Finding True and False values with df.query

May 9, 2023

I have a simple question. I have concatenated two dataframes with consistent columns. One of the column is entitled ‘isForecast’ where the top dataframe has a value of False, and for the dataframe it is True (I am adding a forecast to actuals data). From this point I put the data through a sklearn regression which is working but when I try to unpack the forecast I get an error.

allData = pd.concat([actuals, forecast])
allData = adjustment(allData,periodLength) #### this is a seperate function but the 'isForecast'column remains consistent.

forecastOutput = allData.query('isForecast').copy() ## error here

The error I am getting doesn’t make sense. My index is a column entitled ‘datetime’ and is not being referenced.

None of [Index([False, False, False, False, False, False, False, False, False, False,\n …\n True, True, True, True, True, True, True, True, True, True],\n dtype=’object’, name=’datetime’, length=91968)] are in the [index]

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Any suggestions or comments

>Solution :

For me working your solution, alternative is compare by True:

allData = pd.DataFrame({'isForecast':[True, False, True]})

forecastOutput = allData.query('isForecast').copy()
print(forecastOutput)
   isForecast
0        True
2        True

forecastOutput = allData.query('isForecast == True').copy()
print(forecastOutput)
   isForecast
0        True
2        True

Or boolean indexing:

forecastOutput = allData[allData['isForecast']]
print(forecastOutput)
   isForecast
0        True
2        True

If values are not boolean:

allData = pd.DataFrame({'isForecast':['True', 'False', 'True']})

forecastOutput = allData.query('isForecast == "True"').copy()
print(forecastOutput)
  isForecast
0       True
2       True