Scalar broadcasting isn't commutative

[beckernick]

• I am using the latest version of PyGDF from conda or built from master.
• I have included the following environment details:
  Linux Distro, Linux Kernel, GPU Model
• I have included the following version information for:
  Arrow, CUDA, Numpy, Pandas, Python
• I have included below a minimal working reproducer (if you are unsure how
  to write one see http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports).

Operating System:
Linux version 3.10.0-862.9.1.el7.x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.5 LTS"

GPU:  Tesla V100

CUDA 9.2

Python 3.5.5

Arrow: N/A
pandas==0.20.3
numpy==1.14.5

Issue 46 related to scalar broadcasting appears to have been implemented, but the syntax doesn't appear to be commutative.

The code snippet below works correctly:

import pandas as pd
import numpy as np
import pygdf


df = pd.DataFrame({'a': np.arange(10)})
gdf = pygdf.DataFrame.from_pandas(df)

gdf['b'] = gdf['a'] + 0.5

print(
    gdf['a'] + 2
)

The code snippet below results in an error withthe following traceback:

import pandas as pd
import numpy as np
import pygdf


df = pd.DataFrame({'a': np.arange(10)})
gdf = pygdf.DataFrame.from_pandas(df)

gdf['b'] = gdf['a'] + 0.5

print(
    2 + gdf['a']
)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-43-c1812ae3df00> in <module>()
      1 print(
----> 2     2 + gdf.a
      3 )

TypeError: unsupported operand type(s) for +: 'int' and 'Series'

The same TypeError occurs with both attribute and square bracket Series access, and with both addition and multiplication.

[kkraus14]

This is due to the lack of implementations for builtin functions of __radd__, __rsub__, __rmul__, etc. Should be an easy fix and a great first issue for someone!

[beckernick]

Fantastic. I'm happy to take this on, too. @kkraus14, do you envision implementing reflected operations for every operation currently implemented or only those that are commutative (addition / multiplication)?

In pandas, the non-commutative operators are implemented -- though I wonder how frequently they are ultimately used.