NumPy

🐛 Bug

--- Logging error ---
Traceback (most recent call last):
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/logging/__init__.py", line 1025, in emit
    msg = self.format(record)
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/logging/__init__.py", line 869, in format
    return fmt.format(record)
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/site-packages/_pytes

I notice that there are still a fair number LGTM alerts, many of which look pretty simple. I wonder if they would be a good source of issues for the next NumPy sprint?

Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq

Currently assert_eq takes both check_dtypes & check_dtype. The latter is forwarded to assert_series_equal. For example see this code:

https://github.com/dask/

The Github issue tracker URL in error messages should be updated to use issue templates

Originally posted by @esc in numba/numba#7223 (comment)

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

What's wrong?

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

hi,
if possible, please add these indicators as well:
TDI (Traders Dynamic Index)
chandelier exit
pivot points
BOP (balance of power)
CTM (Chande trend meter)
Coppock Curve
Correlation Coefficient
PMO (DecisionPoint Price Momentum Oscillator)
Ulcer Index
most of them except TDI are available on stockcharts.com
thanks

Support Series.between

Is your feature request related to a problem? Please describe.
Sometimes you want to check that data values are present in another array, but only up to a certain tolerance.

Describe the solution you'd like
da.isin(test_values, tolerance=1e-6), where the tolerance argument is optional.

Not sure what the implementation should be but there are two vectorized [suggestions here](http