In this chapter, you’ll gain an understanding of data cleaning approaches when working with PostgreSQL databases and learn the value of cleaning data as early as possible in the pipeline. You’ll also learn basic string editing approaches such as removing unnecessary spaces as well as more involved topics such as pattern matching and string similarity to identify string values in need of cleaning.

Introduction to data cleaning

Developing a data cleaning mindset

Applying functions for string cleaning

Pattern matching

Classifying parking violations by time of day

Masking identifying information with regular expressions

Matching similar strings

Matching inconsistent color names

Standardizing color names

Standardizing multiple colors

Formatting text for colleagues

Data Cleaning Basics

You’ll learn how to write queries to solve common problems of missing, duplicate, and invalid data in the context of PostgreSQL database tables. Through hands-on exercises, you’ll use the COALESCE() function, SELECT query, and WHERE clause to clean messy data.

Handling missing data

Quantifying completeness

Using a fill-in value

Analyzing incomplete records

Handling duplicated data

Duplicate parking violations

Resolving impartial duplicates

Detecting invalid values

Detecting invalid values with regular expressions

Identifying out-of-range vehicle model years

Detecting inconsistent data

Identifying invalid parking violations

Invalid violations with overnight parking restrictions

Recovering deleted data

Missing, Duplicate, and Invalid Data

Sometimes you need to convert data stored in a PostgreSQL database from one data type to another.  In this chapter, you’ll explore the expressions you need to convert text to numeric types and how to format strings for temporal data. 

Data type conversions

Type conversion with a CASE clause

Applying aggregate functions to converted values

Date parsing and formatting

Cleaning invalid dates

Converting and displaying dates

Timestamp parsing and formatting

Extracting hours from a time value

A parking violation report by day of the month

Risky parking behavior

Converting  Data

In the final chapter, you’ll learn how to transform your data and construct pivot tables. Working with real-world postal data, you’ll discover how to combine and split addresses into city, state, and zip codes using a multitude of powerful functions including CONCAT(), SUBSTRING(), and REGEXP_SPLIT_TO_TABLE().

Combining columns

Tallying corner parking violations

Creating a TIMESTAMP with concatenation

Splitting column data

Extracting time units with SUBSTRING()

Extracting house numbers from a string

Splitting data with delimiters

Splitting house numbers with a delimiter

Mapping parking restrictions

Creating pivot tables

Selecting data for a pivot table

Using FILTER to create a pivot table

Aggregating film categories

Course wrap-up

Transforming Data

Parking violations in NYC

Restaurant inspections in NYC

Film permits in NYC

If you surveyed a large number of data scientists and data analysts about which tasks are most common in their workday, cleaning data would likely be in almost all responses. This is the case because real-world data is messy. To help you tame messy data, this course teaches you how to clean data stored in a PostgreSQL database. You’ll learn how to solve common problems such as how to clean messy strings, deal with empty values, compare the similarity between strings, and much more. You’ll get hands-on practice with these tasks using interesting (but messy) datasets made available by New York City's Open Data program. Are you ready to whip that messy data into shape?

Data Manipulation in SQL

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Cleaning Data in PostgreSQL Databases

Developing a data cleaning mindset

Cleaning Data in PostgreSQL Databases

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