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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. | It is a library for building interactive web interfaces. It provides data-reactive components with a simple and flexible API. |
Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. | Reactivity; Components; Modularity; Animations; Routing; Stability; Extendable Data bindings; Plain JS object models; Build UI by composing components; Mix & matching small libraries |
Statistics | |
GitHub Stars - | GitHub Stars 209.7K |
GitHub Forks - | GitHub Forks 33.8K |
Stacks 2.1K | Stacks 55.5K |
Followers 1.3K | Followers 44.7K |
Votes 23 | Votes 1.6K |
Pros & Cons | |
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| No integrations available | |
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