acdick

import pandas as pd

# calculates the content-based product similarity matrix
# items is a Pandas dataframe containing all product details available for comparison
# returns the feature matrix and the correlation matrix
def product_similarity(items):
  
    # drop multicollinear columns and columns not considered for similarity
    items = items.drop(['Item', 'Style', 'Product', 'On Sale'], axis=1)
    print(items.nunique())

acdick

import numpy as np

# generate battleship board and solution
def new_board():
    
    # create a clear board
    dim = 10
    board = np.zeros((dim, dim), dtype=int)

    # randomly place ships on the board

acdick

import plotly.plotly as py
import plotly.graph_objs as go

hover_text = []
color_range = []
for index, row in bcg_matrix.iterrows():
    hover_text.append(('Borough: {borough}<br>'+
                      'Neighborhood: {neighborhood}<br>'+
                      'Share: {share}%<br>'+
                      'Growth: {growth}%<br>'+