10 Essential Python Libraries for Data Scientists

In today’s society, data science is highly important!

So much so that data scientist has been crowned the “Sexiest Job of the Twenty-First Century,” despite no one expecting geeky jobs to be sexy! 

However, because of the enormous importance of data, Data Science is quite popular right now.

Python, with its statistical analysis, data modeling, and readability, is one of the best programming languages for extracting value from this data. 

Python never ceases to amaze its programmers when it comes to overcoming data science challenges. It’s a widely used, object-oriented, open-source, high-performance programming language with a variety of additional features. 

Python has been designed with remarkable libraries for data science that programmers utilize every day to solve difficulties. 

Here are the best Python libraries to consider:

1. Pandas

Pandas is a package designed to assist developers in working with “labeled” and “relational” data in a natural manner. It is built on two major data structures: “Series” (one-dimensional, similar to a list of objects) and “Data Frames” (two-dimensional, like a table with multiple columns). 

Pandas support converting data structures to DataFrame objects, dealing with missing data, adding/deleting columns from DataFrame, imputing missing files, and visualizing data using histograms or plot boxes. 

It also provides a number of tools for reading and writing data between in-memory data structures and several file formats. 

In a nutshell, it is ideal for rapid and simple data processing, data aggregation, data reading and writing, and data visualization. When creating a data science project, you will always use the beast library Pandas to handle and analyze your data.

2. Numpy

NumPy (Numerical Python) is a fantastic tool for doing scientific computations and basic and sophisticated array operations. 

The library provides a number of helpful features for working with n-arrays and matrices in Python. 

It makes it easier to process arrays that contain values of the same data type and to perform arithmetic operations on arrays (including vectorization). In actuality, using the NumPy array type to vectorize mathematical operations improves performance and decreases the execution time. 

The support for multidimensional arrays for mathematical and logical operations is the library’s core feature. NumPy functions can be used to index, sort, reshape, and communicate visuals and sound waves as a multidimensional array of real numbers.

3. Matplotlib

In the Python world, Matplotlib is one of the most extensively used libraries. It is used to generate static, animated, and interactive data visualizations. Matplotlib has a lot of charting and customization options. 

Using histograms, programmers can scatter, tweak, and edit graphs. The open-source library provides an object-oriented API for adding plots into programs. 

When utilizing this library to generate complex visualizations, however, developers must write more code than normal. 

It is worth noting that popular charting libraries coexist with Matplotlib without a hitch. 

Among other things, it’s used in Python scripts, Python and IPython shells, Jupyter notebooks, and web application servers. 

Plots, bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, and any other sort of visualization chart can all be created with it.

4. Seaborn

The Seaborn library is built on Matplotlib. Seaborn can be used to make more attractive and informative statistical graphs than Matplotlib. 

Seaborn includes an integrated data set-oriented API for investigating the interactions between many variables, in addition to full support for data visualization. 

Seaborn offers a staggering number of options for data visualization, including time-series visualization, joint plots, violin diagrams, and many others. 

It uses semantic mapping and statistical aggregation to provide informative visualizations with deep insights. It includes a number of dataset-oriented charting routines that work with data frames and arrays that include whole datasets. 

Its data visualizations can include bar charts, pie charts, histograms, scatterplots, error charts, and other graphics. This Python data visualization library also includes tools for selecting color palettes, which help in uncovering trends in a dataset.

5. Scikit-learn

Scikit-learn is the greatest Python library for data modeling and model assessment. It is one of the most helpful Python libraries. It has a plethora of capabilities designed solely for the purpose of modeling. 

It includes all Supervised and Unsupervised Machine Learning algorithms, as well as fully-defined Ensemble Learning and Boosting Machine Learning functions. 

It is used by data scientists to do routine machine learning and data mining activities such as clustering, regression, model selection, dimensionality reduction, and classification. It also comes with comprehensive documentation and performs admirably. 

Scikit-learn can be used to create a variety of Supervised and Unsupervised Machine Learning models such as Classification, Regression, Support Vector Machines, Random Forests, Nearest Neighbors, Naive Bayes, Decision Trees, Clustering, and so on. 

The Python machine learning library includes a variety of simple-yet-efficient tools for performing data analysis and mining tasks.

For further reading, here’s our guide on Scikit-learn.

6. XGBoost

XGBoost is a distributed gradient boosting toolkit designed for speed, flexibility, and portability. To develop ML algorithms, it employs the Gradient Boosting framework. XGBoost is a fast and accurate parallel tree boosting technique that can solve a wide range of data science problems. 

Using the Gradient Boosting framework, this library can be used to create machine learning algorithms. 

It includes parallel tree boosting, which aids teams in solving a variety of data science issues. Another benefit is that developers can use the same code for Hadoop, SGE, and MPI. 

It’s also dependable in both distributed and memory-constrained situations.

7. Tensorflow

TensorFlow is a free end-to-end open-source AI platform with a large range of tools, libraries, and resources. TensorFlow must be familiar to anybody working on machine learning projects in Python. 

It is an open-source symbolic math toolkit for numerical calculation utilizing data flow graphs that were developed by Google. The graph nodes reflect the mathematical processes in a typical TensorFlow data flow graph. 

The graph edges, on the other hand, are the multidimensional data arrays, also known as tensors, that flow between the network nodes. It lets programmers distribute processing among one or more CPUs or GPUs on a desktop, mobile device, or server without changing code. 

TensorFlow is developed in C and C++. With TensorFlow, you can simply design and train Machine Learning models using high-level APIs like Keras. 

It also has many degrees of abstraction, allowing you to select the best solution for your model. TensorFlow also lets you deploy Machine Learning models to the cloud, a browser, or your own device. 

It is the most effective tool for jobs like object recognition, speech recognition, and many others. It aids in the development of artificial neural networks that must deal with numerous data sources.

Here’s our quick guide on TensorFlow for further reading.

8. Keras

Keras is a free and open-source Python-based neural network toolkit for artificial intelligence, deep learning, and data science activities. Neural networks are also utilized in Data Science to interpret observational data (photos or audio). 

It’s a collection of tools for creating models, graphing data, and evaluating data. It also includes pre-labeled datasets that can be quickly imported and loaded. 

It’s easy to use, versatile, and ideal for exploratory research. Furthermore, it allows you to create fully connected, convolutional, pooling, recurrent, embedding, and other forms of Neural Networks. 

These models can be merged to construct a full-fledged Neural Network for enormous data sets and issues. It’s a fantastic library for modeling and creating neural networks. 

It’s simple to use and gives developers a lot of flexibility. Keras is sluggish in comparison to other Python machine learning packages. 

This is because it first generates a computational graph utilizing the backend infrastructure and then uses it to conduct operations. Keras is incredibly expressive and adaptable when it comes to doing new research.

9. PyTorch

PyTorch is a popular Python package for deep learning and machine learning. It is a Python-based open-source scientific computing software for implementing Deep Learning and Neural Networks on huge datasets. 

Facebook makes extensive use of this toolkit to create neural networks that aid in activities such as facial recognition and auto-tagging. 

PyTorch is a platform for data scientists who wish to complete deep learning jobs quickly. The tool enables tensor calculations to be performed with GPU acceleration. 

It’s also used for other things, including constructing dynamic computational networks and automatically calculating gradients. 

Fortunately, PyTorch is a fantastic package that allows developers to easily transition from theory and research to training and development when it comes to machine learning and deep learning research in order to give maximum flexibility and speed.

10. NLTK

NLTK (Natural Language Toolkit) is a popular Python package for data scientists. Text tagging, tokenization, semantic reasoning, and other tasks related to natural language processing can be accomplished with NLTK. 

NLTK can also be used to complete more complex AI (Artificial Intelligence) jobs. NLTK was originally created to support different AI and machine learning teaching paradigms, such as the linguistic model and cognitive theory. 

It’s currently driving AI algorithm and learning model development in the actual world. It has been extensively embraced for usage as a teaching tool and as an individual study tool, in addition to being utilized as a platform for prototyping and developing research systems. 

Classification, parsing, semantic reasoning, stemming, tagging, and tokenization are all supported.

Conclusion

That concludes the top ten Python libraries for data science. Python data science libraries are updated on a regular basis as data science and machine learning become more popular.

There are several Python libraries for Data Science, and the user’s choice is mostly determined by the type of project they are working on.