Advanced Functional Exploratory Data Analysis using Python

“EDA” is a critical first step in analyzing the data from an experiment or research. Here are the
main reasons we use EDA:
 detection of mistakes
 checking of assumptions
 preliminary selection of appropriate models
 determining relationships among the explanatory variables
 assessing the direction and rough size of relationships between explanatory and outcome
variables.
The data from experiment/research are generally collected into a spreadsheet or database, most
commonly with one row per experimental subject and one column for each subject identifier,
outcome variable, and explanatory variable. Each column contains the numeric values for a
particular quantitative variable or the levels for a categorical variable. (Some more complicated
experiments require a more complex data layout.
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