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import boto3
import pandas as pd


def read_s3(bucket, prefix):
    s3 = boto3.resource("s3")
    bucket = s3.Bucket(bucket)
    prefix_objs = bucket.objects.filter(Prefix=prefix)

    df = pd.concat([pd.read_excel(obj.get()["Body"].read()) for obj in prefix_objs])

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class BinaryTree:
    def __init__(self, root_val):
        self.root = root_val
        self.left = None
        self.right = None
        
    def insert_left(self, node):
        if self.left is None:
            self.left = BinaryTree(node)
        else:

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import numpy as np 
import pandas as pd

from datetime import datetime

# make an example
np.random.seed(0)

item = np.random.choice(['A', 'B'], 10)
year = np.random.choice([2016, 2017], 10)

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iris2 <- as.matrix(iris[, 1:4])

# cetralize data
centered_iris2 <- scale(iris2, center = TRUE, scale = FALSE)

# covariance matrix
cov_iris2 <- (t(centered_iris2) %*% centered_iris2) / (dim(centered_iris2)[1] - 1)
# perform eigendecomposition on covariance matrix
eig_cov <- eigen(cov_iris2)

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from ad import adnumber
from ad import admath
import numpy as np

# ref: http://cs231n.github.io/optimization-2/
w = adnumber(np.array([2, -3, -3]))
x = adnumber(np.array([-1, -2, 1]))

f = 1 / (1 + admath.exp(-sum(w * x)))

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#!usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

def get_alpha(error):
    return 0.5 * np.log((1 - error) / error)
def update_weights(y, weights, g, alpha):
    return weights * np.exp(-alpha * g * y) / sum(weights * np.exp(-alpha * g * y))

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#!usr/local/env python3
# -*- coding: utf-8 -*-

import itertools

def consonant(c):
    return c.lower() not in "aeiou"
# (built-in) filter()
list(filter(consonant, 'Aartttik'))
list(itertools.filterfalse(consonant, 'Aartttik'))