GeezerVison

Add function : void Container.put_rows(pandas.DataFrame input). In python layer, I convert input from pandas.DataFrame to numpy.array because NumPy support C-API. In C++ layer, I use API from NumPy to put data into GridDB. Compare with using Container.multi_puts(input : list[list]) to put large data with LONG type, the time to run reduce about 11% and memory using reduce 20%. With String type, the time to run reduce about 10% and memory using reduce about 8%.

Add function : pandas.DataFrame RowSet.fetch_rows(). In C++ layer, it uses Iterable object (RowList.h/cpp) to wrap output data. In python layer, I convert data from Iterable object to pandas.DataFrame. Compare with using RowSet.next() to query large data with LONG type, the time to run reduce 17% and memory using are the same. When query large data with STRING type, the time to run reduce 11%.

Reduce call function to check NULL field: when get data, for each row field, Python Client is using gsGetRowFieldNull() then gsGetRowFieldAsXXX(). I change to use gsGetRowFieldAsXXX(), then if data is empty or null then I use gsGetRowFieldNull() to check whether field is null.

There is a note for Container.put_rows(). To create DataFrame, we use: "frame = pandas.DataFrame(data)" with "data" is list. However, when list has None value, Pandas library will automatic change value, for example None value to NaN value. To prevent this, in python code should use "frame = pandas.DataFrame(data, dtype=object)".