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())

• CDataFrame.add_prefix()

• CDataFrame.add_suffix()

• CDataFrame.append()

• CDataFrame.assign()

• CDataFrame.describe()

• CDataFrame.project()

• CDataFrame.rename()

• CDataFrame.sample()

• CDataFrame.shuffle()

• CDataFrame.slice()

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

• concat()

• remove_objects()

Any column

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

Table methods

• CDataFrame.filter() [1]

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

• CSeries.as_table()

• CSeries.astype()

• CSeries.get()

• CSeries.if_else() [1]

• CSeries.with_threshold()

• CSeriesColRef.as_table()

• CSeriesColRef.count()

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.

Integer column

These methods apply to integer columns:

Table methods

• CDataFrame.groupby()

• CDataFrame.max()

• CDataFrame.min()

• CDataFrame.validate()

Column methods

• CSeriesColRef.in_range()

• CSeriesColRef.max()

• CSeriesColRef.mean()

• CSeriesColRef.min()

• CSeriesColRef.sum()

• CSeriesColRef.sum_in_range()

• CSeriesColRef.sum_squares()

• CSeriesColRef.var()

Global functions

• cut()

• merge()

• series_max()

• series_min()

Grouping functions

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

Added in version 1.8: Groupings also work for date columns

Performing the following

Grouping table methods

• CDataFrameGroupBy.as_table()

• CDataFrameGroupBy.col()

• CDataFrameGroupBy.size()

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

• CSeriesGroupBy.agg()

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

Added 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

• crlearn.linear_model.LinearRegression.fit() (inherited from Ridge)

• crlearn.linear_model.Ridge.fit()

• crlearn.logistic_regression.LogisticRegression.fit()

Column of vectors of integers

Column methods

• CSeries.inner()

• CSeries.vsum()

String column

Column methods

• CSeries.contains()

• CSeries.fullmatch()

• CSeries.len()

• CSeries.lower()

Added in version 1.8: String matching

Global functions

• merge() [2]

Date column

Added in version 1.8: Date columns

• CSeries.weekday()

• CSeries.day()

• CSeries.month()

• CSeries.year()

Nullable columns

Added in version 1.8: Any column type can be nullable instead of only integers

Table methods

• CDataFrame.fillna()

Column methods

• CSeries.fillna()

• CSeries.isna()

• CSeries.isnull()

• CSeries.notna()

• CSeries.notnull()

[1] (1,2)

These functions require a boolean column input but such input can be computed from a column of any type, e.g. tab["integer"] > 3 or tab["string"] == "test" or simply tab["boolean"].

[2]

Except for how="left" as that requires a CDataFrameGroupBy that can only exist for integer or date columns.