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import scipy.stats as scs

def min_sample_size(bcr, mde, power=0.8, sig_level=0.05):
    """Returns the minimum sample size to set up a split test

    Arguments:
        bcr (float): probability of success for control, sometimes
        referred to as baseline conversion rate

        mde (float): minimum change in measurement between control

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import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as scs

def z_val(sig_level=0.05, two_tailed=True):
    """Returns the z value for a given significance level"""
    z_dist = scs.norm()
    if two_tailed:
        sig_level = sig_level/2
        area = 1 - sig_level

mnguyenngo

import yaml

with open("apikey.yaml", 'r') as f:
    try:
        credentials_dict = yaml.load(f)
    except yaml.YAMLError as exc:
        print(exc)

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# update the virtual machine
sudo apt-get update

# install nginx
sudo apt-get install nginx

# check version
sudo nginx -v

# start the nginx service

mnguyenngo

# install docker
sudo apt-get install docker
# the above command did not work (8/24/18); try the command below
sudo curl -sSL https://get.docker.com/ | sh

# pull nginx image
sudo docker pull nginx:1.10.0
sudo docker images

# verify the versions match

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"""simple Dockerfile
FROM alpine:3.1  # starter image (typical)
MAINTAINER <name> <email>
ADD hello /usr/bin/hello  # ADD <bin> <path-on-image>
ENTRYPOINT ["hello"]  # run hello app on startup
"""

# install GO
wget https://storage.googleapis.com/golang/go1.6.2.linux-amd64.tar.gz
rm -rf /usr/local/bin/go

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# see all images
sudo docker images

# docker tag command help
docker tag --help

# add your tag to the image
sudo docker tag monolith:1.0.0 <your username>/monolith:1.0.0
# you can also rename the image
sudo docker tag monolith:1.0.0 udacity/example-monolith:1.0.0

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plac Quickstart

plac makes parsing arguments from the command line easy.

plac Documentation: https://micheles.github.io/plac/

---

This short guide will only cover the decorator function plac.annotations().

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# set bins to the (number of bins + 1) - 0.5 to center the bar on the right x-label
df['col_name'].hist(xrot=90, figsize=(12,6), rwidth=0.5, bins=np.arange(9)-0.5)

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from flask import Flask
from flask_restful import reqparse, abort, Api, Resource
import pickle
import numpy as np
from model import NLPModel

app = Flask(__name__)
api = Api(app)

model = NLPModel()