pgolding

# coding=utf8
# the above tag defines encoding for this document and is for Python 2.x compatibility

import re

# Find words in the csv list that begin with 'he'
regex = r"\bhe"

test_str = "shoes, footwear, protection, comfort, human, foot, activity, walking, running, high-heel, stiletto, flat, sole, heel, hole, fashion, design, style, cost, sandal, boot, leather, wood, canvas, rubber, plastic, terrain, climate, well-heeled, work, empty, throw, insole, laces, slip, athletic, sneakers, gym, ballet, tap, horseshoe, polish, factory"

pgolding

The update_item method in Boto3 requires preparing something like this:

try:
    result = table.update_item(
        Key={
            'id': event['pathParameters']['id']
        },
        ExpressionAttributeNames=attr_names,
        ExpressionAttributeValues=expression_vals,

pgolding

'''
When rendering SVG files exported from Adobe Illustrator using Phantom JS, 
the injected Adobe ENTITY tags can intefere with the render. This removes
the tags and also removes width/height attributes from the SVG tag.
'''


SVG_FILE = 'Some-adobe-illustrator-export.svg'

# By default, BeautifulSoup will ignore the <!ENTITY> array when parsing the file

pgolding

Handling of StreamingBody Response from Invoking a Lambda Function

When calling a Lambda Function via Boto3, the returned object is something like:

{u'Payload': <botocore.response.StreamingBody object at 0x7f8d5b62ac50>, 
'ResponseMetadata': {'RetryAttempts': 0, 'HTTPStatusCode': 200, 'RequestId': '5bdbb3ca-3b35-11e7-9816-397b140c3bac', 'HTTPHeaders': {'x-amzn-requestid': '5bdbb3ca-3b35-11e7-9816-397b140c3bac', 'content-length': '1636', 'x-amzn-trace-id': 'root=1-591ca199-00d34d5474d16275ec2c8d10;sampled=0', 'x-amzn-remapped-content-length': '0', 'connection': 'keep-alive', 'date': 'Wed, 17 May 2017 19:16:41 GMT', 'content-type': 'application/json'}}, u'StatusCode': 200}

The Payload parameter is <botocore.response.StreamingBody> which is a data streaming object.

pgolding

# http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# The usual creation of arrays produces wrong format (as cosine_similarity works on matrices)
x = np.array([2,3,1,0])
y = np.array([2,3,0,0])

# Need to reshape these
x = x.reshape(1,-1)

pgolding

from __future__ import print_function # Python 2/3 compatibility
import boto3
import json
import decimal
from boto3.dynamodb.conditions import Key, Attr

# Helper class to convert a DynamoDB item to JSON.
class DecimalEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, decimal.Decimal):

pgolding

ABORT
ABSOLUTE
ACTION
ADD
AFTER
AGENT
AGGREGATE
ALL
ALLOCATE
ALTER

pgolding

reserved_words = ['ABORT', 'ABSOLUTE', 'ACTION', 'ADD', 'AFTER', 'AGENT', 'AGGREGATE', 'ALL', 'ALLOCATE', 'ALTER', 'ANALYZE', 'AND', 'ANY', 'ARCHIVE', 'ARE', 'ARRAY', 'AS', 'ASC', 'ASCII', 'ASENSITIVE', 'ASSERTION', 'ASYMMETRIC', 'AT', 'ATOMIC', 'ATTACH', 'ATTRIBUTE', 'AUTH', 'AUTHORIZATION', 'AUTHORIZE', 'AUTO', 'AVG', 'BACK', 'BACKUP', 'BASE', 'BATCH', 'BEFORE', 'BEGIN', 'BETWEEN', 'BIGINT', 'BINARY', 'BIT', 'BLOB', 'BLOCK', 'BOOLEAN', 'BOTH', 'BREADTH', 'BUCKET', 'BULK', 'BY', 'BYTE', 'CALL', 'CALLED', 'CALLING', 'CAPACITY', 'CASCADE', 'CASCADED', 'CASE', 'CAST', 'CATALOG', 'CHAR', 'CHARACTER', 'CHECK', 'CLASS', 'CLOB', 'CLOSE', 'CLUSTER', 'CLUSTERED', 'CLUSTERING', 'CLUSTERS', 'COALESCE', 'COLLATE', 'COLLATION', 'COLLECTION', 'COLUMN', 'COLUMNS', 'COMBINE', 'COMMENT', 'COMMIT', 'COMPACT', 'COMPILE', 'COMPRESS', 'CONDITION', 'CONFLICT', 'CONNECT', 'CONNECTION', 'CONSISTENCY', 'CONSISTENT', 'CONSTRAINT', 'CONSTRAINTS', 'CONSTRUCTOR', 'CONSUMED', 'CONTINUE', 'CONVERT', 'COPY', 'CORRESPONDING', 'COUNT', 'COUN

pgolding

Boto3 documentation for lambda_client.invoke suggests it is possible to pass data via the ClientContext object as a string.

The string is required to be base64 encoded JSON.

To encode such a string, use:

import baseb4
base64.b64encode(b'<string>').decode('utf-8')

pgolding

from botocore import exceptions


try:
 # to update table conditionally
 
except exceptions.ClientError as e:
            if e.response['Error']['Code'] == 'ConditionalCheckFailedException':
                print("ConditionalCheckFailedException detected")
                # do something else, like raise a custom error based on the condition