WillKoehrsen

def house_price_in_aus_from_model(usd_per_floor,
                                  usd_per_bathroom_squared,
                                  floor_count,        
                                  bathroom_count,                      
                                  usd_to_aus_rate):
  """
  Calculate the house price in Australian dollars from
  a polynomial regression model
  """
    

WillKoehrsen

PIXEL_NORMALIZATION_FACTOR = 12.5
PIXEL_OFFSET_FACTOR = 150

for row_index in range(row_count):
    for column_index in range(column_count):
        for color_channel_index in range(color_channel_count):
            normalized_pixel_value = (
                original_pixel_array[row_index][column_index][color_channel_index]
                * PIXEL_NORMALIZATION_FACTOR
            )

WillKoehrsen

define(['base/js/namespace', 'base/js/events'], function (Jupyter, events) {
  // Template cells including markdown and imports
  var setUp = function () {
    Jupyter.notebook.insert_cell_at_index('markdown', 0)
      .set_text(`# Introduction
State notebook purpose here`)
    Jupyter.notebook.insert_cell_at_index('markdown', 1).set_text(`### Imports
Import libraries and write settings here.`)
    // Define imports and settings
    Jupyter.notebook.insert_cell_at_index('code', 2)

WillKoehrsen

import pandas as pd
import numpy as np

# Pandas options
pd.options.display.max_columns = 30
pd.options.display.max_rows = 20

from IPython import get_ipython
ipython = get_ipython()

WillKoehrsen

def show_stats_by_tag(tag):
    return(df.groupby(f'<tag>{tag}').describe()[['views', 'reads']])
    
stats = interact(show_stats_by_tag,
                tag=widgets.Dropdown(options=['Towards Data Science', 'Education', 
                                              'Machine Learning', 'Python', 'Data Science']))

WillKoehrsen

# Create widgets
directory = widgets.Dropdown(options=['images', 'nature', 'assorted'])
images = widgets.Dropdown(options=os.listdir(directory.value))

# Updates the image options based on directory value
def update_images(*args):
    images.options = os.listdir(directory.value)

# Tie the image options to directory value
directory.observe(update_images, 'value')

WillKoehrsen

# Create interactive version of function with DatePickers
interact(stats_for_article_published_between,
        start_date=widgets.DatePicker(value=pd.to_datetime('2018-01-01')),
        end_date=widgets.DatePicker(value=pd.to_datetime('2019-01-01')))

WillKoehrsen

import cufflinks as cf

@interact
def scatter_plot(x=list(df.select_dtypes('number').columns), 
                 y=list(df.select_dtypes('number').columns)[1:],
                 theme=list(cf.themes.THEMES.keys()), 
                 colorscale=list(cf.colors._scales_names.keys())):
    
    df.iplot(kind='scatter', x=x, y=y, mode='markers', 
             xTitle=x.title(), yTitle=y.title(), 

WillKoehrsen

# Correlation between two columns with dropdown
@interact
def correlations(column1=list(df.select_dtypes('number').columns), 
                 column2=list(df.select_dtypes('number').columns)):
    print(f"Correlation: {df[column1].corr(df[column2])}")
    
# Stats of a column with dropdown
@interact
def describe(column=list(df.columns)):
    print(df[column].describe())

WillKoehrsen

# Interact with specification of arguments
@interact
def show_articles_more_than(column=['claps', 'views', 'fans', 'reads'], 
                            x=(10, 100000, 10)):
    return df.loc[df[column] > x]