pre_treat_data.rtf

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\f0\fs24 \cf0 # \uc0\u22312 \u26412 \u22320  Mac \u19978 \u36816 \u34892 \
import pandas as pd\
\
# \uc0\u35835 \u21462 \u21407 \u22987 \u25991 \u20214 \
df = pd.read_csv('data/EURUSD2022_2025.csv')\
\
# \uc0\u20551 \u35774 \u26085 \u26399 \u21015 \u21517 \u20026  'Date'\
if 'Date' in df.columns:\
    # \uc0\u31227 \u38500  GMT \u37096 \u20998 \u24182 \u26631 \u20934 \u21270 \u26684 \u24335 \
    df['Date'] = df['Date'].str.replace(' GMT', '', regex=False)\
    df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y %H:%M:%S.%f %z', utc=True)\
    \
    # \uc0\u37325 \u32622 \u32034 \u24341 \
    df.set_index('Date', inplace=True)\
    \
    # \uc0\u20445 \u23384 \u26631 \u20934 \u21270 \u25991 \u20214 \
    df.to_csv('data/EURUSD_standard.csv')\
    print("\uc0\u9989  \u24050 \u21019 \u24314 \u26631 \u20934 \u21270 \u25991 \u20214 : data/EURUSD_standard.csv")}