Polars: exceptions.ShapeError: unable to vstack, dtypes for column "a" don't match: `f64` and `i64`

Question

Unable to concatenate the polars dataframes with data types of a column being f64 and i64.

I have two pandas dataframes df1, df2 in pandas, where column 'a' in df1 is float and in df2 is int, when I perform pd.concat([df1, df2]) it works.

However, when I try the same operation on polars dataframes, it is throwing the following error: exceptions.ShapeError: unable to vstack, dtypes for column "a" don't match: f64 and i64

pandas code:

import pandas as pd

df1 = pd.DataFrame({'a': [1.0, 2.0, 3.0], 'b': [1, 2, 3]})
df2 = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})

pd.concat() produces the following output:

pd.concat([pd_df1, pd_df2])
           a  b
0 1.00000000  1
1 2.00000000  2
2 3.00000000  3
0 1.00000000  1
1 2.00000000  2
2 3.00000000  3

polars code:

import polars as pl

df1 = pl.DataFrame({'a': [1.0, 2.0, 3.0], 'b': [1, 2, 3]})
df2 = pl.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})

pl.concat() is producing the error, unlike pandas.

pl.concat([df1, df2])
Traceback (most recent call last):
  File "C:\Users\user\.conda\envs\dev\lib\site-packages\IPython\core\interactiveshell.py", line 3362, in run_code
    async def run_code(self, code_obj, result=None, *, async_=False):
  File "<ipython-input-16-4301449ba376>", line 1, in <cell line: 1>
    pl.concat([df1, df2])
  File "C:\Users\user\.conda\envs\dev\lib\site-packages\polars\functions\eager.py", line 22, in concat
    def concat(
exceptions.ShapeError: unable to vstack, dtypes for column "a" don't match: `f64` and `i64`

Here, I am fetching the data from database for various tables and creating a list of dataframes before concatenating them. Kindly help me with a solution where I could have the feasibility of not hard coding the column name in such scenarios.

Answer 1

You can use the vertical_relaxed strategy.

pl.concat([df1, df2], how="vertical_relaxed")

shape: (6, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ f64 ┆ i64 │
╞═════╪═════╡
│ 1.0 ┆ 1   │
│ 2.0 ┆ 2   │
│ 3.0 ┆ 3   │
│ 1.0 ┆ 1   │
│ 2.0 ┆ 2   │
│ 3.0 ┆ 3   │
└─────┴─────┘