The Pandas Bootcamp - Data Analysis with Pandas Python3

The Pandas Bootcamp | Data Analysis with Pandas Python3, available at $49.99, has an average rating of 4.32, with 119 lectures, 1 quizzes, based on 170 reviews, and has 24729 subscribers.

You will learn about Understand the basics of Pandas, its data structures, and how to install it. Work with different types of data structures in Pandas. Use descriptive and inferential statistics methods to analyze data. Apply element-wise, row or column-wise, and table-wise function application on data. Reindex, sort, and iterate through data using Pandas. Use string methods for data cleaning and manipulation. Customize display options and data types in Pandas. Perform indexing and selecting operations based on labels, integers, or Boolean values. Use window functions such as rolling, expanding, and ewm for data analysis. Group data based on single or multiple columns, apply aggregation functions, and filter or transform data. Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas. Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap. Read and write data in different formats such as CSV, Excel, and JSON using Pandas. Work with sparse data and understand its features. This course is ideal for individuals who are Aspiring data analysts who want to learn how to use Pandas for data analysis or Data scientists who want to add Pandas to their skillset or Business analysts who need to analyze data using Pandas or Programmers who want to learn about data manipulation and analysis using Python and Pandas or Anyone interested in learning about Pandas and data analysis with Python It is particularly useful for Aspiring data analysts who want to learn how to use Pandas for data analysis or Data scientists who want to add Pandas to their skillset or Business analysts who need to analyze data using Pandas or Programmers who want to learn about data manipulation and analysis using Python and Pandas or Anyone interested in learning about Pandas and data analysis with Python.

Enroll now: The Pandas Bootcamp | Data Analysis with Pandas Python3

Summary

Title: The Pandas Bootcamp | Data Analysis with Pandas Python3

Price: $49.99

Average Rating: 4.32

Number of Lectures: 119

Number of Quizzes: 1

Number of Published Lectures: 119

Number of Published Quizzes: 1

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics of Pandas, its data structures, and how to install it.

Work with different types of data structures in Pandas.

Use descriptive and inferential statistics methods to analyze data.

Apply element-wise, row or column-wise, and table-wise function application on data.

Reindex, sort, and iterate through data using Pandas.

Use string methods for data cleaning and manipulation.

Customize display options and data types in Pandas.

Perform indexing and selecting operations based on labels, integers, or Boolean values.

Use window functions such as rolling, expanding, and ewm for data analysis.

Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.

Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.

Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.

Read and write data in different formats such as CSV, Excel, and JSON using Pandas.

Work with sparse data and understand its features.

Who Should Attend

Aspiring data analysts who want to learn how to use Pandas for data analysis

Data scientists who want to add Pandas to their skillset

Business analysts who need to analyze data using Pandas

Programmers who want to learn about data manipulation and analysis using Python and Pandas

Anyone interested in learning about Pandas and data analysis with Python

Target Audiences

Aspiring data analysts who want to learn how to use Pandas for data analysis

Data scientists who want to add Pandas to their skillset

Business analysts who need to analyze data using Pandas

Programmers who want to learn about data manipulation and analysis using Python and Pandas

Anyone interested in learning about Pandas and data analysis with Python

Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3

The “Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3” course is designed for anyone who wants to learn how to use Pandas, the popular data manipulation library for Python.

This course covers a wide range of topics, from the basics of Pandas installation and data structures to more advanced topics such as window functions and visualization.

Whether you are a beginner or an experienced programmer, this course will provide you with a comprehensive understanding of how to use Pandas to analyze and manipulate data efficiently.

Through practical programming examples, you will learn how to perform data cleaning and manipulation, aggregation, and grouping, as well as how to work with different data formats such as CSV, Excel, and JSON. By the end of the course, you will have gained the knowledge and skills necessary to work with large datasets and perform complex data analysis tasks using Pandas.

Instructors Experiences and Education:

Faisal Zamiris an experienced programmer and an expert in the field of computer science. He holds a Master’s degree in Computer Science and has over 7 years of experience working in schools, colleges, and university. Faisal is a highly skilled instructor who is passionate about teaching and mentoring students in the field of computer science.

As a programmer, Faisal has worked on various projects and has experience in multiple programming languages, including PHP, Java, and Python.

He has also worked on projects involving web development, software engineering, and database management. This broad range of experience has allowed Faisal to develop a deep understanding of the fundamentals of programming and the ability to teach complex concepts in an easy-to-understand manner.

As an instructor, Faisal has a proven track record of success. He has taught students of all levels, from beginners to advanced, and has a passion for helping students achieve their goals.

Faisal has a unique teaching style that combines theory with practical examples, which allows students to apply what they have learned in real-world scenarios.

Overall, Faisal Zamir is a skilled programmer and a talented instructor who is dedicated to helping students achieve their goals in the field of computer science. With his extensive experience and proven track record of success, students can trust that they are learning from an expert in the field.

What you will learn from  Course Data Analysis with Pandas Python3

1. Understand the basics of Pandas, its data structures, and how to install it.

2. Work with different types of data structures in Pandas.

3. Use descriptive and inferential statistics methods to analyze data.

4. Apply element-wise, row or column-wise, and table-wise function application on data.

5. Reindex, sort, and iterate through data using Pandas.

6. Use string methods for data cleaning and manipulation.

7. Customize display options and data types in Pandas.

8. Perform indexing and selecting operations based on labels, integers, or Boolean values.

9. Use window functions such as rolling, expanding, and ewm for data analysis.

10. Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.

11. Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.

12. Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.

13. Read and write data in different formats such as CSV, Excel, and JSON using Pandas.

14. Work with sparse data and understand its features.

Outlines for Pandas Course for Data Science

Chapter 01

Introduction

What is Pandas

Why need of Pandas

What we can do with Pandas

Pandas Installation

Pandas Basic Program

Chapter 02

Data Structures

Types of Data Structure

Chapter 03

Series

Series different OperationS

Series Attributes

Series methods 

DataFrame

Panel

Chapter 04

DataFrame

DataFrame different OperationS

DataFrame Attributes

DataFrame methods 

Panel

Chapter 05

Descriptive Statistics

Descriptive Statistics Methods & Programming Examples

Inferential statistics functions

Chapter 06

Function Application

Element-wise

Row or Column-wise

Table wise

Chapter 07

Reindexing

Reindexing Method with Programming Examples

Iteration

Iteration Method with Programming Examples

Sorting

Sorting Method with Programming Examples

Chapter 08

String Methods

lower

upper

title

capitalize

swapcase

strip

lstrip

rstrip

split

rsplit

join

replace

contains

startswith

endswith

find

rfind

count

len

Chapter 09

Customization Options

Customizing display options

Customizing data types

Customizing data cleaning and manipulation

Indexing & Selecting

Label-based or integer-based indexing

Boolean indexing

Based on a string (.query)

Chapter 10

Window Function

Rolling window

Expanding window

Exponentially Weighted window

Weighted window

Chapter 11

Groupby operations

Splitting Data

Appling function on that data

Combining the results

Operations on subset data

Aggregation

Transformation

Filtration

Chapter 12

Categorical Data

Benefits

Purpose

Methods used in Categorial Data

astype

value_counts

unique

reorder_categories

set_categories

remove categories

add categories

rename categories

remove unused categories

Chapter 13

Visualization

Line plot

Bar plot

Histogram

Scatter plot

Box plot

Area plot

Heatmap

Density plot

Chapter 14

I/O Tools

Reading CSV

Writing CSV

Reading Excel

Writing CSV

Reading JSON

Writing CSV

Chapter 16

Date Time Functions

to datetime

date range

strftime

Timestamp

30-day money-back guarantee for The Pandas Bootcamp | Data Analysis with Pandas Python3

We are confident that The Pandas Bootcamp | Data Analysis with Pandas Python3 course will provide you with the skills and knowledge needed for successful data analysis using Pandas.

That’s why we offer a 30-day money-back guarantee, giving you peace of mind as you embark on this learning journey.

With our expert instructors and a comprehensive curriculum, you’ll gain a solid understanding of data structures, descriptive statistics, function applications, customization options, and more.

Our course is designed for anyone looking to enhance their data analysis skills, including students, data analysts, business professionals, and aspiring data scientists. Join us today and take the first step towards becoming a proficient Pandas user!

Thank you

Faisal Zamir

Course Curriculum

Chapter 1: Chapter 01

Lecture 1: 01 Pandas Chapter 01 Outlines

Lecture 2: 02 What is Pandas

Lecture 3: 03 Where we can use Pandas

Lecture 4: 04 What we can do with Pandas

Lecture 5: 05 Pandas Installation

Lecture 6: 06 Pandas Basic Program

Chapter 2: Chapter 02

Lecture 1: 01 Pandas Chapter 02 Outlines

Lecture 2: 02 Series Data Structure

Lecture 3: 03 DataFrame Data Strcuture

Lecture 4: 04 Panel Data Structure

Chapter 3: Chapter 03

Lecture 1: 01 Chapter 03 Outlines for Pandas

Lecture 2: 02 Series Creation with 5 Methods

Lecture 3: 03 Indexing with Series

Lecture 4: 04 Slicing with Series

Lecture 5: 05 Arithmetics with Series

Lecture 6: 06 Comparision with Series

Lecture 7: 07 Aggregation with Series

Lecture 8: 08 Filtering with Series

Lecture 9: 09 All Attribues of Series

Lecture 10: 10 head method with Series

Lecture 11: 11 tail method with Series

Lecture 12: 12 describe method with Series

Lecture 13: 13 info method with Series

Lecture 14: 14 mean method with Series

Lecture 15: 15 sum method with Series

Lecture 16: 16 unique method with Series

Lecture 17: 17 value_counts method with Series

Lecture 18: 18 sort_values method with Series

Lecture 19: 19 apply method with Series

Lecture 20: 20 fillna method with Series

Lecture 21: 21 drop method with Series

Lecture 22: 22 concat method with Series

Chapter 4: Chapter 04

Lecture 1: 01 Chapter 04 Outlines for Pandas

Lecture 2: 02 Methods to create DataFrame

Lecture 3: 03 Select Add and Delete Column

Lecture 4: 04 Select Add Delete Row

Lecture 5: 05 Indexing and Slicing in DataFrame

Lecture 6: 06 Arithmetic Operation with DataFrame

Lecture 7: 07 Comparision Operations on DataFrame

Lecture 8: 08 Aggregation with DataFrame

Lecture 9: 09 Filtering in DataFrame

Lecture 10: 10 Missing Data Handling in DataFrame

Lecture 11: 11 Joining Method with DataFrame

Lecture 12: 12 Sorting in DataFrame

Lecture 13: 13 Attributes for DataFrame

Lecture 14: 14 Head and Tail method in DF

Lecture 15: 15 Describe and Info method with DF

Lecture 16: 16 sort_values method with DF

Lecture 17: 17 dropna Method with DF

Lecture 18: 18 fillna and merge method with DF

Lecture 19: 19 apply method with DF

Lecture 20: 20 Panel in Pandas

Chapter 5: Chapter 05

Lecture 1: 01 Chapter 05 Outlines

Lecture 2: 02 Descriptive Statistics in Pandas

Lecture 3: 03 Descriptive Methods in Pandas

Chapter 6: Chapter 06

Lecture 1: 01 Pandas Chapter 06 Outlines

Lecture 2: 02 Function Application in Pandas

Lecture 3: 03 Element Wise Application

Lecture 4: 04 Row or Column Wise Application

Lecture 5: 05 Table wise Application

Chapter 7: Chapter 07

Lecture 1: 01 Pandas Chapter 07 Outlines

Lecture 2: 02 Reindexing in Pandas

Lecture 3: 03 Iteration with items method

Lecture 4: 04 Iteration with iterrows method

Lecture 5: 05 Iteration with itertuples method

Lecture 6: 06 Iteration in Pandas

Lecture 7: 07 Sort Values in Pandas

Lecture 8: 08 Sort Index in Pandas

Lecture 9: 09 nlargest and nsmallest in Pandas

Chapter 8: Chapter 08

Lecture 1: 01 Pandas Chapter 08 Outlines

Lecture 2: 02 lower and upper method

Lecture 3: 03 title and capatilize method

Lecture 4: 04 swapecase method in Pandas

Lecture 5: 05 strip lstrip rstrip in pandas

Lecture 6: 06 join method in Pandas

Lecture 7: 07 replace method in Pandas

Lecture 8: 08 contains method in Pandas

Lecture 9: 09 startswith and endswith in Pandas

Lecture 10: 10 find and rfind in Pandas

Lecture 11: 11 count and len Method in Pandas

Chapter 9: Chapter 09

Lecture 1: 01 Pandas Chapter 09 Outline

Lecture 2: 02 Display Option in Pandas

Lecture 3: 03 Customizing Data Types

Lecture 4: 04 Data Cleaning

Lecture 5: 05 Label integer and boolean based indexing

Lecture 6: 06 Query Method in Pandas

Chapter 10: Chapter 10

Lecture 1: 01 Pandas Chapter 10 Outline

Lecture 2: 02 Rolling Window in Pandas

Lecture 3: 03 Rolling window functions in Pandas

Lecture 4: 04 Expending Window in Pandas

Instructors

Faisal Zamir
Programmer

Jafri Code
Programming and Web Instructor

Pro Python Support

Rating Distribution

1 stars: 2 votes

2 stars: 9 votes

3 stars: 26 votes

4 stars: 54 votes

5 stars: 79 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!