pandas

Pandas version checks

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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("

• Base README.md
• Quizzes
• Introduction base README
  • Defining Data Science README
  • Defining Data Science assignment
  • Ethics README
  • Ethics assignment
  • Defining Data README
  • Defining Data assignment
  • Stats and Probability README
  • Stats and Probability assignment
• Working with Data base README
  • Rel

This is a suggestion to simplify reporting the dask version in the issue template by providing a suggested code snippet to run (in the Environment section of the issue template):

import dask; print("- Dask version:", dask.__version__)
import sys; print("- Python version:", sys.version)

The use of an mr parameter in inplace_bitmask_and, which calls inplace_bitmask_binop, is a little misleading. The allocations there are always temporary and are not part of the return value. It is only used for a few temporary arrays/scalars: https://github.com/rapidsai/cudf/blob/1f8a03e69704562dfac38de40b7172650280c6ea/cpp/include/cudf/detail/null_mask.cuh#L169-L171

It should be possible

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] }));
  

Wondering if this already exists? If not happy to create if valuable.

I'm looking for a mapping from the column names outputted, to the actual technical indicator it represents.

examples:
momentum_ao == "Momentum, Awesome Oscilator"
momentum_kama == "Momentum, Kaufman’s Adaptive Moving Average (KAMA)"

Can help quickly grasp what the features represent without having to refer back to do