timesler

markymarkus

---
Parameters:
  pBucketName:
    Type: String
    Default: ''
    Description: Input S3 Bucket
Resources:
  StateMachine:
    Type: AWS::StepFunctions::StateMachine
    Properties:

wongcyrus

ip=$(curl http://169.254.169.254/latest/meta-data/public-ipv4)
echo "Code Server"
echo "http://$ip:8443"
security_group=$(ec2-metadata -s | cut -d " " -f 2);
aws ec2 authorize-security-group-ingress --group-name $security_group --protocol tcp --port 8443 --cidr 0.0.0.0/0
version="1.32.0-310"
wget https://github.com/codercom/code-server/releases/download/$version/code-server-$version-linux-x64.tar.gz
tar -xvzf code-server-$version-linux-x64.tar.gz
cd code-server-$version-linux-x64
chmod +x code-server

ivankeller

def dataframe_constructor(df):
    return "df = pd.DataFrame(%s)" % (str(df.to_dict()).replace("nan"," float('nan')").replace('array', 'np.array'))

fedme

Run Visual Studio Code for Linux from WSL

Thanks a lot to mredbishop and others for their insturctions posted here. This is just a recap of what they figured out.

This process was tested on WSL Ubuntu 18.04.

Install VcXsrv on Windows

1. Dowload the VcXsrv installer from https://sourceforge.net/projects/vcxsrv/
2. Install the software on Windows

Add VS Code repositories

sysboss

#!/usr/bin/env python3
#
# Query AWS Athena using SQL
# Copyright (c) Alexey Baikov <sysboss[at]mail.ru>
#
# This snippet is a basic example to query Athen and load the results
# to a variable.
#
# Requirements:
# > pip3 install boto3 botocore retrying

schledererj

# Does NOT implement the PEP 249 spec, but the return type is suggested by the .fetchall function as specified here: https://www.python.org/dev/peps/pep-0249/#fetchall

import time
import boto3

# query_string: a SQL-like query that Athena will execute
# client: an Athena client created with boto3
def fetchall_athena(query_string, client):
    query_id = client.start_query_execution(
        QueryString=query_string,

dboyd13

    ___      _____          ___         _   _  __ _        _   ___              _                       _                   _      _       
   /_\ \    / / __|  ___   / __|___ _ _| |_(_)/ _(_)___ __| | |   \ _____ _____| |___ _ __  ___ _ _    /_\   ______ ___  __(_)__ _| |_ ___ 
  / _ \ \/\/ /\__ \ |___| | (__/ -_) '_|  _| |  _| / -_) _` | | |) / -_) V / -_) / _ \ '_ \/ -_) '_|  / _ \ (_-<_-</ _ \/ _| / _` |  _/ -_)
 /_/ \_\_/\_/ |___/        \___\___|_|  \__|_|_| |_\___\__,_| |___/\___|\_/\___|_\___/ .__/\___|_|   /_/ \_\/__/__/\___/\__|_\__,_|\__\___|
                                                                                     |_|                
Notes taken in Jan-2018, from acloud.guru and AWS FAQs.
 
There is a lot of overlap in knowledge areas between Solution Architect Associate, and the Certified Developer Associate.

Hence this doc only covers the deltas for the CDA exam.

DustinAlandzes

# N-CryptoAsset Portfolios: Identifying Highly Correlated 
# Cryptocurrencies using PCA
#
# (c) 2017 QuantAtRisk.com, by Pawel Lachowicz
 
 
import numpy as np
import pandas as pd
from scipy import stats
from matplotlib import pyplot as plt

yassineAlouini

import pandas as pd

def compare_two_dfs(input_df_1, input_df_2):
    df_1, df_2 = input_df_1.copy(), input_df_2.copy()
    ne_stacked = (df_1 != df_2).stack()
    changed = ne_stacked[ne_stacked]
    changed.index.names = ['id', 'col']
    difference_locations = np.where(df_1 != df_2)

    changed_from = df_1.values[difference_locations]