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#!/usr/bin/env python3
#this file create a freq dict of text

import re
import os
import sys
import uu
import operator

class FrequencyDict():

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#!/usr/bin/env python3

import argparse


def parse_args():
    """ Process command line arguments. """
    parser = argparse.ArgumentParser()
    parser.add_argument("-c", "--column", type=int,
                        default=0,

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#!/bin/sh
# chkconfig: - 95 95
### BEGIN INIT INFO
# Provides:          someservice
# Required-Start:    $ALL
# Required-Stop:
# Default-Start:     2 3 4 5
# Default-Stop:
# X-Interactive:     false
# Short-Description: someservice

dmitryhd

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

""" """
from jinja2 import Template


# python3
def render_jinja(template: str, parameters: dict) -> str:
    """

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import pandas as pd
from joblib import Parallel, delayed

data = []
func = None

def process_chunk(chunk_size, num):
    res = data[chunk_size * num: chunk_size * (num + 1)].apply(func)
    return res

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# How to execute ipython
ipython nbconvert --to=html --ExecutePreprocessor.enabled=True test_notebook.ipynb

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#!/bin/bash

# runs ipython notebook by cron
# Add to cron
# 42 14 * * * /home/user/cron_ipython_runner.sh

source $HOME/.profile

WORKDIR=/var/local/
RUNNERLOG=/tmp/recomm_runner.log

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# Postgres
from sqlalchemy import create_engine
engine = create_engine('postgresql://USER:PASS@HOST/')
engine.execute('select 1').fetchall()

# Vertica
import pyodbc
conn_string = 'DSN=Vertica;'
con = pyodbc.connect(conn_string)

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from multiprocessing import Process
from time import sleep
import pandas as pd
series = pd.Series(range(100))

data = series[:3]

def process_chunk(chunk_id, chunk_size):
    local_data = data[chunk_id * chunk_size: (chunk_id + 1) * chunk_size]
    for x in local_data:

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def decor(func):
    def wrap(*args, **kwargs):
        print('before a')
        print(args)
        args[0].log()
        res = func(*args, **kwargs)
        print('after a')
    return wrap

def param_decor(param1):