Fundamentals of Pandas

Fundamentals of Pandas, available at $19.99, has an average rating of 4.25, with 17 lectures, based on 15 reviews, and has 877 subscribers.

You will learn about Perform data analysis with python using the pandas library. Understand some of the basic concepts of data analysis. Learn DataFrames, basic plotting, indexing, and groupby Learn how to work with data more effectively This course is ideal for individuals who are Programmers and Developers or Data analysts and business analysts or Anyone interested in learning Pandas It is particularly useful for Programmers and Developers or Data analysts and business analysts or Anyone interested in learning Pandas.

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Summary

Title: Fundamentals of Pandas

Price: $19.99

Average Rating: 4.25

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Perform data analysis with python using the pandas library.

Understand some of the basic concepts of data analysis.

Learn DataFrames, basic plotting, indexing, and groupby

Learn how to work with data more effectively

Who Should Attend

Programmers and Developers

Data analysts and business analysts

Anyone interested in learning Pandas

Target Audiences

Programmers and Developers

Data analysts and business analysts

Anyone interested in learning Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandasis a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

Pandas provides a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with widespread use of Pandas include: data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.

In this course, you’ll learn how to use the pandas library and tools for data analysis and data structuring. Students will learn about DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, 

By the end of this course, students should have a good understanding of Pandas and gain proficiency using the Python Pandas library for data analysis.

Library Highlights

A fast and efficient DataFrame object for data manipulation with integrated indexing;

Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;

Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;

Flexible reshaping and pivoting of data sets;

Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;

Columns can be inserted and deleted from data structures for size mutability;

Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;

High performance merging and joining of data sets;

Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;

Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;

Highly optimized for performance, with critical code paths written in Cython or C.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: Introduction

Chapter 2: Setup

Lecture 1: Anaconda

Lecture 2: Downloading the data set

Lecture 3: Jupyter notebook

Lecture 4: Using Pandas

Lecture 5: Series and DataFrames

Chapter 3: Data Input and Validation

Lecture 1: Using read_csv() and using shape

Lecture 2: Using head() and tail() and info()

Chapter 4: Basic Analysis

Lecture 1: Using value_counts() and sort_values()

Lecture 2: Boolean indexing and string handling

Lecture 3: Basic Plotting

Lecture 4: Indexing

Lecture 5: Groupby

Lecture 6: Reshaping

Lecture 7: Data Visualizations

Lecture 8: Conclusion

Instructors

SmartBase Analytics
Data Analytics

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 2 votes

5 stars: 9 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!