pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
GET /api/items| Parameter | Type | Description |
|---|---|---|
api_key |
string |
Required. Your API key |
GET /api/items/${id}This page gives an overview of all public pandas objects, functions and methods. All classes and functions exposed in pandas.* namespace are public.
Some subpackages are public which include pandas.errors, pandas.plotting, and pandas.testing. Public functions in pandas.io and pandas.tseries submodules are mentioned in the documentation. pandas.api.types subpackage holds some public functions related to data types in pandas
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Here are just a few of the things that pandas does well:
Easy handling of missing data (represented as NaN, NA, or NaT) 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, date shifting and lagging
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