从数据框字典中调用报告

Question

我是我以前的question，我问过如何遍历多个 csv 文件（例如 100 个不同的股票代码文件）并一次计算它们的每日收益。我想知道如何为每个文件调用这些返回的最大/最小值并打印报告。

这是根据 Trenton McKinney 先生创建的词典：

import pandas as pd
from pathlib import Path

# create the path to the files
p = Path('c:/Users/<<user_name>>/Documents/stock_files')

# get all the files
files = p.glob('*.csv')

# created the dict of dataframes
df_dict = {f.stem: pd.read_csv(f, parse_dates=['Date'], index_col='Date') 
for f in files}

# apply calculations to each dataframe and update the dataframe
# since the stock data is in column 0 of each dataframe, use .iloc
for k, df in df_dict.items():
    df_dict[k]['Return %'] = df.iloc[:, 0].pct_change(-1)*100

问候并感谢您的帮助！

Answer 1

data_dict = dict()  # create an empty dict here
for k, df in df_dict.items():
    df_dict[k]['Return %'] = df.iloc[:, 0].pct_change(-1)*100

    # aggregate the max and min of Return
    mm = df_dict[k]['Return %'].agg(['max', 'min'])  

    # add it to the dict, with ticker as the key
    data_dict[k] = {'max': mm.max(), 'min': mm.min()}  

# convert to a dataframe if you want
mm_df = pd.DataFrame.from_dict(data_dict, orient='index')

# display(mm_df)
          max      min
aapl  8.70284 -4.90070
msft  6.60377 -4.08443

# save
mm_df.to_csv('max_min_return.csv', index=True)