How can I concat polars dataframes that have different columns

In pandas it happens automatically, just by calling pd.concat([df1, df2, df3]) and the frame that didn't have the column previously just gets a column filled with NaNs.

In polars I get a 'shape error' with the message that the columns differ (11 cols in df1 vs 12 cols in df2).

Polars cares about schema correctness by default in operations and prefers throwing an error above silently succeeding as it might indicate a bug in your program.

If you want polars to add the columns, add the kwarg how="diagonal" to pl.concat

df_a = pl.DataFrame({
    "a": [1, 2, 3],
    "b": [True, None, False],
})


df_b = pl.DataFrame({
    "a": [4, 5],
    "c": ["bar", "ham"]
})


pl.concat([df_a, df_b], how="diagonal")

shape: (5, 3)
┌─────┬───────┬──────┐
│ a   ┆ b     ┆ c    │
│ --- ┆ ---   ┆ ---  │
│ i64 ┆ bool  ┆ str  │
╞═════╪═══════╪══════╡
│ 1   ┆ true  ┆ null │
│ 2   ┆ null  ┆ null │
│ 3   ┆ false ┆ null │
│ 4   ┆ null  ┆ bar  │
│ 5   ┆ null  ┆ ham  │
└─────┴───────┴──────┘