OML4Py provides methods that enable you to perform exploratory data analysis and common statistical operations.

These methods are described in the following topics.

• About the Exploratory Data Analysis Methods
  OML4Py provides methods that enable you to perform exploratory data analysis.
• Correlate Data
  Use the corr method to perform Pearson, Spearman, or Kendall correlation analysis across columns where possible in an oml.DataFrame object.
• Cross-Tabulate Data
  Use the crosstab method to perform cross-column analysis of an oml.DataFrame object and the pivot_table method to convert an oml.DataFrame to a spreadsheet-style pivot table.
• Mutate Data
  In preparing data for analysis, a typical operation is to mutate data by reformatting it or deriving new columns and adding them to the data set.
• Sort Data
  The sort_values function enables flexible sorting of an oml.DataFrame along one or more columns specified by the by argument, and returns an oml.DataFrame.
• Summarize Data
  The describe method calculates descriptive statistics that summarize the central tendency, dispersion, and shape of the data in each column.
• Date, Time, and Integer Data
  OML4Py provides the data types that enable you to manipulate date, time and integer.