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What you'll 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.
Course Overview
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.
Pre-requisites
- You should have basic knowledge of Python programming with beginner experince
- You did not have to buy extra software or course
Target Audience
- 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.
Curriculum 119 Lectures 09:33:40
-
Section 1 : Chapter 01
- 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
- 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
- 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
- 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
- Lecture 1 :
- Chapter 05 Outlines
- Lecture 2 :
- Descriptive Statistics in Pandas
- Lecture 3 :
- Descriptive Methods in Pandas
-
Section 6 : Chapter 06
- 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
- 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
- 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
- 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
- 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
- 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
- Lecture 1 :
- Pandas Outline Chapter 12
- Lecture 2 :
- Pandas Categorical Data Introduction
- Lecture 3 :
- Pandas Categorical Data Example
-
Section 13 : Chapter 13
- 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
- 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
- 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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Instructor
505857 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.
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