Data Manipulation in Python A Pandas Crash Course

Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.
Data Manipulation in Python A Pandas Crash Course
File Size :
3.29 GB
Total length :
8h 48m



Samuel Hinton


Last update




Data Manipulation in Python A Pandas Crash Course

What you’ll 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.

Data Manipulation in Python A Pandas Crash Course


Basic knowledge of Python


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 DataFramesDisplaying 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 DataFramesLastly, 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.


Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Who Am I? And how to get help

Lecture 3 BONUS: Learning Path

Lecture 4 Setting up python and editors

Lecture 5 Live Install

Lecture 6 Get the materials

Section 2: Dataset Basics

Lecture 7 Finding Datasets

Lecture 8 Jupyter Notebooks and Loading Data

Lecture 9 Pandas vs Numpy

Lecture 10 Creating DataFrames

Lecture 11 Saving and Serialising

Lecture 12 Inspecting DataFrames

Section 3: Visual exploration

Lecture 13 Introduction and super basic plots

Lecture 14 Pandas vs Matplotlib

Lecture 15 Visualising 1D distributions

Lecture 16 Visualising 2D distributions

Lecture 17 Styling Pandas Table outputs

Lecture 18 Higher dimension visualisations

Lecture 19 Summary

Section 4: Basic Data Manipulations

Lecture 20 Introduction, Labelling and Ordering

Lecture 21 Slicing and Filtering

Lecture 22 Replacing and Thresholding

Lecture 23 Removing and adding data

Lecture 24 Apply, map and vectorised functions

Lecture 25 Summary

Section 5: Grouping

Lecture 26 Introduction and motivation

Lecture 27 Basic grouping syntax

Lecture 28 Intelligent imputation

Lecture 29 Grouping aggregation

Lecture 30 Summary

Section 6: Merging

Lecture 31 Introduction and basic syntax

Lecture 32 Different types of merging

Lecture 33 Helpful merging functions

Lecture 34 Summary

Section 7: Advanced Manipulation – MultiIndex, Pivoting and more

Lecture 35 Introduction and basic MultiIndexes

Lecture 36 MultiIndex II – MultiIndex Strikes Back

Lecture 37 Stacking and Unstacking

Lecture 38 Pivoting

Lecture 39 Pivot Margins

Lecture 40 Crosstab

Lecture 41 Melting

Lecture 42 Summary

Section 8: Time Series Data

Lecture 43 Introduction and the Datetime Index

Lecture 44 Reindexing

Lecture 45 Resampling

Lecture 46 Rolling functions

Lecture 47 Time Zones

Lecture 48 Summary

Section 9: Conclusion

Lecture 49 A recap and a thank you

Lecture 50 Extra – Customising Jupyter Notebooks

Lecture 51 Extra – Chapter 2 Data Runthrough

Lecture 52 Extra – Chapter 3 Visualisation Runthrough

Lecture 53 Extra – Chapter 4 Basics Runthrough

Lecture 54 Extra – Chapter 5 Grouping Runthrough

Lecture 55 Extra – Chapter 6 Merging Runthrough

Lecture 56 Extra – Chapter 7 Advanced Runthrough

Lecture 57 Extra – Chapter 8 TimeSeries Runthrough

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.

Course Information:

Udemy | English | 8h 48m | 3.29 GB
Created by: Samuel Hinton

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