<jupyter_start><jupyter_text>Datasets Description: Train, Test<jupyter_code>from pandas import set_option, reset_option

from contest.data.sets import load
from contest.data.descriptors import DataDescriptor

set_option(
    'display.max_rows', None,
    'display.max_columns', None,
)<jupyter_output><empty_output><jupyter_text>Load<jupyter_code>keys = ['train', 'test']

data = load(keys, info=True)
descriptor = DataDescriptor(data)<jupyter_output>train: train.csv
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1460 entries, 1 to 1460
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MSSubClass     1460 non-null   int64  
 1   MSZoning       1460 non-null   object 
 2   LotFrontage    1201 non-null   float64
 3   LotArea        1460 non-null   int64  
 4   Street         1460 non-null   object 
 5   Alley          91 non-null     object 
 6   LotShape       1460 non-null   object 
 7   LandContour    1460 non-null   object 
 8   Utilities      1460 non-null   object 
 9   LotConfig      1460 non-null   object 
 10  LandSlope      1460 non-null   object 
 11  Neighborhood   1460 non-null   object 
 12  Condition1     1460 non-null   object 
 13  Condition2     1460 non-null   object 
 14  BldgType       1460 non-null   object 
 15  HouseStyle     1460 non-null   object 
 16  OverallQual    1460 non-null   int64  
 17  OverallCond    1460 non-null   int6[...]<jupyter_text>Types<jupyter_code>descriptor.types(transformers=True)<jupyter_output><empty_output><jupyter_text>Nulls<jupyter_code>descriptor.nulls(transformers=True)<jupyter_output><empty_output><jupyter_text>Uniques<jupyter_code>descriptor.uniques(transformers=True)
reset_option('display.*')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0898876404494382, 'fraction_numerical': 0.06741573033707865, 'mean_word_length': 3.0755148741418763, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '253038', 'license': 'MIT', 'n_tokens_mistral': 683, 'n_tokens_neox': 595, 'n_words': 138, 'path': 'exploration/descriptions/2-datasets.ipynb', 'repo_name': 'dmitry-s-danilov/kaggle-house-prices-advanced-regression-techniques', 'size': '1780'}