Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.This is a ultimate guide to python pandas for data science user. In this course user will learn python pandas library. User will learn pandas series, dataframe and panel data structures. Also, they will learn how to create these data structures, store data, access data. The functionality and operations on pandas data structures are also explained in detail. python pandas and its data structures are usefull to store the required for data analytics. The detail content of the course is as follows,1.Python Pandas - Introduction2. Python Pandas - Environment Setup3. Introduction to Data Structures4. Python Pandas - Series5. Python Pandas - DataFrame6. Python Pandas - Basic Functionality7.Python Pandas - Descriptive Statistics8.Python Pandas - Function Application9.Python Pandas - Reindexing10.Python Pandas - Iteration11.Python Pandas - Sorting12.Python Pandas - Working with Text Data13.Python Pandas - Options and Customization14.Python Pandas - Indexing and Selecting Data15.Python Pandas - Statistical Functions16.Python Pandas - Window Functions17.Python Pandas - Aggregations18.Python Pandas - Missing Data19. Python Pandas - GroupBy20.Python Pandas - Merging/Joining21.Python Pandas - Concatenation22.Python Pandas - Date Functionality23.Python Pandas - Timedelta24.Python Pandas - Categorical Data25.Python Pandas - Visualization26.Python Pandas - IO Tools27.Python Pandas - Comparison with SQL
What you'll learn
understand the basics of Python Pandas
create and manipulate Series and DataFrames
perform descriptive statistics and data aggregation
manage missing data and work with categorical data
implement data visualization techniques using Pandas
compare Pandas operations with SQL functionalities
Course objectives
provide a thorough introduction to the Pandas library
equip students with practical skills for data manipulation
enable students to apply Pandas in various data analysis contexts