VertexFrame copy

---

copy(self, columns=None, where=None, name=None)

Create new frame from current frame.

Parameters:

columns : str | list of str | dict (default=None)

If not None, the copy will only include the columns specified. If dict, the string pairs represent a column renaming, {source_column_name: destination_column_name}

where : function (default=None)

If not None, only those rows for which the UDF evaluates to True will be copied.

name : str (default=None)

Name of the copied frame

Returns:

: Frame

A new Frame of the copied data.

Copy frame or certain frame columns entirely or filtered. Useful for frame query.

Examples

Build a Frame from a csv file with 5 million rows of data; call the frame “cust”:

>>> my_frame = ta.Frame(source="my_data.csv")
>>> my_frame.name("cust")

Given the frame has columns id, name, hair, and shoe. Copy it to a new frame:

>>> your_frame = my_frame.copy()

Now we have two frames of data, each with 5 million rows. Checking the names:

>>> print my_frame.name()
>>> print your_frame.name()

Gives the results:

"cust"
"frame_75401b7435d7132f5470ba35..."

Now, let’s copy some of the columns from the original frame:

>>> our_frame = my_frame.copy(['id', 'hair'])

Our new frame now has two columns, id and hair, and has 5 million rows. Let’s try that again, but this time change the name of the hair column to color:

>>> last_frame = my_frame.copy(('id': 'id', 'hair': 'color'))