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It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.\n\n## Main Features\nHere are just a few of the things that pandas does well:\n\n  - Easy handling of [**missing data**][missing-data] (represented as\n    `NaN`) in floating point as well as non-floating point data\n  - Size mutability: columns can be [**inserted and\n    deleted**][insertion-deletion] from DataFrame and higher dimensional\n    objects\n  - Automatic and explicit [**data alignment**][alignment]: objects can\n    be explicitly aligned to a set of labels, or the user can simply\n    ignore the labels and let `Series`, `DataFrame`, etc. automatically\n    align the data for you in computations\n  - Powerful, flexible [**group by**][groupby] functionality to perform\n    split-apply-combine operations on data sets, for both aggregating\n    and transforming data\n  - Make it [**easy to convert**][conversion] ragged,\n    differently-indexed data in other Python and NumPy data structures\n    into DataFrame objects\n  - Intelligent label-based [**slicing**][slicing], [**fancy\n    indexing**][fancy-indexing], and [**subsetting**][subsetting] of\n    large data sets\n  - Intuitive [**merging**][merging] and [**joining**][joining] data\n    sets\n  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of\n    data sets\n  - [**Hierarchical**][mi] labeling of axes (possible to have multiple\n    labels per tick)\n  - Robust IO tools for loading data from [**flat files**][flat-files]\n    (CSV and delimited), [**Excel files**][excel], [**databases**][db],\n    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]\n  - [**Time series**][timeseries]-specific functionality: date range\n    generation and frequency conversion, moving window statistics,\n    date shifting and lagging.\n***  \n```sh\n# conda\nconda install pandas\n```\n\n```sh\n# or PyPI\npip install pandas\n```\n***\n## Dependencies\n- [NumPy](https://www.numpy.org)\n- [python-dateutil](https://labix.org/python-dateutil)\n- [pytz](https://pythonhosted.org/pytz)\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freddyprasade%2Fpandas-practice","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Freddyprasade%2Fpandas-practice","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freddyprasade%2Fpandas-practice/lists"}