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In this course, you will learn :
- Describe the Pandas Data Structure Anatomy.
- Implement several methods for loading and removing data from Pandas DataFrames. Python Native Data Structures, Tabular Data Files, API Queries, and so on are examples of these methods.
- Describe any data contained within a Pandas DataFrame. This will assist you in identifying data issues such as missing values or the use of incorrect data types.
- Data manipulation and cleaning are carried out. This section includes fixing data types, dealing with missing values, removing duplicate records, and many other things.
- Join and merge multiple datasets into Pandas DataFrames.
- Within any DataFrame, perform data summarization and aggregation.
- Make various types of data visualisations.
- Update the Pandas Styling Options.
- Conduct a data analysis project with the Pandas library to collect and investigate COVID-19 infection and the resulting lockdown in various countries.
- Understand Pandas Data Types and the correct use case for each type
Syllabus :
- Introduction to Jupyter Lab
- Getting Started with Pandas
- Getting Data into and from Pandas
- Exploring DataFrames
- Data Cleaning in Pandas
- Merging & Joining Data
- Data Accessing & Aggregation
- Pandas Data Visualization
- Pandas Analysis Project
