I have the following dataframe:
Price Adj Close Close High Low Open Volume ema_10 ema_20 ema_40 ema_50 sma_5 sma_10 n_high n_low
Ticker AAPL AAPL AAPL AAPL AAPL AAPL
Date
2023-11-13 00:00:00+00:00 183.899063 184.800003 186.029999 184.210007 185.820007 43627500 184.800003 184.800003 184.800003 184.800003 NaN NaN NaN NaN
2023-11-14 00:00:00+00:00 186.526199 187.440002 188.110001 186.300003 187.699997 60108400 185.280003 185.051432 184.928784 184.903532 NaN NaN NaN NaN
2023-11-15 00:00:00+00:00 187.093430 188.009995 189.500000 187.779999 187.850006 53790500 185.776365 185.333199 185.079086 185.025354 NaN NaN NaN NaN
2023-11-16 00:00:00+00:00 188.785126 189.710007 190.960007 188.649994 189.570007 54412900 186.491573 185.750038 185.304985 185.209066 NaN NaN NaN NaN
2023-11-17 00:00:00+00:00 188.765244 189.690002 190.380005 188.570007 190.250000 50922700 187.073105 186.125273 185.518888 185.384789 187.930002 NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2024-11-04 00:00:00+00:00 221.766006 222.009995 222.789993 219.710007 220.990005 44944500 228.087639 228.953961 227.718509 226.698000 226.920001 229.660001 236.850006 219.710007
2024-11-05 00:00:00+00:00 223.204422 223.449997 223.949997 221.139999 221.800003 28111300 227.244431 228.429774 227.510289 226.570628 224.876001 228.419000 236.850006 219.710007
2024-11-06 00:00:00+00:00 222.475235 222.720001 226.070007 221.190002 222.610001 54561100 226.421808 227.885986 227.276616 226.419623 223.400000 227.615001 236.850006 219.710007
2024-11-06 00:00:00+00:00 222.475235 222.720001 226.070007 221.190002 222.610001 54561100 226.421808 227.885986 227.276616 226.419623 223.400000 227.615001 236.850006 219.710007
2024-11-06 00:00:00+00:00 222.475235 222.720001 226.070007 221.190002 222.610001 54561100 226.421808 227.885986 227.276616 226.419623 223.400000 227.615001 236.850006 219.710007
2024-11-06 00:00:00+00:00 222.475235 222.720001 226.070007 221.190002 222.610001 54561100 226.421808 227.885986 227.276616 226.419623 223.400000 227.615001 236.850006 219.710007
2024-11-07 00:00:00+00:00 227.229996 227.479996 227.880005 224.570007 224.630005 42137700 226.614206 227.847321 227.286537 226.461206 223.713998 227.306000 236.850006 219.710007
And I'm trying to create a new column using the following statement:
dfDaily['%K'] = (dfDaily['Close'] - dfDaily['n_low']) * 100 / (dfDaily['n_high'] - dfDaily['n_low'])
But I get the following error message:
Traceback (most recent call last):
File "C:\DEV\Fiverr2024\ORDER\VanaromHuot\stockMA6.py", line 107, in <module>
dfDaily['%K'] = (dfDaily['Close'] - dfDaily['n_low']) * 100 / (dfDaily['n_high'] - dfDaily['n_low'])
~~~~~~~^^^^^^
File "C:\DEV\.venv\yfinance\Lib\site-packages\pandas\core\frame.py", line 4301, in __setitem__
self._set_item_frame_value(key, value)
File "C:\DEV\.venv\yfinance\Lib\site-packages\pandas\core\frame.py", line 4459, in _set_item_frame_value
raise ValueError(
ValueError: Cannot set a DataFrame with multiple columns to the single column %K
If there is only one ticker, here AAPL:
Because MultiIndex in columns, it is possible to remove the second level of MultiIndex (Ticker level) by DataFrame.droplevel:
dfDaily = dfDaily.droplevel(axis=1, level=1)
dfDaily['%K'] = ((dfDaily['Close'] - dfDaily['n_low']) * 100 /
(dfDaily['n_high'] - dfDaily['n_low']))
If output is one column DataFrame, it is possible to select it:
dfDaily['%K'] = ((dfDaily['Close'] - dfDaily['n_low']) * 100 /
(dfDaily['n_high'] - dfDaily['n_low'])['AAPL'])
If there are multiple tickers:
df = ((dfDaily['Close'] - dfDaily['n_low']) * 100 /
(dfDaily['n_high'] - dfDaily['n_low']).add_prefix('%K_'))
out = pd.concat([dfDaily, df], axis=1)