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 with complementary information.

The first dataset contains information about bands, and their associated data. However, instead of names, it refers to countries by their IDs. Here are three sample rows:

| artist            | country | plays | genre | fans | 
|-------------------|---------|-------|-------|------| 
| The Beatles       | 1       | 150   | rock  | 50   | 
| Iron Maiden       | 1       | 20000 | metal | 3500 | 
| Judas Priest      | 1       | 5000  | metal | 1000 | 
| Leprous           | 5       | 1000  | metal | 500  |
| Rush              | 6       | 3000  | rock  | 500  | 


The second is a simple mapping between the ID of a country and its name.


| country_id | name    | 
|------------|---------| 
| 1          | UK      | 
| 2          | US      | 
| 3          | Egypt   | 
| 4          | Finland | 

This is very similar to the previous dataset in **Challenge 1**, but with a small difference. In this case, there are some artists in the dataset with an associated country ID that doesn't exist in the `countries` table. This might be due to the data either getting corrupted, or inserted incorrectly.

Your music analyst would like to know how many `plays` were affected due to this error. Can you help the music analyst find the sum of plays of all the artists without a country name associated with them?


This challenge expects you to merge DataFrames that have missing information when combined.

Course Introduction

Selection / Filters

Grouping

Dates Manipulation

Apply / Map

Merge/Concat

Quizzes: Assorted Topics

Conclusion

Challenge 2: Merge with Missing Values (Medium)

Problem definition