Effective Data Manipulation with pandas
INTERACTIVE COURSE

Effective Data Manipulation with pandas

Beginner

89 Lessons

24h

Certificate of Completion

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Effective Data Manipulation with pandas

This course includes:

564 Playgrounds
17 Quizzes
68 Illustrations

Takeaway Skills

An understanding of efficient data manipulation techniques for working with data in pandas

Working knowledge of different techniques for handling missing data in pandas

The ability to reshape and pivot data

Hands-on experience of the best practices for working with pandas

Course Overview

The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data. In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance consid...Show More

Course Content

1

Introduction

Before We Start
Data Structures
Series IntroductionNaN Values in SeriesSimilarities in pandas and NumPyCategorical Data in pandasExercise: Series in pandasSolution: Series in pandasSummaryQuiz: pandas Series
2

Series Deep Dive

Data Loading and Series AttributesOperators and Dunder MethodsAggregate MethodsExercise: Aggregate MethodsSolution: Aggregate MethodsConversion MethodsManipulation MethodsQuiz: Methods in SeriesConditional MethodsHandling Missing DataOther Manipulation MethodsData CleaningQuiz: Duplicate values and NaNIndexing OperationsIndexing Operations (.iloc)Exercise: Data Manipulation and IndexingSolution: Data Manipulation and IndexingQuiz: Indexing, Manipulation Methods, and Data CleaningString ManipulationOptimizing Manipulations Using CythonDate and Time ManipulationDates in the IndexQuiz: String Manipulation, Date-Time Manipulation, and DT IndexingPlotting with a Series: Histogram, Box Plot, and Kernel DensityPlotting with a Series: Line, Bar, and Pie PlotsCategorical ManipulationQuiz: Plotting and Categorical Data
3

DataFrames

Introduction to DataFramesSimilarities between Series and DataFrameMath Methods on DataFramesQuiz: DataFrames and Math Methods on DataFramesExercise: DataFrames in pandasSolution: DataFrames in pandasLooping and AggregationThe apply() MethodExercise: Looping, Aggregation and apply() MethodSolution: Looping, Aggregation, and apply() MethodColumns Types, .assign, and Memory UsageCreating and Updating ColumnsDealing with Missing and Duplicated DataQuiz: Missing and Duplicated DataSorting Columns and IndexesFiltering and Indexing OperationsIndexing by PositionIndexing by NameFiltering with Function and .locLine and Bar PlotsScatter, Area, and Stacked Bar PlotsExercise: Sorting, Columns, Filtering, and Indexing Operations.Solution: Sorting, Columns, Filtering, and Indexing OperationsExercise: PlottingSolution: PlottingQuiz: Sorting Columns, Filtering, and Indexing Operations.Reshaping DataFrames with DummiesReshaping by Pivoting and GroupingAggregations
4

Manipulating Data

GroupingMore AggregationsCross-tabulation Deep DiveExercise: Reshaping/AggregationsSolution: Reshaping/AggregationsQuiz: Reshaping, Pivot Tables, AggregationsMelting and Transposing DataStacking and Flattening DataQuiz: Melting, Transposing, Stacking, and Unstacking DataWorking with Time SeriesAdding Rows and Columns in a DataFrameJoins in DataFramesJoining Real-World DatasetsQuiz: Joining in pandasExporting Data to CSV and ExcelExporting Data to Feather, SQL, and JSONStyling DataFramesChecking if DataFrames are EqualDebugging ChainsDebugging apply (and friends)Debugging Memory Usage and Timing Information
5

Wrapping Up

Conclusion
6

Appendix

1 Lesson

COURSE AUTHOR
Educative

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