Top 10 Python libraries for Data Science

Data Science is an interdisciplinary field with statistical analysis, mathematics, algorithms, machine learning and utilising data visualisation in order to draw data-driven decisions and insights. You can identify data, trends and patterns to make calculative and informed decisions. Data Science requires using tools, techniques and programming languages like R, SQL, and Python to analyse large databases, use machine learning algorithms to make predictions, and classify data and patterns in large data repositories. Almost every industry requires a data scientist, including healthcare, finance, marketing, retail and real estate.

Here are 10 Python libraries for Data Science:

1. NumPy:
   NumPy is an extensive Python library used to provide support for large, multi-dimensional arrays and matrices used in tasks like data manipulation, machine learning, statistical analysis and more. NumPy also offers mathematical support to perform functions on these arrays. NumPy uses arrays to handle these large data sets. NumPy creates and manipulates arrays for mathematical operations and generates random numbers and statistical distributions.
2. Pandas
   Pandas is another popular Python library for data manipulation and analysis. Pandas has a DataFrame data structure which is a two-dimensional table to store different types of data. Pandas is very flexible in handling data manipulation tasks like filtering, merging, and pivoting. Pandas is used for data cleaning, which provides a wide range of clean and easy functions which eventually lead to transforming data. Pandas also support time-series data, like rolling window calculations and time-zone handling. This Python library also integrates wonderfully with Matplotlib, NumPy and Scikit-Learn.
3. PyTorch
   PyTorch is another open-source machine-learning Python library, developed by Facebook. PyTorch uses dynamic computational graphs, facilitating the creation of a graph at run time, rather than defining it statically beforehand, because of which researchers can experiment with architectures and machine-learning models for efficient computation. TorchScript allows the conversion of PyTorch models, which enables it to integrate with other systems. PyTorch has in-built neural network modules due to which models can be created, without having to create the code from scratch. PyTorch has a debugging interface that allows users to resolve issues in their code.
4. Keras:
   Keras a Python library developed by Francois Chollet provides a user-friendly high-level neural network Application Programming Interface (API) that allows users to build neural networks models. Keras also allows the building of complex neural network architectures that gives a wide range of pre-built layers which can be customised. Keras has compatibility with a range of Python libraries, like Theano, and TensorFlow. Keras also has efficient data monitoring and data visualisation tools, and pre-trained models used for solving new problems.
5. Scikit-Learn:
   Scikit-Learn also provides a comprehensive set of machine-learning algorithms like regression, clustering and experiments with dimensionality reduction algorithms and finds the best algorithm with the help of a consistent API. Scikit-Learn offers a wide range of tools for data preprocessing, data normalisation, missing value imputation, and model selection, all of which helps to fine-tune their models which can improve performances. Scikit-Learn also integrates with other Python libraries like NumPy, Pandas, and Matplotlib, making it popular for machine-learning tasks in data science.
6. Light GBM:
   LightGBM is also a popular Python Library providing an open-source gradient framework, that helps to work on large datasets. LightGBM uses a unique approach called Gradient-based Decision Trees, where the most informative data instances for each tree are selected, which reduces the computation time. LightGBM has in-built categorical features to optimise performance when dealing with datasets with variables. LightGBM allows users to train models on large datasets using multiple machine-learning models.
7. SciPy:
   SciPy is an open-source scientific computing Python library, that provides comprehensive mathematical algorithms and functions and this library is used for data analysis and scientific computing. SciPy also integrates with other Python libraries like NumPy, Pandas and Matplotlib. SciPy is also highly-optimisable with many algorithms in C or Fortran. SciPy also provides a wide range of data visualisation tools, making it easy to visualise and interpret data.
8. Matplotlib:
   Matplotlib, also a Python plotting library that integrates well with NumPy, is used for data visualisation. Matlpitlib also provides a wide range of data visualisation tools like line plots, scatter plots, and other graphical representations, and due to the highly customisable performance of Matplotlib, visualisations can be customised and is designed to provide, publication-quality figures. Matplotlib has a wide wealth of resources and contributors of users and developers, which makes it an active community.
9. Tensorflow:
   TensorFlow is a Python Library created by Google, which offers a wide range of machine-learning tasks, and is highly scalable models on large datasets. TensorFlow has a user-friendly interface and can leverage hardware accelerators to speed up model training. TensorFlow also integrates with NumPy, Pandas and Matplotlib. The accelerators like GPUs and TPUs for high performance.
10. Theano:
    Theano is an open-source numerical computation library for Python that was developed by the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. It was designed to enable fast numerical computations, especially those involved in deep learning and other machine learning algorithms. Theano provides a symbolic computation system that allows users to define mathematical expressions symbolically rather than numerically. This enables Theano to optimize and compile these expressions for efficient execution on a variety of hardware platforms, including CPUs and GPUs. Theano is optimized for efficient execution on GPUs, which makes it an attractive choice for training large-scale deep-learning models. Theano provides automatic differentiation, which makes it easy to compute gradients of complex mathematical expressions. This is a key feature in many machine learning algorithms, including gradient-based optimization methods.