Index of functions per column type#

This is a list of the functions that can be applied on tables or columns, depending on the column type.

Table functions#

The following functions can be applies on any table, regardless of the types of columns it contains:

Methods (e.g. table.shuffle())

Global functions (e.g. cd.concat(table, table2)):

Any column#

The following functions can be applied on any column, regardless of its type:

Table methods

Column methods (e.g. table["column_name"].as_table())

Numeric column#

The following functions can be applied to any numeric column. The current column types are integer, nullable integer, integer vector and fixed point numbers.

Numeric column#

These methods apply to integer and fixed-point columns:

Table methods

Column methods

Global functions

Grouping functions

These functions require a GroupBy object (output by CDataFrame.groupby()) which only works for integer columns. Such object represents a grouped table.

Performing the following

Grouping table methods

Grouping column methods

For the following methods, not only must the grouping be created for an integer column, but it can only be applied to an integer column

Given a table tab with an integer index int_index, a column of ages age and one column with names name:

grouping = tab.groupby("int_index")

# This works
age_sum = grouping["age"].agg(cd.groupby.sum)

# This does not
name_sum = grouping["name"].agg(cd.groupby.sum)

Integers or fixed-points#

New in version 1.7: Fixed-point columns

Constructing a logistic or linear regression model can be done with either integer or fixed-point columns using the following methods

Column of vectors of integers#

Column methods

Column of nullable integers#

Table methods

Column methods

String column#

Column methods

Global functions