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

# Concept

The ability to ***group*** or *segment* `DataFrames` by one or more columns is one of the key features of any data analysis application. Therefore it would most likely show up in a data analysis interview or task.

The idea is to divide a `DataFrame` into multiple groups to analyze each group separately.

## Syntax

As a reminder, the syntax is as simple as
`df.groupby(<col_name>)` or, in the case of grouping by multiple columns, `df.groupby([<col1>, <col2>, ..])`.

Operations such as aggregations and apply functions can be applied on `DataFrameGroupBy` objects which can be reset to a normal `DataFrame` using `reset_index()`

# Travel dataset

## Idea

In your travel dataset, it makes sense that not all travelers exhibit the same behavior. Certain nationalities might like to travel to specific destinations, while other nationalities might prefer other destinations. 

In this case, your ***grouping column*** will be `nationality`, and you will be analyzing the `destination` distribution per `nationality`. You might want the most common destinations or the count of unique destinations. This can help in developing marketing strategies for `nation-destination` combinations.

Another example is that certain types of tour packages might be more interesting in specific weather conditions; for example, cruises in the summer.

In this case, your *grouping column* will be `tour_type`, and you will be analyzing the `season` distribution.

In interviews, it is important to understand the business domain and use-case before answering a question. So, it's likely that you'll be using either *grouping* or *filtering* on the most important attributes representing the business core models. 

In the example here, the core models are the traveler and the trip. It's important to pause here and reflect on what attributes would likely affect the behaviors of travelers. Here, you discussed `nationality`, `destination`, `tour_type`, and `season`. Can you come up with other core attributes?

## Interviews

You can be asked open-ended questions interviews, such as the following:

1. How would you create a semi-personalized newsletter to send to previous travelers based on their demographics, characteristics, or previous trips?

One solution here would be to segment or group the previous travelers by their most distinguishing characteristics. Then you can send them suggestions for other trips they are likely to be interested in.

In this lesson, you will be introduced to the concept of grouping related items in Pandas.

Course Introduction

Selection / Filters

Grouping

Dates Manipulation

Apply / Map

Merge/Concat

Quizzes: Assorted Topics

Conclusion

Introduction to Grouping

Concept

Syntax