Data Cleaning With pandas and NumPy

Focused View

Ian Currie

1:28:17

329 View

560 - 00 - intro.mp4

02:43

560 - 01 - Setting Up Work Environment.mp4

08:00

560 - 02 - exploring olympic data.mp4

02:11

560 - 03 - Setting up for cleaning.mp4

07:48

560 - 04 - renaming columns.mp4

07:00

560 - 05 - Exploring with loc.mp4

09:37

560 - 06 - Exploring Uni towns.mp4

02:21

560 - 07 - process data.mp4

06:22

560 - 08 - clean it.mp4

03:55

560 - 09 - assign.mp4

07:34

560 - 10 - books intro.mp4

05:08

560 - 11 - dropping cols.mp4

02:28

560 - 12 - indices.mp4

02:38

560 - 13 - cleaning date.mp4

09:31

560 - 14 - cleaning place.mp4

09:33

560 - 15 - outro.mp4

01:28

pandas data cleaning final code.zip

slides TxQ33sS.pdf

Description

Data scientists spend a large amount of their time cleaning datasets so that they’re easier to work with. In fact, the 80/20 rule says that the initial steps of obtaining and cleaning data account for 80% of the time spent on any given project.

So, if you’re just stepping into this field or planning to step into this field, it’s important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers.

In this video course, you’ll leverage Python’s pandas and NumPy libraries to clean data.

Along the way, you’ll learn about:

To get the most out of this tutorial, you should have a basic understanding of the pandas and NumPy libraries, including pandas’ workhorse Series and DataFrame objects, common methods that can be applied to these objects, and NumPy’s NaN values.