arrow

Feature Request

Many locales have the bare minimum when it comes to test cases. While I understand it can be tedious and repetitive to write out test case

Edited request

Can polars allow mixing string and type selection using col(["x", pl.Int64])

See suggestion here.

Initial request

Any thoughts on an .include() method that is the opposite of .exclude()?

That way these would both work:

df.select(col('x').include(pl.Int64))

df.se

Is your feature request related to a problem? Please describe.
Hi,

While porting some code from Pandas to cuDF, I have noticed that cuDF series do not support unstack method.
As an additional request, It would be great if fill_values could be supported in both cudf.DataFrame.unstack and cudf.Series.unstack methods. Thanks!

Describe the solution you'd like
To have that meth

We no longer need to control the number of concurrent kernels, since now we control the number of concurrent tasks

Describe the bug
If the column names in a CSV file are uppercase, then typing lowercase column names in SQL queries will result in an Error: Invalid identifier for schema.

To Reproduce
Here is a simple example program that runs a query on 2 tables.

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
    let mut ctx = ExecutionContext::new();
    ctx.regis

Note sure if it could be interesting but:

When registering a table:

addr: 0.0.0.0:8084
tables:
  - name: "example"
    uri: "/data/"
    option:
      format: "parquet"
      use_memory_table: false

add in options:
glob

pattern: "file_typev1*.parquet"

or regexp

pattern: "\wfile_type\wv1\w*.parquet"

It would allow selecting in uri's with different exte

It would be helpful to have Fletchgen output warnings for unused metadata fields that start with fletcher_. For example, (this happened to me) when someone adds fletchgen_epc to Schema metadata instead of Field metadata.

Problem description

Reading a dataset with eager's read functionality raises a ValueError when providing columns.

Example code (ideally copy-pastable)

import pandas as pd

from tempfile import TemporaryDirectory
from functools import partial
from storefact import get_store_from_url

from kartothek.io.eager import store_dataframes_as_dataset, read_dataset_as_data

Motivation:

1. Improved compile times (at least by 2x compared to arrow-rs).
2. Faster Parquet impl
3. Projects are migrating to arrow2 (including Datafusion and Polars)