{"id":7871,"date":"2015-11-03T18:50:04","date_gmt":"2015-11-03T18:50:04","guid":{"rendered":"https:\/\/unknownerror.org\/index.php\/2015\/11\/03\/pydata-pandas\/"},"modified":"2015-11-03T18:50:04","modified_gmt":"2015-11-03T18:50:04","slug":"pydata-pandas","status":"publish","type":"post","link":"https:\/\/unknownerror.org\/index.php\/2015\/11\/03\/pydata-pandas\/","title":{"rendered":"pydata\/pandas"},"content":{"rendered":"

<\/p>\n

What is it<\/h2>\n

pandas<\/strong> is a Python package providing fast, flexible, and expressive data structures designed to make working with \u201crelational\u201d or \u201clabeled\u201d data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world<\/strong> 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<\/strong>. It is already well on its way toward this goal.<\/p>\n

Main Features<\/h2>\n

Here are just a few of the things that pandas does well:<\/p>\n

    \n
  • Easy handling of missing data<\/strong> (represented as NaN<\/code>) in floating point as well as non-floating point data<\/li>\n
  • Size mutability: columns can be inserted and deleted<\/strong> from DataFrame and higher dimensional objects<\/li>\n
  • Automatic and explicit data alignment<\/strong>: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series<\/code>, DataFrame<\/code>, etc. automatically align the data for you in computations<\/li>\n
  • Powerful, flexible group by<\/strong> functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data<\/li>\n
  • Make it easy to convert<\/strong> ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects<\/li>\n
  • Intelligent label-based slicing<\/strong>, fancy indexing<\/strong>, and subsetting<\/strong> of large data sets<\/li>\n
  • Intuitive merging<\/strong> and joining<\/strong> data sets<\/li>\n
  • Flexible reshaping<\/strong> and pivoting<\/strong> of data sets<\/li>\n
  • Hierarchical<\/strong> labeling of axes (possible to have multiple labels per tick)<\/li>\n
  • Robust IO tools for loading data from flat files<\/strong> (CSV and delimited), Excel files<\/strong>, databases<\/strong>, and saving\/loading data from the ultrafast HDF5 format<\/strong><\/li>\n
  • Time series<\/strong>-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.<\/li>\n<\/ul>\n

    Where to get it<\/h2>\n

    The source code is currently hosted on GitHub at: http:\/\/github.com\/pydata\/pandas<\/p>\n

    Binary installers for the latest released version are available at the Python package index<\/p>\n

    http:\/\/pypi.python.org\/pypi\/pandas\/\n<\/code><\/pre>\n
    And via easy_install<\/code>:<\/p>\n
    easy_install pandas\n<\/code><\/pre>\n
    or pip<\/code>:<\/p>\n
    pip install pandas\n<\/code><\/pre>\n
    or conda<\/code>:<\/p>\n
    conda install pandas\n<\/code><\/pre>\n
    Dependencies<\/h2>\n
    Highly Recommended Dependencies<\/h3>\n
      \n
    • numexpr\n
        \n
      • Needed to accelerate some expression evaluation operations<\/li>\n
      • Required by PyTables<\/li>\n<\/ul>\n<\/li>\n
      • bottleneck\n
          \n
        • Needed to accelerate certain numerical operations<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
          Optional dependencies<\/h3>\n
          Notes about HTML parsing libraries<\/h4>\n
            \n
          • \n
            If you install BeautifulSoup4 you must install either lxml or html5lib or both. pandas.read_html<\/code> will not<\/strong> work with only<\/em> BeautifulSoup4<\/code> installed.<\/p>\n<\/li>\n
          • \n
            You are strongly encouraged to read HTML reading gotchas. It explains issues surrounding the installation and usage of the above three libraries.<\/p>\n<\/li>\n
          • \n
            You may need to install an older version of BeautifulSoup4:<\/p>\n
              \n
            • Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and 32-bit Ubuntu\/Debian<\/li>\n<\/ul>\n<\/li>\n
            • \n
              Additionally, if you\u2019re using Anaconda you should definitely read the gotchas about HTML parsing libraries<\/p>\n<\/li>\n
            • \n
              If you\u2019re on a system with apt-get<\/code> you can do<\/p>\n
              sudo apt-get build-dep python-lxml\n<\/code><\/pre>\n
              to get the necessary dependencies for installation of lxml. This will prevent further headaches down the line.<\/p>\n<\/li>\n<\/ul>\n
              Installation from sources<\/h2>\n
              To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from pypi:<\/p>\n
              pip install cython\n<\/code><\/pre>\n
              In the pandas<\/code> directory (same one where you found this file after cloning the git repo), execute:<\/p>\n
              python setup.py install\n<\/code><\/pre>\n
              or for installing in development mode:<\/p>\n
              python setup.py develop\n<\/code><\/pre>\n
              Alternatively, you can use pip<\/code> if you want all the dependencies pulled in automatically (the -e<\/code> option is for installing it in development mode):<\/p>\n
              pip install -e .\n<\/code><\/pre>\n
              On Windows, you will need to install MinGW and execute:<\/p>\n
              python setup.py build --compiler=mingw32\npython setup.py install\n<\/code><\/pre>\n
              See http:\/\/pandas.pydata.org\/ for more information.<\/p>\n
              License<\/h2>\n
              BSD<\/p>\n
              Documentation<\/h2>\n
              The official documentation is hosted on PyData.org: http:\/\/pandas.pydata.org\/<\/p>\n
              The Sphinx documentation should provide a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on.<\/p>\n
              Background<\/h2>\n
              Work on pandas<\/code> started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.<\/p>\n
              Discussion and Development<\/h2>\n
              Since pandas development is related to a number of other scientific Python projects, questions are welcome on the scipy-user mailing list. Specialized discussions or design issues should take place on the PyData mailing list \/ Google group:<\/p>\n
              https:\/\/groups.google.com\/forum\/#!forum\/pydata<\/p>\n","protected":false},"excerpt":{"rendered":"
              What is it pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with \u201crelational\u201d or \u201clabeled\u201d data both easy and intuitive. 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-7871","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/posts\/7871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/comments?post=7871"}],"version-history":[{"count":0,"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/posts\/7871\/revisions"}],"wp:attachment":[{"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/media?parent=7871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/categories?post=7871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/unknownerror.org\/index.php\/wp-json\/wp\/v2\/tags?post=7871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}