Thank you for this contribution. I am utilizing this concept of converting pd.DataFrame to GDB Table as part of a larger workflow that moves queried database records into a .gdb as a table participating in a relationship class with a feature class.
To accomplish the pd.DataFrame to GDB Table step: first the pd.DataFrame must become a numpy.array with df.to_records(). Then the array is converted to GDB Table with arcpy.da.NumPyArrayToTable(array, table_path). The snag, as noted, is that the data type 'object'/'O' can't be inferred by GDB Table fields when reading the numpy.array. I declared the 'object' data type as string for each column using the process below to convert columns with the 'O' type into String fields. Note that if some other field type should be applied for a field that is typed as 'O' then the code will need adjusting. Hope this helps the next viewer of this thread!
numpy data types ref: https://numpy.org/doc/stable/user/basics.types.html#data-types-for-strings-and-bytes
```
dtypes = {}
for col,type in df.dtypes.items():
if str(type) == 'object':
dtypes[col] = f'U{len(col)}'
else:
dtypes[col] = type
array = df.to_records(column_dtypes=dtypes)
