There are several useful libraries in Python which helped it stand the test of time. Even though Python is from the 90s', it's still one of the high-rating programming languages. You might wonder the reason behind such popularity. In fact, there are many reasons for it:
- super easy to code
- flat learning curve
- an extensive set of libraries and packages
- strong community support
- is most used in developing modern-day technologies
What are libraries in a programming language?
Before we delve into useful libraries in Python, let's understand what a library is and what role it plays in a programming language. In simple words, libraries are pre-written codes and resources to solve a use case problem. In other words, the resources could be a template, configuration data, subroutines, etc.
What are the most useful libraries in Python?
Python is one of the most highly-rated programming languages as of today. Also, a huge set of libraries and modules is what makes Python so accessible. Libraries are customizable pieces of code to solve a problem of a specific use case. For various use-cases, there are many libraries in Python. For instance, Data Visualization has Matplotlib and Seaborn as its libraries.
Yet, we added Seaborn to the list of most useful libraries in Python list. Because Seaborn is an extension of Matplotlib. And, Matplotlib has lesser functionalities when compared to Seaborn. Thus, we will be discussing the most useful libraries in Python. And they are;
- Pandas, for Data Cleansing
- NumPy, for managing multi-dimensional arrays
- Scikit-Learn, for ML
- TensorFlow, for ML & DL
- Keras, for DL & Neural Networks
- and SciPy, for Scientific and mathematical functions.
Let's discuss these most useful libraries in the Python programming language in detail:
A Data Scientist's job is to extract insights from a large data set. Now, this data set may contain a wide range of data from different sources. For instance, data could be an image, text, video, or even log data. But, you must understand one thing that all these data sets are not clean and organized. Every Data Scientist must perform Data Exploration and Data Munging upon a raw data set. In simple words, they have to explore and cleanse the data before using it for analytics purposes.
In fact, Pandas is one of the most popular libraries that are in use in the Data Science domain. Data Science professionals use it for exploring, cleaning, and analyzing data. Also, you can load your structured data, prepare it, manipulate it, and analyze it to extract insights. Check out this link here to solve hands-on challenges to master Data Manipulation skills using Pandas.
NumPy helps you manage multi-dimensional or N-dimensional arrays. To assert its importance, I will tell how important this library is to Python users. Many Data Science libraries use NumPy to function. For instance, Pandas uses NumPy. In conclusion, NumPy is crucial to Data Science.
NumPy is far more robust to lists in Python. Therefore, makes it a favorite for Data Scientists. NumPy helps in solving numerical problems by offering precompiled functions. Besides, they also use array-oriented computations, which eases working with many classes. Check out the Kaggle website to learn and practice questions in NumPy. Also, you can visit this official NumPy website to access various resources to learn NumPy.
For Machine Learning professionals, it's one of the best libraries. After data cleansing, manipulation using Pandas and NumPy library. After that, use the Scikit-Learn library to build ML models. Also, it has predictive modeling and analysis tools which makes it one of the best for creating ML models. Scikit-Learn has every module to start with like:
- classification methods
- regression methods
- model selection
- model validation
- feature extraction
- dimensionality reduction, etc.
Also, you can use Scikit-Learn to build different ML models (structured or unstructured). You can also use it to confirm the model accuracy and conduct feature importance. Check out this link here to learn Machine Learning with the Scikit-Learn library.
Google created TensorFlow to develop and train Machine Learning and Deep Learning models. TensorFlow uses tensors (multi-dimensional arrays) to perform several operations on a particular input. In other words, it helps in hosting models to platforms like CPU, GPU, and TPU (Tensor Processing Unit). Also, you can get full access to collections of tools, APIs, and other resources from TensorFlow.
In conclusion, you can use these resources to create apps using ML. And play around with their flexible architecture. Above all, TensorFlow can build scalable and efficient models by training Neural Networks and GPUs. Also, you can explore TensorFlow by learning it from the official website.
Keras helps in solving Deep Learning and Neural Networks problems. The founder of Keras describes this library to be a flexible and powerful API to work with DL models. In addition, to being powerful, this library is simple and offers lesser user interaction from the ground up. This makes it one of the best Python libraries to learn Deep Learning and Neural Networks.
Keras made using TensorFlow and Theano, thus very easy to scale models to clusters of GPUs. But, the downside to Keras is that it uses back-end infrastructure to generate a computational graph. Which slows Keras down. Learn from the Keras official website by clicking on this link.
It helps users in mathematical and scientific functions. SciPy relies on the NumPy library. SciPy uses NumPy library for:
- N-dimensional arrays
- linear algebra
- numerical routines for integration
- optimization, etc.
This library offers many useful functions like stat, signal processing, and optimization functions. You can also solve differential equations using computing integrals. SciPy library can perform certain functions, like:
- can process multi-dimensional image
- could solve Fourier transforms, and differential equations
- could also solve algebra computations
You can learn and understand this library by clicking on this link.
It is one of the most important libraries that every Data Science aspirant must know. Data Visualization is a big aspect of Data Science. Seaborn library is a beautiful library containing data visuals. You can use this library to represent the extracted insights from data sets. Using this, Data Scientists could communicate information and also understand models.
Seaborn has many customizable themes and high-level interfaces. It helps in creating beautiful Data Visualizations. You can learn Seaborn by visiting their user guides and tutorials.
There are far more libraries than mentioned here. But the list encompasses the most popular and useful libraries. These libraries help a wide variety of professionals. In conclusion, these libraries are vital for every professional using Python in their day-to-day life.