<jupyter_start><jupyter_text>Data Anaysis  duration_precipitation, daily_precipitation (lots of null filled with zero)<jupyter_code>from util.build_dataset import build_origin_weather

origin_w, o_key = build_origin_weather(date="201908")
preci = origin_w[["강수 계속시간(hr)", "일강수량(mm)"]]
preci.head(3)
from util.handle_null import NullHandler
filled = NullHandler.fillna_with_zero(precipitation)
filled.head(3)
from util.visualize import show_hist
show_hist([preci["강수 계속시간(hr)"], filled["강수 계속시간(hr)"]], name="precipitation")
show_hist([preci["일강수량(mm)"], filled["일강수량(mm)"]], name="precipitation")
show_hist([preci["일강수량(mm)"],
from util.build_dataset import build_process_price, build_origin_price

p_df, p_key = build_origin_price(date="201908", prefix="clean")
p_df.info()
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
p_df.groupby("품목명").count().sort_values(by="조사일자")
from util.transform import CustomTransformer
from sklearn.preprocessing import OneHotEncoder

e = OneHotEncoder()
result =e.fit_transform(p_df["품목명"].to_frame())
print(result)
from util.visualize import show_hist

show_hist([filter_origin_df["일강수량(mm)"], clean_origin_df["일강수량(mm)"]], name="amount")
show_hist([filter_origin_df["일강수량(mm)"], clean_origin_df["일강수량(mm)"]], name="amount")
show_hist(ser=filter_origin_df["강수 계속시간(hr)"], name="time")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15496872828353023, 'fraction_numerical': 0.01667824878387769, 'mean_word_length': 11.0, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '417546', 'license': 'MIT', 'n_tokens_mistral': 579, 'n_tokens_neox': 634, 'n_words': 88, 'path': 'src/main/python/data_analysis.ipynb', 'repo_name': 'meowpunch/bobsim-research', 'size': '1439'}