List of Top Python Libraries
In this article, we will explore some of the top Python libraries that are used by data scientists, developers, and researchers for various purposes like data analysis, machine learning, deep learning, etc.
A library is a collection of pre-written code that can be used to perform specific tasks. It helps developers to save time and effort to write complex code from scratch. Python has an extensive collection of libraries that are available to use.
Here are the top 10 Python libraries that you should know:
- Pandas: Used for data manipulation and analysis.
- NumPy: Used for scientific computing and working with arrays.
- Keras: Used for building and training deep learning models.
- TensorFlow: Used for building and deploying machine learning models.
- Scikit Learn: Used for machine learning and data mining.
- Eli5: Used for debugging machine learning classifiers and explain the predictions.
- SciPy: Used for scientific and technical computing.
- PyTorch: Used for building and training machine learning models.
- LightGBM: Used for gradient boosting.
- Theano: Used for fast numerical computation on GPUs.
Using these libraries, you can perform complex data analysis, build and train machine learning models, and much more.
Conclusion:
Python has a vast collection of libraries that can be used for various purposes like data analysis, machine learning, deep learning, etc. In this article, we have explored the top 10 Python libraries that are widely used. However, there are many other libraries available that can be used depending on your requirements.
Frequently Asked Questions (FAQs):
- Q.1: What are Python libraries used for?
- Q.2: Are all Python libraries free?
- Q.3: How do libraries work in Python?
- Q.4: How do I list all libraries in Python?
Answer: Libraries are collections of pre-written code that can be used to perform specific tasks in Python. They help developers to save time and effort to write complex code from scratch.
Answer: Most Python libraries are free and open-source.
Answer: You can import the libraries in your code and use the pre-written code to perform specific tasks.
Answer: You can use the pip list command in the terminal to list all the installed libraries.
Additional Resources:
Why Python is a Popular Language for Real-world Problems
In today’s tech-driven world, choosing a programming language that can efficiently address real-world issues is crucial. Python is a popular choice, used extensively in various industries like software engineering, machine learning, and data science, among others, thanks to its multitude of libraries. Many upcoming talents prefer Python as their primary programming language because of this reason.
In this article, we will share knowledge about the most popular Python libraries’ usage in today’s modern world.
What is a Library?
A library is a collection of pre-written utility methods, classes, and modules that can be used by your application code to perform specific tasks without writing everything from scratch. Libraries have a narrow scope, such as Strings, Input/Output, and Sockets, resulting in smaller APIs and fewer dependencies. They are essentially lists of class definitions. The main benefit of libraries is code reusability, which allows developers to use code already written by others instead of reinventing the wheel.
For example, a library may contain a function called findLastIndex(char) that returns the last index of a character in a string. Rather than writing this functionality from scratch, we can simply call the library’s findLastIndex(charToFind) function and pass the desired character as a parameter. By using libraries, programmers can focus on solving the real problems instead of constantly reinventing known solutions.
Introduction to Python Libraries
Python is a widely-used programming language that has gained popularity due to its simplicity, ease of use, and extensive range of libraries. These libraries provide collections of functions that allow developers to build applications without starting from scratch.
Python is a beginner-friendly language, and developers can be more productive in development, deployment, and maintenance. Additionally, Python’s programming syntax is easy to learn and has a high level of abstraction, making it easier than languages such as C, Java, and C++.
Python libraries offer a wide array of benefits, including their portability. With more than 137,000 libraries, Python can be used in various fields, such as data science, machine learning, data visualization, image manipulation, and more.
Top Python Libraries
Having learned about libraries and Python, it’s time to explore the most frequently used Python libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
These libraries are essential for data analysis and visualization tasks in Python.
PANDAS
Pandas is an open-source library under the BSD license, widely used in data science for data analysis, manipulation, and cleaning. It provides simple data modeling and analysis operations in Python, eliminating the need to switch to R. Pandas works with various types of data, including labeled matrix data, ordered and unordered time series data, unlabeled information, and other statistical information.
[Pandas]
Pandas can perform a broad range of tasks, including slicing data frames, joining and merging data frames, concatenating columns, changing index values in a data frame, and modifying column headers. Additionally, it can convert data into multiple formats, among other operations.
NumPy Library
NumPy is an open-source Python library that focuses on scientific computations. It provides built-in mathematical functions for fast computations and supports big matrices and multidimensional data. NumPy stands for “Numerical Python”. The library has various use cases, such as in linear algebra, multi-dimensional container for generic data, and as a random number generator.
In NumPy, the main object is the NumPy Array, which defines an N-dimensional array with rows and columns and is preferred over lists for its faster performance and convenience. The library features important functions such as arcsin(), arccos(), tan(), radians(), etc.
The advantages of the NumPy library include being interactive, user-friendly, intuitive, and having a lot of open source contribution. It is useful for representing images, sound waves, and other binary raw streams as a real value N-dimensional array for visualizations.
Full-stack developers need knowledge of NumPy for implementing machine learning models.
Keras: Python-based Neural Network Library
Keras is a popular open-source neural network library written in Python. It provides an API designed for humans that allows us to experiment with deep neural networks quickly. Keras runs smoothly on both CPU and GPU and supports most neural network models, including convolutional, pooling, recurrent, and more. Its modular design makes it adaptable and suited for cutting-edge research. Keras is already utilized by several major companies such as Uber, Netflix, and Yelp, particularly those employing deep learning to power their products. It also includes pre-trained models and pre-processed data sets, e.g., MNIST, VGG, Inception, SqueezeNet, and ResNet. Installing TensorFlow before Keras is recommended.
What is TensorFlow?
TensorFlow is an open-source high-performance numerical calculation library utilized in machine learning and deep learning algorithms. It was created by researchers from the Google Brain team and is employed for complicated mathematical computations by researchers in math, physics, and machine learning.
Some of the features of TensorFlow are its adaptable and responsive construct that allows easy visualization and easy modularization of its Machine Learning models. TensorFlow also allows for simple training of Machine Learning models on both the CPU and GPU and permits parallel neural network training. TensorFlow has an active and large community due to being open-source.
TensorFlow is regularly used by Google services such as Google Voice Search and Google Photos. Its libraries are primarily developed in C and C++ and have a sophisticated Python front end. TensorFlow has numerous applications that make it an appealing tool for machine learning research.
# Sample code utilizing TensorFlow's modularity for training
# a Machine Learning model on both CPU and GPU
import tensorflow as tf
# Set up a TensorFlow graph
graph = tf.Graph()
with graph.as_default():
# Define the operations for the graph
X = tf.placeholder(tf.float32, shape=(None, n_features))
y = tf.placeholder(tf.float32, shape=(None, n_targets))
W = tf.Variable(tf.zeros([n_features, n_targets]))
b = tf.Variable(tf.zeros([n_targets]))
y_pred = tf.matmul(X, W) + b
mse = tf.reduce_mean(tf.square(y_pred - y))
# Train the model with TensorFlow
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(mse)
with tf.Session(graph=graph) as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
sess.run(train_op, feed_dict={X: data, y: targets})
Scikit Learn: An Overview
Scikit Learn is a Python-based open-source library for machine learning algorithms. It supports supervised and unsupervised learning techniques, and includes popular algorithms as well as packages like NumPy, Matplotlib, and SciPy. Scikit Learn is widely used in music recommendation algorithms by Spotify. Some key features of Scikit Learn are:
* Cross-Validation: Scikit Learn offers several methods like TRAIN_TEST_SPLIT and CROSS_VAL_SCORE for checking model accuracy on unseen data.
* Unsupervised Learning: Scikit Learn provides a variety of unsupervised learning algorithms including clustering, factor analysis, principal component analysis, and unsupervised neural networks.
* Feature Extraction: The library can be used for feature extraction from both photos and text.
Scikit Learn includes a vast collection of algorithms and can perform machine learning and data mining tasks such as dimensionality reduction, classification, regression, clustering, and model selection.
Eli5: A Python Library for Debugging Machine Learning Models
Eli5 is a Python library used for inspecting machine learning algorithms and explaining their predictions, particularly when dealing with inaccurate outcomes. It supports various libraries such as scikit-learn, XGBoost, catboost, Keras, and more. Eli5 is crucial when other Python packages are involved, and it is commonly used to debug algorithms such as classifiers and regressors in sklearn. Eli5 has practical applications in many industries that use legacy software and innovative approaches to implementing machine learning algorithms.
Scipy: A Powerful Python Library for Scientific Computing
Scipy is a Python library, which is widely used for scientific computation, data processing, and high-performance computing. It provides an enormous range of user-friendly routines optimized for quick computation. It is built on top of NumPy, enabling data processing and visualization, and provides high-level commands for mathematical computing.
Scipy provides various useful sub-packages like constants, cluster, fftpack, interpolate, linalg, integrate, io, ndimage, optimize, odt, signal, spatial, sparse, special, and stats. You can use `from scipy import subpackage-name` to import them easily.
All of SciPy’s submodules are well-documented, making it easy to understand and use. SciPy handles a wide range of scientific programming applications such as linear algebra, calculus, ordinary differential equations, and signal processing effortlessly.
The essential packages of SciPy include NumPy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.
PyTorch Overview
PyTorch is a Python library created by Facebook in 2017 that combines tensor computation with GPU acceleration and deep neural networks. It is widely used for natural language processing applications and is popular for its simplicity and speed.
Code written in PyTorch has the following features:
– Supports Python and its libraries.
– Dynamic setup and computation of graphs at any point during code execution.
– Simple coding and fast processing.
– Supports CUDA for GPU acceleration.
PyTorch was developed by Facebook’s AI research lab and is utilized by Uber’s probabilistic programming software “Pyro”. It has gained popularity due to its superior performance compared to TensorFlow.
LightGBM
LightGBM is a machine learning package that helps in developing new algorithms by modifying basic models like decision trees. It is one of the three popular libraries, the other two being XGBoost and CatBoost, which can be used for implementing Gradient Boosting quickly and efficiently.
Some features of LightGBM are:
– Provides high production efficiency through quick computation.
– It is user-friendly and intuitive.
– Faster training compared to many other deep learning libraries.
– It does not encounter errors when NaN values or other canonical values are used.
These libraries offer highly scalable and efficient gradient boosting implementations, making them popular among machine learning engineers.
THEANO: Mathematical Operations Library for Efficient Processing
Theano is a library used to define, optimize, and evaluate mathematical expressions using multi-dimensional arrays, resulting in more efficient processing. It offers fast implementation of mathematical operations due to its rich library. The library computes unstable expressions more accurately compared to NumPy, making it more useful.
Features:
– NumPy integration: Theano can use NumPy arrays entirely in compiled functions.
– Transparent GPU: It can perform data-intensive operations faster than a CPU.
– Symbolic differentiation: Theano does efficient symbolic differentiation for functions with one or more inputs.
– Optimizations for speed and stability: Theano is stable for efficient processing of large problems.
– Dynamic C code generation: Theano evaluates expressions fast, resulting in a significant increase in efficiency.
– Self-verification: Theano has extensive unit testing to help diagnose problems and ambiguities.
Theano can handle processing for complex neural network algorithms required for deep learning. Its symbolic syntax may be confusing for new users, but it is widely used across the industry and is the backbone of several neural network projects.
Python: The Language for the Future
Python, the high-level, dynamically typed, interpreted language, is becoming increasingly popular for error debugging in real-world applications. It’s being used globally by major companies, including YouTube, DropBox, and others, and has a variety of libraries that make tasks easier to complete. Learning Python and its libraries is crucial for success in fields like Data Science, Machine Learning, and Software Engineering. Undoubtedly, Python is the language of the future.
Frequently Asked Questions (FAQs)
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Python Libraries: Their Purpose and Usage
Python libraries serve as a foundation for developing and deploying various types of applications and models. These libraries can be used for a wide range of applications such as machine learning, data science, data visualization, image and data manipulation, and much more.
Are All Python Libraries Free?
Yes, the majority of Python libraries are free. Python is developed under an OSI-approved open-source license, which makes it free to use and distribute, even for commercial purposes.
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Working with Python Libraries
Python libraries can be imported into the code, and the functions and methods they offer can be utilized.
How to List All Installed Libraries in Python?
We can list all the installed libraries in Python in two ways:
help("modules")
The above command can be run on the Python prompt to display a list of all the installed modules on the system using the help function. However, we need to filter the list manually to find the required module.
pip freeze | grep "module_name"
The above command can be used to list all the installed libraries containing the specified “module_name” using Python PIP. We can easily search or filter the result with the grep command.
Additional Resources
Here are some additional resources for learning more about Python:
