Data Manipulation in Python: A Pandas Crash Course

Data Manipulation in Python: A Pandas Crash Course, available at $94.99, has an average rating of 4.73, with 63 lectures, based on 1841 reviews, and has 38750 subscribers.

You will learn about Visualise data using methods from histograms to dimensionality reduction. Create, save and serialise data frames in and out of multiple formats. Clean and format data easily. Detect and intelligently fill missing values. Group, aggregate and summarise your data. Merge data sources into a beautiful whole. Pivot and cross-tabulate data like a pro. Intersplice, summarise and investigate time series data. Seamlessly work with data from different time zones. Learn the common pitfalls and traps that ensnare beginners and how to avoid them. This course is ideal for individuals who are Python students that want to learn how to manipulate data professionally. or Aspiring data analysts and scientists looking to upgrade their skillset. or People who would prefer to spend more time solving interesting problems than formatting data. or Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade. It is particularly useful for Python students that want to learn how to manipulate data professionally. or Aspiring data analysts and scientists looking to upgrade their skillset. or People who would prefer to spend more time solving interesting problems than formatting data. or Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.

Enroll now: Data Manipulation in Python: A Pandas Crash Course

Summary

Title: Data Manipulation in Python: A Pandas Crash Course

Price: $94.99

Average Rating: 4.73

Number of Lectures: 63

Number of Published Lectures: 58

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 58

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Visualise data using methods from histograms to dimensionality reduction.
Create, save and serialise data frames in and out of multiple formats.
Clean and format data easily.
Detect and intelligently fill missing values.
Group, aggregate and summarise your data.
Merge data sources into a beautiful whole.
Pivot and cross-tabulate data like a pro.
Intersplice, summarise and investigate time series data.
Seamlessly work with data from different time zones.
Learn the common pitfalls and traps that ensnare beginners and how to avoid them.

Who Should Attend

Python students that want to learn how to manipulate data professionally.
Aspiring data analysts and scientists looking to upgrade their skillset.
People who would prefer to spend more time solving interesting problems than formatting data.
Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.

Target Audiences

Python students that want to learn how to manipulate data professionally.
Aspiring data analysts and scientists looking to upgrade their skillset.
People who would prefer to spend more time solving interesting problems than formatting data.
Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.

In the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.

If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.

Own your data, don’t let your data own you!

When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.

Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.

This course prepares you to do just that!

With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.

If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.

Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton:

Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible.
Achieve better results by spending more time problem-solving and less time data-wrangling.
Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible.
Utilize the latest version of Python and the industry-standard Pandas library.

Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:

1. Pandas has a steep learning curve:As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas.

2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help.

After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of data analysis.

Here’s a closer look at the curriculum:

Loading and creating Pandas DataFrames
Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations.
Performing basic DataFrame manipulations: indexing, labeling, ordering slicing, filtering and more.
Performing advanced Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more.
Carrying out DataFrame grouping: aggregation, imputation, and more.
Mastering time series manipulations: reindexing, resampling, rolling functions, method chaining and filtering, and more.
Merging Pandas DataFrames

Lastly, this course is packed with a cheatsheet and practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Who Am I? And how to get help

Lecture 3: EXTRA: Learning Path

Lecture 4: Setting up python and editors

Lecture 5: Live Install

Lecture 6: Get the materials

Chapter 2: Dataset Basics

Lecture 1: Finding Datasets

Lecture 2: Jupyter Notebooks and Loading Data

Lecture 3: Pandas vs Numpy

Lecture 4: Creating DataFrames

Lecture 5: Saving and Serialising

Lecture 6: Inspecting DataFrames

Chapter 3: Visual exploration

Lecture 1: Introduction and super basic plots

Lecture 2: Pandas vs Matplotlib

Lecture 3: Visualising 1D distributions

Lecture 4: Visualising 2D distributions

Lecture 5: Styling Pandas Table outputs

Lecture 6: Higher dimension visualisations

Lecture 7: Summary

Chapter 4: Basic Data Manipulations

Lecture 1: Introduction, Labelling and Ordering

Lecture 2: Slicing and Filtering

Lecture 3: Replacing and Thresholding

Lecture 4: Removing and adding data

Lecture 5: Apply, map and vectorised functions

Lecture 6: Summary

Chapter 5: Grouping

Lecture 1: Introduction and motivation

Lecture 2: Basic grouping syntax

Lecture 3: Intelligent imputation

Lecture 4: Grouping aggregation

Lecture 5: Summary

Chapter 6: Merging

Lecture 1: Introduction and basic syntax

Lecture 2: Different types of merging

Lecture 3: Helpful merging functions

Lecture 4: Summary

Chapter 7: Advanced Manipulation – MultiIndex, Pivoting and more

Lecture 1: Introduction and basic MultiIndexes

Lecture 2: MultiIndex II – MultiIndex Strikes Back

Lecture 3: Stacking and Unstacking

Lecture 4: Pivoting

Lecture 5: Pivot Margins

Lecture 6: Crosstab

Lecture 7: Melting

Lecture 8: Summary

Chapter 8: Time Series Data

Lecture 1: Introduction and the Datetime Index

Lecture 2: Reindexing

Lecture 3: Resampling

Lecture 4: Rolling functions

Lecture 5: Time Zones

Lecture 6: Summary

Chapter 9: Conclusion

Lecture 1: A recap and a thank you

Lecture 2: Extra – Customising Jupyter Notebooks

Lecture 3: Extra – Chapter 2 Data Runthrough

Lecture 4: Extra – Chapter 3 Visualisation Runthrough

Lecture 5: Extra – Chapter 4 Basics Runthrough

Lecture 6: Extra – Chapter 5 Grouping Runthrough

Lecture 7: Extra – Chapter 6 Merging Runthrough

Lecture 8: Extra – Chapter 7 Advanced Runthrough

Lecture 9: Extra – Chapter 8 TimeSeries Runthrough

Chapter 10: Congratulations!! Don't forget your Prize 🙂

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Samuel Hinton
Astrophysicist, Software Engineer and Presenter
SuperDataScience Team
Helping Data Scientists Succeed
Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 12 votes
2 stars: 22 votes
3 stars: 108 votes
4 stars: 550 votes
5 stars: 1149 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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