Comparing DataFrames and Returning Rows Based on Conditions

In this article, we’ll explore how to compare two dataframes and return rows based on conditions. We’ll use the popular R programming language with its dplyr library, but the concepts can be applied to other languages as well.

Introduction

When working with data, it’s often necessary to compare two datasets or dataframes. In this article, we’ll focus on how to achieve this comparison and return rows based on specific conditions.

We’ll use a customer transaction list and a product mapping table as an example. The goal is to find customers who have not purchased any category B products and return their similar type of category B products that they haven’t purchased.

Setting Up the Data

To demonstrate our approach, we first need to set up our dataframes. Let’s create two tables: Cust_list and Product_Table.

# Create the customer transaction list
Cust_list <- data.frame(
  stringsAsFactors = FALSE,
  Customer = c("Mike S.", "Tim P."),
  Product_ID = c(233, 6546)
)

# Create the product mapping table
Product_Table <- data.frame(
  stringsAsFactors = FALSE,
  Product_ID = c(233, 256, 296, 8536, 6546, 8946),
  Type = c("Shoes", "Shoes", "Shoes", "Socks", "Socks", "Socks"),
  Category = c("A", "B", "B", "A", "B", "B")
)

Merging the Tables

Next, we’ll merge the two tables using a right-outer join. This will give us a combined output where all customers are included, even if they don’t have any matching products.

# Merge the customer transaction list and product mapping table
df <- merge(x = Cust_list, y = Product_Table, by = "Product_ID", all.y = TRUE)

  Product_ID Customer  Type   Category
1        233          Mike S.  Shoes    A
2        256          &lt;NA&gt;     Shoes    B
3        296          &lt;NA&gt;     Shoes    B
4        6546         Tim P.   Socks    B
5        8536         &lt;NA&gt;     Socks    A
6        8946         &lt;NA&gt;     Socks    B

Filtering and Returning Rows

Now that we have our merged table, we can filter the rows based on specific conditions. We want to find customers who haven’t purchased any category B products. To do this, we’ll first filter the product mapping table to get only the products of category B.

# Filter the product mapping table for products of category B
B_products <- Product_Table %>% 
  filter(Category == "B")

Next, we’ll join the customer transaction list with the filtered product mapping table using a left join. This will give us all customers and their corresponding products of category B.

# Join the customer transaction list with the filtered product mapping table
left_join <- Cust_list %>% 
  left_join(B_products, by = "Product_ID")

Now we can filter the rows to get only the customers who haven’t purchased any category B products. We’ll do this by checking if the sum of category B products is greater than 0 for each customer.

# Filter the rows to get only customers with no category B products
no_B_products <- left_join %>% 
  group_by(Customer) %>% 
  filter(!sum(Category == "B") > 0)

Finally, we’ll return the similar type of category B products that these customers haven’t purchased. We can do this by joining the customer transaction list with the filtered product mapping table again.

# Join the customer transaction list with the filtered product mapping table
result <- no_B_products %>% 
  left_join(B_products, by = "Product_ID") %>% 
  filter(Type != Product_Type)

Output

The final output will be a table containing the similar type of category B products that each customer hasn’t purchased.

# Print the result
result

This should meet our expectation.

Last modified on 2024-03-14