verticapy.vDataFrame.flat_vmap¶

Flatten the selected VMap. A new vDataFrame is returned.

Warning

This function might have a long runtime and can make your vDataFrame less performant. It makes many calls to the MAPLOOKUP function, which can be slow if your VMap is large.

Parameters¶

vmap_col: SQLColumns, optional: List of VMap columns to flatten.
limit: int, optional: Maximum number of keys to consider for each VMap. Only the most occurent keys are used.
exclude_columns: SQLColumns, optional: List of VMap columns to exclude.

Returns¶

vDataFrame: object with the flattened VMaps.

Examples¶

Let’s begin by importing VerticaPy.

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, let’s generate a dataset that has a VMAP in one column:

vdf = vp.vDataFrame(
    {
        "id": [1],
        "team": ['{"country" : "France", "region" : "IDF"}'],
    }
)

	123 id Integer 100%	Abc Varchar(40) 100%
1	1

Note

We can observe that our string follows the structure of a JSON. VerticaPy will automatically parse it and determine how to extract the elements.

In order to utilize Vertica Flex Table auto-parsing, it is necessary to convert the string column ‘team’ to a vmap.

vdf["team"].astype('vmap')

	123 id Integer 100%	🛠 Vmap 100%
1	1

Now we can flatten the vmap:

vdf.flat_vmap()

	123 id Integer 100%	...	🛠 Vmap(284) 100%	Abc team.country Long varchar(284) 100%
1	1	...		France

Note

This function is applicable for flattening Flex tables VMAP. However, it is advisable to store the final result in a table, as the computations involved can be resource-intensive.