将函数调用的结果合并到一个数据框中，使用 apply 函数。

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英文:

Combining results of a function call into a dataframe using apply

问题

以下是您要翻译的代码部分：

  1. import pandas as pd
  2. import yfinance as yf
  4. def get_opt_data(ticker, expiration):
  5.     try:
  6.         data = yf.Ticker(ticker)
  7.         calls = data.option_chain(expiration).calls
  8.         calls['Type'] = 'CALL'
  9.         puts = data.option_chain(expiration).puts
  10.         puts['Type'] = 'PUT'
  11.         combined = pd.concat([calls, puts])
  12.         combined['Ticker'] = ticker
  13.         info = data.info
  14.         combined['Sector'] = info['sector']
  15.         combined['Industry'] = info['industry']
  16.         return combined
  17.     except:
  18.         pass
  20. ticker_list = pd.Series(['AAPL', 'GOOGL', 'MSFT', 'NVDA'])
  21. option_data = ticker_list.apply(lambda x: get_opt_data(x, '2023-07-21'))

请注意，我只翻译了代码部分，没有包括问题的回答。如果您需要更多帮助，请随时提问。

英文:

I am trying to use .apply to create a dataframe containing the concatenated results of the following function which simply pulls option data and puts it into tabular form

  1. import pandas as pd
  2. import yfinance as yf
  4. def get_opt_data(ticker, expiration):
  5.     try:
  6.         data = yf.Ticker(ticker)
  7.         calls = data.option_chain(expiration).calls
  8.         calls['Type'] = 'CALL'
  9.         puts = data.option_chain(expiration).puts
  10.         puts['Type'] = 'PUT'
  11.         combined = pd.concat([calls,puts])
  12.         combined['Ticker'] = ticker
  13.         info = data.info
  14.         combined['Sector'] = info['sector']
  15.         combined['Industry'] = info['industry']
  16.         return combined
  17.     except:
  18.         pass

When I try to pull the data using:

  1. ticker_list = pd.Series(['AAPL','GOOGL','MSFT','NVDA'])
  2. option_data = ticker_list.apply(lambda x: get_opt_data(x, '2023-07-21'))

I get a series containing the resulting dataframes, instead of the dataframes concatenated to create a single dataframe.

I have done something similar in the past that included grouby and worked, I guess I don't understand why the grouby was necessary for it to work. Here is that previous code that combined the results correctly.

  1. grouped_data = raw_data.groupby('Ticker', group_keys=False).apply(lambda x: get_indicators(x))

答案1

得分: 1

这是一个简单的例子。应用变换以1:1的方式进行 - 也就是说，你得到的返回的方框数量与你发送的一样多。你可能想要在每个元素上调用get_opt_data，然后连接结果：

  1. option_data = pd.concat(get_opt_data(x, '2023-07-21') for x in ticker_list)
  2. print(option_data[:5].to_string())

产生的结果如下：

  1.         contractSymbol             lastTradeDate  strike  lastPrice     bid     ask     change  percentChange  volume  openInterest  impliedVolatility  inTheMoney contractSize currency  Type Ticker      Sector              Industry
  2. 0  AAPL230721C00050000 2023-06-13 19:24:32+00:00    50.0     133.50  134.40  137.35   0.000000       0.000000     1.0          21.0           2.383793        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  3. 1  AAPL230721C00055000 2023-06-16 13:36:40+00:00    55.0     131.17  129.40  132.35  37.170000      39.542550     2.0          23.0           2.218754        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  4. 2  AAPL230721C00060000 2023-06-16 13:36:40+00:00    60.0     126.22  123.20  127.40   1.220001       0.976001     2.0        2540.0           1.773439        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  5. 3  AAPL230721C00065000 2023-06-15 17:56:08+00:00    65.0     121.00  119.45  122.45   0.000000       0.000000     2.0         403.0           1.959473        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  6. 4  AAPL230721C00070000 2023-05-04 14:07:49+00:00    70.0      95.90  110.20  112.10   0.000000       0.000000     4.0         214.0           0.000010        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics

希望这对你有所帮助。

英文:

This is an easy one. Apply transforms things in a 1:1 way - that is, you get the same number of boxes back as you sent in. You probably want to call get_opt_data on each element and concat the results:

  1. option_data = pd.concat(get_opt_data(x, '2023-07-21') for x in ticker_list)
  2. print(option_data[:5].to_string())

yields

  1.         contractSymbol             lastTradeDate  strike  lastPrice     bid     ask     change  percentChange  volume  openInterest  impliedVolatility  inTheMoney contractSize currency  Type Ticker      Sector              Industry
  2. 0  AAPL230721C00050000 2023-06-13 19:24:32+00:00    50.0     133.50  134.40  137.35   0.000000       0.000000     1.0          21.0           2.383793        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  3. 1  AAPL230721C00055000 2023-06-16 13:36:40+00:00    55.0     131.17  129.40  132.35  37.170000      39.542550     2.0          23.0           2.218754        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  4. 2  AAPL230721C00060000 2023-06-16 13:36:40+00:00    60.0     126.22  123.20  127.40   1.220001       0.976001     2.0        2540.0           1.773439        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  5. 3  AAPL230721C00065000 2023-06-15 17:56:08+00:00    65.0     121.00  119.45  122.45   0.000000       0.000000     2.0         403.0           1.959473        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics
  6. 4  AAPL230721C00070000 2023-05-04 14:07:49+00:00    70.0      95.90  110.20  112.10   0.000000       0.000000     4.0         214.0           0.000010        True      REGULAR      USD  CALL   AAPL  Technology  Consumer Electronics

huangapple
  • 本文由 发表于 2023年6月19日 00:07:53
  • 转载请务必保留本文链接：https://go.coder-hub.com/76501437.html
  • apply
  • group-by
  • pandas
  • python
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