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Recently, Python has seen a strong emergence in the data science community, challenging more traditional players such as R and Matlab. Aside from the very intuitive coding style, transferability, and other features described above, there are a number of reasons for this. First amongst these is its strong set of packages aimed at making mathematical analysis easy. In the mid‐1990s the Python community strongly supported the development of a package known as numeric whose purpose was to take the strengths of Matlab's mathematical analysis packages and bring them over to the Python ecosystem. Numeric evolved into numpy, which is one of the most heavily used Python packages today. The same approach was taken to build matplotlib – which as the name suggests was built to take the Matlab plotting library over to python. These were bundled with other libraries aimed at scientific applications (such as optimisation) and turned into scipy – Python's premier scientific‐orientated package.

Having taken some of the best pieces out of Matlab, the Python community turned its attention to R; the other behemoth language of data science. Key to the functionality of R is its concept of the data frame, and the Python package pandas emerged to challenge in this arena. Pandas' data frame has proven extremely adept for data ingestion and manipulation, especially of time series data, and has now been linked into multiple packages, facilitating an easy end to end data analytics and machine learning experience.

It is in the area of machine learning in which Python has really separated itself from the rest of the pack. Taking a leaf out of R's book, the scikit‐learn module was built to mimic the functionality of the R module caret. Scikit‐learn offers a plethora of algorithms and data manipulation features which make some of the routine tasks of data science very simple and intuitive. Scikit‐learn is a fantastic example of how powerful the pythonic method for creating libraries can be.