Navigation

  • index
  • modules |
  • next |
  • previous |
  • pandas 0.16.2 documentation »
  • API Reference »

Table Of Contents

  • What’s New
  • Installation
  • Contributing to pandas
  • Frequently Asked Questions (FAQ)
  • Package overview
  • 10 Minutes to pandas
  • Tutorials
  • Cookbook
  • Intro to Data Structures
  • Essential Basic Functionality
  • Working with Text Data
  • Options and Settings
  • Indexing and Selecting Data
  • MultiIndex / Advanced Indexing
  • Computational tools
  • Working with missing data
  • Group By: split-apply-combine
  • Merge, join, and concatenate
  • Reshaping and Pivot Tables
  • Time Series / Date functionality
  • Time Deltas
  • Categorical Data
  • Plotting
  • IO Tools (Text, CSV, HDF5, ...)
  • Remote Data Access
  • Enhancing Performance
  • Sparse data structures
  • Caveats and Gotchas
  • rpy2 / R interface
  • pandas Ecosystem
  • Comparison with R / R libraries
  • Comparison with SQL
  • API Reference
    • Input/Output
    • General functions
    • Series
    • DataFrame
    • Panel
    • Panel4D
      • Constructor
      • Attributes and underlying data
      • Conversion
    • Index
    • CategoricalIndex
    • DatetimeIndex
    • TimedeltaIndex
    • GroupBy
    • General utility functions
  • Internals
  • Release Notes

Search

Enter search terms or a module, class or function name.

pandas.Panel4D.values¶

Panel4D.values¶

Numpy representation of NDFrame

Notes

The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcase to int32.

Navigation

  • index
  • modules |
  • next |
  • previous |
  • pandas 0.16.2 documentation »
  • API Reference »
© Copyright 2008-2014, the pandas development team. Created using Sphinx 1.2.3.