Set default Check n_failure_cases to None and add documentation for n_failure_cases behavior #577

Describe the bug
pa.errors.SchemaErrors.failure_cases only returns the first 10 failure_cases

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of pandera. 0.6.5
• (optional) I have confirmed this bug exists on the master branch of pandera.

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

import pandas as pd
import pandera as pa

df = pd.DataFrame({'n': range(20)})
schema = pa.DataFrameSchema({
    "n": pa.Column(pa.Int, pa.Check.greater_than(30))
})
try:
    schema.validate(df, lazy=True)
except pa.errors.SchemaErrors as err:
    print(err.failure_cases)

Expected behavior

err.failure_cases should be 20 lines long

Usage

Using pandera for data validation at the start of data processing. When errors appear I want the clean data to be able to continue the pipeline, while corrupted data is removed from the dataframe and indexes stored somewhere so I can fix the issue and plan a recovery pipeline run later.
Was trying the following code when I discovered the issue. (I've got MultiIndexes)

import pandas as pd
import pandera as pa

df = pd.DataFrame({'n': range(20), 'a': range(20), 'b': range(20)}).set_index(['a', 'b'])
schema = pa.DataFrameSchema({
   "n": pa.Column(pa.Int, pa.Check.greater_than(30))
})

try:
   schema.validate(df, lazy=True)
except pa.errors.SchemaErrors as err:
   f = err.failure_cases  # dataframe of schema errors
   d = err.data  # invalid dataframe
   df_failures = pd.DataFrame(err.failure_cases['index'].apply(literal_eval).values.tolist(), columns=err.data.index.names)
   print(df_failures)

df_failures could contain the indexes that I need to remove the corrupted data from the dataframe and store to prepare the recovery run, but it only has the first 10.