Fix export (hdf5) of joined dataframe which contains missing values

[JovanVeljanoski]

This PR address the issue raised in #1413

Notes:

• The added test checks the export against hdf5, arrow, parquet and csv. Only hdf5 and csv report problems, but for different reasons;
• HDF5 test fails because the data seems to be saved incorrectly;
• The csv file is exported correctly. However when the data is read, the missing values are converted do nan. I think this should now automatically be turned into a missing (masked) values, not np.nan. What do you think @maartenbreddels @xdssio. This might be more tricky tho.. so I am happy to leave it as is and adjust the test. Just wondering what the ideal case should be - in my opinion missing values.

Checklist:

• Add tests
• Make tests pass

[Ben-Epstein]

fwiw i believe the issue with hdf5 stems from the fact that you cannot have "missing" (nan) values in int dtypes in numpy.

import numpy as np

np.array([1,2,3,4,None], dtype=np.int64)

>>> TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

Converting the column to something non-int would work, but that may not be the desired outcome. Or maybe you could convert to a pyarrow array?

For example, this works:

import vaex
import pandas as pd
import pyarrow as pa

df1 = vaex.from_pandas(pd.DataFrame([1,2,3], columns=['idx']))
df2 = vaex.from_pandas(pd.DataFrame({'idx':[1,6,7], 't1': [0,0,0]}))
df3 = df1.join(df2, how='left', on='idx', rsuffix='_y')

df3["idx_y"] = df3.idx_y.astype(pa.int64())
df3["t1"] = df3.idx_y.astype(pa.int64())
display(df3)

df3.export('test.hdf5')
vaex.open('test.hdf5')