The ultimate guide to data analytics interviews using Pandas. Strengthen your skills with advanced concepts and practice with real-world interview questions. Developed by FAANG engineers.

In this course, you will face a number of challenges across different concepts, all with the data analytics library Pandas.

This course is mainly to refresh your knowledge, as well as help you understand more advanced concepts. By solving multiple challenges, you will surely strengthen your skills and it will help you ace your interview involving Pandas.

You will cover many topics such as: grouping, filtering, date manipulation, and more. By the time you finish this course, you will have the confidence you need to land your dream job.

Data Analytics Interview Prep Using Pandas

# Problem definition

Your music analyst now has two datasets. The first is the standard music dataset with the band names and their associated data.



| artist      | country | plays | genre | fans | 
|-------------|---------|-------|-------|------| 
| Leprous     | 5       | 1000  | metal | 500  | 
| ABBA        | 5       | 2000  | pop   | 100  | 
| Bryan Adams | 6       | 2500  | pop   | 200  | 
| Rush        | 6       | 3000  | rock  | 500  | 

---

The second dataset contains an extra piece of information for each band (the bands in both datasets are the same), which is the number of monthly listeners.

| artist      | monthly_listeners | 
|-------------|-------------------| 
| Leprous     | 300               | 
| ABBA        | 20                | 
| Bryan Adams | 40                | 
| Rush        | 280               | 

---
Can you help the music analyst find the bands that have a ratio of monthly listeners to fans greater than 0.5?


This challenge expects you to perform a horizontal concatenation of two DataFrames .

Course Introduction

Selection / Filters

Grouping

Dates Manipulation

Apply / Map

Merge/Concat

Quizzes: Assorted Topics

Conclusion

Challenge 4: Concat on Columns [Bands] (Medium)

Problem definition