Python for Data Analysis: Step-By-Step with Projects - Extracting Date and Time

The video tutorial covers techniques to transform datetime columns in a dataset into new features for data analysis. It explains how to extract components like year, month, and day, and how to calculate differences between dates using pandas. The tutorial also demonstrates creating Boolean indicators for date comparisons and highlights the importance of datetime data in uncovering insights. The session concludes with a call to explore further into time series analysis.

Explain the importance of time series in data analysis.

What are the basic properties of a datetime that can be extracted from a data frame?

What steps are necessary to convert a column from object dtype to datetime dtype in pandas?

How can you extract the year from a datetime column in a pandas DataFrame?

What methods can be used to explore the range of datetime values in a dataset?

What is a time delta object and how is it used in datetime calculations?

Describe how to calculate the difference between two datetime objects in pandas.

Explain how to convert time differences into different units such as days or weeks.

How can you create a new column in a DataFrame that indicates whether a date is before a specific date?

What insights can be gained from analyzing datetime data in relation to air quality?