Rahul Das RahulDas-dev

The Jupyter Notebook Installation for the Virtual Environment

Installation can be done pip install Jupyter. However big list dependency of Jupyter can be avoided by installting ipython kernel module only, if you have Jupyter installed Globally.

Install the ipython kernrl pip install ipykernel
Create a new kernel for your current virtual environment python -m ipykernel install --user --name=<promt>
Launch the notebook with jupyter notebbok from virtual Environment.
You should now be able to see your kernel in the IPython notebook menu: Kernel -> Change kernel

	from dataclasses import dataclass, field
	from pprint import pprint

	import inspect

	@dataclass(frozen=True)
	class Dimension:
	height: float
	width: float
	depth: float

	@dataclass(frozen=True, init= False)
	class Dimension:
	height: float
	width: float
	depth: float

	def __init__(self,height,width,depth ):
	object.__setattr__(self, "height", int(round(height)))
	object.__setattr__(self, "width", int(round(width)))
	object.__setattr__(self, "depth", int(round(depth)))

	from dataclasses import dataclass, field
	from pprint import pprint

	import inspect

	@dataclass(frozen=True, init= False)
	class Dimension:
	height: float
	width: float
	depth: float

	import os
	import sys
	import inspect
	currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
	#print(currentdir)
	parentdir = os.path.dirname(currentdir)
	lib_path = os.path.join(parentdir,'src')
	#print(parentdir, lib_path)
	sys.path.insert(0,lib_path)

	import math
	from typing import List


	def sliding_window(input_: List[int], kernel_: List[int]) -> List[int]:
	# Checking input and kernel length constraint
	if len(kernel_) > len(input_):
	raise ValueError("Input Size must be gratter than Kernel size")

	start_index = 0

	import math
	import pandas

	# Loading data into pandas Dataframe
	dataset = pd.read_csv(dataset_path)

	print(f'dataset {dataset.shape}')
	dataset.head()

	#Shuffling the Dataset

	from functools import reduce

	# creates pipeline object from list of callables
	def make_pipeline(list_of_callables):
	return reduce(lambda f,g: lambda x: g(f(x)), list_of_callables)

	add1 = lambda x: x+'1' # Oprtaion 1
	add2 = lambda x: x+'2' # Oprtaion 2
	add3 = lambda x: x+'3' # Oprtaion 3
	add4 = lambda x: x+'4' # Oprtaion 4

	def make_pipeline2(list_of_callables):
	def map_2_partials(item):
	func = item[0]
	#return func if len(item) < 2 else partial(func, **item[1])
	if len(item) < 2:
	return func
	else :
	return partial(func, **item[1])

	list_of_partials = map(map_2_partials, list_of_callables)