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Pandas for Data Analysis in Python

PDFs and exam guides are not so efficient, right? Prepare for your Software Engineering Courses examination with our training course. The Pandas for Data Analysis in Python course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Software Engineering Courses certification exam. Pass the Software Engineering Courses Pandas for Data Analysis in Python test with flying colors.

134 Students Enrolled 31 Lectures 04:45:54 Hours

Curriculum For This Course

1

Introduction to the Course

1Lectures

Time 00:00:49

2

IPython Notebooks and Raw Python Data Analysis

5Lectures

Time 00:39:21

3

The Basics of NumPy

6Lectures

Time 01:03:30

4

Pandas Basics

1Lectures

Time 00:10:40

5

pandas Series

5Lectures

Time 00:43:33

6

pandas DataFrame

9Lectures

Time 01:22:51

7

BONUS: Advanced pandas Topics

4Lectures

Time 00:45:10

Introduction to the Course
	1. Installation Instructions	00:49

IPython Notebooks and Raw Python Data Analysis
	1. Tools of the Trade, The IPython Notebook	10:06
	2. Maps	07:07
	3. Filters	05:45
	4. List Comprehensions	06:40
	5. Lambda Functions	09:44

The Basics of NumPy
	1. NumPy Array Basics	06:09
	2. Boolean Selection	10:20
	3. Helpful Methods and Shortcuts	12:59
	4. Vectorization	07:08
	5. Multi-Dimensional Arrays	14:12
	6. Querying Slicing, Combining, and Splitting Arrays	12:42

Pandas Basics
	1. General pandas Concepts	10:40

pandas Series
	1. Overview of the pandas Series	07:25
	2. Look Ups, Selections, and Indexing	11:01
	3. Advanced Indexing Options	06:56
	4. Handling NaN Values, Reindexing, Filling Methods and Series Addition	12:06
	5. Series Multiplication, More Reindexing, and Mapping	06:05

pandas DataFrame
	1. DataFrame Basics	10:04
	2. Reading Files, Plotting, and Basic Methods	10:45
	3. More Plotting, Joins, Basic DateTime Indexing, and Writing to Files	11:53
	4. Adding & Reseting Columns, Mapping with Functions	07:14
	5. More Mapping, Filling NaN values, Plotting, Correlations, and Histograms	10:17
	6. More Plotting, Rolling Calculations, Basic DateTime Indexing	05:52
	7. Analysis Concepts, Filling NaN Values, Cumulative Sums and Value Counts	09:20
	8. Data Maintenance, Adding/ Removing Columns and Rows	08:18
	9. Basic Grouping, Concepts of Aggregate Functions	09:08

BONUS: Advanced pandas Topics
	1. pandas.IO.Data, Panels and Hierarchical Indexing	11:42
	2. Advanced Reading Csvs/HTML, Binning, Categorical Data	12:43
	3. Advanced Groupings and Aggregate Functions	07:56
	4. More Grouping Functions including Apply and Transform	12:49

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