Delete duplicates in a Pandas Dataframe based on two columns

A dataframe is a two-dimensional, size-mutable tabular data structure with labeled axes (rows and columns). It can contain duplicate entries and to delete them there are several ways. 

The dataframe contains duplicate values in column order_id and customer_id. Below are the methods to remove duplicate values from a dataframe based on two columns.

Method 1: using drop_duplicates() 

Approach:

• We will drop duplicate columns based on two columns
• Let those columns be ‘order_id’ and ‘customer_id’
• Keep the latest entry only
• Reset the index of dataframe

Below is the python code for the above approach.

Python3

# import pandas library
import pandas as pd

# load data
df1 = pd.read_csv("super.csv")

# drop rows which have same order_id
# and customer_id and keep latest entry
newdf = df1.drop_duplicates(
  subset = ['order_id', 'customer_id'],
  keep = 'last').reset_index(drop = True)

# print latest dataframe
display(newdf)

Output:

Method 2: using groupby()

Approach:

• We will group rows based on two columns
• Let those columns be ‘order_id’ and ‘customer_id’
• Keep the first entry only

The python code for the above approach is given below.

Python3

# import pandas library
import pandas as pd

# read data
df1 = pd.read_csv("super.csv")

# group data over columns 'order_id'
# and 'customer_id' and keep first entry only
newdf1 = df1.groupby(['order_id', 'customer_id']).first()

# print new dataframe
print(newdf1)

Output: