Master Data Analysis with Pandas Python3 – From Beginner to Advanced. Enroll in The Pandas Bootcamp today!
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.
Description
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 Zamir is 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
- 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.
Outlines for Data Analysis with Pandas Python3
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
DataFrame
Panel
Chapter 04
Descriptive Statistics
Descriptive Statistics Methods & Programming Examples
Inferential statistics functions
Chapter 05
Function Application
Element-wise
Row or Column-wise
Table-wise
Chapter 06
Reindexing
Reindexing Method with Programming Examples
Iteration
Iteration Method with Programming Examples
Sorting
Sorting Method with Programming Examples
Chapter 07
String Methods
lower()
upper()
title()
capitalize()
swapcase()
strip()
lstrip()
rstrip()
split()
rsplit()
join()
replace()
contains()
startswith()
endswith()
find()
rfind()
count()
len()
Chapter 08
Customization Options
Customizing display options
Customizing data types
Customizing data cleaning and manipulation
Indexing & Selecting
Label-based or integer-based indexing (.loc[] and .iloc[] )
Boolean indexing
Based on a string (.query())
Chapter 09
Window Functions
rolling()
rolling().apply()
rolling().agg()
rolling().corr()
rolling().cov()
rolling().max()
rolling().mean()
rolling().median()
rolling().min()
rolling().quantile()
rolling().std()
rolling().sum()
rolling().var()
expanding()
ewm()
Chapter 10
Group By
Grouping by a single column
Grouping by multiple columns
Aggregating data
Applying multiple aggregation functions
Applying custom functions
Filtering data
Transforming data
Grouping by time
Iterating over groups
Chapter 11
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()
ordered
min(), max()
Chapter 12
Visualization
Line plot
Bar plot
Histogram
Scatter plot
Box plot
Area plot
Heatmap
Density plot
Chapter 13
I/O Tools
Reading CSV
Writing CSV
Reading Excel
Writing CSV
Reading JSON
Writing CSV
Chapter 14
Sparse Data
Features
Programming Example
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
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