Join a time-series dataframe with an "interval" dataframe

Question

I am struggling to join data from an interval dataframe to a time series dataframe. For each row of my time series, I want to look in which interval it is included and return a specific value from the interval dataframe.

I got inspired by this solution: How to join two dataframes for which column values are within a certain range?

But it is not working for a too complex reason from my knowledge.

Here is my error message:

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_13072/1034504056.py in <module>
      1 #df_test.index = pd.IntervalIndex.from_arrays(df_test['Start'],df_test['End'],closed='both')
----> 2 data_test['Product'] = data_test.index.to_series().apply(lambda x : df_test.iloc[df_test.index.get_loc(x)]['Product'])

~\Anaconda3\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwargs)
   4355         dtype: float64
   4356         """
-> 4357         return SeriesApply(self, func, convert_dtype, args, kwargs).apply()
   4358 
   4359     def _reduce(

~\Anaconda3\lib\site-packages\pandas\core\apply.py in apply(self)
   1041             return self.apply_str()
   1042 
-> 1043         return self.apply_standard()
   1044 
   1045     def agg(self):

~\Anaconda3\lib\site-packages\pandas\core\apply.py in apply_standard(self)
   1097                 # List[Union[Callable[..., Any], str]]]]]"; expected
   1098                 # "Callable[[Any], Any]"
-> 1099                 mapped = lib.map_infer(
   1100                     values,
   1101                     f,  # type: ignore[arg-type]

~\Anaconda3\lib\site-packages\pandas\_libs\lib.pyx in pandas._libs.lib.map_infer()

~\AppData\Local\Temp/ipykernel_13072/1034504056.py in <lambda>(x)
      1 #df_test.index = pd.IntervalIndex.from_arrays(df_test['Heure début réelle'],df_test['Hre fin réelle'],closed='both')
----> 2 data_test['Designation'] = data_test.index.to_series().apply(lambda x : df_test.iloc[df_test.index.get_loc(x)]['Désignation article'])

~\Anaconda3\lib\site-packages\pandas\core\indexes\interval.py in get_loc(self, key, method, tolerance)
    631         matches = mask.sum()
    632         if matches == 0:
--> 633             raise KeyError(key)
    634         elif matches == 1:
    635             return mask.argmax()

KeyError: Timestamp('2021-10-23 23:59:29')

The function I want to succeed.

df_test.index = pd.IntervalIndex.from_arrays(df_test['Start'],df_test['End'],closed='both')
data_test['Product'] = data_test.index.to_series().apply(lambda x : df_test.iloc[df_test.index.get_loc(x)]['Product'])

Sample value for data_test

{'Ordre': {92: 3149484,
  93: 3149484,
  94: 3149484,
  95: 3149610,
  96: 3149610,
  97: 3149610,
  98: 3149610,
  99: 3149610,
  100: 3149610,
  101: 3149610,
  102: 3149611},
 'Start': {92: Timestamp('2021-10-26 06:55:00'),
  93: Timestamp('2021-10-26 06:55:00'),
  94: Timestamp('2021-10-26 06:55:00'),
  95: Timestamp('2021-10-26 07:25:00'),
  96: Timestamp('2021-10-26 07:25:00'),
  97: Timestamp('2021-10-26 07:25:00'),
  98: Timestamp('2021-10-26 08:30:00'),
  99: Timestamp('2021-10-26 08:30:00'),
  100: Timestamp('2021-10-26 08:30:00'),
  101: Timestamp('2021-10-26 08:30:00'),
  102: Timestamp('2021-10-26 11:37:00')},
 'End': {92: Timestamp('2021-10-26 07:25:00'),
  93: Timestamp('2021-10-26 07:25:00'),
  94: Timestamp('2021-10-26 07:25:00'),
  95: Timestamp('2021-10-26 08:30:00'),
  96: Timestamp('2021-10-26 08:30:00'),
  97: Timestamp('2021-10-26 08:30:00'),
  98: Timestamp('2021-10-26 11:37:00'),
  99: Timestamp('2021-10-26 11:37:00'),
  100: Timestamp('2021-10-26 11:37:00'),
  101: Timestamp('2021-10-26 11:37:00'),
  102: Timestamp('2021-10-26 12:30:00')},
 'Product': {92: 'Product_1',
  93: 'Product_1',
  94: 'Product_1',
  95: 'Product_2',
  96: 'Product_2',
  97: 'Product_2',
  98: 'Product_2',
  99: 'Product_2',
  100: 'Product_2',
  101: 'Product_2',
  102: 'Product_2'}}

Sample value for df_test

{'Temperature_1': {Timestamp('2021-10-26 06:55:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:56:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:57:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:58:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:59:29'): 62.9905242919922,
  Timestamp('2021-10-26 08:25:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:26:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:27:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:28:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:29:29'): 65.0611953735352},
 'Temperature_2': {Timestamp('2021-10-26 06:55:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:56:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:57:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:58:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:59:29'): 66.8290863037109,
  Timestamp('2021-10-26 08:25:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:26:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:27:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:28:29'): 66.0404281616211,
  Timestamp('2021-10-26 08:29:29'): 66.0404281616211}}

The output would be a new column that indicate which product is concerned regarding timestamp included or not in the interval:

{'Temperature_1': {Timestamp('2021-10-26 06:55:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:56:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:57:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:58:29'): 62.9905242919922,
  Timestamp('2021-10-26 06:59:29'): 62.9905242919922,
  Timestamp('2021-10-26 08:25:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:26:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:27:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:28:29'): 65.0611953735352,
  Timestamp('2021-10-26 08:29:29'): 65.0611953735352},
 'Temperature_2': {Timestamp('2021-10-26 06:55:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:56:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:57:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:58:29'): 66.8290863037109,
  Timestamp('2021-10-26 06:59:29'): 66.8290863037109,
  Timestamp('2021-10-26 08:25:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:26:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:27:29'): 67.0449523925781,
  Timestamp('2021-10-26 08:28:29'): 66.0404281616211,
  Timestamp('2021-10-26 08:29:29'): 66.0404281616211},
'Product': {Timestamp('2021-10-26 06:55:29'): 'Product_1',
  Timestamp('2021-10-26 06:56:29'): 'Product_1',
  Timestamp('2021-10-26 06:57:29'): 'Product_1',
  Timestamp('2021-10-26 06:58:29'): 'Product_1',
  Timestamp('2021-10-26 06:59:29'): 'Product_1',
  Timestamp('2021-10-26 08:25:29'): 'Product_2',
  Timestamp('2021-10-26 08:26:29'): 'Product_2',
  Timestamp('2021-10-26 08:27:29'): 'Product_2',
  Timestamp('2021-10-26 08:28:29'): 'Product_2',
  Timestamp('2021-10-26 08:29:29'): 'Product_2'}}

Thank you for your help and advice

Answer 1

The intervals should be created on the data_test, not df_test. Also, your data_test has duplicates:

data_test = data_test.drop_duplicates()
data_test.index = pd.IntervalIndex.from_arrays(data_test['Start'],
                                               data_test['End'],
                                               closed='both')

product = (df_test
           .index
           .to_series()
           .apply(lambda df: data_test.iloc[data_test.index.get_loc(df), 
                                            data_test.columns.get_loc('Product')])
          )

df_test.assign(Product = product)
 
Temperature_1  Temperature_2    Product
2021-10-26 06:55:29      62.990524      66.829086  Product_1
2021-10-26 06:56:29      62.990524      66.829086  Product_1
2021-10-26 06:57:29      62.990524      66.829086  Product_1
2021-10-26 06:58:29      62.990524      66.829086  Product_1
2021-10-26 06:59:29      62.990524      66.829086  Product_1
2021-10-26 08:25:29      65.061195      67.044952  Product_2
2021-10-26 08:26:29      65.061195      67.044952  Product_2
2021-10-26 08:27:29      65.061195      67.044952  Product_2
2021-10-26 08:28:29      65.061195      66.040428  Product_2
2021-10-26 08:29:29      65.061195      66.040428  Product_2