podhmo

[<__main__.Sub object at 0x1f44250>, <__main__.Sub object at 0x1f44390>, <__main__.Sub object at 0x1f44350>]
[u'a', u'b', u'c']



pre commit
------
	ok
after commit
------

szs8

#!/usr/bin/env python

import wx
from wx import EVT_CLOSE
import wx.grid as gridlib

EVEN_ROW_COLOUR = '#CCE6FF'
GRID_LINE_COLOUR = '#ccc'

class PandasTable(wx.Frame):

cmslewis

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>


/* ==========================================================================
 * COMPARATORS
 * ========================================================================== */

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






\

aldraco

def gcd(m,n):
    while m % n != 0:
        oldm = m
        oldn = n
        m = oldn
        n = oldm % oldn
    return n

class Rational:
    def __init__(self, n, d):

DaniSancas

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

• Node: Graph data records
• Relationship: Connect nodes (has direction and a type)
• Property: Stores data in key-value pair in nodes and relationships
• Label: Groups nodes and relationships (optional)

malexer

# Note: it is a copy of great answer by "mgoldwasser" from Stackoverflow
# Check the original answer here: http://stackoverflow.com/a/26018934/1032439

# Imagine that post1, post5, and post1000 are posts objects with ids 1, 5 and 1000 respectively
# The goal is to "upsert" these posts.
# we initialize a dict which maps id to the post object

my_new_posts = {1: post1, 5: post5, 1000: post1000} 

for each in posts.query.filter(posts.id.in_(my_new_posts.keys())).all():

thomas-a-neil

from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator

from datetime import datetime, timedelta


# default arguments for each task
default_args = {
    'owner': 'nthomas',

kdubovikov

import numpy as np
import tensorflow as tf

from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):

uhho

def s3_to_pandas(client, bucket, key, header=None):

    # get key using boto3 client
    obj = client.get_object(Bucket=bucket, Key=key)
    gz = gzip.GzipFile(fileobj=obj['Body'])
    
    # load stream directly to DF
    return pd.read_csv(gz, header=header, dtype=str)

def s3_to_pandas_with_processing(client, bucket, key, header=None):