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@BOOK{McKinney:353388,
      author       = {McKinney, Wes},
      title        = {{P}ython for data analysis: [data wrangling with {P}andas,
                      {N}um{P}y, and {IP}ython ]; {F}irst edition},
      address      = {Sebastopol},
      publisher    = {O'Reilly},
      reportid     = {PUBDB-2017-138916},
      isbn         = {9781449319793},
      pages        = {xiv, 452 pages : illustrations},
      year         = {2012},
      note         = {Includes unchanged reprints with later publication date},
      abstract     = {Python for Data Analysis is concerned with the nuts and
                      bolts of manipulating, processing, cleaning, and crunching
                      data in Python. It is also a practical, modern introduction
                      to scientific computing in Python, tailored for
                      data-intensive applications. This is a book about the parts
                      of the Python language and libraries you’ll need to
                      effectively solve a broad set of data analysis problems.
                      This book is not an exposition on analytical methods using
                      Python as the implementation language. Written by Wes
                      McKinney, the main author of the pandas library, this
                      hands-on book is packed with practical cases studies. It’s
                      ideal for analysts new to Python and for Python programmers
                      new to scientific computing. * Use the IPython interactive
                      shell as your primary development environment * Learn basic
                      and advanced NumPy (Numerical Python) features * Get started
                      with data analysis tools in the pandas library * Use
                      high-performance tools to load, clean, transform, merge, and
                      reshape data * Create scatter plots and static or
                      interactive visualizations with matplotlib * Apply the
                      pandas groupby facility to slice, dice, and summarize
                      datasets * Measure data by points in time, whether it’s
                      specific instances, fixed periods, or intervals * Learn how
                      to solve problems in web analytics, social sciences,
                      finance, and economics, through detailed examples},
      keywords     = {Python (DE-H253) / programming languages (DE-H253) / data
                      analysis (DE-H253)},
      ddc          = {005.133},
      shelfmark    = {C McK},
      typ          = {PUB:(DE-HGF)3},
      url          = {https://bib-pubdb1.desy.de/record/353388},
}