zkan

# ---- Base python ----
FROM python:3.6 AS base
# Create app directory
WORKDIR /app

# ---- Dependencies ----
FROM base AS dependencies  
COPY gunicorn_app/requirements.txt ./
# install app dependencies
RUN pip install -r requirements.txt

zkan

import unittest


class FizzBuzz:
    def say(self, number):
        if number % 3 == 0 and number % 5 == 0:
            return 'FizzBuzz'
        elif number % 3 == 0:
            return 'Fizz'
        elif number % 5 == 0:

zkan

class FizzBuzz:
    def say(self, number):
        if number % 3 == 0 and number % 5 == 0:
            return 'FizzBuzz'
        elif number % 3 == 0:
            return 'Fizz'
        elif number % 5 == 0:
            return 'Buzz'
        else:
            return number

zkan

import pytest


class FizzBuzz:
    def say(self, number):
        if number % 3 == 0 and number % 5 == 0:
            return 'FizzBuzz'
        elif number % 3 == 0:
            return 'Fizz'
        elif number % 5 == 0:

zkan

import pytest


class FizzBuzz:
    def say(self, number):
        if number % 3 == 0 and number % 5 == 0:
            return 'FizzBuzz'
        elif number % 3 == 0:
            return 'Fizz'
        elif number % 5 == 0:

zkan

import pytest


class FizzBuzz:
    def say(self, number):
        if number % 3 == 0 and number % 5 == 0:
            return 'FizzBuzz'
        elif number % 3 == 0:
            return 'Fizz'
        elif number % 5 == 0:

zkan

import pytest


@pytest.mark.parametrize('test_input, expected', [
    (3, 'Fizz'),
    (6, 'Fizz'),
    (5, 'Buzz'),
    (10, 'Buzz'),
    (15, 'FizzBuzz'),
    (30, 'FizzBuzz'),

zkan

#!/usr/bin/env/python
# 
# More of a reference of using jinaj2 without actual template files.
# This is great for a simple output transformation to standard out.
#
# Of course you will need to "sudo pip install jinja2" first!
#
# I like to refer to the following to remember how to use jinja2 :)
# http://jinja.pocoo.org/docs/templates/
#

zkan

import pandas as pd
from sklearn import cluster


data = {
    'data': [2, 3, 4, 10, 12, 20, 30, 11, 25]
}
df = pd.DataFrame(data)

kmeans = cluster.KMeans(n_clusters=2)

zkan

import pandas as pd
import seaborn as sns


titanic_df = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv')
titanic_df.head()

sns.countplot(x=titanic_df.survived, hue=titanic_df.sex)
sns.countplot(x=titanic_df.survived, hue=titanic_df.embarked)
sns.boxplot(x=titanic_df.survived, y=titanic_df.age)