Incorrect columns in the result of binary operations

[dchigarev]

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• Modin version (modin.__version__): 0.7.3+200.g8e7a5682
• Python version: 3.7.5
• Code we can use to reproduce:

if __name__ == "__main__":
    import modin.pandas as pd
    import pandas
    import numpy as np

    data = {key: np.arange(i, i + 5) for i, key in enumerate(["C", "B", "A"])}

    md_df, pd_df = pd.DataFrame(data), pandas.DataFrame(data)

    pd_res = pd_df + pd_df.iloc[0, :-1]
    md_res = md_df + md_df.iloc[0, :-1]

    print("pd_result:\n", pd_res, sep="")
    print("\nmd_result:\n", md_res, sep="")

    print("\npd_columns:", pd_res.columns)
    print("md_columns:", md_res.columns)

Output:

pd_result:
    A    B    C
0 NaN  2.0  0.0
1 NaN  3.0  1.0
2 NaN  4.0  2.0
3 NaN  5.0  3.0
4 NaN  6.0  4.0

md_result:
    A    B    C
0 NaN  2.0  0.0
1 NaN  3.0  1.0
2 NaN  4.0  2.0
3 NaN  5.0  3.0
4 NaN  6.0  4.0

pd_columns: Index(['A', 'B', 'C'], dtype='object')
md_columns: Index(['C', 'B', 'A'], dtype='object')

Describe the problem

Columns into partitions seems to be correct, but columns of dataframe itself isn't, that's also the reason why test don't fails on that test case (to_pandas that used in df_equals considers information only from partitions)