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Product Composition Table This file was generated on April 06, 2026 |
 Sponsored by FracTracker Alliance |
This interactive table below lists all Tradenanes (from the Open-FF FULL data set) published in the FracFocus bulk download.
The index of the table is the combination of TradeName, and the set of bgCAS and PercentHigAdditive (the maximum amount of the chemical in the trade-named product) associated with that TradeName in disclosures. Each unique combination of those three produces a row in the table.
How to use this table:¶
Type either a TradeName or CASNumber (or other terms) into the Search bar to filter the list by that search term. You can combine terms in the search to further filter the list. For exmaple, to see the instances of the product Losurf that occurred in 2011, simply use losurf 2011 in the search bar. If you want to find all the product names that contained 111-76-2 in 2016, use 111-76-2 2016.
The table is also sort-able by any column (click a second time for reverse sort).
Note that in many disclosures, entries in the TradeName field are missing or uninformative. Further, some companies aggregate many tradenames together, effectively combining all their ingredients into one record in the following table. Those are less useful in understanding what is reported for a given TradeName.
| Column | Description |
|---|
|TradeName| is a cleaned version of the raw FracFocus text
|cas_per_tuple|shows the cluster of chemicals (bgCAS) and their associated PercentHighAdditive for a given TradeName.|
|size |indicates the number of disclosures for this pair of TradeName & cas_per_tuple|
|year| years in which this pair occurs|
|APINumber| APINumbers for example disclosures with this pair|
|bgSupplier| suppliers associated with this pair|
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[5], line 5 3 df_cas.TradeName = df_cas.TradeName.str.lower() 4 tn_df = df_cas.groupby(['TradeName','DisclosureId','APINumber','year','bgSupplier'])['cas_per_tuple'].apply(set).reset_index() ----> 5 tn_df.cas_per_tuple = tn_df.cas_per_tuple.map(lambda x: sort_id(x)) 6 tn_df.cas_per_tuple = tn_df.cas_per_tuple.map(lambda x: th.xlate_to_str(x,'<br>',trunc=False)) 7 gb1 = tn_df.groupby(['TradeName','cas_per_tuple'],as_index=False).size() File ~\anaconda3\envs\openFF\Lib\site-packages\pandas\core\series.py:4675, in Series.map(self, func, na_action, engine, **kwargs) 4673 if callable(func): 4674 func = functools.partial(func, **kwargs) -> 4675 new_values = self._map_values(func, na_action=na_action) 4676 return self._constructor(new_values, index=self.index, copy=False).__finalize__( 4677 self, method="map" 4678 ) File ~\anaconda3\envs\openFF\Lib\site-packages\pandas\core\base.py:1022, in IndexOpsMixin._map_values(self, mapper, na_action) 1019 if isinstance(arr, ExtensionArray): 1020 return arr.map(mapper, na_action=na_action) -> 1022 return algorithms.map_array(arr, mapper, na_action=na_action) File ~\anaconda3\envs\openFF\Lib\site-packages\pandas\core\algorithms.py:1710, in map_array(arr, mapper, na_action) 1708 values = arr.astype(object, copy=False) 1709 if na_action is None: -> 1710 return lib.map_infer(values, mapper) 1711 else: 1712 return lib.map_infer_mask(values, mapper, mask=isna(values).view(np.uint8)) File pandas/_libs/lib.pyx:3071, in pandas._libs.lib.map_infer() Cell In[5], line 5, in <lambda>(x) 3 df_cas.TradeName = df_cas.TradeName.str.lower() 4 tn_df = df_cas.groupby(['TradeName','DisclosureId','APINumber','year','bgSupplier'])['cas_per_tuple'].apply(set).reset_index() ----> 5 tn_df.cas_per_tuple = tn_df.cas_per_tuple.map(lambda x: sort_id(x)) 6 tn_df.cas_per_tuple = tn_df.cas_per_tuple.map(lambda x: th.xlate_to_str(x,'<br>',trunc=False)) 7 gb1 = tn_df.groupby(['TradeName','cas_per_tuple'],as_index=False).size() Cell In[4], line 3, in sort_id(st) 1 def sort_id(st): 2 l = list(st) ----> 3 l.sort() 4 return l TypeError: '<' not supported between instances of 'float' and 'str'