dask

I naively tried to do dd.merge(a, b, on="column_with_ten_values"), where a and b were both large DataFrames with thousands of partitions each.

Eventually the compute failed with:

[File /opt/conda/envs/coiled/lib/python3.9/site-packages/dask/dataframe/multi.py:275, in merge_chunk()

File /opt/conda/envs/coiled/lib/python3.9/site-packages/pandas/core/frame.py:9329, i

Describe the bug
cudf.pivot doesn't understand "values" when it's column name instead of list and the column name includes numeric

Steps/Code to reproduce bug

import cudf
df = cudf.DataFrame([
    ['A', 'a', 0, 0, 0],
    ['A', 'b', 1, 1, 1],
    ['A', 'c', 2, 2, 2],
    ['B', 'a', 0, 0, 0],
    ['B', 'b', 1, 1, 1],
    ['B', 'c', 2, 2, 2],
    ['C', 'a', 0, 0, 0],
    [

pydata/xarray#5865 (reply in thread)

I wonder if it's possible to implement a built-in function like:
da.str.format("%.2f") or xr.string_format(da, "%.2f)

To wrap:

import xarray as xr

da = xr.DataArray([5., 6., 7.])
das = xr.DataArray("%.2f")
das.str % da

<xarray.DataArray (dim_0: 3)>
array(['5.00', '6.00', '7.00'], dtype='<U4')
Dim

Is your feature request related to a problem? Please describe.
Implements classification_report for classification metrics.(https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)

The stumpy.snippets feature is now completed in #283 which follows this work:

• The original published paper
• The supporting website with data examples

We have a rough notebook t

What happened:

When creating a LocalCluster object the comm is started on a random high port, even if there are no other clusters running.

What you expected to happen:

Should use port 8786.

Minimal Complete Verifiable Example:

$ conda create -n dask-lc-test -c conda-forge -y python=3.8 ipython dask distributed
$ conda activate dask-lc-test

The `d

Feature Request

Is your feature request related to a problem? Please describe.

Whenever I report a bug, I need to confirm what satpy version I am using. This is of course important, but it's also an extra step that could be semi-automated.

Describe the solution you'd like

I would like that debug_on() prints the relevant versions. When we report bugs, we anyway call `debu

Is your feature request related to a problem? Please describe.
Look at here

If taking just one row with our sorting, we may use GROUP BY and FIRST to solve this problem, it can be a lot faster. Let's add this special handling.

Code Sample, a minimal, complete, and verifiable piece of code

from pyresample.boundary import Boundary
b = Boundary(my_lons, my_lats)
print(b.contour_poly.area())

Problem description

The above code doesn't fail if the provided lons/lats are 2D (not sure on 3D+), but the class and all functions/utilities underneath it assume 1D arrays. The end results are incor

The ML implementation is still a bit experimental - we can improve on this:

• SHOW MODELS and DESCRIBE MODEL
• Hyperparameter optimizations, AutoML-like behaviour
• @romainr brought up the idea of exporting models (#191, still missing: onnx - see discussion in the PR by @rajagurunath)
• and some more showcases and examples

Does HyperGBM's make_experiment return the best model?
How does it work on paramter tuning? It's say that, what's its seach space (e.g. in XGboost)???

from dask_jobqueue import SLURMCluster 
cluster = SLURMCluster(cores=1, memory='1GB') 
print(cluster.job_script()) 

#!/usr/bin/env bash

#SBATCH -J dask-worker
#SBATCH -n 1
#SBATCH --cpus-per-task=1
#SBATCH --mem=954M
#SBATCH -t 00:30:00

/home/lesteve/miniconda3/bin/python -m distributed.cli.dask_worker tcp://192.168.0.11:44065 --nthreads 1 --memory-limit 1000.00MB -

Problem description

Reading a dataset with eager's read functionality raises a ValueError when providing columns.

Example code (ideally copy-pastable)

import pandas as pd

from tempfile import TemporaryDirectory
from functools import partial
from storefact import get_store_from_url

from kartothek.io.eager import store_dataframes_as_dataset, read_dataset_as_data

Example for numerical weather prediction

to be added to initialised datasets

Data sources (to) implement(ed):

• GEFS https://www.ncei.noaa.gov/thredds/catalog/model-gefs-003/202008/20200831/catalog.html
• DWD https://opendata.dwd.de/weather/nwp/

relates to #600

Without thinking I put resampling="bilinear" and got an error when I called .compute()

Traceback (most recent call last):
  File "carajas.py", line 92, in <module>
    band_medianNP = band_median.compute()
  File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/xarray/core/dataarray.py", line 899, in compute
    return new.load(**kwargs)
  File "/home/ubuntu/anaco

Hi,

System and Software

• aicsimageio Version: 4.5.2
• Python Version: 3.10
• Operating System: Windows 10

Description

aiscimageio installs fine but when we try pip install aiscimageio[czi]¹ it fails with the following error

ERROR: Command errored out with exit status 1:
command: 'C:\Program Files\Python310\python.exe' -u -c 'import io, os, sys, setuptools, to

Currently all of the metrics computed are independent of a target variable or column, but if lens.summarise took the name of a column as the target variable, the output of some metrics could be more interpretable even if the target variable is not used in any kind of predictive modelling.

A good example of this could be PCA (see #14), which could plot the different categories of the target va