# !pip install deepchecks

import pandas as pd
from deepchecks.tabular.checks import MixedNulls, TrainTestFeatureDrift
# from deepchecks import Suite

!pip show pandas | grep "Version"

Version: 2.1.4

from deepchecks import Suite

import deepchecks
print(deepchecks.__version__)

0.18.1

# Sample data
train_data = pd.DataFrame({
    'age': [25, 35, 45, None, 50],
    'salary': [50000, 60000, None, 80000, 90000],
    'city': ['New York', 'Los Angeles', 'New York', None, 'San Francisco']
})

test_data = pd.DataFrame({
    'age': [30, 40, 50, None, 55],
    'salary': [55000, 65000, 70000, None, 95000],
    'city': ['Chicago', 'Los Angeles', 'San Francisco', 'New York', None]
})

# Running individual checks
null_check = MixedNulls().run(train_data)
print(null_check)

deepchecks - WARNING - Received a "pandas.DataFrame" instance. It is recommended to pass a "deepchecks.tabular.Dataset" instance by initializing it with the data and metadata, for example by doing "Dataset(dataframe, label=label, cat_features=cat_features)"
deepchecks - WARNING - It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data. 3 categorical features were inferred.: age, salary, city


Mixed Nulls: {'n_samples': 5, 'columns': {'age': {'math.nan': {'count': 1, 'percent': 0.2}}, 'salary': {'math.nan': {'count': 1, 'percent': 0.2}}, 'city': {'None': {'count': 1, 'percent': 0.2}}}, 'feature_importance': age       NaN
salary    NaN
city      NaN
dtype: object}