xarray
  • Overview: Why xarray?
    • Features
    • Core data structures
    • Goals and aspirations
  • Examples
    • Quick overview
    • Toy weather data
    • Calculating Seasonal Averages from Timeseries of Monthly Means
  • Installation
    • Required dependencies
    • Optional dependencies
    • Instructions
  • Data Structures
    • DataArray
    • Dataset
    • Coordinates
  • Indexing and selecting data
    • Positional indexing
    • Indexing with labeled dimensions
    • Pointwise indexing
    • Dataset indexing
    • Dropping labels
    • Nearest neighbor lookups
    • Masking with where
    • Multi-dimensional indexing
    • Copies vs. views
    • Orthogonal (outer) vs. vectorized indexing
    • Align and reindex
  • Computation
    • Basic array math
    • Missing values
    • Aggregation
    • Rolling window operations
    • Broadcasting by dimension name
    • Automatic alignment
    • Coordinates
    • Math with datasets
  • GroupBy: split-apply-combine
    • Split
    • Apply
    • First and last
    • Grouped arithmetic
    • Squeezing
  • Reshaping and reorganizing data
    • Reordering dimensions
    • Converting between datasets and arrays
    • Stack and unstack
    • Shift and roll
  • Combining data
    • Concatenate
    • Merge
    • Update
    • Equals and identical
  • Time series data
    • Creating datetime64 data
    • Datetime indexing
    • Datetime components
    • Resampling and grouped operations
  • Working with pandas
    • Hierarchical and tidy data
    • Multi-dimensional data
  • Serialization and IO
    • Pickle
    • netCDF
    • OPeNDAP
    • Formats supported by PyNIO
    • Combining multiple files
  • Out of core computation with dask
    • What is a dask array?
    • Reading and writing data
    • Using dask with xarray
    • Chunking and performance
  • Plotting
    • Introduction
    • One Dimension
    • Two Dimensions
    • Faceting
    • Maps
    • Details
  • API reference
    • Top-level functions
    • Dataset
    • DataArray
    • Universal functions
    • IO / Conversion
    • Plotting
  • Frequently Asked Questions
    • Why is pandas not enough?
    • How do xarray data structures differ from those found in pandas?
    • Should I use xarray instead of pandas?
    • What is your approach to metadata?
    • What other netCDF related Python libraries should I know about?
  • What’s New
    • v0.7.2 (13 March 2016)
    • v0.7.1 (16 February 2016)
    • v0.7.0 (21 January 2016)
    • v0.6.1 (21 October 2015)
    • v0.6.0 (21 August 2015)
    • v0.5.2 (16 July 2015)
    • v0.5.1 (15 June 2015)
    • v0.5 (1 June 2015)
    • v0.4.1 (18 March 2015)
    • v0.4 (2 March, 2015)
    • v0.3.2 (23 December, 2014)
    • v0.3.1 (22 October, 2014)
    • v0.3 (21 September 2014)
    • v0.2 (14 August 2014)
    • v0.1.1 (20 May 2014)
    • v0.1 (2 May 2014)
 
xarray
  • Docs »
  • Examples
  • Edit on GitHub

ExamplesΒΆ

  • Quick overview
    • Create a DataArray
    • Indexing
    • Computation
    • GroupBy
    • Convert to pandas
    • Datasets and NetCDF
  • Toy weather data
    • Examine a dataset with pandas and seaborn
    • Probability of freeze by calendar month
    • Monthly averaging
    • Calculate monthly anomalies
    • Fill missing values with climatology
  • Calculating Seasonal Averages from Timeseries of Monthly Means
    • Some calendar information so we can support any netCDF calendar.
    • A few calendar functions to determine the number of days in each month
    • Open the Dataset
    • Now for the heavy lifting:
Next Previous

© Copyright 2014-2016, xarray Developers.

Built with Sphinx using a theme provided by Read the Docs.