

"""simple docstring"""




from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def 						A							(  snake_case__ = "isbn/0140328726"     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   olid.strip().strip("""/"""     )  # Remove leading/trailing whitespace & slashes
			if new_olid.count("""/"""     ) != 1:
						SCREAMING_SNAKE_CASE__										=   f"""{olid} is not a valid Open Library olid"""
						raise ValueError(snake_case__     )
			return requests.get(f"""https://openlibrary.org/{new_olid}.json"""     ).json()

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   {
			    """title""": """Title""",
			    """publish_date""": """Publish date""",
			    """authors""": """Authors""",
			    """number_of_pages""": """Number of pages:""",
			    """first_sentence""": """First sentence""",
			    """isbn_10""": """ISBN (10)""",
			    """isbn_13""": """ISBN (13)""",
			}
			SCREAMING_SNAKE_CASE__										=   {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
			SCREAMING_SNAKE_CASE__										=   [
			    get_openlibrary_data(author["""key"""]     )["""name"""] for author in data["""Authors"""]
			]
			SCREAMING_SNAKE_CASE__										=   data["""First sentence"""]["""value"""]
			for key, value in data.items():
						if isinstance(snake_case__		,					snake_case__     ):
									SCREAMING_SNAKE_CASE__										=   """, """.join(snake_case__     )
			return data


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						while True:
												A_				:      Tuple           =			input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
												if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																		break

												if len(isbn) not in (10, 13) or not isbn.isdigit():
																		print(F'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
																		continue

												print(F'\nSearching Open Library for ISBN: {isbn}...\n')

												try:
																		A_				:      Optional[int]           =			summarize_book(get_openlibrary_data(F'isbn/{isbn}'))
																		print("\n".join(F'{key}: {value}' for key, value in book_summary.items()))
												except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																		print(F'Sorry, there are no results for ISBN: {isbn}.')





"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A_				:      int           =			pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
A_				:      int           =			dataset.iloc[:, 1:2].values
A_				:      Optional[Any]           =			dataset.iloc[:, 2].values


A_       ,						A_       ,						A_       ,						A_				:      str           =			train_test_split(X, y, test_size=0.2, random_state=0)


A_				:      Optional[Any]           =			PolynomialFeatures(degree=4)
A_				:      Tuple           =			poly_reg.fit_transform(X)
A_				:      str           =			LinearRegression()
pol_reg.fit(X_poly, y)

def 						A							(  ):




			'''simple docstring'''


			plt.scatter(snake_case__		,					snake_case__		,					color="""red"""     )
			plt.plot(snake_case__		,					pol_reg.predict(poly_reg.fit_transform(snake_case__     )     )		,					color="""blue"""     )
			plt.title("""Truth or Bluff (Linear Regression)"""     )
			plt.xlabel("""Position level"""     )
			plt.ylabel("""Salary"""     )
			plt.show()


if __name__ == "__main__":
						viz_polymonial()

						# Predicting a new result with Polymonial Regression
						pol_reg.predict(poly_reg.fit_transform([[5.5]]))
						# output should be 132148.43750003


