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

For pandas API compatibility, we can implement Series.autocorr. autocorr calculates the Pearson correlation between the Series and itself lagged by N steps. Conceptually, this is a combination of shift and corr.

import pandas as pd
​
s = pd.Series([0.25, 0.5, 0.2, -0.05])
print(s.autocorr())
print(s

Are you using Python or Rust?

Python

Which feature gates did you use?

This can be ignored by Python users.

What version of polars are you using?

v0.10.19

What operating system are you using polars on?

RHEL8

Describe your bug.

When the input of from_arrow() is a concatenated pyarrow table, while that table is constructed from multiple chunked ar

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

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
I just tried out the official Ballista 0.6.0 release by following the instructions shown here and here.

Overall, this was really great and this is the first Ballista release that is easy to try out via `cargo

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

Add arrow.rs in benches/ and perform micro-benchmarks for creating ImmutableTable andRawTable to understand how our StructBuilder approach performs.

Using different sizes where the one below is "small".

#[derive(Arrow, Clone)]
pub struct Event {
   pub id: u64,
   pub data: f32,
}