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https://issues.apache.org/jira/browse/ARROW-3065?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16625011#comment-16625011
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Krisztian Szucs commented on ARROW-3065:
----------------------------------------
I've tried to reproduce it, sadly with no luck.
Based on your description, I used the following snippet including the parquet
roundtrip:
{code:python}
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
df1 = pd.DataFrame({"col": list("abcdefgh")})
df2 = pd.DataFrame({"col": [""] * 8})
tbl1 = pa.Table.from_pandas(df1)
tbl2 = pa.Table.from_pandas(df2)
pq.write_table(tbl1, 'tbl1.parquet')
pq.write_table(tbl2, 'tbl2.parquet')
tbl1_ = pq.read_table('tbl1.parquet')
tbl2_ = pq.read_table('tbl2.parquet')
pa.concat_tables([tbl1_, tbl2_])
print(tbl2.schema)
{code}
Also the column which contains empty strings correctly has object numpy_type
instead of float64.
Tested with 0.10.0, 0.9.0, HEAD(391516df8ce084c279e854cf52c8beb4a4fc444a)
[[email protected]] Could You please provide a more reproducible example?
> [Python] concat_tables() failing from bad Pandas Metadata
> ---------------------------------------------------------
>
> Key: ARROW-3065
> URL: https://issues.apache.org/jira/browse/ARROW-3065
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.10.0
> Reporter: David Lee
> Priority: Major
> Fix For: 0.11.0
>
>
> Looks like the major bug from
> https://issues.apache.org/jira/browse/ARROW-1941 is back...
> After I downgraded from 0.10.0 to 0.9.0, the error disappeared..
> {code:python}
> new_arrow_table = pa.concat_tables(my_arrow_tables)
> File "pyarrow/table.pxi", line 1562, in pyarrow.lib.concat_tables
> File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
> pyarrow.lib.ArrowInvalid: Schema at index 2 was different:
> {code}
> In order to debug this I saved the first 4 arrow tables to 4 parquet files
> and inspected the parquet files. The parquet schema is identical, but the
> Pandas Metadata is different.
> {code:python}
> for i in range(5):
> pq.write_table(my_arrow_tables[i], "test" + str(i) + ".parquet")
> {code}
> It looks like a column which contains empty strings is getting typed as
> float64.
> {code:python}
> >>> test1.schema
> HoldingDetail_Id: string
> metadata
> --------
> {b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [
> {"name": "HoldingDetail_Id", "field_name": "HoldingDetail_Id", "pandas_type":
> "unicode", "numpy_type": "object", "metadata": null},
> >>> test1[0]
> <Column name='HoldingDetail_Id' type=DataType(string)>
> [
> [
> "Z4",
> "SF",
> "J7",
> "W6",
> "L7",
> "Q9",
> "NE",
> "F7",
> >>> test2.schema
> HoldingDetail_Id: string
> metadata
> --------
> {b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [
> {"name": "HoldingDetail_Id", "field_name": "HoldingDetail_Id", "pandas_type":
> "unicode", "numpy_type": "float64", "metadata": null},
> >>> test2[0]
> <Column name='HoldingDetail_Id' type=DataType(string)>
> [
> [
> "",
> "",
> "",
> "",
> "",
> "",
> "",
> "",
> {code}
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