Pandas is one of the most popular of the Python data science libraries to work with data. Expressing data in a tabular format makes it easy and intuitive to perform data cleaning, aggregations and other analysis. Pandas offers many built-in functions for common data manipulation techniques making it very simple for data analysts and scientists to clean and explore datasets before performing further analysis.
Here is what this course covers:
Series and Dataframes: Work with a vector of values stored in a Series or tabular data stored in a Dataframe
Indexing and iterating over Dataframes: Access individual records and columns within your data
Importing and exporting data: Read from CSV files and store to CSV, Excel and Text files
Grouping, aggregations and sorting: Perform analysis on interesting bits of data with built-in methods
MultiIndex: Index data at multiple levels based on your use case
Concat, Merge and Join: Bring together data stored in different structures in a variety of ways
Missing data: Clean data by removing missing or invalid values
Time-series data: Use date time as an index into your Dataframe
This course is built around hands on demos, built to explicitly explain concepts underpinning each topic.
Software used: Python 3, Pandas
