datatable.Namespace.__getitem__()

datatable.Namespace.

self

[

*items

]

source

Retrieve column(s) by their indices/names/types.

By “retrieve” we actually mean that an expression is created such that when that expression is used within the DT[i,j] call, it would locate and return the specified column(s).

Parameters

items

int | str | slice | None | type | stype | ltype | list | tuple

The column selector:

int

Retrieve the column at the specified index. For example, f[0] denotes the first column, while f[-1] is the last.

str

Retrieve a column by name.

slice

Retrieve a slice of columns from the namespace. Both integer and string slices are supported.

Note that for string slicing, both the start and stop column names are included, unlike integer slicing, where the stop value is not included. Have a look at the examples below for more clarity.

None

Retrieve no columns (an empty columnset).

type | stype | ltype

Retrieve columns matching the specified type.

list/tuple

Retrieve columns matching the column names/column positions/column types within the list/tuple.

For example, f[0, -1] will return the first and last columns. Have a look at the examples below for more clarity.

return

FExpr

An expression that selects the specified column from a frame.

See also

• f-expressions – user guide on using f-expressions.

Notes

Changed in version 1.0.0

f-expressions containing a list/tuple of column names/column positions/column types are accepted within the j selector.

Examples

from datatable import dt, f, by df = dt.Frame({'A': [1, 2, 3, 4], 'B': ["tolu", "sammy", "ogor", "boondocks"], 'C': [9.0, 10.0, 11.0, 12.0]}) df

	A	B	C
	int32	str32	float64
0	1	tolu	9
1	2	sammy	10
2	3	ogor	11
3	4	boondocks	12

4 rows × 3 columns

Select by column position:

df[:, f[0]]

	A
	int32
0	1
1	2
2	3
3	4

4 rows × 1 column

Select by column name:

df[:, f["A"]]

	A
	int32
0	1
1	2
2	3
3	4

4 rows × 1 column

Select a slice:

df[:, f[0 : 2]]

	A	B
	int32	str32
0	1	tolu
1	2	sammy
2	3	ogor
3	4	boondocks

4 rows × 2 columns

Slicing with column names:

df[:, f["A" : "C"]]

	A	B	C
	int32	str32	float64
0	1	tolu	9
1	2	sammy	10
2	3	ogor	11
3	4	boondocks	12

4 rows × 3 columns

Note

For string slicing, both the start and stop are included; for integer slicing the stop is not included.

Select by data type:

df[:, f[dt.str32]]

	B
	str32
0	tolu
1	sammy
2	ogor
3	boondocks

4 rows × 1 column

df[:, f[float]]

	C
	float64
0	9
1	10
2	11
3	12

4 rows × 1 column

Select a list/tuple of columns by position:

df[:, f[0, 1]]

	A	B
	int32	str32
0	1	tolu
1	2	sammy
2	3	ogor
3	4	boondocks

4 rows × 2 columns

Or by column names:

df[:, f[("A", "B")]]

	A	B
	int32	str32
0	1	tolu
1	2	sammy
2	3	ogor
3	4	boondocks

4 rows × 2 columns

Note that in the code above, the parentheses are unnecessary, since tuples in python are defined by the presence of a comma. So the below code works as well:

df[:, f["A", "B"]]

	A	B
	int32	str32
0	1	tolu
1	2	sammy
2	3	ogor
3	4	boondocks

4 rows × 2 columns

Select a list/tuple of data types:

df[:, f[int, float]]

	A	C
	int32	float64
0	1	9
1	2	10
2	3	11
3	4	12

4 rows × 2 columns

Passing None within an f-expressions returns an empty columnset:

df[:, f[None]]

0
1
2
3

4 rows × 0 columns