dask-ml | Scalable Machine Learning with Dask | Machine Learning library

ValueError: Array assignment only supports 1-D arrays

dfy = y.to_dask_dataframe(
    columns=['percentage_qt'],
    index=ddf.index)

ddf_out = ddf.join(dfy)

print(d

Dask-XGBoost has been deprecated and is no longer maintained.
The functionality of this project has been included directly
in XGBoost. To use Dask and XGBoost together, please use
xgboost.dask instead
https://xgboost.readthedocs.io/en/late

conda install -c conda-forge dask-ml

or

pip install dask-ml

import time
from sklearn.datasets import make_classification
from sklearn.preprocessing import QuantileTransformer as skQT
from dask_ml.preprocessing im

from jcrist/alpine-dask

USER root
RUN /opt/conda/bin/conda create -p /pyenv -y
RUN /opt/conda/bin/conda install -p /pyenv dask scikit-learn flask waitress gunicorn \
    pytest apscheduler matplotlib pyodbc -y
RUN /opt/conda/bin/conda ins

import dask.dataframe as dd
from dask_ml.preprocessing import MinMaxScaler

# or read directly from csv with ddf = dd.read_csv('data.csv')
ddf = dd.from_pandas(df, npartitions=10)
scaler = MinMaxScaler(feature_range=(0, 5))
scaler.fit(ddf[

category_mapping = dd.concat([test, test.cat.codes], axis=1)
category_mapping.columns = ["Category", "Code"]
category_mapping = category_mapping.drop_duplicates()
print(category_mapping.compute())

       Category