Use pandas for data analysis in Python
pandas is an open source package that provides flexible and high-performance data structure manipulation, modeling, and analysis tools for Python. Data analysis and modeling were never the strong side of Python programming language and its functionality in this sphere, except data wrangling, leaves much to be desired. pandas library changed this situation. Now work with data in Python becomes intuitive. In collaboration with the powerful IPython toolkit and other libraries, pandas improves performance and productivity of Python data analysis.
But this is only the start, since pandas aims at becoming the most powerful and expressive data manipulation tool available in any language. This library has already done first steps to this goal and now it is broadly used in a variety of academic and commercial areas, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, etc.
Such popularity was obtained because of the use of the two pandas primary data structures: Series (1-dimensional) and DataFrame (2-dimensional) that cover the vast majority of typical use cases in finance, social science, statistics, and engineering. pandas supports wide range of different data kinds:
- tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet;
- arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels;
- ordered and unordered (not necessarily fixed-frequency) time series data;
- any other form of observational/statistical data sets.
- the data actually does not need to be labeled at all to be placed into a pandas data structure.
Main pandas features:
- size mutability and integrated handling of missing data;
- automatic and explicit data alignment;
- powerful performance of split/apply/combine operations on data sets, for both aggregating and transforming data;
- flexible reshaping and pivoting of data sets;
- intuitive merging and joining of data sets;
- intelligent label-based slicing, indexing, and subsetting of large data sets;
- robust IO tools for loading, reading, and writing data between in-memory data structures and different formats: CSV, Excel files, SQL databases, and ultrafast HDF5 format;
- hierarchical axis indexing that provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure.
Time series-specific functionality includes date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, creating domain-specific time offsets and joining time series without losing data, etc.
pandas is fast and easy-to-use library that integrates well within a scientific computing environment. More information can be found on pandas website. Contact Quintagroup if you are interested in Python solution for practical and efficient data analysis.