Python for Data Analysis - Boston University
Python for Data AnalysisResearch Computing ServicesKatia Oleinik Content2Overview of Python Libraries for Data ScientistsReading Data; Selecting and filtering the Data; Data manipulation, sorting, grouping, rearranging Plotting the dataDescriptive statisticsInferential statisticsPython Libraries for Data ScienceMany popular Python toolboxes/libraries: NumPy SciPy Pandas SciKit-LearnVisualization libraries matplotlib Seabornand many more ...3All these libraries are installed on the SCCPython Libraries for Data ScienceNumPy: introduces objects for multidimensional arrays and matrices, as well as functions that allow to easily perform advanced mathematical and statistical operations on those objects provides vectorization of mathematical operations on arrays and matrices which significantly improves the performance many other Python libraries are built on NumPy4Link: Libraries for Data ScienceSciPy: collection of algorithms for linear algebra, differential equations, numerical integration, optimization, statistics and more part of SciPyStack built on NumPy5Link: Libraries for Data SciencePandas: adds data structures and tools designed to work with table-like data (similar to Series and Data)
mean(), median() return mean/median values for all numeric columns std() standard deviation ... Data Frame: filtering 29 To subset the data we can apply Boolean indexing. This indexing is commonly known as a filter. For example if we want to subset the rows in which the salary
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