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import contextlib
from functools import partial

from sklearn.pipeline import Pipeline

@contextlib.contextmanager
def intermediate_transforms(pipe: Pipeline, keys: list=[], bypass_list: list=[]):
    """Allows for the retrieval of all or parts of the transformations in a
    sklearn Pipeline, as well as the ability to dynamically bypass parts of
    the pipeline.

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This borrows from https://jordanthomasg.medium.com/python-development-on-macos-with-pyenv-2509c694a808.

Install Homebrew if necessary.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Before installing pyenv do the following:

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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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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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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)