pandas

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Reproducible Example

import numpy as np
import pandas as pd

s = pd.Series([

Describe the bug

Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

Steps to reproduce the bug

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("

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#9130 adds a sort_results argument to assert_eq, which allows us to assert DataFrame equality without sorting them (by default, assert_eq sorts the input DataFrames before checking equality). It'll be nice to identify+update the tests that would benefit from it.

A first step could be to run the dataframe tests with check_index=False and sort_results=False as defaults.

Problem statement

The algorithms cudf::reduce and cudf::segmented_reduce do not accept an initial value.

Similar algorithms like [std::reduce](https://e

Reading currencies, alphavantage returns a greeting note ("welcome") and this note raises an error in alphavantage.py line 363.

            elif "Note" in json_response and self.treat_info_as_error:
                raise ValueError(json_response["Note"])

For this reason, alphavantage does not work in home assistant.

I would like to convert a DataFrame to a JSON object the same way that Pandas does with to_dict().

toJSON() treats rows as elements in an array, and ignores the index labels. But to_dict() uses the index as keys.

Here is an example of what I have in mind:

function to_dict(df) {
    const rows = df.toJSON();
    const entries = df.index.map((e, i) => ({ [e]: rows[i] }));