Python Libraries
Python libraries are a vast collection of mostly open source libraries that can be used by project development. These libraries allow for rapid development by providing pre-coded modules for functionality such as sending emails or developing web interfaces.
Libraries are written by individuals, corporations, and non-profit foundations. Developers see a need and usually build open-source solutions for the python community to use.
Modules are single .py files containing functions and variables, Packages are folders are a collection of these modules and Libraries are a collection of these packages.
Libraries need to first be installed into your development environment, then imported by each individual python .py program and executed when the program is called.
The pip command (Python Package Index) is written in python and used as a package-management system. It installs these libraries from various python third party organizations for local development system use.
After the pip install, the python import keyword is used inside your python program for use.
Below is a description of some of the main python used libraries.
Data Science
NumPy library : Provides modules for efficient multi-dimensional array handling.
Pandas library: Used for data manipulation and analysis leveraging the concept of DataFrames.
Scikit-learn library: Used for machine learning concepts like regression and classification.
TensorFlow & PyTorch: Used for deep learning and AI models. models.
We will now focus on NumPy and Pandas and look at the ML libraries later.
The NumPy library supports large, multi-dimensional arrays and matrices,and mathematical functions to act on these arrays.
NumPy arrays are much faster and have more functionality than traditional python lists and provide extensive capabilities on machine learning and data science examples.
Install numpy:
$ pip3 install numpy
Note: It may already be installed by default
Python program numpy_example.py
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$ python3 num.py
[1 2 3 4 5]
<class 'numpy.ndarray'>
john_iacovacci1@cloudshell:~/py-exam (uconn-engr)$
Python program numpy_example.py
===================================================
#import numpy library
import numpy as np
#customer details
name = input("Name: ")
address = input("Address: ")
#data in numpy (np) arrays
items = np.array(['Plates', 'Cups'])
prices = np.array([2, 4])
#quantities
num_plates = int(input("How many plates? "))
num_cups = int(input("How many cups? "))
#store quantities in a numpy array
quantities = np.array([num_plates, num_cups])
#calculate total for each item and overall total
totals = prices * quantities
total_cost = np.sum(totals)
#results
print("\nCustomer Information:")
print(f"Name: {name}")
print(f"Address: {address}")
print("\nInvoice:")
for i, item in enumerate(items): #iterate through numpy array to print result
print(f"{item}: Quantity = {quantities[i]}, Unit Price = ${prices[i]}, Total = ${totals[i]}")
print(f"\nTotal Amount Due: ${total_cost}")
===================================================
Results
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$ python3 numpy_example.py
Name: John Iacovacci
Address: 55 Daffodil Road
How many plates? 4
How many cups? 4
Customer Information:
Name: John Iacovacci
Address: 55 Daffodil Road
Invoice:
Plates: Quantity = 4, Unit Price = $2, Total = $8
Cups: Quantity = 4, Unit Price = $4, Total = $16
Total Amount Due: $24
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$
The Pandas library was designed for data manipulation and analysis. It provides data structures and operations for table manipulation.
Built-in tools to clean data for accurate processing. Very fast parts written in C programming language. Can read data from multiple formats and transform data for easy integration.
Install pandas
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$ pip3 install pandas
Defaulting to user installation because normal site-packages is not writeable
Collecting pandas
Downloading pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.1/13.1 MB 18.3 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.9.0.post0)
Collecting tzdata>=2022.7
Downloading tzdata-2024.1-py2.py3-none-any.whl (345 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 345.4/345.4 KB 26.6 MB/s eta 0:00:00
Collecting pytz>=2020.1
Downloading pytz-2024.2-py2.py3-none-any.whl (508 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 508.0/508.0 KB 27.1 MB/s eta 0:00:00
Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)
Installing collected packages: pytz, tzdata, pandas
Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.1
Python program pandas_example.py
===================================================
#!/usr/bin/python3
#import pandas library
import pandas as pd
#customer details
name = input("Name: ")
address = input("Address: ")
#item data, name and price
data = {
'Item': ['Plates', 'Cups'],
'Price': [2, 4]
}
df = pd.DataFrame(data) #dataframe
#quantities
num_plates = int(input("How many plates? "))
num_cups = int(input("How many cups? "))
#add quantities to the DataFrame
df['Quantity'] = [num_plates, num_cups]
#calculate total for each item and overall total
df['Total'] = df['Price'] * df['Quantity']
total_cost = df['Total'].sum()
#results
print(f"\n{name}\n{address}")
print(df[['Item', 'Quantity', 'Total']])
print(f"Amount Due = ${total_cost}")
====================================================
Results
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$ python3 numpy_example.py
Name: John Iacovacci
Address: 55 Daffodil Road
How many plates? 4
How many cups? 4
Customer Information:
Name: John Iacovacci
Address: 55 Daffodil Road
Invoice:
Plates: Quantity = 4, Unit Price = $2, Total = $8
Cups: Quantity = 4, Unit Price = $4, Total = $16
Total Amount Due: $24
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$ python3 pandas_example.py
Name: John Iacovacci
Address: 55 Daffodil Road
How many plates? 4
How many cups? 4
John Iacovacci
55 Daffodil Road
Item Quantity Total
0 Plates 4 8
1 Cups 4 16
Amount Due = $24
john_iacovacci1@cloudshell:~/py-exam (cloud-project-examples)$
Data Visualization
Observing information trends is an important part of data analytics. This is done by creating graphs and charts where users can detect patterns that appear in the data. A stock price with a downward trend may cause a trader to wait until the chart stabilizes before buying.
Matplotlib is one of the main tools that python developers use to produce charts and graphs for data visualization. The library is designed to generate images based upon these graphs. A common use would be a traditional X and Y data point access. It can create plots, histograms, bar charts, scatter plots, etc., with small amounts of code..
====================================================
import pandas as pd # Data frame
from datetime import datetime
from matplotlib import pyplot as plt # Plotting Library
from google.cloud import storage # Leverage Google Storage Buckets
#Google Cloud project ID
# use your project ID from Google cloud
PROJECT_ID = 'cloud-project-examples'
# The ID of your GCS bucket (bucket name)
bucket_name = "cloud-storage-exam"
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
raw_data = [
("1/28/2026", 481.63),
("1/29/2026", 433.5),
("1/30/2026", 430.29),
("2/2/2026", 423.37),
("2/3/2026", 411.21),
("2/4/2026", 414.19),
("2/5/2026", 393.67),
("2/6/2026", 401.14)
]
# Parse dates using %m/%d/%Y (Month/Day/Year)
dates = [datetime.strptime(d[0], "%m/%d/%Y") for d in raw_data]
prices = [d[1] for d in raw_data]
# Plotting
plt.figure(figsize=(10, 5))
plt.plot(dates, prices, marker='o', linestyle='-', color='#e63946', linewidth=2)
# Styling
plt.title('Daily Price MSFT (Jan - Feb 2026)', fontsize=14)
plt.xlabel('Date', fontsize=12)
plt.ylabel('Price ($)', fontsize=12)
plt.grid(True, alpha=0.3)
# Rotate labels so they don't overlap
plt.xticks(rotation=45)
plt.tight_layout()
# Show results
plt.show()
plt.savefig('msft_prc.png')
# send plot file to your bucket
plot_filename = 'msft_prc.png'
destination_blob_name = f'{plot_filename}'
source_file_name = 'msft_prc.png'
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name) #upload file to specified destination
print(f'File {source_file_name} uploaded to {destination_blob_name}.')
====================================================
Had the output file moved to a public storage bucket.
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