Selecting Data

Selecting Data – Columns

Column selection is via the j section in the DT[i, j, ...] syntax. First, let’s construct a simple Frame:

from datatable import dt, f from datetime import date source = {"dates" : [date(2000, 1, 5), date(2010, 11, 23), date(2020, 2, 29), None], "integers" : range(1, 5), "floats" : [10.0, 11.5, 12.3, -13], "strings" : ['A', 'B', None, 'D'] } DT = dt.Frame(source) DT

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D

4 rows × 4 columns

Column selection is possible via a number of options:

By column name

DT[:, 'dates']

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA

4 rows × 1 column

When selecting all rows, the i section can also be ....

By position

DT[..., 2] # 3rd column

	floats
	float64
0	10
1	11.5
2	12.3
3	-13

4 rows × 1 column

With position, you can select with a negative number – the column will be selected from the end; this is similar to indexing a python list:

DT[:, -2] # 2nd column from the end

	floats
	float64
0	10
1	11.5
2	12.3
3	-13

4 rows × 1 column

For a single column, it is possible to skip the : in the i section and pass the column name or position only

DT['dates']

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA

4 rows × 1 column

DT[0]

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA

4 rows × 1 column

When selecting via column name or position, an error is returned if the name or position does not exist:

DT[:, 5]

ValueError: Column index 5 is invalid for a Frame with 4 columns

DT[:, 'categoricals']

KeyError: Column categoricals does not exist in the Frame

By data type

Column selection is possible by using python’s built-in types that correspond to one of the datatable’s types:

DT[:, int]

	integers
	int32
0	1
1	2
2	3
3	4

4 rows × 1 column

Or datatable’s Type:

DT[:, dt.Type.float64]

	floats
	float64
0	10
1	11.5
2	12.3
3	-13

4 rows × 1 column

DT[:, dt.Type.date32]

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA

4 rows × 1 column

A list of types can be selected as well:

DT[:, [date, str]]

	dates	strings
	date32	str32
0	2000-01-05	A
1	2010-11-23	B
2	2020-02-29	NA
3	NA	D

4 rows × 2 columns

By list

Using a list allows for selection of multiple columns:

DT[:, ['integers', 'strings']]

	integers	strings
	int32	str32
0	1	A
1	2	B
2	3	NA
3	4	D

4 rows × 2 columns

A tuple of selectors is also allowed, although not recommended from stylistic perspective:

DT[:, (-3, 2, 3)]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D

4 rows × 3 columns

Selection via list comprehension/generator expression is possible:

DT[:, [num for num in range(DT.ncols) if num % 2 == 0]]

	dates	floats
	date32	float64
0	2000-01-05	10
1	2010-11-23	11.5
2	2020-02-29	12.3
3	NA	-13

4 rows × 2 columns

Selecting columns via a mix of column names and positions (integers) is not allowed:

DT[:, ['dates', 2]]

TypeError: Mixed selector types are not allowed. Element 1 is of type integer, whereas the previous element(s) were of type string

Via slicing

When slicing with strings, both the start and end column names are included in the returned frame:

DT[:, 'dates':'strings']

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D

4 rows × 4 columns

However, when slicing via position, the columns are returned up to, but not including the final position; this is similar to the slicing pattern for Python’s sequences:

DT[:, 1:3]

	integers	floats
	int32	float64
0	1	10
1	2	11.5
2	3	12.3
3	4	-13

4 rows × 2 columns

DT[:, ::-1]

	strings	floats	integers	dates
	str32	float64	int32	date32
0	A	10	1	2000-01-05
1	B	11.5	2	2010-11-23
2	NA	12.3	3	2020-02-29
3	D	-13	4	NA

4 rows × 4 columns

It is possible to select columns via slicing, even if the indices are not in the Frame:

DT[:, 3:10] # there are only four columns in the Frame

	strings
	str32
0	A
1	B
2	NA
3	D

4 rows × 1 column

Unlike with integer slicing, providing a name of the column that is not in the Frame will result in an error:

DT[:, "integers" : "categoricals"]

KeyError: Column categoricals does not exist in the Frame

Slicing is also possible with the standard slice function:

DT[:, slice('integers', 'strings')]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D

4 rows × 3 columns

With the slice function, multiple slicing on the columns is possible:

DT[:, [slice("dates", "integers"), slice("floats", "strings")]]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D

4 rows × 4 columns

DT[:, [slice("integers", "dates"), slice("strings", "floats")]]

	integers	dates	strings	floats
	int32	date32	str32	float64
0	1	2000-01-05	A	10
1	2	2010-11-23	B	11.5
2	3	2020-02-29	NA	12.3
3	4	NA	D	-13

4 rows × 4 columns

Slicing on strings can be combined with column names during selection:

DT[:, [slice("integers", "dates"), "strings"]]

	integers	dates	strings
	int32	date32	str32
0	1	2000-01-05	A
1	2	2010-11-23	B
2	3	2020-02-29	NA
3	4	NA	D

4 rows × 3 columns

But not with integers:

DT[:, [slice("integers", "dates"), 1]]

TypeError: Mixed selector types are not allowed. Element 1 is of type integer, whereas the previous element(s) were of type string

Slicing on position can be combined with column position:

DT[:, [slice(1, 3), 0]]

	integers	floats	dates
	int32	float64	date32
0	1	10	2000-01-05
1	2	11.5	2010-11-23
2	3	12.3	2020-02-29
3	4	-13	NA

4 rows × 3 columns

But not with strings:

DT[:, [slice(1, 3), "dates"]]

TypeError: Mixed selector types are not allowed. Element 1 is of type string, whereas the previous element(s) were of type integer

Via booleans

When selecting via booleans, the sequence length must be equal to the number of columns in the frame:

DT[:, [True, True, False, False]]

	dates	integers
	date32	int32
0	2000-01-05	1
1	2010-11-23	2
2	2020-02-29	3
3	NA	4

4 rows × 2 columns

Booleans generated from a list comprehension/generator expression allow for nifty selections:

DT[:, ["i" in name for name in DT.names]]

	integers	strings
	int32	str32
0	1	A
1	2	B
2	3	NA
3	4	D

4 rows × 2 columns

In this example we want to select columns that are numeric (integers or floats) and whose average is greater than 3:

DT[:, [column.type.is_numeric and column.mean1() > 3 for column in DT]]

	floats
	float64
0	10
1	11.5
2	12.3
3	-13

4 rows × 1 column

Via f-expressions

All the selection options above (except boolean) are also possible via f-expressions:

DT[:, f.dates]

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA

4 rows × 1 column

DT[:, f[-1]]

	strings
	str32
0	A
1	B
2	NA
3	D

4 rows × 1 column

DT[:, f['integers':'strings']]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D

4 rows × 3 columns

DT[:, f['integers':]]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D

4 rows × 3 columns

DT[:, f[1::-1]]

	integers	dates
	int32	date32
0	1	2000-01-05
1	2	2010-11-23
2	3	2020-02-29
3	4	NA

4 rows × 2 columns

DT[:, f[date, int, float]]

	dates	integers	floats
	date32	int32	float64
0	2000-01-05	1	10
1	2010-11-23	2	11.5
2	2020-02-29	3	12.3
3	NA	4	-13

4 rows × 3 columns

DT[:, f["dates":"integers", "floats":"strings"]]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D

4 rows × 4 columns

Note

If the columns names are python keywords (def, del, …), the dot notation is not possible with f-expressions; you have to use the brackets notation to access these columns.

Note

Selecting columns with DT[:, f[None]] returns an empty Frame. This is different from DT[:, None], which currently returns all the columns. The behavior of DT[:, None] may change in the future:

DT[:, None]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D

4 rows × 4 columns

DT[:, f[None]]

0
1
2
3

4 rows × 0 columns

Selecting Data – Rows

There are a number of ways to select rows of data via the i section.

Note

The index labels in a Frame are just for aesthetics; they serve no actual purpose during selection.

By Position

Only integer values are acceptable:

DT[0, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A

1 row × 4 columns

DT[-1, :] # last row

	dates	integers	floats	strings
	date32	int32	float64	str32
0	NA	4	-13	D

1 row × 4 columns

Via Sequence of Positions

Any acceptable sequence of positions is applicable here. Listed below are some of these sequences.

• List (tuple):

  DT[[1, 2, 3], :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2010-11-23	2	11.5	B
  1	2020-02-29	3	12.3	NA
  2	NA	4	-13	D

  3 rows × 4 columns

• An integer numpy 1-D Array:

  DT[np.arange(3), :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2000-01-05	1	10	A
  1	2010-11-23	2	11.5	B
  2	2020-02-29	3	12.3	NA

  3 rows × 4 columns

• A one column integer Frame:

  DT[dt.Frame([1, 2, 3]), :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2010-11-23	2	11.5	B
  1	2020-02-29	3	12.3	NA
  2	NA	4	-13	D

  3 rows × 4 columns

• An integer pandas Series:

  DT[pd.Series([1, 2, 3]), :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2010-11-23	2	11.5	B
  1	2020-02-29	3	12.3	NA
  2	NA	4	-13	D

  3 rows × 4 columns

• A python range:

  DT[range(1, 3), :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2010-11-23	2	11.5	B
  1	2020-02-29	3	12.3	NA

  2 rows × 4 columns

• A generator expression:

  DT[(num for num in range(4)), :]

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2000-01-05	1	10	A
  1	2010-11-23	2	11.5	B
  2	2020-02-29	3	12.3	NA
  3	NA	4	-13	D

  4 rows × 4 columns

If the position passed to i does not exist, an error is raised

DT[(num for num in range(7)), :]

ValueError: Index 4 is invalid for a Frame with 4 rows

The set sequence is not acceptable in the i or j sections.

Except for lists/tuples, all the other sequence types passed into the i section can only contain positive integers.

Via booleans

When selecting rows via boolean sequence, the length of the sequence must be the same as the number of rows:

DT[[True, True, False, False], :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B

2 rows × 4 columns

DT[(n%2 == 0 for n in range(DT.nrows)), :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2020-02-29	3	12.3	NA

2 rows × 4 columns

Via slicing

Slicing works similarly to slicing a python list:

DT[1:3, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2010-11-23	2	11.5	B
1	2020-02-29	3	12.3	NA

2 rows × 4 columns

DT[::-1, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	NA	4	-13	D
1	2020-02-29	3	12.3	NA
2	2010-11-23	2	11.5	B
3	2000-01-05	1	10	A

4 rows × 4 columns

DT[-1:-3:-1, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	NA	4	-13	D
1	2020-02-29	3	12.3	NA

2 rows × 4 columns

Slicing is also possible with the slice function:

DT[slice(1, 3), :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2010-11-23	2	11.5	B
1	2020-02-29	3	12.3	NA

2 rows × 4 columns

It is possible to select rows with multiple slices. Let’s increase the number of rows in the Frame:

DT = dt.repeat(DT, 3) DT

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D
4	2000-01-05	1	10	A
5	2010-11-23	2	11.5	B
6	2020-02-29	3	12.3	NA
7	NA	4	-13	D
8	2000-01-05	1	10	A
9	2010-11-23	2	11.5	B
10	2020-02-29	3	12.3	NA
11	NA	4	-13	D

12 rows × 4 columns

DT[[slice(1, 3), slice(5, 8)], :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2010-11-23	2	11.5	B
1	2020-02-29	3	12.3	NA
2	2010-11-23	2	11.5	B
3	2020-02-29	3	12.3	NA
4	NA	4	-13	D

5 rows × 4 columns

DT[[slice(5, 8), 1, 3, slice(10, 12)], :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2010-11-23	2	11.5	B
1	2020-02-29	3	12.3	NA
2	NA	4	-13	D
3	2010-11-23	2	11.5	B
4	NA	4	-13	D
5	2020-02-29	3	12.3	NA
6	NA	4	-13	D

7 rows × 4 columns

Via f-expressions

f-expressions return booleans that can be used to filter/select the appropriate rows:

DT[f.dates < dt.Frame([date(2020,1,1)]), :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B

2 rows × 4 columns

DT[f.integers % 2 != 0, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2020-02-29	3	12.3	NA

2 rows × 4 columns

DT[(f.integers == 3) & (f.strings == None), ...]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2020-02-29	3	12.3	NA
1	2020-02-29	3	12.3	NA
2	2020-02-29	3	12.3	NA

3 rows × 4 columns

Selection is possible via the data types:

DT[f[float] < 1, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	NA	4	-13	D
1	NA	4	-13	D
2	NA	4	-13	D

3 rows × 4 columns

DT[dt.rowsum(f[int, float]) > 12, :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2010-11-23	2	11.5	B
1	2020-02-29	3	12.3	NA
2	2010-11-23	2	11.5	B
3	2020-02-29	3	12.3	NA
4	2010-11-23	2	11.5	B
5	2020-02-29	3	12.3	NA

6 rows × 4 columns

Select rows and columns

Specific selections can occur in rows and columns simultaneously:

DT[0, slice(1, 3)]

	integers	floats
	int32	float64
0	1	10

1 row × 2 columns

DT[2 : 6, ["i" in name for name in DT.names]]

	integers	strings
	int32	str32
0	3	NA
1	4	D
2	1	A
3	2	B

4 rows × 2 columns

DT[f.integers > dt.mean(f.floats) - 3, f['strings' : 'integers']]

	strings	floats	integers
	str32	float64	int32
0	NA	12.3	3
1	D	-13	4
2	NA	12.3	3
3	D	-13	4
4	NA	12.3	3
5	D	-13	4

6 rows × 3 columns

Single value access

Passing single integers into the i and j sections returns a scalar value:

DT[0, 0]

datetime.date(2000, 1, 5)

DT[0, 2]

10.0

DT[-3, 'strings']

'B'

Deselect rows/columns

Deselection of rows/columns is possible via list comprehension/generator expression

• Deselect a single column/row:

  # The list comprehension returns the specific column names DT[:, [name for name in DT.names if name != "integers"]]

  	dates	floats	strings
  	date32	float64	str32
  0	2000-01-05	10	A
  1	2010-11-23	11.5	B
  2	2020-02-29	12.3	NA
  3	NA	-13	D
  4	2000-01-05	10	A
  5	2010-11-23	11.5	B
  6	2020-02-29	12.3	NA
  7	NA	-13	D
  8	2000-01-05	10	A
  9	2010-11-23	11.5	B
  10	2020-02-29	12.3	NA
  11	NA	-13	D

  12 rows × 3 columns

  # A boolean sequence is returned in the list comprehension DT[[num != 5 for num in range(DT.nrows)], 'dates']

  	dates
  	date32
  0	2000-01-05
  1	2010-11-23
  2	2020-02-29
  3	NA
  4	2000-01-05
  5	2020-02-29
  6	NA
  7	2000-01-05
  8	2010-11-23
  9	2020-02-29
  10	NA

  11 rows × 1 column

• Deselect multiple columns/rows:

  DT[:, [name not in ("integers", "dates") for name in DT.names]]

  	floats	strings
  	float64	str32
  0	10	A
  1	11.5	B
  2	12.3	NA
  3	-13	D
  4	10	A
  5	11.5	B
  6	12.3	NA
  7	-13	D
  8	10	A
  9	11.5	B
  10	12.3	NA
  11	-13	D

  12 rows × 2 columns

  DT[(num not in range(3, 8) for num in range(DT.nrows)), ['integers', 'floats']]

  	integers	floats
  	int32	float64
  0	1	10
  1	2	11.5
  2	3	12.3
  3	1	10
  4	2	11.5
  5	3	12.3
  6	4	-13

  7 rows × 2 columns

  DT[:, [num not in (2, 3) for num in range(DT.ncols)]]

  	dates	integers
  	date32	int32
  0	2000-01-05	1
  1	2010-11-23	2
  2	2020-02-29	3
  3	NA	4
  4	2000-01-05	1
  5	2010-11-23	2
  6	2020-02-29	3
  7	NA	4
  8	2000-01-05	1
  9	2010-11-23	2
  10	2020-02-29	3
  11	NA	4

  12 rows × 2 columns

  # an alternative to the previous example DT[:, [num not in (2, 3) for num, _ in enumerate(DT.names)]]

  	dates	integers
  	date32	int32
  0	2000-01-05	1
  1	2010-11-23	2
  2	2020-02-29	3
  3	NA	4
  4	2000-01-05	1
  5	2010-11-23	2
  6	2020-02-29	3
  7	NA	4
  8	2000-01-05	1
  9	2010-11-23	2
  10	2020-02-29	3
  11	NA	4

  12 rows × 2 columns

• Deselect by data type:

  # This selects columns that are not numeric DT[2:7, [not coltype.is_numeric for coltype in DT.types]]

  	dates	strings
  	date32	str32
  0	2020-02-29	NA
  1	NA	D
  2	2000-01-05	A
  3	2010-11-23	B
  4	2020-02-29	NA

  5 rows × 2 columns

Slicing could be used to exclude rows/columns. The code below excludes rows from position 3 to 6:

DT[[slice(None, 3), slice(7, None)], :]

	dates	integers	floats	strings
	date32	int32	float64	str32
0	2000-01-05	1	10	A
1	2010-11-23	2	11.5	B
2	2020-02-29	3	12.3	NA
3	NA	4	-13	D
4	2000-01-05	1	10	A
5	2010-11-23	2	11.5	B
6	2020-02-29	3	12.3	NA
7	NA	4	-13	D

8 rows × 4 columns

Columns can also be deselected via the remove() method, where the column name, column position, or data type is passed to the f symbol:

DT[:, f[:].remove(f.dates)]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D
4	1	10	A
5	2	11.5	B
6	3	12.3	NA
7	4	-13	D
8	1	10	A
9	2	11.5	B
10	3	12.3	NA
11	4	-13	D

12 rows × 3 columns

DT[:, f[:].remove(f[0])]

	integers	floats	strings
	int32	float64	str32
0	1	10	A
1	2	11.5	B
2	3	12.3	NA
3	4	-13	D
4	1	10	A
5	2	11.5	B
6	3	12.3	NA
7	4	-13	D
8	1	10	A
9	2	11.5	B
10	3	12.3	NA
11	4	-13	D

12 rows × 3 columns

DT[:, f[:].remove(f[1:3])]

	dates	strings
	date32	str32
0	2000-01-05	A
1	2010-11-23	B
2	2020-02-29	NA
3	NA	D
4	2000-01-05	A
5	2010-11-23	B
6	2020-02-29	NA
7	NA	D
8	2000-01-05	A
9	2010-11-23	B
10	2020-02-29	NA
11	NA	D

12 rows × 2 columns

DT[:, f[:].remove(f['strings':'integers'])]

	dates
	date32
0	2000-01-05
1	2010-11-23
2	2020-02-29
3	NA
4	2000-01-05
5	2010-11-23
6	2020-02-29
7	NA
8	2000-01-05
9	2010-11-23
10	2020-02-29
11	NA

12 rows × 1 column

DT[:, f[:].remove(f[int, float])]

	dates	strings
	date32	str32
0	2000-01-05	A
1	2010-11-23	B
2	2020-02-29	NA
3	NA	D
4	2000-01-05	A
5	2010-11-23	B
6	2020-02-29	NA
7	NA	D
8	2000-01-05	A
9	2010-11-23	B
10	2020-02-29	NA
11	NA	D

12 rows × 2 columns

DT[:, f[:].remove(f[:])]

0
1
2
3
4
5
6
7
8
9
10
11

12 rows × 0 columns

Delete rows/columns

To actually delete a row (or a column), use the del statement; this is an in-place operation, and as such no reassignment is needed

• Delete multiple rows:

  del DT[3:7, :] DT

  	dates	integers	floats	strings
  	date32	int32	float64	str32
  0	2000-01-05	1	10	A
  1	2010-11-23	2	11.5	B
  2	2020-02-29	3	12.3	NA
  3	NA	4	-13	D
  4	2000-01-05	1	10	A
  5	2010-11-23	2	11.5	B
  6	2020-02-29	3	12.3	NA
  7	NA	4	-13	D

  8 rows × 4 columns

• Delete a single row:

  del DT[3, :] DT

  	dates	integers	floats
  	date32	int32	float64
  0	2000-01-05	1	10
  1	2010-11-23	2	11.5
  2	2020-02-29	NA	NA
  3	2000-01-05	NA	NA
  4	2010-11-23	2	11.5
  5	2020-02-29	3	12.3
  6	NA	4	-13

  7 rows × 3 columns

• Delete a column:

  del DT['strings'] DT

  	dates	integers	floats
  	date32	int32	float64
  0	2000-01-05	1	10
  1	2010-11-23	2	11.5
  2	2020-02-29	3	12.3
  3	NA	4	-13
  4	2000-01-05	1	10
  5	2010-11-23	2	11.5
  6	2020-02-29	3	12.3
  7	NA	4	-13

  8 rows × 3 columns

• Delete multiple columns:

  del DT[:, ['dates', 'floats']] DT

  	integers
  	int32
  0	1
  1	2
  2	NA
  3	NA
  4	2
  5	3
  6	4

  7 rows × 1 column