Python for Data Analysis: Dealing With Dates

This video covers the basics of loading in and extracting features from date data using the Pandas library in Python.

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This is lesson 17 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Dealing With Dates

Link to Pandas Date code information:

Link to Pandas Time Series functionality documentation:

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:

Link to the Python for Data Analysis written guide index page:

[mcottenjr]

Pure genius - great explanation and under 12 minutes too! Thank you

[duttashyamal]

I am using parse_date argument while loading the data, but then when I check the type of columns, it still shows as string only. What could be the reason?

dates_df=pd.read_csv("/kaggle/input/lesson-16-dates/dates_lesson_16.csv", parse_dates=[0, 1, 2, 3])

for i in dates_df:
print(type(i))

Output:
<class 'str'>
<class 'str'>
<class 'str'>
<class 'str'>