def print_everything(*args):
        for count, thing in enumerate(args):
            print( '{0}. {1}'.format(count, thing))

print_everything('apple', 'banana', 'cabbage', 'spinach')

0. apple
1. banana
2. cabbage
3. spinach

import numpy as np

a = np.array(
    [
        [1,2,44,7], 
        [9,88,6,45], 
        [19,76,3,4]
    ]
)

a

array([[ 1,  2, 44,  7],
       [ 9, 88,  6, 45],
       [19, 76,  3,  4]])

a.size

12

a.shape

(3, 4)

a.ndim

2

np.argmax(a)

5

a.flatten()

array …

from pandas import read_csv
from datetime import datetime
from matplotlib import pyplot as plt
from pandas.plotting import autocorrelation_plot
from pandas import DataFrame
from statsmodels.tsa.arima.model import ARIMA

# Define the parser function
def parser(x):
    return datetime.strptime('190' + x, '%Y-%m')

# Read the dataset
series = read_csv(
    'shampoo-sales …

import pandas as pd
from matplotlib import pyplot as plt
from pandas.plotting import lag_plot

# Load the dataset using pandas.read_csv
series = pd …

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.models import ColumnDataSource

# Set Bokeh output to notebook
output_notebook()

import numpy as np

x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#print(x)

x = x[1:7:5]
#print(x)

x = np.array([
            [
                [1],[2],[3]
            ], 
            [
                [4],[5],[6]
            ]
        ])
#print(x)
print(x.shape)

(2, 3, 1)

import numpy as np

a = np.array([
    [1, 2],
    [3, 4]
])

a

array([[1, 2],
       [3, 4]])

inverse_a = np.linalg.inv(a)

print(inverse_a)

[[-2.   1. ]
 [ 1.5 -0.5]]

import numpy as np

a = np.array([1, 2, 3])  # Create a rank 1 array

print (type(a))            # Prints "<type 'numpy.ndarray'>"
print (a.shape)           # Prints "(3,)"
print (a[0], a[1], a[2])   # Prints "1 2 3"
a[0] = 5                 # Change an element of the array
print (a)                  # Prints …

import requests
from bs4 import BeautifulSoup

# Collect and parse first page
page = requests.get('https://www.wired.com/story/intel-great-american-microchip-mobilization/')
soup = BeautifulSoup(page.text, 'html.parser')    

content = soup.select('article.article-body-component')

content

[]

from IPython.display import Markdown, display

# Path to your PNG file
image_path = 'deer.jpg'

# Display the image inline
display(Markdown(f"![Image Description]({image_path})"))