liquidgenius

import pymysql
from pprint import pprint as pprint
__author__ = "Liquidgenius"

class Query:

    def __init__(self, host, port, user, password, schema):
        self.host = host
        self.port = port
        self.user = user

liquidgenius

import asyncio
import threading
import random


def thr(i):
    # we need to create a new loop for the thread, and set it as the 'default'
    # loop that will be returned by calls to asyncio.get_event_loop() from this
    # thread.
    loop = asyncio.new_event_loop()

liquidgenius

"""
"""

import multiprocessing
import boto
import os
import sys
import datetime
import logging
import Queue

liquidgenius

# -*- coding: utf-8 -*-
"""Utilities for working with S3."""
from __future__ import unicode_literals

import logging
import threading
import zlib
import six

from boto3.session import Session

liquidgenius

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$ docker

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liquidgenius

# vim: set fileencoding=utf-8 :
#
# How to store and retrieve gzip-compressed objects in AWS S3
###########################################################################
#
#   Copyright 2015 Vince Veselosky and contributors
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at

liquidgenius

from io import BytesIO
import gzip
import shutil


def upload_gzipped(bucket, key, fp, compressed_fp=None, content_type='text/plain'):
    """Compress and upload the contents from fp to S3.

    If compressed_fp is None, the compression is performed in memory.
    """

liquidgenius

def pendulum_to_pandas(pend_dt):
  """Converts a Pendulum datetime to a Pandas datetime

  Parameters
  -----------
  pend_dt (Pendulum.datetime): Any Pendulum datetime. Pendulum datetimes include 
      nanoseconds that Pandas does not support.

  Returns
  --------

liquidgenius

# Pipenv Cheat Sheet

## Install pipenv
```
pip3 install pipenv
```
## Activate
```
pipenv shell
```

liquidgenius

import pendulum
import pandas as pd

def generate_pd_timestamp_series(start_year,
                                 start_month,
                                 start_day,
                                 end_year,
                                 end_month,
                                 end_day,
                                 time_zone,