<jupyter_start><jupyter_text>[Module 1.0] 원본 데이터 파일 내용 확인이 노트북은 원본 데이터의 각 테이블의 일부를 보여서, 어떤 데이터가 있는지를 확인 합니다.전체 테이블은 아래와 같습니다.- Customer- Order- Order Item- Product- Product Category- Seller- Order Payment- Order Review- GeoLocationBrazilian E-Commerce Public Dataset by Olist- https://towardsdatascience.com/xgboost-for-multi-class-classification-799d96bcd368<jupyter_code>import pandas as pd
pd.options.display.max_rows = 5
from IPython.display import display

def show_df(file_name):
    df = pd.read_csv(file_name)
    display(df)
    return df<jupyter_output><empty_output><jupyter_text>Customer<jupyter_code>file_name = 'data/olist_customers_dataset.csv'    
df = show_df(file_name)     
df.nunique()<jupyter_output><empty_output><jupyter_text>Order<jupyter_code>file_name = 'data/olist_orders_dataset.csv'    
df = show_df(file_name)    
pd.options.display.max_rows = 10
df.nunique()
df.order_status.value_counts()<jupyter_output><empty_output><jupyter_text>Order Item<jupyter_code>file_name = 'data/olist_order_items_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Product<jupyter_code>file_name = 'data/olist_products_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Product Category<jupyter_code>file_name = 'data/product_category_name_translation.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Seller<jupyter_code>file_name = 'data/olist_sellers_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Order Payment<jupyter_code>file_name = 'data/olist_order_payments_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Order Review<jupyter_code>file_name = 'data/olist_order_reviews_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output><jupyter_text>Geolocation<jupyter_code>file_name = 'data/olist_geolocation_dataset.csv'    
df = show_df(file_name)    
df.nunique()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11015325670498084, 'fraction_numerical': 0.006226053639846743, 'mean_word_length': 6.823970037453184, 'pattern_counts': {'":': 0, '<': 41, '<?xml version=': 0, '>': 41, 'https://': 1, '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': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '31513', 'license': 'MIT', 'n_tokens_mistral': 880, 'n_tokens_neox': 906, 'n_words': 100, 'path': 'brazil_ecommerce/1.0.Show_Raw_Data.ipynb', 'repo_name': 'gonsoomoon-ml/predict-delivery-time', 'size': '2088'}