Book Image

Data Wrangling with Python 3.x [Video]

By : Jamshaid Sohail
Book Image

Data Wrangling with Python 3.x [Video]

By: Jamshaid Sohail

Overview of this book

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

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Data Wrangling with Python 3.x [Video]
Jamshaid Sohail
Jan, 2019 | 3 hours 35 minutes
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Table of Contents (7 chapters)
Free Chapter
1
Gathering and Parsing Data
The Course Overview
Installing Anaconda Navigator on Windows/Linux
Importing and Parsing CSV in Python
Importing and Parsing JSON in Python
Scraping Data from Public Web – Part 1
Scraping Data from Public Web – Part 2
2
Working with Data from Excel and PDF Files
Importing and Parsing Excel Files – Part 1
Importing and Parsing Excel Files – Part 2
Manipulating PDF Files in Python – Part 1
Manipulating PDF Files in Python – Part 2
3
Storing Data in Persistent Storage
Difference between Relational and Non-Relational Databases
Storing Data in SQLite Databases
Storing Data in MongoDB
Storing Data in Elasticsearch
Comparative Study of Databases for Storage
4
Cleaning Structured Data
The Most Important Step in Data Analysis
Viewing/Inspecting DataFrames
Renaming/Adding/Removing the DataFrame Columns
Dropping Duplicate Rows
Indexing DataFrame to Retrieve Specific Columns and Rows
Merging/Concatenating/Joining DataFrames
Dealing with Missing Values
5
More Data Cleaning and Transformation
Filtering and Sorting of DataFrame
Encoding/Mapping Existing Values – Part 1
Encoding/Mapping Existing Values – Part 2
Rescale/Standardize Column Values
Common Cleaning Operations
Exporting Datasets for Future Use
6
Performing Statistical Analysis
Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)
Types of Column Names/Features/Attributes in Structured Data
Split-Apply-Combine (Performing Group By Operation)
Descriptive Statistics Using Python – Part 1
Descriptive Statistics Using Python – Part 2
7
Let the Visualizations Tell the Story
Using Visualizations
Cool Visualization of Real-World Datasets of World Population Evolution
Visualizations in Python – Part 1
Visualizations in Python – Part 2
Exploring an Online Visualization Tool (RAWGraphs)
Chapter 4
Cleaning Structured Data
Content Locked
Section 5
Indexing DataFrame to Retrieve Specific Columns and Rows
Sometimes we need to extract just required columns and rows out of the data frames. In this video we are going to look at the lines of codes which can be used to do so. - Read in the dataset and then retrieving the first, last rows and columns - Retrieve the first five columns and first five rows - Retrieve certain rows and columns
Previous Section
End of Section 5
Next Section