GT.cols_move(self, columns, after)
Move one or more columns.
On those occasions where you need to move columns this way or that way, we can make use of the cols_move() method. While it’s true that the movement of columns can be done upstream of Great Tables, it is much easier and less error prone to use the method provided here. The movement procedure here takes one or more specified columns (in the columns argument) and places them to the right of a different column (the after argument). The ordering of the columns to be moved is preserved, as is the ordering of all other columns in the table.
The columns supplied in columns must all exist in the table and none of them can be in the after argument. The after column must also exist and only one column should be provided here. If you need to place one more or columns at the beginning of the column series, the cols_move_to_start() method should be used. Similarly, if those columns to move should be placed at the end of the column series then use cols_move_to_end().
| Name | Type | Description | Default |
|---|---|---|---|
columns |
SelectExpr |
The columns to target. Can either be a single column name or a series of column names provided in a list. | required |
after |
str |
The column after which the columns should be placed. This can be any column name that exists in the table. |
required |
| Type | Description |
|---|---|
| GT | The GT object is returned. This is the same object that the method is called on so that we can facilitate method chaining. |
Let’s use the countrypops dataset to create a table. We’ll choose to position the population column after the country_name column by using the cols_move() method.
from great_tables import GT
from great_tables.data import countrypops
countrypops_mini = countrypops.loc[countrypops["country_name"] == "Japan"][
["country_name", "year", "population"]
].tail(5)
(
GT(countrypops_mini)
.cols_move(
columns="population",
after="country_name"
)
)| country_name | population | year |
|---|---|---|
| Japan | 126811000 | 2018 |
| Japan | 126633000 | 2019 |
| Japan | 126261000 | 2020 |
| Japan | 125681593 | 2021 |
| Japan | 125124989 | 2022 |