Python Pandas Library Tutorial - Explore Data in DataFrames and Series Objects

In this video, we introduce Python Pandas library built-in functions to explore DataFrame and Series objects such as size, info(), describe(), unique() , and value_counts().

These tools can provide users with information about the dataset size, data types, missing values, in addition to basic summary statistics. This information is important for users to identify what changes they would need to make to prepare the dataset for further analysis. The video also explains the 7 different data types supported by Pandas library. It's really important for data professionals to understand the usability of each data type and they should also be able to convert any DataFrame column from one data type to another depending on the analysis needs.

Timecodes:
0:00 INTRO
1:02 Describe information in DataFrame
2:05 Pandas Shape Function
2:57 Pandas Size Function
4:08 Pandas info() Function
6:45 Pandas describe() Function
8:34 Pandas unique() Function
9:08 Pandas value_counts() Function
10:01 Pandas Data Types

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? Jupyter notebook used in this video: https://github.com/NabuRika/pandas_crash_course
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