Pandas The ultimate guide on python pandas for data science

Python pandas for data analytics: Complete reference to python pandas for data analytics, python pandas data structures

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

Requirements
For better understanding of the topic student should know python programming and Numpy library
Description
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

Who this course is for
This course is for python programmer.
Also, this course is for data analysts.