`validate` is slow with when coercing several hundreds columns.

Describe the bug

Validating against a SchemaModel with several hundred is used with coerce takes a lot of time, even if the dataframe is already valid. It doesn’t occur when there is no coerce.

[x] I have checked that this issue has not already been reported.
[x] I have confirmed this bug exists on the latest version of pandera.
[ ] (optional) I have confirmed this bug exists on the master branch of pandera.

Code Sample, a copy-pastable example

import pandas as pd
import pandera as pa
import numpy as np
from pandera.typing import Series

class TestCoerce(pa.SchemaModel):
    a: Series[float] = pa.Field(alias="a\d+", regex=True, coerce=True)

class TestNoCoerce(pa.SchemaModel):
    a: Series[float] = pa.Field(alias="a\d+", regex=True, coerce=False)

def gen_df(
    value: float = 1.618,
    col_number: int = 40,
    row_number: int = int(1e6),
):

    return pd.DataFrame(
        {
            f"a{i}": np.full(row_number, value)
            for i in range(col_number)
        }
    )

df = gen_df()
TestCoerce.validate(df)

In this gist you will find a script that compares execution time with and without coerce : https://gist.github.com/koalp/0e70303c014712a6f7f790b5743482a3

Expected behavior

That the coercion doesn’t take so much time when the dtype is already good. It would be even better to not be slow when all the columns must be converted.

Desktop (please complete the following information):

OS: linux
Python 3.9, 3.10
Pandera 0.13.4

Additional context

After running benchmarks, I found out that the __setattr__ function¹ from pandas (replacing a column) takes a lot of time to run. (python 3.9) If I modify pandera to only setattr it the result from try_coercion differs from the previous column it solves my issue as I currently only have 1 or less column that need to be changed (wrong dtype). However, it isn’t a generic solution as it doesn’t help when a lot of columns have a wrong dtype.

On discord, a modification was suggested:

I think an alternative and potentially faster solution would be to check if the dtype of obj[matched_colname] is the same as col_schema.dtype. If so, then coercion isn't necessary. If not, then apply coercion and reassign the column.

unionai-oss / pandera