Data Analysis with Python Proficiency (Advanced Level)

Topics covered

• apply a multi-index to a DataFrame and reshape it using the stack and melt operations
• apply regular expressions and other advanced techniques to select and drop columns
• clean missing data in mismatched DataFrames
• compare the use cases for swarm plots, bar plots strip plots, and categorical plots
• configure a FacetGrid to convey more information and to draw one's focus to specific plots
• create a FacetGrid to visualize distributions within a range of categories
• describe how operations can be performed between arrays of mismatched shapes using broadcasting
• identify kinds of masking operations
• implement joins using the join() method
• load data into a Pandas DataFrame from a table in a relational database
• manipulate and analyze time series data
• parse and manipulate datetime values
• perform advanced manipulations on string data
• perform conditional filtering using the query function
• perform filtering operations on categorical data
• perform operations between arrays of mismatched shapes by applying broadcasting rules
• recognize default and custom indexes and reindex DataFrames
• transform data with user-defined functions
• troubleshoot data with duplicates
• use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
• use pivot tables to explore data
• use pivot tables to summarize data