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This is a recipe for using Sklearn to build a cosine similarity matrix and then to build dendrograms from it.

import numpy as np
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy
import scipy.spatial.distance
from scipy.spatial.distance import pdist
from sklearn.metrics.pairwise import cosine_similarity

Make a "feature matrix" of 15 items that will be the binary representation of each index.

tommct

This is a recipe for making box plots overlaying histograms in Tableau version 9.3. It largely borrows from http://vizpainter.com/some-tableau-tips-options-for-box-and-whisker/ and http://vizdiff.blogspot.com/2015/11/overlaying-histogram-with-box-and.html.

1. Create a fixed continuous variable for number of objects per dimension. For example, the number of unique assignments per user:

    [Assignments Per User] = {FIXED [Userid] : COUNTD([Assignmentid])}
   

2. Set the variable's Default Aggregation to COUNT.

3. Drag the variable from Measures to the columns shelf.

4. Set it to "Dimension" instead of CNT().

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def columns_via_merge(df: pd.DataFrame, df2: pd.DataFrame, oncols: list, assigning: list):
    """
    Add (or replace) columns to df that map via a merge with df2.

    Examples:

        # Add the ord value to a subset of a DataFrame
        ABC = [chr(x) for x in range(ord('A'), ord('Z') + 1)]
        AABBCC = [chr(x)+chr(x) for x in range(ord('A'), ord('Z') + 1)]
        abc = [chr(x) for x in range(ord('a'), ord('z') + 1)]

tommct

From an open Jupyter Notebook homework assignment, select "Coursera" to take you to the home page. Make a new notebook and fill it with the following and excute the cell with:

%%bash
tar cvfz hw.tar.gz .

This may take a little while to run depending on the packages. Select "Coursera" again to take you to the Home directory. Check the hw.tar.gz file and then Download. After the file is downloaded, delete it.

tommct

This creates a normalized mass density histogram in matplotlib

bins = np.linspace(-1, 1, 101)
# To get a normalized mass density histogram, we have to do it this way...
hist, bins = np.histogram(df['some_column'], bins=bins, density=True)
hist /= len(bins)
width = bins[1]-bins[0]
fig = plt.figure(figsize=(8, 4))
ax = fig.add_axes([.15, .15, .75, .75])

plt.bar(left=bins[:-1], height=hist, width=width)

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To get use MongoDB from Tableau, start a mongosqld instance...

mongosqld --mongo-uri "mongodb://<host>:<port>/?connect=direct"

Then from Tableau, select Servers->MongoDB BI Connector with 127.0.0.1 and 3307 as connection details.

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import logging
from collections.abc import Iterable 

def is_container(obj):
    return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes, bytearray))

# https://stackoverflow.com/a/54000999/394430
def walk_collection(obj, callback=None, _path: list=[], **kwargs):
    """Walk an arbitrarily nested structure of lists and/or dicts such as would be made when
    reading JSON as an object. Walking is performed in a depth-first search manner.

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