Python pandas for data analytics: Complete reference to python pandas for data analytics, python pandas data structures
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.
What you’ll learn
- What python pandas library and its usage in storing data required for data analytics.
- Python pandas data structures: Series, DataFrame and Panel.
- How to create Series, DataFrame and Panel types of data structures.
- How to access data from Series, DataFrame and Panel data structures.
- What types of operations can be performed on pandas data structures.
Course Content
- Introduction –> 2 lectures • 3min.
- Types of pandas data structures –> 3 lectures • 59min.
- Functionality of pandas –> 3 lectures • 36min.
- Indexing and reindexing –> 2 lectures • 20min.
- Working with data in pandas –> 3 lectures • 29min.
- Advanced indexing –> 1 lecture • 13min.
- Functions on pandas –> 3 lectures • 18min.
- Handling missing data –> 1 lecture • 11min.
- Groupby function –> 1 lecture • 13min.
- Database operations –> 2 lectures • 14min.
- Date data handling –> 2 lectures • 14min.
- Categorical data handling –> 1 lecture • 14min.
- Data visualization and IO operations –> 3 lectures • 24min.
Requirements
- For better understanding of the topic student should know python programming and Numpy library.
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 – Introduction
2. Python Pandas – Environment Setup
3. Introduction to Data Structures
4. Python Pandas – Series
5. Python Pandas – DataFrame
6. Python Pandas – Basic Functionality
7.Python Pandas – Descriptive Statistics
8.Python Pandas – Function Application
9.Python Pandas – Reindexing
10.Python Pandas – Iteration
11.Python Pandas – Sorting
12.Python Pandas – Working with Text Data
13.Python Pandas – Options and Customization
14.Python Pandas – Indexing and Selecting Data
15.Python Pandas – Statistical Functions
16.Python Pandas – Window Functions
17.Python Pandas – Aggregations
18.Python Pandas – Missing Data
19. Python Pandas – GroupBy
20.Python Pandas – Merging/Joining
21.Python Pandas – Concatenation
22.Python Pandas – Date Functionality
23.Python Pandas – Timedelta
24.Python Pandas – Categorical Data
25.Python Pandas – Visualization
26.Python Pandas – IO Tools
27.Python Pandas – Comparison with SQL