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pythonpandasdataframeconcatenationyfinance

TypeError: cannot concatenate object of type '<class 'yfinance.ticker.Options'>'; only Series and DataFrame objs are valid

Below is my code to pull stock option data from a list of stock tickers then concatenate all of the stock option's dataframes into one. However, I am getting the following error message: "TypeError: cannot concatenate object of type '<class 'yfinance.ticker.Options'>'; only Series and DataFrame objs are valid"

opt_appended = []

for symbol in tickers:
    try:
        ticker = yf.Ticker(symbol)
        opt = ticker.option_chain('2021-07-30')
        opt_appended.append(opt)
    except ValueError:
        continue
opt_appended = pd.concat(opt_appended)
Solution

  • Sequential appends to a DataFrame are extremely costly as it requires a new DataFrame to be build every iteration. For this reason, they are generally avoided. Since option_chain returns an iterable, instead of appending to the list we should extend the list. Then perform a single concat at the end.

    import pandas as pd
import yfinance as yf

tickers = ['AAPL', 'AA', 'AAL']

opts_list = []

for symbol in tickers:
    try:
        ticker = yf.Ticker(symbol)
        opt = ticker.option_chain('2021-07-30')
        opts_list.extend(opt)
    except ValueError:
        continue

new_df = pd.concat(opts_list)

    new_df:

             contractSymbol       lastTradeDate  ...  contractSize  currency
0   AAPL210730C00065000 2021-07-28 19:37:45  ...       REGULAR       USD
1   AAPL210730C00070000 2021-07-22 18:17:27  ...       REGULAR       USD
2   AAPL210730C00075000 2021-07-28 17:19:38  ...       REGULAR       USD
3   AAPL210730C00080000 2021-07-22 14:59:05  ...       REGULAR       USD
4   AAPL210730C00085000 2021-07-27 16:09:57  ...       REGULAR       USD
..                  ...                 ...  ...           ...       ...
28   AAL210730P00029000 2021-07-26 13:31:18  ...       REGULAR       USD
29   AAL210730P00029500 2021-07-26 13:32:22  ...       REGULAR       USD
30   AAL210730P00030000 2021-07-22 16:52:08  ...       REGULAR       USD
31   AAL210730P00031000 2021-07-22 15:53:55  ...       REGULAR       USD
32   AAL210730P00032000 2021-07-26 13:30:11  ...       REGULAR       USD

[253 rows x 14 columns]