Describe the bug

A ValueError is raised in pandas when a pandas.DataFrame object with MultiIndexed Columns is lazily validated (using the parameter lazy=True) by a pandera.DataFrameSchema object, and there is at least one failed check for the columns.

Running the code below, the following exception is raised:

Traceback (most recent call last):
  line 18, in <module>
    print(schema.validate(df, lazy=True))
  File "Y:\Python39\lib\site-packages\pandera\schemas.py", line 613, in validate
    raise errors.SchemaErrors(
  File "Y:\Python39\lib\site-packages\pandera\errors.py", line 87, in __init__
    error_counts, failure_cases = self._parse_schema_errors(schema_errors)
  File "Y:\Python39\lib\site-packages\pandera\errors.py", line 172, in _parse_schema_errors
    failure_cases = err.failure_cases.assign(
  File "Y:\Python39\lib\site-packages\pandas\core\frame.py", line 3699, in assign
    data[k] = com.apply_if_callable(v, data)
  File "Y:\Python39\lib\site-packages\pandas\core\frame.py", line 3044, in __setitem__
    self._set_item(key, value)
  File "Y:\Python39\lib\site-packages\pandas\core\frame.py", line 3120, in _set_item
    value = self._sanitize_column(key, value)
  File "Y:\Python39\lib\site-packages\pandas\core\frame.py", line 3768, in _sanitize_column
    value = sanitize_index(value, self.index)
  File "Y:\Python39\lib\site-packages\pandas\core\internals\construction.py", line 747, in sanitize_index
    raise ValueError(
ValueError: Length of values (2) does not match length of index (1)

Checking the line 172 in errors.py in pandera, i.e.

failure_cases = err.failure_cases.assign(
                    schema_context=err.schema.__class__.__name__,
                    check=check_identifier,
                    check_number=err.check_index,
                    column=column,
                )

It could be seen that the MultiIndexed Column with the name ("foo", "baz") , which has the type tuple, would not be interpreted as a single value by pandas, which then failed to be broadcasted to err.failure_cases and causing the ValueError from pandas during the assign method call.

• I have checked that this issue has not already been reported.
• 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
from pandera import Column, DataFrameSchema

schema = DataFrameSchema({
    ("foo", "bar"): Column(int),
    ("foo", "baz"): Column(int)
})

df = pd.DataFrame({
    ("foo", "bar"): [1, 2, 3],
    ("foo", "baz"): ["a", "b", "c"],
})

print(schema.validate(df, lazy=True))

Expected behavior

A pandera.SchemasError should be raised with the type mismatch on the column ("foo", "baz") logged, which has the value ("foo", "baz") in the column Column.

Desktop:

• OS: Windows 10
• Version: Python 3.9.0, with pandera 0.7.0, pandas 1.1.4 installed

Additional context

If we change the code above to

import pandas as pd
from pandera import Column, DataFrameSchema, Check

schema = DataFrameSchema({
    ("foo", "bar"): Column(int, checks=Check(lambda s: s == 1)),
    ("foo", "baz"): Column(str, name="b")
})

df = pd.DataFrame({
    ("foo", "bar"): [1, 2, 3],
    ("foo", "baz"): ["a", "b", "c"],
})

try:
    schema.validate(df, lazy=True)
except Exception as e:
    print(e.failure_cases)

The output would be

  schema_context column     check  check_number  failure_case  index
0         Column    foo  <lambda>             0             2      1
1         Column    bar  <lambda>             0             3      2

which shows that the column name ("foo", "bar") is incorrectly interpreted as a pandas.Series-like object and treated as a column object when calling the method assign from err.failure_cases.

Potential Fix

A band-aid fix would be manually broadcast the input for the column Column before assigning the column to err.failure_cases, i.e.

failure_cases = err.failure_cases.assign(
                    schema_context=err.schema.__class__.__name__,
                    check=check_identifier,
                    check_number=err.check_index,
                    column=[column] * len(err.failure_cases),
                )

which seems to have fixed the problem.