10 Best Python Libraries for Data Science
Libraries that data scientists should know and top 5 books to learn them.
Learning data science is a challenge. Knowing a programming language is not enough to do data science projects. You also need to learn about some libraries. In this post, I’ll cover the 10 best Python libraries that data scientists should know. Also, at the end of the post, I’ll talk about 5 books, which I love to read. I highly recommend reading these books on your data science journey.
Before getting started, please don’t forget to subscribe to my youtube channel where I create content about ai, data science, machine learning, and deep learning.
Let’s dive in!
What is Data Science?
Data science is the science of finding hidden patterns in data. You can extract meaningful information from data with data science techniques. No deep math knowledge is required for data science. You can easily perform data analysis with Python libraries. There are many libraries and tools that you can use in data science, especially written in Python. You can make your analysis easier by using these libraries. But knowing all of these libraries is of course very difficult. Let’s take a look at the 10 best libraries.
1- NumPy
NumPy is short for numerical Python and is one of Python’s most important libraries. Numpy is used for matrix and multidimensional array operations in Python. As you know, Python is an easy language to learn, on the other hand, it is slow in mathematical operations. But multidimensional array operations with NumPy are about 100 times faster than Python. It is very often used with other libraries such as Pandas, Scikit-Learn, or TensorFlow.
2- Pandas
One of the most used libraries for data science is Pandas. It is one of my favorite libraries. The most time-consuming step in a data science project is data preprocessing. The data is made suitable for analysis with this step. Pandas is used for data cleaning and data preprocessing. After preprocessing the data, you can easily perform your analysis with libraries such as Scikit-Learn.
3- Matplotlib
It is very important to understand the data before analyzing the data. Data visualization is the easiest way to explore data. In addition, data visualization is one of the important stages of data analysis. For example, plots help us find outliers in data and decide which model to use. In Python, the most used library for data visualization is Matplotlib. It works with libraries like Scikit-Learn, or TensorFlow. You can also draw three-dimensional plots with Matplotlib.
4- Seaborn
Matplotlib is an important library for visualizing data. However, Seaborn is often used to draw statistical plots. Also, Matplotlib is a low-level library and requires more code for advanced graphics. Another problem with Matplotlib was that it was introduced about 10 years before Pandas, so it was not designed to use DataFrame, an important data structure of Pandas. To overcome these problems and to make statistical analyzes easier, Seaborn library working on Matplotlib was developed. You can draw easier and more useful plots with Seaborn. You can also make the themes of your graphics more beautiful with this library.
5- Scikit-Learn
One of the important stages of data analysis after data preprocessing is to build a model. The most used library in Python for building a machine learning model is Scikit-Learn. Scikit-Learn has supervised and unsupervised machine learning algorithms. You can also do data preprocessing such as data scaling and data encoding with Scikit-Learn.
6- SciPy
SciPy is a library built on NumPy. As the name suggests, it combines scientific functions and mathematical algorithms. In this library, you can find useful functions that you can use in areas such as mathematics, statistics, linear algebra, or optimization.
7- Streamlite
Once you have some meaningful results, you want to share those results with others. You can use the Streamlite library to deploy your analysis results interactively. Streamlite is a framework that helps develop web applications. Applications made with this framework are fascinatingly fast and flexible.
8- OpenCV
With the development of social media and smartphones, we take a lot of pictures and share these pictures. Data scientists analyze these images with AI algorithms. You can use the OpenCV library for computer vision analysis. OpenCV is primarily written in C++, but can also be used in Python and Java. You can use OpenCV to manipulate images or find objects in images.
9- TensorFlow
Deep learning is one of the subfields of AI. You can perform analyses such as image classification, voice recognition, natural language processing, and translation from language to language with deep learning.
One of the most used libraries for deep learning is TensorFlow developed by Google. With TensorFlow, you can do end-to-end data science projects. So you can use TensorFlow at every stage of your data science analysis, from data preprocessing to model deployment.
One advantage of TensorFlow is that it works with Keras, a high-level API. Keras was embedded in TensorFlow as an API in 2019. Keras is automatically installed when you install TensorFlow. Thus, you can easily do your data science or artificial intelligence analysis with Keras.
You can also use the PyTorch library developed by Facebook for deep learning analysis. PyTorch is mainly used for academic research.
10- Flask
Another library you can use to deploy your model is Flask. You can easily make web applications with Flask. You don’t need to be a web developer to deploy a model. You can develop web APIs with Flask, a micro framework.
You can also use the Django library to develop web applications. But Django is mostly used for larger projects. Flask, on the other hand, is mostly used for developing small applications.
With the libraries I mentioned, you can become an expert data scientist. To learn about these libraries, you can look at the official documentation or you can find many free resources on the internet. Now I would like to talk about 5 books for data science that I also enjoy reading.
Top 5 Books for Data Science
As I just mentioned, being a data scientist is not as easy as it seems. It took me two years to become a data scientist from scratch. The libraries I mentioned above have their documentation. I suggest reading this documentation to stay up to date. But it is difficult for beginners to learn data science from the documentation. Let’s take a look at the books to learn data science.
1- Python for Data Analysis
The first book I would recommend is Python for data analysis book. The author of this book is the person who wrote the Pandas library. This book starts with NumPy and focuses specifically on the Pandas. You can also find examples of data preprocessing with real-world datasets. The third edition of this book will be out soon.
2- Python Data Science Handbook
The second book I would recommend is the Python Data Science Handbook, which you can access online. The author of the book is currently working at Google. In this book, you can learn the Numpy, Pandas, Matplotlib libraries, and machine learning algorithms with practical applications.
3- Introduction to Machine Learning with Python
Another book I would recommend is Introduction to Machine Learning with Python, which focuses on the Scikit-Learn library. I highly recommend this book, especially for those who do not know statistics. In this book, machine learning is explained without going into the theory of algorithms. An excellent book for data science beginners.
4- Hands-On Machine Learning
Another book I would recommend for data science is Hands-On Machine Learning. When asked for a book recommendation for data science or machine learning, this is often the first book data scientists recommend. This book consists of two parts. In the first part, machine learning with Scikit-Learn, and in the second part deep learning with TensorFlow and Keras are explained. It is one of the rare books that combines theory and practice.
5- Deep Learning with Python
The last book I would recommend is Deep Learning with Python. This book was written by the developer of Keras. Currently working as a Keras team leader at TensorFlow. A great book for those who want to learn the theory and practice of data science with Keras and TensorFlow. The second edition just came out.
Conclusion
Data is the new oil and models are the new refineries. Data science is a broad term that describes the processes used to extract meaningful information from any type of data. Learning data science is a challenge. In this post, I told you what you need to know to be an expert in data science and mentioned 5 books.
That’s it. I hope you enjoy it. Thanks for reading. Don’t forget to follow us on YouTube | GitHub | Twitter | Kaggle | LinkedIn 👍
You may be interested in the following articles 👇
If this post was helpful, please click the clap 👏 button below a few times to show me your support 👇
