Python Libraries | Standard, Third-Party & More (Lists + Examples)

24 mins read

Python is one of the most popular and versatile programming languages out there, and one of the key reasons for its widespread appeal is the huge number of libraries available. But what exactly are Python libraries, and why should you care about them? Simply put, a Python library is a collection of pre-written code that you can use to perform common tasks without having to reinvent the wheel. Libraries make it easier to work with everything from data analysis and machine learning to web development and image processing. Let’s dive deeper into what makes these libraries so powerful and how they work!

What Are Python Libraries?

In Python programming, a library is a bundle of code that provides various functionalities, modules, and tools that you can use in your programs. These libraries contain collections of modules, which are individual files that group related functions, classes, and variables together. 

Think of a library as a toolbox that contains many smaller tools (modules), each designed to perform specific tasks. For example, the math library provides a set of functions for performing mathematical operations such as square roots, trigonometric functions, and logarithms. The datetime library, on the other hand, provides tools for handling and manipulating dates and times. You don’t have to write the code for these operations from scratch—just import the library, and you are good to go.

Python libraries can be categorized into two main types:

1. Standard Libraries: These come pre-installed with Python and include common functionalities that you can use out of the box (e.g., math, datetime, os).
2. Third-Party Libraries: These are developed by the Python community or organizations and can be installed using a package manager like pip (e.g., NumPy, Pandas, TensorFlow).

In essence, Python libraries are your best friends when it comes to speeding up development, making your code cleaner, and leveraging pre-written, optimized solutions.

How Do Python Libraries Work?

Python libraries offer pre-written code, i.e., reusable functions, classes, and modules that you can import and utilize in your Python applications. Here’s how Python libraries generally work in a step-by-step process:

Step 1: Importing Libraries
The first step in using a Python library is importing it into your script. You can import an entire library or specific modules/functions depending on your needs. For example, to import the entire math library, you would write the following:

import math

Step 2: Accessing/ Using Functions and Classes 

Once a library is imported, you can access and use the functions, classes, and constants it contains using dot notation. For example, you can use the built-in function sqrt() to find the square root of a number as follows:

math.sqrt(16)  # Uses the sqrt function from the math library

Step 3: Interacting with Returned Objects

Many Python functions return objects that can be further manipulated. For example, some functions may return data structures like lists, and you can call other methods on them. For example, you can use the factorial() function to find the factorial of a number and then print what it returns as follows:

data = math.factorial(5)  # Returns the factorial of 5
print(data)  # Outputs 120

Step 4: Customization and Configuration

Some libraries allow you to customize or configure their behavior to suit your needs. You might use specific arguments when calling a function or configure global settings. For example:

data = pandas.DataFrame(data=my_data)
data.describe()  # Provides statistical details about the DataFrame

Step 5: Handling Exceptions and Errors

Python libraries often raise exceptions when something goes wrong (e.g., invalid input). To handle such cases, you can use try and except blocks. For example:

try:
    data_frame.dropna()
except ValueError as e:
    print("An error occurred:", e)

Step 6: Performance Optimization

Some libraries are optimized for performance through features like native code or parallel processing. This is why libraries like NumPy are faster than writing custom Python code for numerical operations—they use optimized, low-level implementations for speed.

In essence, Python libraries encapsulate reusable code that handles common tasks, making your development process faster and more efficient. You import the library, access its functions, use them in your project, and handle errors appropriately, all while taking advantage of the performance optimizations built into the libraries themselves.

Importing & Using Python Libraries

To use any functionality from a library, you first need to import it into your code. The process of importing and using Python libraries is straightforward. Here's how it typically works:

Import the Library: You begin by using the import keyword to bring the library into your script. If you'd like a shorter name for the library, you can use the as keyword to alias it. For example:

import math  # Imports the math library

Use the Library: Once imported, you can use the functions, classes, or variables provided by the library. The syntax for using an imported library typically follows the pattern:

library_name.function_name() or library_name.class_name(). 

Example: Let's say you want to calculate the square root of 25 and display the value of pi. Here is how this would look in code:

import math
print(math.sqrt(25))  # Output: 5.0
print(math.pi)  # Output: 3.141592653589793

Importing specific items from a library module

Sometimes, you may not need the entire library, just specific functions or classes. In such cases, Python allows you to import only what you need, reducing memory usage and avoiding unnecessary imports.

Importing Specific Items: You can use the from keyword to import specific functions or variables directly from a module. This way, you don’t have to prefix the library name each time you call them. For example:

from math import sqrt, pi  # Importing only sqrt and pi from math

Using the Imported Functions: Once imported, you can directly use the functions or classes without needing to prefix them with the library name. For example:

print(sqrt(25))  # Directly using sqrt function

Example: If you want to generate a random number between 1 and 10 and pick a random item from a list:

from random import randint, choice
print(randint(1, 10))  # Generates a random integer between 1 and 10
print(choice(['apple', 'banana', 'orange']))  # Picks a random item from the list

Standard Python Libraries (With List)

The Standard Library is a treasure trove of pre-built modules and packages bundled with every Python installation. These Python libraries are integral to the langauge and provide a broad range of functionality, from basic operations to advanced system tasks. 

• Note that you don’t need to install them separately—they are readily available as part of the Python standard library.
• Standard libraries include built-in modules that can help you perform a variety of tasks such as file handling, mathematical operations, system interactions, data processing, and more. 
• These libraries are tested, reliable, and widely used, making them an essential part of the Python ecosystem.

Some popular standard Python libraries include math, datetime, os, sys, and json, which offer utilities for specific tasks like working with numbers, managing files, and handling JSON data. 

List Of Essential Standard Python Libraries

The table below lists some of the most commonly used standard libraries in Python, along with their descriptions and use cases.

Example Of Standard Python Library os– Directory Management

The simple Python program example below illustrates how to use the os standard Python library to to work with an existing directory and make a new one.

Code Example:

# Step 1: Import the os module
import os

# Step 2: Get the current working directory
current_directory = os.getcwd()

# Step 3: Create a new directory
os.mkdir('new_folder')

# Step 4: List all files and directories in the current directory
directory_contents = os.listdir()

# Step 5: Print the results
print("Current Directory:", current_directory)
print("Directory Contents:", directory_contents)

Output:

Current Directory: /home
Directory Contents: ['main.py', 'new_folder']

Explanation:

In the basic Python program example,

1. We begin by importing the os module using the import statement.
2. Then, we use the built-in function getcwd() to fetch the current working directory.
3. Next, we create a new directory named new_folder using the mkdir() function, i.e., os.mkdir().
4. We then use the lstdir() function on the current directors, which lists all files and directories in the current directory, i.e., os.listdir().
5. After that, we use the print() function to display the current directory and its contents.

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Important Python Libraries For Data Science

Data Science is where Python shines, thanks to its extensive library ecosystem. These libraries simplify data manipulation, visualization, and advanced analytics. Python's data science libraries provide a robust framework for handling everything from basic data operations to advanced machine learning. Below is a breakdown of the most important Python libraries for data science, their descriptions, and typical use cases.

Example Of Pandas Python Library –Data Analysis

The Python program example below illustrates how to use the pandas Python library and its contents to analyse a dataset.

Code Example:

# Step 1: Import the Pandas library
import pandas as pd

# Step 2: Create a simple DataFrame (no external CSV required)
data = {
    'Name': ['Shivani', 'Anisha', 'Tara', 'Ananya', 'Srishti'],
    'Age': [24, 27, 22, 32, 29],
    'City': ['Delhi', 'Bengalore', 'Coimbatore', 'Kolkata', 'Patna']
}

# Create a DataFrame from the dictionary
df = pd.DataFrame(data)

# Step 3: Display the first few rows of the dataset
print("First 3 rows of the dataset:")
print(df.head(3))

# Step 4: Get a summary of the dataset
print("\nSummary of the dataset:")
print(df.describe())

Output:

First 3 rows of the dataset:
      Name  ...        City
0  Shivani  ...       Delhi
1   Anisha  ...   Bengalore
2     Tara  ...  Coimbatore
[3 rows x 3 columns]
Summary of the dataset:
             Age
count   5.000000
mean   26.800000
std     3.962323
min    22.000000
25%    24.000000
50%    27.000000
75%    29.000000
max    32.000000

Explanation:

In the Python code example, we demonstrate the use of Pandas, one of the most widely used Python libraries for data manipulation and analysis.

1. We start by importing the pandas library, commonly abbreviated as pd, so we can use its functions in our code.
2. Then, using pd.read_csv('sample_data.csv'), we read a CSV file named 'sample_data.csv' into a Pandas DataFrame. The DataFrame is essentially a table-like structure, which makes it easy to handle and analyze data in a structured format (rows and columns).
3. By calling data.head(), we display the first 5 rows of the DataFrame. This function is helpful to quickly inspect the data, ensuring it was loaded correctly and get an initial sense of the structure.
4. Finally, we use data.describe() to get the summary of the dataset, including statistical details like the mean, standard deviation, min, max, and quartiles for each numerical column. This helps us understand the distribution and range of the data at a glance.

By following these steps, we use Pandas to easily load, inspect, and summarize data, which are critical tasks in data science workflows. This flow illustrates how Pandas facilitates the efficient handling of data, empowering data scientists to focus on analysis and model building without worrying about raw data manipulation. The same applies to other Python libraries.

Important Python Libraries For Machine & Deep Learning

Python's dominance in machine learning (ML) and deep learning (DL) stems from its versatile libraries. These libraries simplify tasks such as building predictive models, handling big data, and deploying neural networks. Whether you're training a simple regression model or developing state-of-the-art neural networks, Python's ML and DL libraries have you covered.

In the table below, we have listed some of the most important Python libraries for this segment, along with their functionalities and applications.

List Of Python Libraries For Machine & Deep Learning

Note: All the libraries listed in this section are third-party libraries. We will discuss how to work with these third-party Python libraries in a later section, including installation and usage methods, so you can easily integrate them into your projects.

Example Of Scikit-learn Python Library– Simple Linear Regression

In the basic Python code example below, we have illustrated how to use the Scikit-learn library to build and evaluate a simple linear regression model.

Code Example:

from sklearn.linear_model import LinearRegression
import numpy as np

# Data (independent and dependent variables)
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1.5, 3.7, 2.8, 4.9, 5.2])

# Step 1: Create a Linear Regression model
model = LinearRegression()

# Step 2: Fit the model with the data
model.fit(X, y)

# Step 3: Make a prediction
predicted_value = model.predict([[6]])

# Step 4: Print coefficients and predicted value
print("Slope (Coefficient):", model.coef_[0])
print("Intercept:", model.intercept_)
print("Prediction for X=6:", predicted_value[0])

Output:

Slope (Coefficient): 0.75
Intercept: 1.1
Prediction for X=6: 5.6

Explanation:
In the example Python program,

• We start by importing LinearRegression from Scikit-learn third-party Python library and the numpy library as np.
• Then, we prepare a simple dataset with independent and dependent variables. The dataset contains five data points where X is a 2D array representing our features, and y is a 1D array representing the target values.
• Next, we initialize a LinearRegression object, which serves as our model. 
• The model is then fitted to the dataset using model.fit(X, y), allowing it to learn the relationship between X and y.
• After training, we make a prediction using model.predict([[6]]). This predicts the output for a new input value of X=6.
• Finally, we print the model's slope (coefficient), intercept, and predicted value, providing insight into the linear relationship it has learned.

Important Note: Before running this code, ensure that the Scikit-learn library is installed in your Python environment. If it's not installed, you'll encounter an error similar to this:

ModuleNotFoundError: No module named 'sklearn'

To resolve this, you can install Scikit-learn by running the following command in your terminal or command prompt (we will discuss this in detail in a later section):

pip install scikit-learn

If you're working within a Python IDE, the IDE may prompt you to install the required library, or you may need to install it manually in your terminal before executing the code.

Other Important Python Libraries You Must Know

While Python's most popular libraries are often those tied to specific fields like data science, machine learning, or web development, there are numerous other libraries that are essential for everyday tasks, making your life as a developer easier and more productive. 

In the table below, we have listed the Python libraries covering a wide range of uses, from handling HTTP requests to working with databases or automating repetitive tasks.

Example For Requests Python Library

The following example demonstrates how to make a simple GET request to a website and handle the response.

Code Example:

# Step 1: Import the Requests library
import requests

# Step 2: Make a GET request to a website (e.g., fetching Python's official homepage)
response = requests.get('https://www.python.org')

# Step 3: Check if the request was successful (status code 200 means success)
if response.status_code == 200:
    print("Request was successful!")
    # Step 4: Print the first 500 characters of the HTML content
    print(response.text[:500])
else:
    print("Failed to retrieve the website.")

Explanation:

In the example Python code,

• We first import the requests library so we can make HTTP requests easily.
• Then, we use requests.get() to send a GET request to Python’s official website. The response object contains all the information returned by the server.
• Next, we use an if-statement to check if the status_code of the response is 200, which indicates that the request was successful. If successful, we proceed to print part of the HTML content.
• If the request is successful, the first 500 characters of the response text (the HTML content of the website) are printed. If not, an error message is displayed.

This simple example demonstrates how to fetch data from a website using the Requests library and handle responses effectively, which is useful in scenarios like web scraping, API interaction, or interacting with web servers in general. Note that this is also a third-party library, so check out the next section to learn how to work with these Python libraries.

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Working With Third-Party Python Libraries

Third-party libraries in Python are those that are not included in the standard Python distribution but offer valuable functionality. They are created by other developers and are made available for public use. 

• These libraries extend Python's capabilities, allowing you to avoid reinventing the wheel for common (and complex) tasks. 
• Examples include libraries like requests for HTTP operations, matplotlib for plotting, and scikit-learn for machine learning.

To use these libraries in your Python projects, you need to install them (since they are not a part of the default Python environment) and then import them into your code.

Installing Third-Party Python Libraries

While Python provides a large set of standard libraries, third-party libraries need to be installed separately, as they are not bundled with Python by default. Installing third-party libraries ensures that you can access extra functionality that is useful for specialized tasks such as data science, machine learning, web scraping, and more.

Import vs Installation: Importing brings a library into your current environment, but you can only import a library if it’s already installed. Without installation, Python won’t know where to find the library.

Here are the most common methods to install third-party libraries:

1. Using pip (Python Package Installer): The most widely used tool for installing third-party libraries in Python is pip. It downloads and installs the library from the Python Package Index (PyPI). To install a library, simply run the following command in your terminal:

pip install library_name
pip install requests #This installs the requests library

2. Using conda: If you use Anaconda (a popular distribution for data science and machine learning), you can use the conda command to install third-party libraries. This method is especially useful when you're managing a data science environment where compatibility with certain libraries is critical.
   To install a library using conda:

conda install library_name
conda install numpy # This installs the numpy library

3. Installing from a Local File or URL: If the library isn't available on PyPI or conda, or if you have a custom version, you can install it from a local file or GitHub URL. Use the following command to install from a local file:

pip install /path/to/library.tar.gz

Or, to install directly from a GitHub repository:

pip install git+https://github.com/username/repository.git

4. Installing Multiple Libraries at Once: If you have several libraries to install, you can list them all in a requirements.txt file. This is especially useful for projects where you want to ensure that every team member is using the same set of libraries. Here’s how you do it:
   • Create a requirements.txt file with the names of the libraries you need.
   • Run the command:

pip install -r requirements.txt

This will install all the libraries listed in the file.

Creating Your Own Python Libraries

Creating your own third-party libraries allows you to share reusable code with others or within your team. When creating a Python library, you typically want it to be modular, well-documented, and easy to install. 

Here’s how you can create your own Python library:

1. Create a New Directory for Your Library: Create a new folder and name it after your library. For example, if you are making a library called mylibrary, create a directory named mylibrary.

2. Add a Python File: Inside this directory, create a Python file (e.g., mylibrary.py). Here’s an example of a simple function:

# mylibrary.py
def greet(name):
    return f"Hello, {name}!"

3. Add a Setup File: To make your library installable, add a setup.py file. This file contains metadata about your library, such as its name and version.

from setuptools import setup
setup(
    name="mylibrary",
    version="0.1",
    description="A simple greeting library",
    py_modules=["mylibrary"],
    install_requires=[],
)

4. Install Your Library Locally: Once you’ve set up your library, you can install it locally by running:

pip install .

This command installs your library in the current environment, and you can now use it by importing it like any other Python library.

Troubleshooting Common Issues For Python Libraries

Even though Python libraries are incredibly useful, you may run into problems during their installation, import, or usage. In this section, we will discuss the two most common issues you might encounter when working with Python libraries and provide solutions to resolve them.

Python Library Import Errors

One of the most common issues when working with Python libraries is import errors. This happens when Python cannot find the library you're trying to import or if the library isn’t installed correctly.

Common Causes:

• The library is not installed.
• You are trying to import a module or function that doesn’t exist in the library.
• There are naming conflicts, such as using the same name for a variable and a module.

Example of Import Error:

import nonexistent_library

Output:

ModuleNotFoundError: No module named 'nonexistent_library'

Solution:

• Ensure that the library is installed using pip install library_name or conda install library_name.
• Double-check that you’ve spelt the library name correctly.
• Make sure the library is available in the current Python environment. If you’re using a virtual environment, activate it before running your code.

Best Practice: Use try-except blocks to handle import errors gracefully

try:
    import numpy
except ImportError:
    print("Numpy is not installed. Installing now...")
    !pip install numpy

Version Conflicts When Using Python Libraries

Sometimes, you may encounter version conflicts between different libraries or between a library and the Python version you're using. This often happens when multiple versions of a library exist or when an outdated version is incompatible with the other libraries in your project.

Example of Version Conflict:

pip install numpy==1.18
pip install pandas==1.0

Output (potential conflict message):

ERROR: Could not find a version that satisfies the requirement pandas==1.0 (from versions: 1.1, 1.2, ...)

Solution:

• Use pip freeze to check the installed versions of libraries and check if the versions are compatible.
• You can create a requirements.txt file to pin exact versions of libraries that work well together:

pip freeze > requirements.txt

• Use a virtual environment to avoid conflicts between global and project-specific libraries.
• Upgrade or downgrade libraries using:

pip install library_name==version_number

Python Libraries In Larger Projects

When working on larger projects, managing multiple Python libraries and dependencies can become complicated. It's crucial to ensure that all libraries are compatible and easy to manage as your project grows.

Best Practices For Managing Python Libraries In Larger Projects

• Use a Virtual Environment: Isolate your project’s libraries from the global Python environment to avoid version conflicts. You can create a virtual environment using:

python -m venv myenv

• And activate it using:

source myenv/bin/activate  # on macOS/Linux
myenv\Scripts\activate     # on Windows

• Use requirements.txt: Keep track of the libraries required for your project in a requirements.txt file. This makes it easy to recreate the environment in the future or share the project with others:

pip freeze > requirements.txt

• Use Docker: For more advanced setups, you can containerize your project using Docker, ensuring that the entire development environment (including libraries) is consistent across different machines and operating systems.

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Importance Of Python Libraries

Python libraries play a crucial role in simplifying programming tasks and enhancing productivity. Here’s why they matter:

• Simplify Complex Tasks: Libraries provide pre-built functions and tools, allowing you to execute complex tasks with minimal effort.
  Example: numpy simplifies numerical operations, while pandas streamlines data manipulation.
• Save Time and Effort: Instead of writing code from scratch, you can leverage libraries to achieve results faster.
  Example: Plotting data using matplotlib takes seconds compared to manually coding the entire visualization.
• Enhance Code Efficiency: Libraries are optimized for performance, often outperforming custom solutions.
  Example: Machine learning tasks are significantly faster using scikit-learn or TensorFlow.
• Ensure Code Reusability: By using well-tested libraries, you can avoid duplicating work and focus on the unique aspects of your project.
  Example: Web development frameworks like Flask or Django offer reusable components for building scalable applications.
• Access to Cutting-Edge Technologies: Libraries provide easy access to advanced features like deep learning, big data analysis, and real-time applications.
  Example: PyTorch and Keras allow even beginners to build neural networks for deep learning.
• Community Support and Reliability: Popular libraries are maintained by large communities, ensuring regular updates, bug fixes, and extensive documentation.
  Example: The requests is a highly reliable library for handling HTTP requests, with a robust support network.

Conclusion

In this article, we've explored the power and versatility of Python libraries, from understanding their role in simplifying code to diving deep into installation, usage, and troubleshooting. Libraries are at the heart of what makes Python an efficient and powerful tool for developers, whether you're analyzing data, building machine learning models, or automating tasks. We’ve also learned about the installation of third-party libraries and how to create and manage your own, ensuring you’re fully equipped to harness the full potential of Python’s ecosystem.

By now, you should have a solid understanding of how to import and use both standard and third-party Python libraries, troubleshoot common issues, and structure your Python projects for maximum efficiency.

Frequently Asked Questions

Q1. What is a module in Python?

A Python module is a file containing Python definitions and statements. It can define functions, classes, and variables. A module allows you to organize your code into reusable sections, making your projects more manageable and easier to maintain.

Q2. What's the difference between a module and a library in Python?

A module is a single file of Python code, whereas a library is a collection of modules that provides additional functionality to your code. Python libraries are often made up of multiple modules that handle different aspects of functionality.

Q3. Can I use Python libraries without installing them?

Yes, many Python libraries come pre-installed with the standard Python distribution, so you can directly import them into your code without installing them. However, third-party libraries need to be installed using tools like pip or conda.

Q4. How do I check which libraries are installed in my environment?

You can use the following command to list all installed libraries:

pip list

Q5. What is the purpose of using virtual environments in Python?

Virtual environments help isolate project-specific libraries from the global Python environment. This ensures that dependencies for one project do not interfere with another and makes it easier to manage different versions of Python libraries.

Q. How do I resolve an ImportError?

An ImportError typically occurs when a library or module isn't installed or isn't found by Python. You can resolve this by ensuring the library is installed with pip or conda and that it's available in the correct environment.

Q. How can I upgrade or downgrade a library?

You can use the following commands to upgrade or downgrade libraries:

To upgrade:

pip install --upgrade library_name

To downgrade to a specific version:

pip install library_name==version_number

Q. Can I create my own Python libraries?

Yes, you can create your own Python libraries by organizing your code into modules and packaging them for reuse. Once packaged, your library can be shared with others or used in different projects.

Quick Quiz On Python Libraries – Let’s Go!

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