This is not exactly right, but we have an inconsitency in the argument specifications of using 'array_like' vs 'array-like', in argument types we should settle on one (prob 'array-like')

(pandas-dev) ~/pandas$ grep -r array-like --include '*.py' pandas|wc
     317    2259   24072
(pandas-dev) ~/pandas$ grep -r array_like --include '*.py' pandas|wc
     104     498    7414

Simila

it would be handy to have a passthrough parameter to dd.concat - currently the union_categoricals call is parameter-free:

    dtype = "category"
            if left is not None and right is not None:
                dtype = union_categoricals(
                    [left.astype("category").values, right.astype("category").values]
                ).dtype

This can be made more fle

This happens after a map operation when num_proc is set to >1. I tested this by cleaning up the json before running the map op on the dataset so it's unlikely it's coming from an earlier concatenation.

Example result:

"citation": "@ONLINE {wikidump,\n    author = {Wikimedia Foundation},\n    title  = {Wikimedia Downloads},\n    url    = {https://dumps.wikimedia.org}\n}\n\n@ONLINE 

Describe the bug
After applying the unstack function, the variable names change to numeric format.

Steps/Code to reproduce bug

def get_df(length, num_cols, num_months, acc_offset):
    cols = [ 'var_{}'.format(i) for i in range(num_cols)]
    df = cudf.DataFrame({col: cupy.random.rand(length * num_months) for col in cols})
    df['acc_id'] = cupy.repeat(cupy.arange(length), nu

Hello,

I want to get intraday data without split/dividend adjustment. How can I get raw intraday data?

From API docs:

Optional: adjusted

By default, adjusted=true and the output time series is adjusted by historical split and dividend events. Set adjusted=false to query raw (as-traded) intraday values.

get_intraday and `g

Implements {DataFrame,Series}.empty