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 columns. Such object represents a grouped table.
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)
Column of vectors of integers¶
Column methods
CSeries.inner()
CSeries.vsum()
Column of nullable integers¶
Table methods
CDataFrame.fillna()
Column methods
CSeries.fillna()
CSeries.isna()
CSeries.isnull()
CSeries.notna()
CSeries.notnull()
String column¶
Column methods
CSeries.len()
CSeries.lower()
Global functions
merge()
[2]