larsmans

"""
Three ways of computing the Hellinger distance between two discrete
probability distributions using NumPy and SciPy.
"""

import numpy as np
from scipy.linalg import norm
from scipy.spatial.distance import euclidean

rvl

Pushing to Multiple Git Repos

If a project has to have multiple git repos (e.g. Bitbucket and Github) then it's better that they remain in sync.

Usually this would involve pushing each branch to each repo in turn, but actually Git allows pushing to multiple repos in one go.

If in doubt about what git is doing when you run these commands, just

patpohler

Big List of Real Estate APIs

Listings / Property Data

####Rets Rabbit http://www.retsrabbit.com

Rets Rabbit removes the nightmare of importing thousands of real estate listings and photos from RETS or ListHub and gives you an easy to use import and Web API server so you can focus on building your listing search powered website or app.

HenrikEckermann

import pandas as pd
import numpy as np
from plotnine import *

#import data
bdf = pd.read_csv('http://www.stat.columbia.edu/~gelman/arm/examples/beauty/ProfEvaltnsBeautyPublic.csv')
#Rename btystdave to beauty for convenience
bdf.columns = ['beauty' if x=='btystdave' else x for x in bdf.columns]
bdf.info()

esmitt

# Numpy is needed because OpenCV images in python are actually numpy arrays.
import numpy
import cv2

class iris_detection():
    def __init__(self, image_path):
        '''
        initialize the class and set the class attributes
        '''
        self._img = None

hellock

conda create -n open-mmlab python=3.7 -y
source activate open-mmlab

conda install -c pytorch pytorch torchvision -y
conda install cython -y
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .

mkdir data

steinwaywhw

Preface

In general, AWS services can be accessed using

1. AWS web interface,
2. API libraries in a programming language, such as boto3 for Python 3,
3. AWS command-line interface, i.e. awscli.

I opted for the API library since it is

ejlp12

import boto3

client = boto3.client('glue')

response = client.create_crawler(
    Name='SalesCSVCrawler',
    Role='AWSGlueServiceRoleDefault',
    DatabaseName='sales-cvs',
    Description='Crawler for generated Sales schema',

andrewodri

This script performs the following actions:

1. Creates a TLS certificate in ACM
2. Upserts a validation CNAME record in Route 53
3. Waits for the validation CNAME record to complete/update
4. Waits for the certificate to validate and issue
5. Outputs a description of the certificate

This obviously assumes that your domain's DNS is hosted on Route 53. It also uses the AWS credentials and region for the environment it is executed in.

thomasbrandon