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?

0.9.12

What operating system are you using polars on?

macOS

Describe your bug.

When reading a CSV file with variable number of columns, polars assumes all rows have the number of columns inferred from th

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.
There are some discussions in #1228

Describe the solution you'd like
like #1290

Describe alternatives you've considered
The task is mechanical, however, it is ideal for newcomers to familiarize themselves with datafusion's build, test, and PR processes.

**Additional context

Hi,

When I configure a Google Spreadsheet using the Docker environment, I get the following error:

[2021-11-12T11:35:25Z INFO  roapi_http::api] loading `uri(https://docs.google.com/spreadsheets/d/<redacted>)` as table `<redacted>`
thread 'main' panicked at 'no CA certificates found', /usr/local/cargo/registry/src/github.com-1ecc6299db9ec823/hyper-rustls-0.22.1/src/connector.rs:45:13
`

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,
}