verticapy.sql.functions.overlaps#

Evaluates two time periods and returns true when they overlap, false otherwise.

Parameters#

start0: SQLExpression: DATE, TIME, or TIMESTAMP/TIMESTAMPTZ value that specifies the beginning of a time period.
end0: SQLExpression: DATE, TIME, or TIMESTAMP/TIMESTAMPTZ value that specifies the end of a time period.
start1: SQLExpression: DATE, TIME, or TIMESTAMP/TIMESTAMPTZ value that specifies the beginning of a time period.
end1: SQLExpression: DATE, TIME, or TIMESTAMP/TIMESTAMPTZ value that specifies the end of a time period.

Returns#

StringSQL: SQL string.

Examples#

First, let’s import the vDataFrame in order to create a dummy dataset.

from verticapy import vDataFrame

Now, let’s import the VerticaPy SQL functions.

import verticapy.sql.functions as vpf

We can now build a dummy dataset.

df = vDataFrame(
    {
        "start0": ['11-03-1993'],
        "end0": ['12-03-1993'],
        "start1": ['11-30-1993'],
        "end1": ['11-30-1994'],
    },
)
df["start0"].astype("timestamp")
df["start1"].astype("timestamp")
df["end0"].astype("timestamp")
df["end1"].astype("timestamp")

Now, let’s go ahead and apply the function.

df["overlaps"] = vpf.overlaps(df["start0"], df["end0"], df["start1"], df["end1"])
display(df)

	📅 start0 Timestamp 100%	...	📅 end1 Timestamp 100%	010 overlaps Boolean 100%
1	1993-11-03 00:00:00	...	1994-11-30 00:00:00	✅

Note

It’s crucial to utilize VerticaPy SQL functions in coding, as they can be updated over time with new syntax. While SQL functions typically remain stable, they may vary across platforms or versions. VerticaPy effectively manages these changes, a task not achievable with pure SQL.