Python is an open-source (free), general-purpose (unlimited), programming language which is the most popular for doing data science. Many companies and businesses are now using Python to gain insights from their data, which helps them to have competitive advantage. This course will take a practical approach by using codes written in Jupyter notebooks (a tool that most Data Scientists use on a daily basis) to teach you how to program with Python with the focus on doing data science.
Data Manipulation with Pandas
Data Manipulation with Pandas
The Pandas library is the de facto approach to work with tabular data in Python. Pandas DataFrames and Series can be viewed as an extremely powerful version of Excel, with a lot more features and flexibility. This module will help you to get hands-on practice with creating, manipulating and analyzing data very efficiently with Pandas.
In this module, you will learn:
- About Pandas and how to use it for data manipulation in Python.
- About Pandas data structures: Series and DataFrames.
- How to create Pandas Series from lists, NumPy arrays and dictionaries.
- How to create Pandas DataFrames using the .DataFrame method and by data importation.
- How to add and remove rows and columns from DataFrames.
- How to read in and write out data in different formats with Pandas.
- How to check different properties and summary statistics of a DataFrame.
- How to handle missing data.
- How to merge, join and concatenate DataFrames.
- How to filter data by boolean masking.
- How to segment and aggregate data with Pandas.
You will receive links to download this course in zip format during Checkout, along with an emailed link that will last for 30 days.