pandas

Pandas version checks

• I have checked that the issue still exists on the latest versions of the docs on main here

Location of the documentation

https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.rename.html#pandas.DataFrame.rename

Documentation problem

The docs for the inplace parameter state: "Whether to return a

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

We're trying to introduce Parquet into our team, and the largest blocker that we've seen is the dreaded "schemas are inconsistent" error message:

RuntimeError: Schemas are inconsistent, try using to_parquet(..., schema="infer"), or pass an explicit pyarrow schema. Such as to_parquet(..., schema={"column1": pa.string()})

This error message is super unhelpful: surely Dask knows what th

Describe the bug
series.unique() returns a cuDF.Series while it returns a numpy.ndarray for pandas.

Steps/Code to reproduce bug

In [1]: import cudf
In [2]: import pandas as pd

In [3]: type(pd.Series([1,1]).unique())
Out[3]: numpy.ndarray

In [4]: type(cudf.Series([1,1]).unique())
Out[4]: cudf.core.series.Series

Expected behavior
I would exp

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