Today I learned that Polars allows non-strict vertical concatenation of dataframes with the parameter how="vertical".

Implicit casting in dataframe concatenation

Polars dataframes have an associated schema, a piece of metadata that describes the columns and their types:

import polars as pl

close_family = pl.DataFrame(
    {
        "name": ["John", "Anne"],
        "age": [27, 35],
    }
)

print(close_family.schema)
# Schema({'name': String, 'age': Int64})

By default, Polars uses the type pl.Int64 when a column contains integers. However, since ages don't tend to get very big, and because they're never negative, it's enough to use the data type pl.UInt8:

extended_family = pl.DataFrame(
    {
        "name": ["Rob", "Jessica"],
        "age": [47, 28],
    },
    schema_overrides={
        "age": pl.UInt8,
    },
)

print(extended_family.schema)
# Schema({'name': String, 'age': UInt8})

Now, if I try to use pl.concat to concatenate these two vertically, Polars complains because the columns age in both dataframes have different types:

pl.concat([close_family, extended_family], how="vertical")

polars.exceptions.SchemaError: type UInt8 is incompatible with expected type Int64

Polars is very strict about data types (and rightfully so) and that is why it complains. In many situations, you can ask Polars to be more lenient by specifying strict=False but pl.concat does not support this argument. Instead, today I learned that it supports how="relaxed"¹:

pl.concat([close_family, extended_family], how="vertical_relaxed")

shape: (4, 2)
┌─────────┬─────┐
│ name    ┆ age │
│ ---     ┆ --- │
│ str     ┆ i64 │
╞═════════╪═════╡
│ John    ┆ 27  │
│ Anne    ┆ 35  │
│ Rob     ┆ 47  │
│ Jessica ┆ 28  │
└─────────┴─────┘

I don't know for sure, but I'm guessing the reason we have how="vertical_relaxed" instead of strict=False is because the parameter strict is completely irrelevant for the other types of concatenation supported by pl.concat, so the Polars devs decided to fold that functionality into the parameter how.

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