Start Your Project with Us

Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.

  • Any feature, you ask, we develop
  • 24x7 support worldwide
  • Real-time performance dashboard
  • Complete transparency
  • Dedicated account manager
  • Customized solutions to fulfill data scraping goals
Careers

For job seekers, please visit our Career Page or send your resume to hr@actowizsolutions.com

Airbnb-Python-Interview-Question-How-to-Perform-Data-Aggregation-and-Labeling-in-Python.jpg

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.

Find-the-min-average-max-rental-prices-for-a-particular-host-s-popularity-rating.jpg

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.

Dataset-Exploration.jpg

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.

Dataset-Exploration.jpg-2

The output is:

The-output-is.jpg

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.

We-will-continue-with-data-exploration.jpg

The output is

The-output-is-2.jpg

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

Approach Writing

Approach-Writing.jpg

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-1.jpg

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-2.jpg

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-3.jpg

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.

Step-4.jpg

The output is:

The-output-is-3.jpg

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:

The-output-is-4.jpg

And the output is:

And-the-output-is.jpg

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:

Our-code-will-appear-like-this.jpg

The output is

The-output-is-5.jpg

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:

Step-6.jpg

The output is

The-output-is-6.jpg

Conclusion:

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.

For more information, get in touch with Actowiz Solutions now! You can also contact us for all your web scraping service and mobile app data scraping service requirements.

RECENT BLOGS

View More

Turo Car Rental Data Analysis - Understanding Consumer Preferences and Behavior

Explore how Turo Car Rental Data Analysis helps businesses uncover consumer preferences, identify trends, and optimize pricing strategies for better decision-making and growth.

How to Scrape Coupang eCommerce Market Insights from Coupang Korea and Japan?

Learn how to scrape Coupang eCommerce market insights from Coupang in Korea and Japan. Gain valuable data for market analysis and business growth.

RESEARCH AND REPORTS

View More

Research Report - Decathlon 2024 Sales Analysis - Key Metrics and Consumer Behavior

An in-depth Decathlon 2024 sales analysis, exploring key trends, consumer behavior, revenue growth, and strategic insights for future success.

Cosmetic Product API Datasets - Market Trends, Retail Data & Ingredient Analysis

Explore cosmetic product API datasets for retail trends, ingredient analysis, and market insights to enhance business decisions in the beauty industry.

Case Studies

View More

Real-Time Insights Unlocked - A Case Study on Google Maps POI Data Extraction

Discover how Google Maps POI Data Extraction delivers real-time insights for smarter business decisions, location analysis, and competitive advantage.

Case Study: Transforming Online Shopping in India with ChatGPT – Powered by Actowiz Solutions

Actowiz Solutions built a ChatGPT shopping assistant to compare prices, delivery times, and links across Blinkit, Zepto, BigBasket & more in real-time.

Infographics

View More

Unlock Best Buy Product Insights with Web Scraping

Extract real-time Best Buy data on pricing, features, and stock availability. Optimize decisions with web scraping insights. Learn more in our expert guide!

Stay Competitive with the Best Price Monitoring Tools

Track competitor prices in real time with Actowiz Solutions. Monitor Amazon, Walmart, and Shopify pricing trends, optimize your strategy, and boost profits effortlessly.