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from sqlalchemy import Table, MetaData, create_engine
from sqlalchemy.engine import reflection

# Configurações de conexão abstraídas em variáveis de ambiente setadas num arquivo .env
DB_USER = os.getenv('DB_USER')
DB_PASS = os.getenv('DB_PASS')
DB_HOST = os.getenv('DB_HOST')

DB_NAME = os.getenv('DB_NAME')
SQLALCHEMY_DATABASE_URL = f"postgresql+psycopg2://{DB_USER}:{DB_PASS}@{DB_HOST}:5432/{DB_NAME}"

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import pandas as pd
import numpy as np
import fastparquet
from sqlalchemy import create_engine, schema, Table

# Copied from pandas with modifications


def __get_dtype(column, sqltype):
    import sqlalchemy.dialects as sqld

hilam

init python:

    class DynamicBlink(renpy.display.layout.DynamicDisplayable):
        """
        A dynamic image that blinks every now and then
        """

        def __init__(self, *args, **kwargs):
            self.current_image = args[1]
            self.blink_st = -1.0 # arbitrary number to force normal start

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#!/bin/bash
#Removes old revisions of snaps
#CLOSE ALL SNAPS BEFORE RUNNING THIS
set -eu
LANG=en_US.UTF-8 snap list --all | awk '/disabled/{print $1, $3}' |
    while read snapname revision; do
        snap remove "$snapname" --revision="$revision"
    done

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INSERT GRAPHIC HERE (include hyperlink in image)

Repository Title Goes Here

Subtitle or Short Description Goes Here

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import cProfile, pstats, StringIO 

pr = cProfile.Profile() 
pr.enable() 
# ... do something ... 
pr.disable() 

s = StringIO.StringIO()
ps = pstats.Stats(pr, stream=s).sort_stats(‘cumulative’) 
ps.print_stats() 

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#!/usr/bin/env python

import tkinter as tk
import math
import re
from collections import ChainMap

Nrows = 5
Ncols = 5

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#!/bin/bash
# Adicione um novo remote; pode chamá-lo de "upstream":

git remote add upstream https://github.com/usuario/projeto.git

# Obtenha todos os branches deste novo remote,
# como o upstream/master por exemplo:

git fetch upstream

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How to split a file into smaller chunks?

You may want to upload a 100GB file over an unstable network, or feed your scripts smaller inputs to load in RAM. In both cases, just splitting your file into smaller chunks is an easy solution.

Great news is Unix/Linux systems have the split utility already installed. And using it is simple as pie:

Cut a binary file into chunks of X bytes:

split -b X bigfile.avi

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# enable color support of ls and also add handy aliases
if [ -x /usr/bin/dircolors ]; then
    test -r ~/.dircolors && eval "$(dircolors -b ~/.dircolors)" || eval "$(dircolors -b)"
    alias ls='ls --color=auto'
    alias grep='grep --color=auto'
fi
 
# some more ls aliases
alias ll='ls -la -h'