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Change the Default Browser for Jupyter Notebooks in OS X

Step 1. Create an editable config file for Jupyter notebooks.

To do this, open Terminal and type:

jupyter notebook --generate-config

This generates the file:

~/.jupyter/jupyter_notebook_config.py

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AussieAnimals = ['kangaroo', 'cassowary', 'wombat', 'possum', 'echidna', 'ibis', 'wallaby', 'koala', 
                 'tasmanian devil', 'kookaburra', 'numbat', 'platypus', 'lyre bird', 'quokka', 'quoll', 
                 'sugar glider', 'bandicoot', 'thorny devil', 'dingo', 'wallaroo', 'yabby', 'bilby']

GalapagosAnimals = ['rice rat', 'hoary bat', 'bottlenose dolphin', 'beaked whale', 'lava lizard', 
                    'tortoise', 'flightless cormorant', 'green sea turtle', 'blue-footed booby', 
                    'marine iguana', 'pink land iguana', 'darwins finches', 'brown noddy']

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# Define a function to get the name field from the first item in a dictionary list
def get_genre1(x):
    x = json.loads(x)
    if len(x) > 0:
        return x[0]['name']

# Now use pandas.apply to use the function on one column
# In thise case create a new column called genres1 to hold the new data
movies['genre1'] = movies['genres'].apply(get_genre1)

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# Import ast for ast.literal_eval
import ast

# Remove JSON from TMDB fields
# for genres, spoken_languages, production_companies, production_countries
# Works only with non-null values, so filter out null values before applying
# Requires import ast -- or use simply eval vs ast.literal_eval
def remove_json(content):
    # Interpret the content as a Python list of dictionaries
    content = ast.literal_eval(content)

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Pandas .describe() formatted

Format numbers output from the pandas df.describe() method. For instance, instead of outputting scientific notation, we can have numbers with thousands separators and a desired number of decimals.

When using .describe with an entire dataframe, use .apply and a lambda function to apply the formatting to every number.

• To change the number of decimals, change the number before the f
• To remove the thousands separator remove the comma

df.describe().apply(lambda s: s.apply('{:,.0f}'.format))

When using .describe with a single column or a series, use the .map method instead:

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# Function for generating model scores and confusion matrices with custom colors and descriptive labels
# https://stackoverflow.com/questions/70097754/confusion-matrix-with-different-colors
# https://medium.com/@dtuk81/confusion-matrix-visualization-fc31e3f30fea

def report_scores(model, features, labels):
    '''
    Generating model scores and confusion matrices with custom colors and descriptive labels
    model = model variable
    features = features of desired split
    labels = labels of desired split

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# Format using Markdown ================================================

# Format Jupyter Code Output using Markdown
# https://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html?highlight=display_markdown#IPython.display.display_markdown
from IPython.display import display_markdown

markdowntext = 'Markdown Heading Level 3'
display_markdown(f'### Code Output Formatted as {markdowntext}', raw=True)
display_markdown(f'_Code output italicized using_ `display_markdown`', raw=True)