common python errors

Python can Drive Almost any Data Application

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In the data science realm, the Python programming language is the king. Organizations including CERN, Google, and NASA, use Python for almost every programming objective, including data science.

I have often heard of the Python programming language described as a kind of Swiss Army knife for coders. It supports a range of programming schemes including structured programming, functional programming and object-oriented programming among others. A joke in the Python community is, “Python is generally the second-best language for everything.”

While many of the “best of” coding solutions quickly become incompatible and unmaintainable, Python can readily manage almost every kind of task.

Python’s inherently readable syntax and relative simplicity make it somewhat easy to learn. Also, the number of dedicated analytical libraries available means that data scientists in almost every field can obtain freely available and downloadable packages tailored to fit their needs.

Some programmers may acknowledge that Python is not exceptionally well suited for data analytics. However, it is the enormous number of available libraries that make it highly adaptable to almost any purpose. Some 113,000 of them are in the Python Package Index (PyPI), and this library is continuously growing.

With Python, programmers don’t need to waste their time reinventing the wheel.

While the language was explicitly created to have a stripped-down core, tools have been added to strengthen the standard library for every sort of programming chore. This extensibility lets language users quickly get down to solving problems without having to choose among competing function libraries.

Python is Free

It comes in the form of free open-source software. Consequently, anyone can write a library package to expand its functionality. Data science has been an early recipient of these extensions, particularly Pandas, the largest library of data science python functions.

The Pandas Python Data Analysis Library can be used for applications ranging from importing data from Excel spreadsheets to processing sets for time-series analysis.

Pandas can also transform raw data into more useful forms for analytics. This process is known as data wrangling or data munging.

Data manipulations from basic cleanup to more advance processes are possible with Pandas’ powerful dataframes. Pandas was built atop of NumPy, one of Python’s earliest data science libraries. NumPy’s functions in Pandas allow advanced numeric analysis.

Some more specialized tools have been created including

  • Statsmodels that focuses on tools for statistical analysis.
  • SciPy  offers tools and procedures for the analysis of scientific data.
  • Scilkit-Learn and PyBrain are machine learning libraries that offer modules for building neural networks and data preprocessing.

Other popular specialized libraries include:

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