ajpotts

Categorical.categories should return an index to match pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Categorical.categories.html

Add a `.base` attribute similar to numpy: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.base.html

Add a `assert_frame_equal` function similar to pandas: https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_frame_equal.html This will make testing easier.

Add an `assert_index_equal` function similar to pandas: https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_index_equal.html This will make testing easier.

Add an `assert_series_equal` function similar to pandas: https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_series_equal.html This will make testing easier.

Update/verify series_test.py in PROTO_tests to ensure all tests pass and full coverage.

Update/verify that index_test.py in PROTO_tests passes all tests and has full coverage.

DataFrame.groupby.prod converts int to float

1

```python def build_ak_df_with_nans(): data = { "key1": ["valuew", "valuex", "valuew", "valuex"], "key2": ["valueA", "valueB", "valueA", "valueB"], "nums1": [1, np.nan, 3, 4], "nums2": [1, np.nan, np.nan, 7], "nums3": [10, 8, 9,...

```python def build_ak_df_with_nans(): data = { "key1": ["valuew", "valuex", "valuew", "valuex"], "key2": ["valueA", "valueB", "valueA", "valueB"], "nums1": [1, np.nan, 3, 4], "nums2": [1, np.nan, np.nan, 7], "nums3": [10, 8, 9,...

```python def build_ak_df_with_nans(): data = { "key1": ["valuew", "valuex", "valuew", "valuex"], "key2": ["valueA", "valueB", "valueA", "valueB"], "nums1": [1, np.nan, 3, 4], "nums2": [1, np.nan, np.nan, 7], "nums3": [10, 8, 9,...