`add_missing_columns` sometimes adds same missing column multiple times

[r-terada]

`add_missing_columns` sometimes adds same missing column multiple times

[derinwalters]

Nice find! Looks like a bug in the missing column insertion logic that occurs when multiple columns not in the schema, in this case "col_b" and "col_c", are positioned after the missing column location in the dataframe to be validated. Thank you for the great working example. I'll submit a pull request shortly.

[r-terada]

Thank you for the investigation and quick fix!
I'm waiting for your pull request to be merged :)

[aphorton]

First, thank you very much for this fantastic package.

The code in OP's example runs as intended now for pandera 0.18.0, but adding non-unique column names causes a similar column-duplication problem.

import pandas as pd
import pandera as pa

schema = pa.DataFrameSchema(
    {
        "col_a": pa.Column(str),
        "col_missing": pa.Column(str, nullable=True)
    },
    add_missing_columns=True
)

df = pd.DataFrame({
    "col_a": ["a", "b", "c"],
    "col_b": ["d", "e", "f"]
})

print(schema.validate(df))
# -> works well
#   col_a col_missing col_b
# 0     a        None     d
# 1     b        None     e
# 2     c        None     f

df.columns = ["col_a", "col_a"]
print(schema.validate(df))
# -> duplicates columns
#   col_a col_a col_missing col_a col_a
# 0     a     d        None     a     d
# 1     b     e        None     b     e
# 2     c     f        None     c     f

Expected behavior

add only 1 col_missing
#   col_a col_a col_missing
# 0     a     d        None
# 1     b     e        None
# 2     c     f        None