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Here we will discuss the Airbnb Python interview question and throw the limelight on performing data aggregation and labeling in Python.

Air Bed and Breakfast, abbreviated as Airbnb, is a platform that connects property owners to rent their places to those looking for places to stay. The additional advantage of this platform is that it helps customers to provide ratings for the place and vice versa. It is the best way to eliminate disgusting hosts and guests.

The Airbnb super hosts welcome several guests. If hosts and their places receive better reviews, the chances of increasing business get high.

If you want to be a super host, there are four conditions: Response rate (90%), overall ratings (4.8+), stays (10+), and cancelation rate (<1%).

Let’s have a look at the question:

Find the min, average, & max rental prices for a particular host’s popularity rating.


In the above question, Airbnb asks to categorize the host’s popularity based on the number of reviews and the minimum, maximum, and average prices for each popular category.

The output will contain four columns, including

  • Host_popularity
  • Minimum
  • Maximum
  • Average prices

For each host popularity category, the conditions are:

  • 0 reviews
  • Between 1-5 reviews - New
  • Between 6-15 reviews- Trending
  • Between 16-40 popular reviews - Trending up
  • More than 40 reviews - Hot

First, we will explore our data to solve this question

1. Dataset Exploration

Airbnb provides us data frame of Airbnb_host_seaches. Below are the columns and data types.


The hosted data contains id, property type, price, etc.

As we have column information, we will use the first row of data using the HEAD() function to collect more information.


The output is:


Here, you can see that our price columns have decimals. It can create problems while calculating the min, max, and average.

Also, the two listings will have the same reviews and prices when selecting the price and reviewing for popularity classification. It will need additional effort to remove the duplicates.

We will continue with data exploration.


The output is


The data types, length of columns, and columns number are available with non-null counts. We will write the approach.

Approach Writing


After data exploration, we will split it into codable stages.

Step 1: Importing Libraries: We will import the Python libraries. We will also import NumPy. Pandas manipulate the dataset.


Step 2: Format up to two decimals: As we will calculate min, max, and average, the columns will have float values. We need to format data by rounding it up to two decimal points. The code for performing is:


Step 3: Rename the Data Frame: In this step, we will rename the data frame to make code syntax more short and readable. We will use the Data framer multiple times and rename it as .df. Our code will appear like this:


Step 4: Drop the Duplicates: The data we will work on possess users' searches. Hence, chances are there with duplicate data. We will remove all duplicates and perform calculations for each host. We will create a column with the host ID and use the INFO() method to view the length of the result.


The output is:


You can see that not forming a host ID column can accidentally remove match listings. So, we will generate a host_id column. The code is:


And the output is:


Step 5: Conditional Statements with Lambda Functions: The primary step for classifying your host popularity category is to use an if-else block. But it needs much effort and is difficult to read. Hence, we will use the lambda function to make the code shorter and neater.

The criteria for host popularity categories are like this:

  • The host having no reviews is known as ‘New.’
  • Between 1-5 reviews is known as ‘Rising.’
  • Between 6-15 is known as ‘Trending Up.’
  • Between 16-40 reviews is known as ‘Popular.’
  • More than 40 as ‘Hot.’

Our code will appear like this:


The output is


Step 6: Group the Columns to Calculate Min, Max, and Average: This is the final step to calculate the min, max, and average for a particular host popularity category. We will then assign it as a column to our data frame. Whenever the coding question consists of ‘each,’ you must use the groupby() function.

First, we will group by host popularity, then calculate the min, max, and average and assign them to Data frame columns. The easiest way to perform this is by using agg() with the groupby() function. Our final code will appear like this:


The output is



We first formatted the Data frame and removed the duplicates to solve the Airbnb Python interview question. Next, we categorized the hosts’ popularity based on reviews and calculated min, max, and average prices.

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