Data Analysis and Exploration with Pandas [Video]

Preview in Mapt

Data Analysis and Exploration with Pandas [Video]

Theodore Petrou
New Release!

Get idiomatic solutions to common data problems while working on real-world datasets and get surprising insights from the pandas library
Mapt Subscription
FREE
$29.99/m after trial
Video
$106.25
RRP $124.99
Save 14%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$106.25
$29.99 p/m after trial
RRP $124.99
Subscription
Video
Start 14 Day Trial

Frequently bought together


Data Analysis and Exploration with Pandas [Video] Book Cover
Data Analysis and Exploration with Pandas [Video]
$ 124.99
$ 106.25
Data Analysis with Pandas and Python [Video] Book Cover
Data Analysis with Pandas and Python [Video]
$ 39.99
$ 34.00
Buy 2 for $35.00
Save $129.98
Add to Cart

Video Details

ISBN 139781789343205
Course Length5 hours and 12 minutes

Video Description

Are you looking for a gigantic boost in your productivity? Are you searching for some interesting and fun tricks to solve your data problems? If so, then this course is indeed a perfect choice for you. This course provides you with unique, idiomatic, and amazing solutions for both fundamental and advanced data manipulation tasks with pandas.

Some solutions focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. A few others will delve into a particular dataset, and let you uncover new and unexpected insights along the way.

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced solutions combine several different features across the pandas library to generate results.

The code bundle for the video course is available at - https://github.com/PacktPublishing/Data-Analysis-and-Exploration-with-Pandas

Style and Approach

This course includes interesting and illustrative examples and delivers very detailed explanations for each line of code in all of the examples. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data. In other words, this is an easy guide with a problem/solution approach for real-world datasets.

Table of Contents

Pandas Foundations
The Course Overview
Dissecting the Anatomy of a DataFrame
Accessing the Main DataFrame Components
Understanding Data Types
Selecting a Single Column of Data as a Series
Calling Series Methods
Working with Operators on a Series
Chaining Series Methods Together
Making the Index Meaningful
Renaming Row and Column Names
Creating and Deleting Columns
Essential DataFrame Operations
Selecting Multiple DataFrame Columns
Selecting Columns with Methods
Ordering Column Names Sensibly
Operating on the Entire DataFrame
Chaining DataFrame Methods Together
Working with Operators on a DataFrame
Comparing Missing Values
Transposing the Direction of a DataFrame
Determining College Campus Diversity
Beginning Data Analysis
Developing a Data Analysis Routine
Reducing Memory by Changing Data Types
Selecting the Smallest of the Largest
Selecting the Largest of Each Group by Sorting
Replicating nlargest with sort_values
Selecting Subsets of Data
Selecting Series Data
Selecting DataFrame Rows
Selecting DataFrame Rows and Columns Simultaneously
Selecting Data with Both Integers and Labels
Speeding Up Scalar Selection
Slicing Rows Lazily
Slicing Lexicographically
Boolean Indexing
Calculating Boolean Statistics
Constructing Multiple Boolean Conditions
Filtering with Boolean Indexing
Replicating Boolean Indexing with Index Selection
Selecting with Unique and Sorted Indexes
Gaining Perspective on Stock Prices
Translating SQL WHERE Clauses
Determining the Normality of Stock Market Returns
Improving Readability of Boolean Indexing with the Query Method
Preserving Series with the WHERE Method
Masking DataFrame Rows
Selecting with Booleans, Integer Location, and Labels
Index Alignment
Examining the Index Object
Producing Cartesian Products
Exploding Indexes
Filling Values with Unequal Indexes
Appending Columns from Different DataFrames
Highlighting the Maximum Value from Each Column
Replicating idxmax with Method Chaining
Finding the Most Common Maximum
Grouping for Aggregation, Filtration, and Transformation
Defining an Aggregation
Grouping and Aggregating with Multiple Columns and Functions
Removing the MultiIndex After Grouping
Customizing an Aggregation Function
Customizing Aggregating Functions with *args and **kwargs
Examining the groupby Object
Filtering for States with a Minority Majority
Transforming through a Weight Loss Bet
Calculating Weighted Mean SAT Scores Per State with Apply
Grouping By Continuous Variables
Counting the Total Number of Flights Between Cities
Finding the Longest Streak of On-Time Flights
Restructuring Data into a Tidy Form
Tidying Variable Values as Column Names with Stack
Tidying Variable Values as Column Names with Melt
Stacking Multiple Groups of Variables Simultaneously
Inverting Stacked Data
Unstacking After a groupby Aggregation
Replicating pivot_table with a groupby Aggregation
Renaming Axis Levels for Easy Reshaping
Tidying When Multiple Variables are Stored as Column Names
Tidying When Multiple Variables are Stored as Column Values
Tidying When Two or More Values are Stored in the Same Cell
Tidying When Variables are Stored in Column Names and Values
Tidying When Multiple Observational Units are Stored in the Same Table
Combining Pandas Objects
Appending New Rows to DataFrames
Concatenating Multiple DataFrames Together
Comparing President Trump's and Obama's Approval Ratings
Understanding the Differences Between concat, join, and merge
Connecting to SQL Databases

What You Will Learn

  • Master the fundamentals of pandas to quickly begin exploring any dataset
  • Explore the most crucial and common operations that you will perform during data analysis
  • Build customized functions to apply to your groups.
  • Restructure and tidy data to make data analysis and visualization easier
  • Prepare real-world messy datasets for machine learning
  • Combine and merge data from different sources through pandas SQL-like operations

Authors

Table of Contents

Pandas Foundations
The Course Overview
Dissecting the Anatomy of a DataFrame
Accessing the Main DataFrame Components
Understanding Data Types
Selecting a Single Column of Data as a Series
Calling Series Methods
Working with Operators on a Series
Chaining Series Methods Together
Making the Index Meaningful
Renaming Row and Column Names
Creating and Deleting Columns
Essential DataFrame Operations
Selecting Multiple DataFrame Columns
Selecting Columns with Methods
Ordering Column Names Sensibly
Operating on the Entire DataFrame
Chaining DataFrame Methods Together
Working with Operators on a DataFrame
Comparing Missing Values
Transposing the Direction of a DataFrame
Determining College Campus Diversity
Beginning Data Analysis
Developing a Data Analysis Routine
Reducing Memory by Changing Data Types
Selecting the Smallest of the Largest
Selecting the Largest of Each Group by Sorting
Replicating nlargest with sort_values
Selecting Subsets of Data
Selecting Series Data
Selecting DataFrame Rows
Selecting DataFrame Rows and Columns Simultaneously
Selecting Data with Both Integers and Labels
Speeding Up Scalar Selection
Slicing Rows Lazily
Slicing Lexicographically
Boolean Indexing
Calculating Boolean Statistics
Constructing Multiple Boolean Conditions
Filtering with Boolean Indexing
Replicating Boolean Indexing with Index Selection
Selecting with Unique and Sorted Indexes
Gaining Perspective on Stock Prices
Translating SQL WHERE Clauses
Determining the Normality of Stock Market Returns
Improving Readability of Boolean Indexing with the Query Method
Preserving Series with the WHERE Method
Masking DataFrame Rows
Selecting with Booleans, Integer Location, and Labels
Index Alignment
Examining the Index Object
Producing Cartesian Products
Exploding Indexes
Filling Values with Unequal Indexes
Appending Columns from Different DataFrames
Highlighting the Maximum Value from Each Column
Replicating idxmax with Method Chaining
Finding the Most Common Maximum
Grouping for Aggregation, Filtration, and Transformation
Defining an Aggregation
Grouping and Aggregating with Multiple Columns and Functions
Removing the MultiIndex After Grouping
Customizing an Aggregation Function
Customizing Aggregating Functions with *args and **kwargs
Examining the groupby Object
Filtering for States with a Minority Majority
Transforming through a Weight Loss Bet
Calculating Weighted Mean SAT Scores Per State with Apply
Grouping By Continuous Variables
Counting the Total Number of Flights Between Cities
Finding the Longest Streak of On-Time Flights
Restructuring Data into a Tidy Form
Tidying Variable Values as Column Names with Stack
Tidying Variable Values as Column Names with Melt
Stacking Multiple Groups of Variables Simultaneously
Inverting Stacked Data
Unstacking After a groupby Aggregation
Replicating pivot_table with a groupby Aggregation
Renaming Axis Levels for Easy Reshaping
Tidying When Multiple Variables are Stored as Column Names
Tidying When Multiple Variables are Stored as Column Values
Tidying When Two or More Values are Stored in the Same Cell
Tidying When Variables are Stored in Column Names and Values
Tidying When Multiple Observational Units are Stored in the Same Table
Combining Pandas Objects
Appending New Rows to DataFrames
Concatenating Multiple DataFrames Together
Comparing President Trump's and Obama's Approval Ratings
Understanding the Differences Between concat, join, and merge
Connecting to SQL Databases

Video Details

ISBN 139781789343205
Course Length5 hours and 12 minutes
Read More

Read More Reviews

Recommended for You

Data Analysis with Pandas and Python [Video] Book Cover
Data Analysis with Pandas and Python [Video]
$ 39.99
$ 34.00
Amazon EC2 Master Class (with Auto Scaling and Load Balancer) [Video] Book Cover
Amazon EC2 Master Class (with Auto Scaling and Load Balancer) [Video]
$ 47.99
$ 40.80
Apache Spark with Python - Big Data with PySpark and Spark [Video] Book Cover
Apache Spark with Python - Big Data with PySpark and Spark [Video]
$ 149.99
$ 127.50
Creating Data Visualization with D3 [Video] Book Cover
Creating Data Visualization with D3 [Video]
$ 124.99
$ 106.25
Text Mining with Machine Learning and Python [Video] Book Cover
Text Mining with Machine Learning and Python [Video]
$ 124.99
$ 106.25
Hands-On Web Development with Vue.js [Video] Book Cover
Hands-On Web Development with Vue.js [Video]
$ 124.99
$ 106.25