![]() ![]() There are lots of different ways to run Python programs, and I don’t want to prescribe any one way as being the ‘best’. Here we briefly discuss the different ways you can folow this tutorial. You can read more about the Pandas package at the Pandas project website. Pandas is a dependency of another library called statsmodels, making it an important part of the statistical computing ecosystem in Python.But deep down in the internals of Pandas, it is actually written in C, and so processing large datasets is no problem for Pandas. Python sometimes gets a bad rap for being a bit slow compared to ‘compiled’ languages such as C and Fortran. Some other important points to note about Pandas are: The data actually need not be labelled at all to be placed into a pandas data structure. Any other form of observational / statistical data sets.Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels.Ordered and unordered (not necessarily fixed-frequency) time series data.Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet.The official Pandas website describes Pandas’ data-handling strengths as: ![]() Pandas is best suited for structured, labelled data, in other words, tabular data, that has headings associated with each column of data. These graphs of course should be taken with a pinch of salt, as there is no agreed way of absolutely determing programming langauge and library popularity, but they are interesting to think about nonetheless. A similar graph has been produced showing the growth of Pandas compared to some other Python software libraries! (Based on StackOverflow question views per month). In the Introduction to Python tutorial we had a look at how Python had grown rapidly in terms of users over the last decade or so, based on traffic to the StackOverflow question and answer site. Pandas is a hugely popular, and still growing, Python library used across a range of disciplines from environmental and climate science, through to social science, linguistics, biology, as well as a number of applications in industry such as data analytics, financial trading, and many others. But if not, don’t worry because this tutorial doesn’t assume any knowledge of NumPy or R, only basic-level Python. If you have used R’s dataframes before, or the numpy package in Python, you may find some similarities in the Python pandas package. The features provided in pandas automate and simplify a lot of the common tasks that would take many lines of code to write in the basic Python langauge. It simplifies the loading of data from external sources such as text files and databases, as well as providing ways of analysing and manipulating data once it is loaded into your computer. Pandas is a package commonly used to deal with data analysis. Be inspired to experiment further with Matplotlib!. ![]()
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