The Pandas Bootcamp | Data Analysis with Pandas Python3

Learn Data Science with Pandas to Analyse Data with Programming Examples

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Last Updated: 2023-12-15 10:12:46 English Course Type: Self Learning

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.

Introduction to Pandas for Data Science Course

Welcome to the "Data Science | Data Analysis Pandas Complete Course" taught by Faisal Zamir. This comprehensive and in-depth course aims to provide students with the necessary skills and knowledge to excel in data analysis using the powerful Pandas library within the field of data science. With over 10 hours and 30 minutes of video content, this course delves into the intricacies of Pandas, equipping students with a solid foundation in data analysis techniques and strategies.

The course is thoughtfully structured to encompass both theoretical concepts and hands-on applications, ensuring a well-rounded learning experience. Under the expert guidance of Faisal Zamir, a seasoned instructor in the realm of data science, students will acquire a profound understanding of effectively analyzing and manipulating data using Pandas. By the completion of this course, students will possess the confidence and expertise to proficiently handle data analysis tasks, extract valuable insights, and make informed decisions based on data-driven approaches. Whether you're a novice starting your data science journey or an aspiring data scientist, this course will equip you with the necessary skills to thrive in the field of data analysis using Pandas.

You should have basic knowledge of Python programming with beginner experince
You did not have to buy extra software or course

Data Analysts: If you work with data and need to perform tasks such as data cleaning, data transformation, data exploration, and data visualization, learning pandas can greatly enhance your data analysis skills.
Data Engineers: Data engineers work on building data pipelines and data infrastructure. Understanding pandas can help in the transformation and preparation of data within the pipeline.
Researchers and Academics: Researchers and academics from various disciplines who work with data can benefit from pandas. It enables efficient data manipulation and analysis, making it easier to extract insights from research data.
Python Developers: If you are a Python developer who wants to expand your skillset in data manipulation and analysis, learning pandas can be highly advantageous. It allows you to work with structured data effectively and perform common data tasks efficiently.
Data Scientists: As a data scientist, you often need to preprocess and analyze large datasets. pandas offers powerful tools for handling and manipulating data, making it a valuable asset for data scientists.

Section 1 : Chapter 01 6 Lectures 00:21:26
Lecture 1 :
Pandas Chapter 01 Outlines Preview
Lecture 2 :
What is Pandas
Lecture 3 :
Where we can use Pandas
Lecture 4 :
What we can do with Pandas
Lecture 5 :
Pandas Basic Program
Lecture 6 :
Pandas Installation
Section 2 : Chapter 02 4 Lectures 00:26:07
Lecture 1 :
Pandas Chapter 02 Outlines
Lecture 2 :
Series Data Structure
Lecture 3 :
DataFrame Data Strcuture
Lecture 4 :
Panel Data Structure
Section 3 : Chapter 03 22 Lectures 01:34:09
Lecture 1 :
Chapter 03 Outlines for Pandas
Lecture 2 :
Series Creation with 5 Methods
Lecture 3 :
Indexing with Series
Lecture 4 :
Slicing with Series
Lecture 5 :
Arithmetics with Series
Lecture 6 :
Comparision with Series
Lecture 7 :
Aggregation with Series
Lecture 8 :
Filtering with Series
Lecture 9 :
All Attribues of Series
Lecture 10 :
head method with Series
Lecture 11 :
tail method with Series
Lecture 12 :
describe method with Series
Lecture 13 :
info method with Series
Lecture 14 :
mean method with Series
Lecture 15 :
sum method with Series
Lecture 16 :
unique method with Series
Lecture 17 :
value_counts method with Series
Lecture 18 :
sort_values method with Series
Lecture 19 :
apply method with Series
Lecture 20 :
fillna method with Series
Lecture 21 :
drop method with Series
Lecture 22 :
concat method with Series
Section 4 : Chapter 04 20 Lectures 01:55:25
Lecture 1 :
Chapter 04 Outlines for Pandas
Lecture 2 :
Methods to create DataFrame
Lecture 3 :
Select Add and Delete Column
Lecture 4 :
Select Add Delete Row
Lecture 5 :
Indexing and Slicing in DataFrame
Lecture 6 :
Arithmetic Operation with DataFrame
Lecture 7 :
Comparision Operations on DataFrame
Lecture 8 :
Aggregation with DataFrame
Lecture 9 :
Filtering in DataFrame
Lecture 10 :
Missing Data Handling in DataFrame
Lecture 11 :
Joining Method with DataFrame
Lecture 12 :
Sorting in DataFrame
Lecture 13 :
Attributes for DataFrame
Lecture 14 :
Head and Tail method in DF
Lecture 15 :
Describe and Info method with DF
Lecture 16 :
sort_values method with DF
Lecture 17 :
dropna Method with DF
Lecture 18 :
fillna and merge method with DF
Lecture 19 :
apply method with DF
Lecture 20 :
Panel in Pandas
Section 5 : Chapter 05 3 Lectures 00:18:26
Lecture 1 :
Chapter 05 Outlines
Lecture 2 :
Descriptive Statistics in Pandas
Lecture 3 :
Descriptive Methods in Pandas
Section 6 : Chapter 06 5 Lectures 00:24:07
Lecture 1 :
Pandas Chapter 06 Outlines
Lecture 2 :
Function Application in Pandas
Lecture 3 :
Element Wise Application
Lecture 4 :
Row or Column Wise Application
Lecture 5 :
Table wise Application
Section 7 : Chapter 07 9 Lectures 00:49:02
Lecture 1 :
Pandas Chapter 07 Outlines
Lecture 2 :
Reindexing in Pandas
Lecture 3 :
Iteration with items method
Lecture 4 :
Iteration with iterrows method
Lecture 5 :
Iteration with itertuples method
Lecture 6 :
Iteration in Pandas
Lecture 7 :
Sort Values in Pandas
Lecture 8 :
Sort Index in Pandas
Lecture 9 :
nlargest and nsmallest in Pandas
Section 8 : Chapter 08 11 Lectures 00:35:37
Lecture 1 :
Pandas Chapter 08 Outlines
Lecture 2 :
lower and upper method
Lecture 3 :
title and capatilize method
Lecture 4 :
swapecase method in Pandas
Lecture 5 :
strip Istrip rstrip in pandas
Lecture 6 :
join method in Pandas
Lecture 7 :
replace method in Pandas
Lecture 8 :
contains method in Pandas
Lecture 9 :
startswith and endswith in Pandas
Lecture 10 :
find and find in Pandas
Lecture 11 :
count and len Method in Pandas
Section 9 : Chapter 09 6 Lectures 00:36:13
Lecture 1 :
Pandas Chapter 09 Outline
Lecture 2 :
Display Option in Pandas
Lecture 3 :
Customizing Data Types
Lecture 4 :
Data Cleaning
Lecture 5 :
Label integer and boolean based indexing
Lecture 6 :
Query Method in Pandas
Section 10 : Chapter 10 4 Lectures 00:28:05
Lecture 1 :
Pandas Chapter 10 Outline
Lecture 2 :
Rolling Window in Pandas
Lecture 3 :
Rolling window functions in Pandas
Lecture 4 :
Expending Window in Pandas
Section 11 : Chapter 11 7 Lectures 00:39:06
Lecture 1 :
Pandas Chapter 11 Outline
Lecture 2 :
groupby Method in Pandas
Lecture 3 :
Aggregation on groupby in Pandas
Lecture 4 :
Transformation on groupby in Pandas
Lecture 5 :
Filteration in Pandas
Lecture 6 :
Transformation Example on groupby
Lecture 7 :
Aggregation Example with groupby
Section 12 : Chapter 12 3 Lectures 00:00:00
Lecture 1 :
Pandas Outline Chapter 12
Lecture 2 :
Pandas Categorical Data Introduction
Lecture 3 :
Pandas Categorical Data Example
Section 13 : Chapter 13 7 Lectures 00:29:56
Lecture 1 :
Pandas Chapter 13 Outline
Lecture 2 :
Lineplot in Pandas
Lecture 3 :
Barplot in Pandas
Lecture 4 :
Scatter plot in Pandas
Lecture 5 :
Boxplot in Pandas
Lecture 6 :
Area plot in Pandas
Lecture 7 :
Density Plot in Pandas
Section 14 : Chapter 14 7 Lectures 00:56:01
Lecture 1 :
Pandas Chapter 14 Outline
Lecture 2 :
Reading CSV File with Pandas
Lecture 3 :
Writing CSV file with Pandas
Lecture 4 :
Reading Excel file with Pandas
Lecture 5 :
Writing Excel file with Pandas
Lecture 6 :
Reading JSON file with Pandas
Lecture 7 :
Writing JSON file with Pandas
Section 15 : Chapter 15 5 Lectures 00:00:00
Lecture 1 :
Pandas Chapter 15
Lecture 2 :
to_datetime in Pandas
Lecture 3 :
Date Range in Numpy
Lecture 4 :
strftime in Numpy
Lecture 5 :
Timestamp in Numpy

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Faisal Zamir,

506133 Course Views

29 Courses

Hello, my name is Faisal Zamir and I am a Master of Computer Science with over 7 years of experience in teaching. I have a passion for teaching and have dedicated my career to educating students on various topics related to web designing, web development, and programming languages such as Python, PHP, JavaScript, C++, C#, and more. As an instructor, I believe in providing students with a comprehensive learning experience that goes beyond just theory. That's why I always provide programming examples, discussion (Q&A), quizzes, assignments, problems with solutions, exercises, and more to ensure that my students have a hands-on approach to learning. My courses are designed to provide students with lifetime access to all course materials and resources, as well as ongoing support. I am committed to helping my students achieve their goals, whether it's to improve their skills for personal or professional reasons. In addition to teaching, I have also worked on various projects in the field of computer science and have acquired practical experience in implementing programming concepts in real-world scenarios. I bring this experience into my teaching to provide my students with a practical and relevant education. Thank you for considering my courses and I look forward to helping you achieve your goals in the world of computer science.