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import os
import pandas as pd
# add_fund_data.py
from app_test . tiantian_jijin_spider import get_fund_data_by_code
def normalize_df ( df ) :
"""
对 DataFrame 对象进行最小最大标准化。
Args:
df (DataFrame): 要进行标准化的 DataFrame 对象。
Returns:
df_normalized (DataFrame): 进行最小最大标准化后的 DataFrame 对象。
"""
# 如果列的数据类型是布尔值、有符号整型、无符号整型、浮点数或复数浮点数的话，就进行最大最小标准化，否则保留原列的数据
df_normalized = df . apply ( lambda x : ( x - x . min ( ) ) / ( x . max ( ) - x . min ( ) )
if x . dtype . kind in ' biufc ' else x )
return df_normalized
def add_fund_data ( fund_code ) :
df = pd . read_csv ( ' filled_row_data.csv ' )
# print(df)
fund_data = get_fund_data_by_code ( fund_code )
# print('基金数据')
# print(fund_data)
#将需要预测的因变量基金数据添加到预处理后数据
# del fund_data['id'] # 删除 'id' 列
df_merged = pd . merge ( df , fund_data , how = ' inner ' , on = ' date ' )
df_merged [ ' date ' ] = pd . to_datetime ( df_merged [ ' date ' ] )
df_merged . set_index ( ' date ' , inplace = True )
# print(type(df_merged.index.max()))
# print('开始保存数据')
# df_merged.to_csv('row_data.csv',encoding='utf-8')
# 对缺失值进行线性插值(其他方法?多项插值?)
df_merged = df_merged . interpolate ( )
# 如果有剩余的NaN值，
df_merged . dropna ( inplace = True )
# df_merged = normalize_df(df_merged)
# print(df_merged)
print ( ' 添加基金数据成功 ' )
return df_merged
