Extract, transform, and load data for effective data analysis
About This Video
- Perform effective data wrangling to achieve your analytical goals by working with real-world problems.
- A step-by-step guide to acquiring and then pre-processing datasets to draw useful insights from them.
- Use the in-built features of Python to acquire, clean, analyze, and present data efficiently.
You might be working in an organization, or have your own business, where data is being generated continuously (structured or unstructured) and you are looking to develop your skillset so you can jump into the field of Data Science. This hands-on guide shows programmers how to process information.
In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.
Towards the end of the course, we will build an intuitive understanding of all the aspects available in Python for Data Wrangling.
All codes and supporting files are placed on GitHub at this link: https://github.com/PacktPublishing/-Data-Wrangling-with-Python-3.x
Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
Table of Contents
- Chapter 1 : Gathering and Parsing Data
- Chapter 2 : Working with Data from Excel and PDF Files
Chapter 3 : Storing Data in Persistent Storage
- Difference between Relational and Non-Relational Databases 00:03:46
- Storing Data in SQLite Databases 00:08:27
- Storing Data in MongoDB 00:06:26
- Storing Data in Elasticsearch 00:07:18
- Comparative Study of Databases for Storage 00:02:29
Chapter 4 : Cleaning Structured Data
- The Most Important Step in Data Analysis 00:02:36
- Viewing/Inspecting DataFrames 00:06:44
- Renaming/Adding/Removing the DataFrame Columns 00:06:05
- Dropping Duplicate Rows 00:06:42
- Indexing DataFrame to Retrieve Specific Columns and Rows 00:07:08
- Merging/Concatenating/Joining DataFrames 00:08:04
- Dealing with Missing Values 00:08:37
- Chapter 5 : More Data Cleaning and Transformation
Chapter 6 : Performing Statistical Analysis
- Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib) 00:01:59
- Types of Column Names/Features/Attributes in Structured Data 00:01:53
- Split-Apply-Combine (Performing Group By Operation) 00:05:37
- Descriptive Statistics Using Python – Part 1 00:05:32
- Descriptive Statistics Using Python – Part 2 00:05:13
- Chapter 7 : Let the Visualizations Tell the Story
- Title: Data Wrangling with Python 3.x
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789956597