JasonCrowe

import requests

data = {
    'view_name': 'discover_artist_subpages_content_block',
    'view_display_id': 'block_1',
    'view_args': '46,51,56/all/206/all/all',
    'view_path': 'discover/artists',
    'view_dom_id': '95603486a7f8f13392af1f532e99b6ad',
    'pager_element': '0',
    'featured': 'all',

JasonCrowe

coins = [
    {'name': 'ETH', 'qty': 0.81945672, 'price': 215.15},
    {'name': 'USDT', 'qty': 125.99, 'price': .99},
    {'name': 'BTC', 'qty': 0.02497291, 'price': 6895.15},
    {'name': 'BNB', 'qty': 20.91476451, 'price': 10.25},
]

def generate_balance(coins, prec=8):
    min = 999*999
    max = 0

JasonCrowe

import os, sys
import Image

size = 128, 128

for infile in sys.argv[1:]:
    outfile = os.path.splitext(infile)[0] + ".thumbnail"
    if infile != outfile:
        try:
            im = Image.open(infile)

JasonCrowe

from xlsxwriter import Workbook
players = [
    {'dailyWinners': 3, 'dailyFree': 2, 'user': 'Player1', 'bank': 0.06},
    {'dailyWinners': 3, 'dailyFree': 2, 'user': 'Player2', 'bank': 4.0, 'level': 'Gold'},
    {'dailyWinners': 1, 'dailyFree': 2, 'user': 'Player3', 'bank': 3.1},            
    {'dailyWinners': 3, 'dailyFree': 2, 'user': 'Player4', 'bank': 0.32, 'time': 'AM'}
]

def write_excel(dict_objects, filename, sheetname=None):
    ordered_list = []

JasonCrowe

import json
from glob import glob

def save_data(data):
    try:
        asin = data['asin']
        file_loc = 'data/{}.json'.format(asin)
        file = open(file_loc, 'w+')
        json.dump(data, file)
    except KeyError:

JasonCrowe

user_agent_list = [
    "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",
    "Mozilla/5.0 (IE 11.0; Windows NT 6.3; Trident/7.0; .NET4.0E; .NET4.0C; rv:11.0) like Gecko",
    "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36"
]
num = random.randint(0, (len(user_agent_list) - 1))


def get_page_object(url):
    response = requests.get(

JasonCrowe

import pandas as pd

df = pd.read_csv('file.tab', sep='\t', lineterminator='\r')
import pandas as pd

fwidths = [34, 4, 4, 34, 11, 1, 1, 1, 1, 1, 8, 8, 2, 11, 1, 40, 1, 40, 1, 7, 7, 1, 1, 1, 4, 40, 40, 40, 40, 40, 2, 12, 1, 8, 40, 1, 11, 11, 11, 11, 1 ]


df = pd.read_fwf('file.tab', widths = fwidths, names = ['ACCOUNT', 'YEAR', 'JURISDICTION', 'TAX-UNIT-ACCT', 'LEVY', 'HOMESTEAD', 'OVER65', 'VETERAN', 'DISABLED', 'AG', 'DATE-PAID', 'DUE-DATE', 'OMIT-FLAG', 'LEVY-BALANCE', 'SUIT', 'CAUSENO', 'BANKCODE', 'BANKRUPTNO', 'ATTORNEY', 'COURT-COST', 'ABSTRACT-FEE', 'DEFERRAL', 'BILLSUPP', 'SPLIT-PMTFLAG', 'CATEGORY-CODE', 'OWNER', 'ADDRESS2', 'ADDRESS3', 'ADDRESS4', 'CITY', 'STATE', 'ZIP', 'ROLL-CODE', 'PARCEL NO.', 'PARCEL NAME', 'PAYMENT AGREEMENT', 'TOT_AMT_DUE', 'TOT_AMT_DUE-30', 'TOT_AMT_DUE-60', 'TOT_AMT_DUE-90', 'AMOUNT INDICATOR'])
df[:10]

JasonCrowe

SELECT   item.item,
        qty AS inventory_level,
        description,
        product_code,
        family_code,
        plan_code,
        unit_weight,
        weight_units,
        charfld1 AS upc,
        stat,

JasonCrowe

import nltk, string
from sklearn.feature_extraction.text import TfidfVectorizer

nltk.download('punkt') # if necessary...


stemmer = nltk.stem.porter.PorterStemmer()
remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)

def stem_tokens(tokens):

JasonCrowe

import tempfile
import win32api
import win32print


filename = tempfile.mktemp (".txt")
open (filename, "w").write (str(df))
win32api.ShellExecute (
  0,
  "print",