**Top Python Data Science Libraries**

**Top Python Data Science Libraries**

Python’s popularity as a developer’s option has been on the rise in recent years. IEEE Spectrum recognized the programming language as the best in 2017. Python is useful in web development, DevOps, and data science, so if you want to create a career in the technical industry, now is the perfect time to learn it.

Python’s use is well recognized in machine learning, data science, and related studies. Python is a popular general-purpose language for data scientists because it can handle data mining and processing, machine learning/deep learning methods, and data visualization.

Python has a stronghold in the data science field thanks to its extensive library of data science tools. Packages group together important functions and objects for certain activities. Libraries are a collection of these packages that can then be imported into scripts.

To get started with data science in Python, you’ll need to familiarize yourself with these libraries, which will allow you to accomplish everything from simple to expert data science activities. We’ve developed a list of these libraries that you should familiarize yourself with. The IPython notebook, often known as the Jupyter Notebook, has more information about these libraries.

**Basic Libraries**

**Basic Libraries**

**NumPy**

**NumPy**

NumPy is a core library for performing scientific computations of any kind. Multidimensional arrays with a set of algorithms that conduct logical, statistical, and mathematical operations are the main aim of this library. NumPy organizes many sorts of data into arrays, making it simple to handle and interface with databases. It can also do Fourier transforms, linear algebra, generate random numbers, and change the shape of matrices.

NumPy is used by many libraries for basic input and output functions. NumPy lacks significant data analytics capabilities, but it lays the groundwork for them. It may be used for technical computations when combined with SciPy and Matplotlib.

**SciPy**

SciPy stands for Scientific Python and is essentially a NumPy-based library, implying that they should be used together. Understanding how array shapes and data types can be changed is critical to understanding SciPy’s functions. To execute high-level routines in linear algebra, interpolation, Fourier transform calculations, optimization, and so on, SciPy includes task-specific sub-modules such as scipy.linalg, scipy.fftback, and so on. Because many machine learning jobs rely significantly on linear algebra and statistical approaches, familiarity with SciPy is required.

**Pandas**

**Pandas**

Pandas are a basic package that includes data structures for working with many types of data. It is one of the most powerful data analysis tools, with applications in finance, economics, statistics, and other fields. It is capable of doing fundamental data functions such as loading, modeling, and analysis.

Pandas can convert data structures into dataframes (2-D data) and modify data within them with ease. Pandas make it simple to deal with missing data and to align data automatically. Row and column addition, index resetting and deletion, pivoting and altering data frames are just a few of the functions it can accomplish. Finally, pandas allow you to export your table to Excel or any other SQL database.

**Data Visualization**

**Data Visualization**

**Matplotlib**

**Matplotlib**

As the name implies, this is a plotting library that makes considerable use of the sub module pyplot() to plot values obtained with the ndarray package. The matplotlob.pyplot package contains tools to conduct 2D charting in Matlab. It’s used to make basic graphs, which may then be combined with graphics toolkits like PyQt to make more complicated graphs like scatter plots, spectrograms, histograms, quiver plots, and so on.

**Seaborn**

**Seaborn**

Seaborn is also a Matplotlib-based data visualization tool that generates visually appealing statistics charts and graphs. Matplotlib can also be used to customize the plots that Seaborn generates. It provides functions for fitting and visualizing linear regression models, plotting time series data, and performing operations on arrays and dataframes, as well as aggregating, to generate the correct result visualizations. It’s crucial to note, however, that Seaborn is not a replacement for Matplotlib, but rather a supplement to it.

Bokeh and Plotly are two excellent visualization tools that are not dependent on matlplotlib and are primarily web-based.

**Machine Learning / Deep Learning**

**Machine Learning / Deep Learning**

**SciKit-Learn**

**SciKit-Learn**

Scikit-learn are a machine learning library that enables supervised and unsupervised learning on medium-sized datasets. This library is based on the SciPy programming language. Before you can use sciKit, you must first install NumPy and SciPy. NumPy and SciPy are focused on data manipulation and wrangling, whereas SciKit is focused on data modeling. Scikit-learn can execute a variety of techniques, including regression and classification, dimensionality reduction, ensemble approaches, and feature extraction functions.

**Theano**

**Theano**

Theano, like NumPy, allows users to efficiently create, optimize, and evaluate mathematical procedures involving multi-dimensional arrays. It also serves as a framework for deep learning jobs in Python. It is a sophisticated mathematical compiler that combines native libraries such as BLAS and C compiler to run on both GPU and CPU. Theano for deep learning isn’t used on its own; instead, it’s wrapped around libraries like Keras or Lasagne to create models that vastly boost computation speed.

**Keras**

**Keras**

Keras is a library for modeling artificial neural networks that runs on the backend of Theano or TensorFlow. It is not an end-to-end machine learning framework like the SciKit library and was built primarily for experimentation with deep neural networks.

Keras can create a neural network in the form of a sequential model, which is essentially a stack of layers that make up a neural network. The data is organized into tensors, which are delivered at the input layer together with a suitable activation function, and the output layer is the last layer. Keras has made the synthesis of ANNs easier.

**TensorFlow**

**TensorFlow**

It is a relatively recent machine learning library that was created by Google as the engine of their neural network training environment. TensorFlow was used to create high-profile Google applications such as Google Translate. It also boosts both CPU and GPU calculations. TensorFlow competes with Theano for popularity as a backend library, with strengths and cons that vary depending on the application. It contains a multilayer node architecture that makes working with enormous datasets simple; however, it may be a little slower than Theano in terms of execution performance.

**Natural Language Processing**

**Natural Language Processing**

**Natural Language Toolkit**

**Natural Language Toolkit**

The Natural Language Toolkit (NLTK) is a collection of libraries for symbolic and statistical tasks in natural language processing (NLP). When there is a human-machine interaction, natural language processing is applied. To mention a few applications, NLP is used for subject segmentation, opinion mining, and sentiment analysis. Tokenization, classification, tagging, parsing, semantic analysis of input data, and other NLP activities are possible with NLTK. By arranging the input data and tokenization operations, it aids in the conversion of written words into vectors.

**Conclusion**

**Conclusion**

For the tasks described, there are various alternatives to the libraries mentioned above. These are, however, the libraries that have acquired traction in the data science community. Apart from the libraries listed above, data scientists should be familiar with data mining libraries such as BeautifulSoup, Scrapy, and Pattern for web crawling, which are not covered here but are very important.

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