe
print('Reversed Dataframe: \n',car_data_reversed)The following is the output, which shows the reversed dataframe.
Original Dataframe: Unique_Number Brand Location Year 0 12 Tesla CA 2019 1 89 Tesla CA 2018 2 63 Tesla NY 2020 3 43 Ford MA 2019 Reversed Dataframe: Unique_Number Brand Location Year 3 43 Ford MA 2019 2 63 Tesla NY 2020 1 89 Tesla CA 2018 0 12 Tesla CA 2019