Python libraries in data analysis

One of the fundamental packages used for scientific computing in Python is Numpy. Among other things, it contains the following:

Besides this, it can also be used as an efficient multidimensional container of generic data. Arbitrary data types can be defined and integrated with a wide variety of databases.

Pandas is a Python package that supports rich data structures and functions for analyzing data, and is developed by the PyData Development Team. It is focused on the improvement of Python's data libraries. Pandas consists of the following things:

Due to these features, Pandas is an ideal tool for systems that need complex data structures or high-performance time series functions such as financial data analysis applications.

Matplotlib is the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats: line plots, contour plots, scatter plots, and Basemap plots. It comes with a set of default settings, but allows customization of all kinds of properties. However, we can easily create our chart with the defaults of almost every property in Matplotlib.

MongoDB is a type of NoSQL database. It is highly scalable, robust, and perfect to work with JavaScript-based web applications, because we can store data as JSON documents and use flexible schemas.

PyMongo is a Python distribution containing tools for working with MongoDB. Many tools have also been written for working with PyMongo to add more features such as MongoKit, Humongolus, MongoAlchemy, and Ming.

The scikit-learn is an open source machine-learning library using the Python programming language. It supports various machine learning models, such as classification, regression, and clustering algorithms, interoperated with the Python numerical and scientific libraries NumPy and SciPy. The latest scikit-learn version is 0.16.1, published in April 2015.