CODEWITHSUNDEEP
pythonpandas
jwd
jwd

Reputation: 11114

filter out "empty array" values in Pandas DataFrame

Suppose I have a dataframe, d which has a column containing Python arrays as the values.

>>> d = pd.DataFrame([['foo', ['bar']], ['biz', []]], columns=['a','b'])
>>> print d

     a      b
0  foo  [bar]
1  biz     []

Now, I want to filter out those rows which have empty arrays.

I have tried various versions, but no luck so far:

Trying to check it as a 'truthy' value:

>>> d[d['b']]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 2682, in __getitem__
    return self._getitem_array(key)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 2726, in _getitem_array
    indexer = self.loc._convert_to_indexer(key, axis=1)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1314, in _convert_to_indexer
    indexer = check = labels.get_indexer(objarr)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/indexes/base.py", line 3259, in get_indexer
    indexer = self._engine.get_indexer(target._ndarray_values)
  File "pandas/_libs/index.pyx", line 301, in pandas._libs.index.IndexEngine.get_indexer
  File "pandas/_libs/hashtable_class_helper.pxi", line 1544, in pandas._libs.hashtable.PyObjectHashTable.lookup
TypeError: unhashable type: 'list'

Trying an explicit length check. It seems len() is being applied to the series, not the value of the data.

>>> d[ len(d['b']) > 0 ]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 2688, in __getitem__
    return self._getitem_column(key)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
    return self._get_item_cache(key)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/generic.py", line 2489, in _get_item_cache
    values = self._data.get(item)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/internals.py", line 4115, in get
    loc = self.items.get_loc(item)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: True

Comparing to empty array directly, just as we might compare to an empty string (which, by the way, does work, if we use strings rather than arrays).

>>> d[ d['b'] == [] ]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/ops.py", line 1283, in wrapper
    res = na_op(values, other)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/ops.py", line 1143, in na_op
    result = _comp_method_OBJECT_ARRAY(op, x, y)
  File "/home/myname/.local/lib/python2.7/site-packages/pandas/core/ops.py", line 1120, in _comp_method_OBJECT_ARRAY
    result = libops.vec_compare(x, y, op)
  File "pandas/_libs/ops.pyx", line 128, in pandas._libs.ops.vec_compare
ValueError: Arrays were different lengths: 2 vs 0

Upvotes: 5

Views: 8765

Answers (3)

jwd
jwd

Reputation: 11114

Scott's answer is better, but just for others' knowledge, another option is to use a tuple rather than a list, and check against an empty tuple directly.

d[d['b'] != ()]

Which gives:

     a       b
0  foo  (bar,)

This doesn't work with lists; see the last error in the original question.

Upvotes: 0

user3483203
user3483203

Reputation: 51165

Empty lists will evaluate to False using all. This will not work if you have other Falsey values in a row (unless you want to drop those rows as well).

d[d.all(1)]


    a      b
0  foo  [bar]

If you only want to filter using column b, you can use astype:

d[d.b.astype(bool)]


     a      b
0  foo  [bar]

Upvotes: 5

Scott Boston
Scott Boston

Reputation: 153460

Use the string accessor, .str to check the length of list in pandas series:

d[d.b.str.len()>0]

Output:

     a      b
0  foo  [bar]

Upvotes: 8

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