合并某些行的时间序列数据。

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英文:

Combine time series data for certain rows

问题

The provided code appears to be in Python and deals with time series data using the pandas library. Here's the translated code:

  1. 什么是合并某些行的时间序列数据的最有效方式？
  3. 在我的情况下，我有四只不同数据框中的四只股票的时间序列数据。
  4. 它们都有一些列，但我只关心'Close'列。
  6. 下面是我想出的方式，这是最有效的方式吗（对pandas和时间序列不熟悉）？
  8. 干杯
  10. def start(verbose=False):
  11.     price_tsla = download_normalize_prices('TSLA', 'Tsla')
  12.     price_aapl = download_normalize_prices('AAPL', 'Aapl')
  13.     price_amzn = download_normalize_prices('AMZN', 'Amzn')
  14.     price_sp500 = download_normalize_prices('^GSPC', 'SP500')
  16.     combined_df = pd.concat([price_tsla, price_aapl, price_amzn, price_sp500], axis=1)
  17.     combined_df.plot()
  18.     plot.show()
  19.     print(combined_df.head(5))
  21. def download_normalize_prices(ticker, new_col_name):
  22.     cols_to_drop = ['Open', 'High', 'Low', 'Volume', 'Dividends', 'Stock Splits']
  23.     prices = yf.Ticker(ticker).history(start='2022-1-1', end='2022-12-31')
  24.     prices = prices.drop(cols_to_drop, axis=1)
  25.     prices = normalize_price(prices)
  26.     prices = prices.rename(columns={'Close': new_col_name})
  28.     return prices
  31. def normalize_price(price_df):
  32.     price_at_row_0 = price_df['Close'].iloc[0]
  33.     return price_df.div(price_at_row_0).mul(100)

Is there anything else you would like to know or any specific modifications you need?

英文:

What's the most efficient way to combine time series data for certain rows?

In my case, I have time series data for four stocks in different dfs.
They all have a few columns but I am only interested in the 'Close' column.

Below is what I came up with, is this the most efficient way (new to pandas and time series)?

Cheers

  1. def start(verbose: False):
  2.     price_tsla = download_normalize_prices('TSLA', 'Tsla')
  3.     price_aapl = download_normalize_prices('AAPL', 'Aapl')
  4.     price_amzn = download_normalize_prices('AMZN', 'Amzn')
  5.     price_sp500 = download_normalize_prices('^GSPC', 'SP500')
  7.     combined_df = pd.concat([price_tsla, price_aapl, price_amzn, price_sp500], axis=1)
  8.     combined_df.plot()
  9.     plot.show()
  10.     print( combined_df.head(5))
  12. def download_normalize_prices(ticker, new_col_name):
  13.     cols_to_drop = ['Open', 'High', 'Low', 'Volume', 'Dividends', 'Stock Splits']
  14.     prices = yf.Ticker(ticker).history(start='2022-1-1', end='2022-12-31')
  15.     prices = prices.drop(cols_to_drop, axis=1)
  16.     prices = normalize_price(prices)
  17.     prices = prices.rename(columns={'Close': new_col_name})
  19.     return prices
  22. def normalize_price( price_df ):
  23.     price_at_row_0 = price_df['Close'].iloc[0]
  24.     return price_df.div(price_at_row_0).mul(100)

答案1

得分: 1

以下是翻译好的代码部分：

  1. 这里有一个提议：
  3. def start():
  4.     d = {"TSLA": "Tsla", "AAPL": "Aapl", "AMZN": "Amzn", "^GSPC": "SP500"}
  5.     prices = {ticker: download_prices(ticker) for ticker in d}
  6.     combined_df = pd.DataFrame(prices, columns=d)
  7.     combined_df.plot()
  8.     print(combined_df.head(5))
  10. def download_prices(ticker):
  11.     prices = yf.Ticker(ticker).history(
  12.         start="2022-01-01", end="2022-12-31")["Close"]
  13.     return normalize_prices(prices)
  15. def normalize_prices(prices):
  16.     return prices.div(prices.iloc[0]).mul(100)
  18. start()

希望这对你有所帮助！如果你需要更多信息，请随时告诉我。

英文:

Here is a proposition :

  1. def start():
  2.     d = {"TSLA": "Tsla", "AAPL": "Aapl", "AMZN": "Amzn", "^GSPC": "SP500"}
  3.     prices = {ticker: download_prices(ticker) for ticker in d}
  4.     combined_df = pd.DataFrame(prices, columns=d)
  5.     combined_df.plot()
  6.     print(combined_df.head(5))
  8. def download_prices(ticker):
  9.     prices = yf.Ticker(ticker).history(
  10.         start="2022-01-01", end="2022-12-31")["Close"]
  11.     return normalize_prices(prices)
  13. def normalize_prices(prices):
  14.     return prices.div(prices.iloc[0]).mul(100)
  16. start()

Output :

  1.                                  Tsla        Aapl        Amzn       SP500
  2. Date                                                                     
  3. 2022-01-03 00:00:00-05:00  100.000000  100.000000  100.000000  100.000000
  4. 2022-01-04 00:00:00-05:00   95.816730   98.730844   98.308441   99.937038
  5. 2022-01-05 00:00:00-05:00   90.693293   96.104607   96.451091   97.998983
  6. 2022-01-06 00:00:00-05:00   88.741268   94.500311   95.803809   97.904535
  7. 2022-01-07 00:00:00-05:00   85.595694   94.593707   95.393024   97.508000

合并某些行的时间序列数据。

huangapple
  • 本文由 发表于 2023年6月15日 01:46:43
  • 转载请务必保留本文链接：https://go.coder-hub.com/76476285.html
  • pandas
  • python
  • time-series
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