Data Manipulation in Python: A Pandas Crash Course
3.0 /5- Offered byUDEMY
Data Manipulation in Python: A Pandas Crash Course at UDEMY Overview
Duration | 9 hours |
Total fee | ₹399 |
Mode of learning | Online |
Difficulty level | Beginner |
Credential | Certificate |
Data Manipulation in Python: A Pandas Crash Course at UDEMY Highlights
- 30-Day Money-Back Guarantee
- Certificate of completion
- Full lifetime access
- Learn from 1 downloadable resource and 2 articles
Data Manipulation in Python: A Pandas Crash Course at UDEMY Course details
- 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 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
- 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
- Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible
- Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible
Data Manipulation in Python: A Pandas Crash Course at UDEMY Curriculum
Introduction
Introduction
Who Am I? And how to get help
BONUS: Learning Path
Setting up python and editors
Live Install
Get the materials
Dataset Basics
Finding Datasets
Jupyter Notebooks and Loading Data
Pandas vs Numpy
Creating DataFrames
Saving and Serialising
Inspecting DataFrames
Visual exploration
Introduction and super basic plots
Pandas vs Matplotlib
Visualising 1D distributions
Visualising 2D distributions
Styling Pandas Table outputs
Higher dimension visualisations
Summary
Basic Data Manipulations
Introduction, Labelling and Ordering
Slicing and Filtering
Replacing and Thresholding
Removing and adding data
Apply, map and vectorised functions
Summary
Grouping
Introduction and motivation
Basic grouping syntax
Intelligent imputation
Grouping aggregation
Summary
Merging
Introduction and basic syntax
Different types of merging
Helpful merging functions
Summary
Advanced Manipulation - MultiIndex, Pivoting and more
Introduction and basic MultiIndexes
MultiIndex II - MultiIndex Strikes Back
Stacking and Unstacking
Pivoting
Pivot Margins
Crosstab
Melting
Summary
Time Series Data
Introduction and the Datetime Index
Reindexing
Resampling
Rolling functions
Time Zones
Summary
Conclusion
A recap and a thank you
Extra - Customising Jupyter Notebooks
Extra - Chapter 2 Data Runthrough
Extra - Chapter 3 Visualisation Runthrough
Extra - Chapter 4 Basics Runthrough
Extra - Chapter 5 Grouping Runthrough
Extra - Chapter 6 Merging Runthrough
Extra - Chapter 7 Advanced Runthrough
Extra - Chapter 8 TimeSeries Runthrough
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