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.
Let us consider the following dataset.
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 libraryimport pandas as pd # load datadf1 = pd.read_csv("super.csv") # drop rows which have same order_id# and customer_id and keep latest entrynewdf = df1.drop_duplicates( subset = ['order_id', 'customer_id'], keep = 'last').reset_index(drop = True) # print latest dataframedisplay(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 libraryimport pandas as pd # read datadf1 = pd.read_csv("super.csv") # group data over columns 'order_id'# and 'customer_id' and keep first entry onlynewdf1 = df1.groupby(['order_id', 'customer_id']).first() # print new dataframeprint(newdf1) |
Output:
