3개 이상 merge

data.py

import pandas as pd
import numpy as np

files = ["nvda.csv", "vix.csv", "eur.csv"]

def open_df(fn):
    df = pd.read_csv(fn)
    df.drop(["Vol.","Change %","Open","Low","High"], axis=1, inplace=True)
    df["Price"] = df["Price"].astype(float)
    df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y')
    df = df.sort_values(by='Date')
    df.set_index('Date', inplace=True)
    return df

def inverse_min_max_scaling(normalized_data, min_value, max_value):
    original_data = normalized_data * (max_value - min_value) + min_value
    return original_data
def normalize_all_columns(df):
    cols = ["Price"]
    for col in cols:
        df[col] = (df[col]-df[col].min())/(df[col].max()-df[col].min())
    return df
def df(files):
    dfs = []
    merged = ""
    for fn in files:
        name = fn.split('.')[0]
        df = open_df(fn)
        df = normalize_all_columns(df)
        df.rename(columns={'Price': name+'_Price'}, inplace=True)
        dfs.append(df)
        if(len(dfs) == 1):
            merged = df
        if(len(dfs) > 1):
            merged = merged.merge(df, on='Date')
    return merged
def get_df():
    return df(files)