This is the code repository for Numpy and Pandas Tips, Tricks, and Techniques [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
This course will empower you with new possibilities using NumPy and pandas that you probably never knew existed, and tips to use them to increase your efficiency and productivity in your daily tasks. Each section will cover key tips, tricks, and techniques for efficient data analysis in NumPy and pandas that you can apply in your own real-world scenarios to increase your output and efficiency. You’ll learn how to make your data more meaningful and contextual by adding customization. We’ll also cover the new features introduced in NumPy and pandas and leverage them to simplify the way you use them for your data science requirements. By the end of this course, you will be able to get the best out of your code much faster and and more efficiently.
- Get a NumPy refresher with lessons you can reuse in general data settings
- Take a deeper dive into NumPy to learn how to leverage the power of ndim arrays
- Get a pandas functionality refresher covering everyday data handling concepts
- Review how to process Excel data quickly and automatically with pandas and re-import into Excel
- See how to work with complex data using merging and data-joining with pandas
- Discover the functionality of pandas to help you sub-set, split, and aggregate data
- Create a Capstone project with NumPy and pandas to produce a data analysis tool for stock prices as a working model
This course is for Python developers and analysts who want to speed up the way they perform data analysis, use some proven techniques to write code with ease, and are looking for best practices in pandas and NumPy. Knowledge of Python programming is expected, along with some familiarity with NumPy and pandas.
Minimum Hardware Requirements For successful completion of this course, students will require the computer systems with at least the following:
OS: Recent distribution of MAC OS, Linux or Windows
Processor: Any in a computer 5 or fewer years old
Memory: 4Gb+
Storage: N/A
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Recent distribution of MAC OS, Linux or Windows
Processor: Any in a computer 5 or fewer years old
Memory: 4Gb+
Storage: N/A
Software Requirements
Operating system: MAC OS, Linux, Windows
Browser: Chrome or Firefox
Anaconda for Python 3.6+ (https://anaconda.com)