I have a Pandas DataFrame called 'dt', which has two columns called 'A' and 'B'. The values of column 'B' are numpy arrays; Something like this:

index   A   B
0       a   [1,2,3]
1       b   [2,3,4]
2       c   [3,4,5]

Where:

type (dt["B"][0])

returns: numpy.ndarray

I want to filter this DataFrame to get another DataFrame, where only rows that have a certain element in the numpy array stored in 'B' are present.

I've tried this:

dt [element in dt["B"]]

So for example:

dt [2 in dt["B"]]

should return:

index   A   B
0       a   [1,2,3]
1       b   [2,3,4]

But this results in an error, namely "KeyError: True"

If the values of column "B" were strings, I could done the same with no error:

dt [dt["B"]==value]

So I wonder why my code doesn't work, and what does "KeyError: True" mean.

The complete error is this:

KeyError                                  Traceback (most recent call last)
~/Applications/Conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)2645             try:
-> 2646                 return self._engine.get_loc(key)2647             except KeyError:pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()KeyError: TrueDuring handling of the above exception, another exception occurred:KeyError                                  Traceback (most recent call last)
<ipython-input-151-aa9ea046a48f> in <module>
----> 1 quotes_of_base["BTC" in quotes_of_base["quote"]]~/Applications/Conda/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)2798             if self.columns.nlevels > 1:2799                 return self._getitem_multilevel(key)
-> 2800             indexer = self.columns.get_loc(key)2801             if is_integer(indexer):2802                 indexer = [indexer]~/Applications/Conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)2646                 return self._engine.get_loc(key)2647             except KeyError:
-> 2648                 return self._engine.get_loc(self._maybe_cast_indexer(key))2649         indexer = self.get_indexer([key], method=method, tolerance=tolerance)2650         if indexer.ndim > 1 or indexer.size > 1:pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()KeyError: True

• lets say you have something like:

        A         B0  10   [11, 0]1  20  [11, 10]2  30  [11, 10]3  40   [10, 0]4  50   [11, 0]5  60   [10, 0]  
  

• And would like to filter only those in the array containing 10

        A         B1  20  [11, 10]2  30  [11, 10]3  40   [10, 0]5  60   [10, 0]
  

• You can use .apply

    #create the dataframedf = pd.DataFrame(columns = ['A','B'])df.A = [10,20,30,40,50,60]df.B = [[11,0],[11,10],[11,10],[10,0],[11,0],[10,0]]# results is a boolean indicating whether the value is found in the list# apply the filter in the column 'B' of the dataframeresults = df.B.apply(lambda a: 10 in a)# filter the dataframe based on the booleandf_filtered = df[results]print(df_filtered)
  

• Then you get:

              A   B1         20  [11, 10]2         30  [11, 10]3         40   [10, 0]5         60   [10, 0]
  

you can find more details at: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html