Understand the fundamentals of Pandas, its importance in data analysis, and the wide range of tasks it can accomplish.
Install Pandas and execute basic programs to perform data analysis.
Familiarize themselves with different data structures in Pandas, such as Series, DataFrame, and Panel.
Apply descriptive statistics methods and inferential statistics functions for data analysis.
Utilize various function application techniques, including element-wise, row or column-wise, and table-wise operations.
Master reindexing, iteration, and sorting techniques to manipulate and organize data effectively.
Explore the rich set of string methods available in Pandas for text data processing and manipulation.
Customize display options, data types, and data cleaning/manipulation processes according to specific requirements.
Learn indexing and selecting techniques using label-based or integer-based indexing, boolean indexing, and string-based querying.
Perform window functions such as rolling, expanding, and exponentially weighted moving average (ewm) for data analysis.
Understand the concept of grouping data, apply aggregation functions, and filter and transform data based on groups.
Handle categorical data using various methods, including type conversion, value counting, reordering categories, and handling missing categories.
Visualize data using line plots, bar plots, histograms, scatter plots, box plots, area plots, heatmaps, and density plots.
Master I/O tools for reading and writing data in different formats such as CSV, Excel, and JSON.
Explore sparse data features and learn to handle and analyze data with missing values efficiently.