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from zeep import Client

client = Client("https://webservices.cibg.nl/Ribiz/OpenbaarV4.asmx?wsdl")
client.service.ListHcpApprox4(WebSite="Ribiz", Name="Kerkhoven", Initials="M")

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def sparse_matrix_to_tensor(X):
    """Transforms SciPy sparse matrix to tensorflow.sparse.SparseTensor."""
    row_nnz = np.diff(X.indptr)
    indices = np.asarray([[row_i, col_i]
                          for row_i, nnz in enumerate(row_nnz)
                          for col_i in range(nnz)], dtype=np.int64)
    values = X.data
    return SparseTensor(indices=indices, values=values, dense_shape=X.shape)

dkapitan

Base image

• install Windows Terminal
• install wsl
• install Ubuntu
• sudo apt update && sudo apt upgrade
• install Homebrew
• install pyenv
  • Issue with ctypes module: pyenv/pyenv#1933
• install oh-my-posh
• install jump

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def make_polynomial(dataframe, degree=MAX_DEGREE):
    """Function for creating higher-order polynomial features from dataframe.

    Dataframe df should be like [Y, X1, X2, .. Xi].
    Returns dataframe polynomial features of X1 ... Xi up to degree polynomials."""

    df = dataframe.copy()
    cols = df.columns[1:]
    for i in range(2, degree + 1):
        for col in cols:

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# baseURI: http://opendata.stelselcatalogus.nl/id/dataset/sc
# imports: http://purl.org/dc/elements/1.1/
# imports: http://rdfs.org/ns/void
# imports: http://www.w3.org/2004/02/skos/core

@prefix adms: <http://www.w3.org/ns/adms#> .
@prefix begrip_banken: <http://opendata.stelselcatalogus.nl/banken/id/begrip/> .
@prefix begrip_bgt: <http://opendata.stelselcatalogus.nl/bgt/id/begrip/> .
@prefix begrip_bri: <http://opendata.stelselcatalogus.nl/bri/id/begrip/> .
@prefix begrip_brk: <http://opendata.stelselcatalogus.nl/brk/id/begrip/> .

dkapitan

Title

When released, this story will ENTER TEXT HERE.

Description

As a ENTER ROLE I want ENTER GOAL, so that ENTER REASON(S).

Requirements

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KWB = {
    2016: "83487NED",
    2017: "83765NED",
    2018: "84286NED",
    2019: "84583NED",
    2020: "84799NED"
}

dkapitan

• 3.6: f-strings
• 3.7: async and await; dataclasses
• 3.8: assignment expression (:=)
• 3.9: type hinting generics in standard library

dkapitan

Zan Armstrong's comet chart has been on my list of hobby projects for a while now. I think it is an elegant solution to visualize statistical mix effects and address Simpson's paradox, and particularly useful when working with longitudinal data involving different sub-populations. Recently I found a good excuse to spend some time to actually use it as part of a exploratory data analysis on a project.

Since I mostly work in Python and have recently fallen in love with Altair - for the same reasons as Fernando explains here - I wondered how the comet chart could be implemented using the grammar of interactive graphics. It took me a while to figure out how to actually plot the c

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import altair as alt
import pandas as pd
import vega_datasets


# Use airline data to assess statistical mix effects of delays
flights = vega_datasets.data.flights_20k()
aggregation = dict(
    number_of_flights=("destination", "count"),
    mean_delay=("delay", "mean"),