# Import necessary libraries
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Ensure plots show in notebook
%matplotlib inline

# Define a function to calculate Bollinger Bands
def bollinger_bands(data, window=20, num_sd=2):
    rolling_mean = data['Close'].rolling(window=window).mean()
    rolling_std = data['Close'].rolling(window=window …

import pandas as pd
import numpy as np

abc = np.array([
    [9, 13, 10],
    [7, 12, 9],
    [19, 11, 8]
])

abc

array([[ 9, 13, 10],
       [ 7, 12,  9],
       [19, 11,  8]])

df2 = pd.DataFrame(abc, columns=['breakfast', 'lunch', 'dinner'])

df2.plot.bar()

<Axes: >

df2.plot.barh()

<Axes: >

df2.plot.barh …

#tuples

t = (1, 2, 3)
print(len(t))

3

print(max((4, 7, 2)))

7

print(min((4, 7, 2)))

2

print(sum((10, 20, 30)))

60

print(any((0, 0, 1)))

True

print(all((1, 1, 1)))

True

print(tuple([1, 2, 3]))

(1, 2, 3)

print(sorted((5 …

import pandas as pd

# Publicly available dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"

# Define column names (from UCI description)
columns = [
    "age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
    "occupation", "relationship", "race", "sex", "capital-gain", "capital-loss",
    "hours-per-week", "native-country", "income"
]

# Read CSV with defined parameters
census = pd.read_csv(url, names …

import numpy as np
import pandas as pd
import random

def xrange(x):
    return iter(range(x))

rnd_1 = [random.randrange(1, 20) for x in xrange(10)]

rnd_1

[5, 3, 10, 18, 10, 18, 19, 13, 1, 5]

rnd_2 = [random.randrange(1, 20) for x in xrange(10)]

rnd_2

[4 …

from collections import defaultdict

user = defaultdict(lambda: 'Kevin')

user

defaultdict(<function __main__.<lambda>()>, {})

type(user)

collections.defaultdict

user['abc']

'Kevin'

user['name'] = 'Peter'
user['age'] = 21
user['city'] = 'Toronto'

user

defaultdict(<function __main__.<lambda>()>,
            {'abc': 'Kevin', 'name': 'Peter', 'age': 21, 'city': 'Toronto'})

defaultdict(<function __main__.<lambda>>,
            {'abc': 'Kevin',
             'age': 21 …

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.3
vals = np.array([[60, 32], [37, 40]])

cmap = plt.get_cmap("tab20c")
# https://matplotlib.org/3.3.1/tutorials/colors/colormaps.html

# print(cmap)

outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1 …

animals = ['cat', 'dog', 'monkey']

for idx, animal in enumerate(animals):
    print ('#%d: %s' % (idx , animal))

#0: cat
#1: dog
#2: monkey

import random
import time

# PythonDecorators/entry_exit_class.py
class execution_time_calculator(object):

    def __init__(self, f):
        self.f = f

    def __call__(self):
        start = int(round(time.time() * 1000))
        print("Entering", self.f.__name__)
        self.f()
        end = int(round(time.time() * 1000))
        #print("Exited", self.f.__name__)
        print("Time taken : "+str((end-start …

def fib(n):
    if n <= 1:
        return n
    else:
        return fib(n - 1) + fib(n - 2)

print(fib(0))  # Expected output: 0
print(fib(1))  # Expected output: 1

0
1

print(fib(8))  # Expected output: 21

21

def fib_list(n):
    return [fib(i) for i in range(n + 1)]

print …