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Total [37] sections
Availability of courseware: Yes

You will gain

Learning Python's built-in regular re library

Processing Excel data using regular expressions in python

re Regularly organizing cluttered data

population (esp. of a group of people)

Accountants, cashiers, storekeepers, statisticians, shopkeepers, warehousemen, data analysts, and other jobs that require data handling.
Courses

A regular expression is a special sequence of characters that helps you easily check whether a string matches a certain pattern. Regular expressions are not part of Python. Regular expressions are powerful tools for working with strings, with their own unique syntax and a separate processing engine

This course applies the techniques of python's regular expressions to excel data processing to improve data handling even more.

Course Catalog

Chapter 1: Python Regular Expressions Common Functions and Methods to Use
1.01 Two ways to use Python regular expressions 09:12
1.02 Matching strings from scratch (match) 17:45
1.03 Example application (withdrawing amounts) 12:44
1.04 Arbitrary match string 09:09
1.05 Example Application (Extracting Unit Price and Quantity for Amount) 11:47
1.06 Splitting strings 10:39
1.07 Example Application (Organizing Data Method 1) 14:51
1.08 find string-1 (findall) 09:25
1.09 Example Application (Data Organization Method 2) 08:58
1.10 Finding strings-2 (finditer) 09:54
1.11 Example Application (Tabular Data Organization) 11:17
1.12 Sub functions and methods 12:01
1.13 Example Application (Determining Numbers in a String) 10:46

Chapter 2: Python Regular Expression Metacharacters Explained
2.01 Individual characters of metacharacters (letters, numbers, arbitrary) 16:27
2.02 Example Applications (Word Organization) 14:18
2.03 Individual characters (character groups) of metacharacters 13:18
2.04 Example application (counting the number of subjects meeting standards) 06:53
2.05 Counting of metacharacters (length representation) 08:21
2.06 Example Application (Extracting Data to Reconstruct a Table) 12:49
2.07 Positional matches for metacharacters (word boundaries, beginnings and endings) 11:26
2.08 Example application (counting the number of words beginning with 26 letters) 10:30
2.09 Packet Capture 10:41
2.10 Example Application (Counting and Sorting Fractions in Strings) 12:34
2.11 Grouping references 18:27
2.12 Example Application (Summarizing Merchandise Amounts in Text) 10:54
2.13 Group selection matching 16:28
2.14 Example Application (Filtering Amounts by Condition) 11:22
2.15 Non-capture grouping 12:47
2.16 Example Application (Multi-Conditional Extraction Data Summarization) 16:21
2.17 Zero-width assertion 18:07
2.18 Example application (normalizing test numbers) 09:12
2.19 Greed vs. non-greed (inertia) 10:41
2.20 Example Application (Organizing Messy Employee Information) 15:25
2.21 Common methods and properties of re.Match objects 30:29

Chapter 3: Regular Expressions in Python for Excel Data Comprehensive Application
3.1 Comprehensive Application Case 1: Extracting Specified Data to Other Table Storage 18:25
3.2 Comprehensive Application Case 2: Extracting Specified Data and Doing Data Summarization 11:25
3.3 Comprehensive Application Case 3: Processing Unregulated Time into Regulated Time 15:37

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