Columns Info#

This notebook provides an overview for using and understanding the columns info check.


What are columns info#

The ColumnsInfo check returns the role and logical type of each column (e.g. date, categorical, numerical etc.).


import numpy as np
import pandas as pd

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import ColumnsInfo

Generating data#

num_fe = np.random.rand(500)
cat_fe = np.random.randint(3, size=500)
num_col = np.random.rand(500)
date = range(1635693229, 1635693729)
index = range(500)
data = {'index': index, 'date': date, 'a': cat_fe, 'b': num_fe, 'c': num_col, 'label': cat_fe}
df = pd.DataFrame.from_dict(data)

dataset = Dataset(df, label='label', datetime_name='date', index_name='index', features=['a', 'b'], cat_features=['a'])

Running columns info check#

check = ColumnsInfo()
Columns Info

Total running time of the script: (0 minutes 0.015 seconds)

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