Skip to content

Instantly share code, notes, and snippets.

View moritzkoerber's full-sized avatar

Moritz Körber moritzkoerber

View GitHub Profile
@moritzkoerber
moritzkoerber / stack.tf
Created February 13, 2024 17:05
A terraform script to deploy a service principal for Workload identity federation (OIDC)
terraform {
required_providers {
azurerm = {
source = "hashicorp/azurerm"
version = ">=3.81.0"
}
azuredevops = {
source = "microsoft/azuredevops"
version = ">= 0.10.0"
}
import pandas as pd
df = pd.DataFrame(
dict(
week=[1, 1, 2, 2, 3, 3] * 2,
layout=["classic", "classic", "modern", "modern"] * 3,
response=["conversion", "exit"] * 6,
cnt=[26, 23, 45, 34, 55, 44, 53, 27, 28, 25, 30, 34],
)
)
import plotly.graph_objects as go
fig = go.Figure()
fig.update_layout(
template="simple_white",
xaxis=dict(title_text="Week"),
yaxis=dict(title_text="Count"),
barmode="stack",
)
@moritzkoerber
moritzkoerber / release.yml
Created August 14, 2021 09:58
Manual PyPi Release GitHub Actions Workflow
name: Manual PyPi Release
on:
workflow_dispatch:
inputs:
package_repository:
description: '"testpypi" or "pypi"'
required: true
tag:
required: true
import yaml
import great_expectations as ge
import os
from great_expectations.cli.datasource import sanitize_yaml_and_save_datasource
from great_expectations.core.batch import BatchRequest
from great_expectations.core.expectation_configuration import ExpectationConfiguration
from contextlib import suppress
project_dir = f"{os.getcwd()}/own_de_project/great_expectations"
@moritzkoerber
moritzkoerber / model_training_for_text_analysis.py
Last active March 8, 2021 08:30
Trains a model to analyze text messages.
import argparse
import pickle
import string
import sys
import nltk
import pandas as pd
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tokenize import word_tokenize
df["scoops"] = df["ice_cream_cones"] * 2
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
x=0.7,
y=1.2,
showactive=True,
def convert_to_sixpack(x):
return x / 6
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
x=0.7,
import pandas as pd
df = pd.DataFrame(
dict(temperature=[24, 26, 28], ice_cream_cones=[14, 20, 23], drinks=[18, 22, 28])
)
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
x=0.7,
y=1.2,
showactive=True,
buttons=list(
[