Solve different challenging problems to prepare yourself for data analytics interviews.

Pandas: Python for Data Analysis

In this module, we’ll try to solve a number of challenges covering different concepts related to data analytics in pandas, such as grouping, filtering, date manipulation, and more. This module will mainly test our knowledge while also helping us understand more advanced concepts. By the end of this module, we’ll have the confidence we need to land our dream job in data science.

Data Analytics Interview Prep Using Pandas

Assuming `date` is a column of type `datetime` , how can you access the year information?

Assuming <code>date</code> is a column of type <code>datetime</code> , how can you access the year information?

`pd.date_range(start='2015-01-01', freq='1M', period=24)`

`pd.date_range(start='2015-01-01', freq='30D', period=24)`

`pd.date_range(start='2015-01-01', freq='1M', period='24M')`

How can you generate a list of dates starting `2015-01-01` for a period of 24 months, with a frequency of one month? 

How can you generate a list of dates starting <code>2015-01-01</code> for a period of 24 months, with a frequency of one month?

What date accessors are predefined in `Pandas`?

What date accessors are predefined in <code>Pandas</code>?

Final date manipulation quiz

Final date manipulation quiz

Hopefully, you got the answers right. The next lesson will be the section summary, and up next you will be using Apply/Map in DataFrames.


This quiz will make sure you've covered the necessary knowledge of the date manipulation functionality in Pandas.

Introduction

Selection / Filters

Grouping

Dates Manipulation

Apply / Map

Merge/Concat

Quizzes: Assorted Topics

Conclusion

Final Quiz