onyxfish

import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()
     
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)

dwyerk

bonzanini

curl -XPOST http://localhost:9200/test/articles/1 -d '{
    "content": "The quick brown fox"
}'
curl -XPOST http://localhost:9200/test/articles/2 -d '{
    "content": "What does the fox say?"
}'
curl -XPOST http://localhost:9200/test/articles/3 -d '{
    "content": "The quick brown fox jumped over the lazy dog"
}'
curl -XPOST http://localhost:9200/test/articles/4 -d '{

jqtrde

Libraries for a modern geospatial workflow

Distribution

• Writing, Running, and Distributing
• Tox driven development
• Publishing python libraries

Numpy

DaniSancas

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

• Node: Graph data records
• Relationship: Connect nodes (has direction and a type)
• Property: Stores data in key-value pair in nodes and relationships
• Label: Groups nodes and relationships (optional)

evansde77

#!/usr/bin/env python
"""
mocking requests calls

"""
import mock
import unittest
import requests
from requests.exceptions import HTTPError

Orbifold