• Xarray reindex

    Xarray reindex

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    Similarly to pandas objects, xarray objects support both integer and label based lookups along each dimension. However, xarray objects also have named dimensions, so you can optionally use dimension names instead of relying on the positional ordering of dimensions. Thus in total, xarray supports four different kinds of indexing, as described below and summarized in this table:. Indexing a DataArray directly works mostly just like it does for numpy arrays, except that the returned object is always another DataArray:.

    Positional indexing deviates from the NumPy when indexing with multiple arrays like arr[[0, 1], [0, 1]]as described in Orthogonal outer vs. See Pointwise indexing for how to achieve this functionality in xarray.

    Because we use a pandas. Index under the hood, label based indexing is very fast. To do label based indexing, use the loc attribute:. You can perform any of the label indexing operations supported by pandasincluding indexing with individual, slices and arrays of labels, as well as indexing with boolean arrays.

    Like pandas, label based indexing in xarray is inclusive of both the start and stop bounds. With labeled dimensions, we do not have to rely on dimension order and can use them explicitly to slice data. There are two ways to do this:. Use a dictionary as the argument for array positional or label based array indexing:. Use the sel and isel convenience methods:. The arguments to these methods can be any objects that could index the array along the dimension given by the keyword, e.

    Depending on whether the underlying numpy indexing returns a copy or a view, the method will fail, and when it fails, it will fail silently. Instead, you should use normal index assignment:. We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:.

    Positional indexing on a dataset is not supported because the ordering of dimensions in a dataset is somewhat ambiguous it can vary between different arrays. However, you can do normal indexing with labeled dimensions:.

    Using indexing to assign values to a subset of dataset e. The drop method returns a new object with the listed index labels along a dimension dropped:. The method parameter allows for enabling nearest neighbor inexact lookups by use of the methods 'pad''backfill' or 'nearest' :. Using tolerance requries pandas version 0. The method parameter is not yet supported if any of the arguments to.Preferably an Index object to avoid duplicating data.

    Method to use for filling holes in reindexed DataFrame. Maximum distance between original and new labels for inexact matches. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

    To further illustrate the filling functionality in reindexwe will create a dataframe with a monotonically increasing index for example, a sequence of dates.

    Basic data structures of xarray

    If desired, we can fill in the missing values using one of several options. For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword. Please note that the NaN value present in the original dataframe at index value will not be filled by any of the value propagation schemes.

    This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna method. See the user guide for more. Home What's New in 1. DataFrame pandas. T pandas. NaN Value to use for missing values.

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    New in version 0. See also DataFrame.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have the following array, which I would like to reindex so the keys are reversed ideally starting at 1 :.

    Then you could use the algorithm above if you just want printed elements to start at 1. Well, I would like to think that for whatever your end goal is, you wouldn't actually need to modify the array to be 1-based as opposed to 0-based, but could instead handle it at iteration time like Gumbo posted. Howeverto answer your question, this function should convert any array into a 1-based version.

    You may want to consider why you want to use a 1-based array at all. Zero-based arrays when using non-associative arrays are pretty standard, and if you're wanting to output to a UI, most would handle the solution by just increasing the integer upon output to the UI.

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    Think about consistency—both in your application and in the code you work with—when thinking about 1-based indexers for arrays. Someone else showed me this a long time ago. So I can't take credit for coming up with it. But it is very simple and fast. Similar to Nick's contribution, I came to the same solution for reindexing an array, but enhanced the function a little since from PHP version 5.

    Example reindexing function is then like this using use keyword closure:. Here's my own implementation. This will also remove blank, null, false, 0 zero values. The returned array will have numeric keys, starting at 0 and increase by 1.

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    In above output we want to remove blank, null, 0 zero values and then reindex array elements. Sorting is just a sortreindexing seems a bit silly but if it is needed this will do it. Though not in-place. If you need reversal in-place, you need to run backwards so you don't stomp on indexes that you need:. Learn more. How do you reindex an array in PHP? Ask Question. Asked 11 years, 1 month ago.

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    Active 1 month ago. Viewed k times. Where is there something reversed? Just a format matter or for internal processing? Active Oldest Votes.

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    Andrew Moore Andrew Moore See imagiro solution. Bram Vanroy Sandra Sandra 1, 11 11 silver badges 17 17 bronze badges. Elias Sep 4 '12 at GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

    Already on GitHub? Sign in to your account. Added sparse option to reindex and unstack.

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    I just added a minimal set of codes necessary to unstack and reindex. There is still a lot of space to complete the sparse support as discussed in Hello fujiisoup! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:. Currently, this is a private method. Probably we can expose it to the public and add the same method to DataArray and Dataset as well in the future.

    We should make these public in DataArray and Dataset. See discussion here: comment. Thanks, dcherian. I would like to expose them to public, but what is the best name of these functions?

    I'll add TODO comment for this. I like the idea of not hard-coding supported sparse formats, but I wonder if we could be a little more careful here if AttributeError is raised. We should probably catch and re-raise Attribute error with a more informative message if this fails. Otherwise, I expect we might see bug reports from confused users, e. I am a little surprised this is necessary. Well, yes. I'll add a TODO here. Hopefully sparse will raise an error if you try to convert a dask array into a sparse array!

    If not, we should do that ourselves. Long term, the best solution would be to convert a dask array from dense chunks to sparse chunks. Thank you fujiisoup! I think it could be a little cleaner to entirely avoid sparse inside any of the reindex functions, but this is fine for now. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

    The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have a code as below. So basically i have predefined a grid, and i make a loop every single small grid to this larger predefined grid. But the code stuck at the reindex part. Is there other way i can snap the small grid to a larger grid? You are making some gigantic arrays here -- an array with shapeand uint8 dtype requires 10 GB to represent in memory.

    Typically you need at least x the size of your largest arrays to comfortably manipulate arrays with NumPy, so I would not be surprised if this fails on any machine with less than about 64 GB of total RAM. You should try opening your datasets with daskwhich works with xarray to support working with arrays larger than fit into memory. This could be as simple as adding a chunks argument to your call to xr. Learn more. Asked 1 year, 7 months ago. Active 1 year, 7 months ago.

    Viewed 31 times. Active Oldest Votes. The code not even pass one loop. It's also quite likely that reindex uses more memory than simply allocating an array with np. It's doing a lot more work to reorder all of the data. Sign up or log in Sign up using Google.

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    xarray reindex

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    Technical site integration observational experiment live on Stack Overflow. Dark Mode Beta - help us root out low-contrast and un-converted bits. Triage needs to be fixed urgently, and users need to be notified upon…. Related This section covers indexing with a MultiIndex and other advanced indexing features.

    See the Indexing and Selecting Data for general indexing documentation. Whether a copy or a reference is returned for a setting operation may depend on the context.

    This is sometimes called chained assignment and should be avoided. See Returning a View versus Copy. See the cookbook for some advanced strategies. In essence, it enables you to store and manipulate data with an arbitrary number of dimensions in lower dimensional data structures like Series 1d and DataFrame 2d.

    Changed in version 0. The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects.

    You can think of MultiIndex as an array of tuples where each tuple is unique. A MultiIndex can be created from a list of arrays using MultiIndex. The Index constructor will attempt to return a MultiIndex when it is passed a list of tuples. The following examples demonstrate different ways to initialize MultiIndexes.

    When you want every pairing of the elements in two iterables, it can be easier to use the MultiIndex. This is a complementary method to MultiIndex. As a convenience, you can pass a list of arrays directly into Series or DataFrame to construct a MultiIndex automatically:. All of the MultiIndex constructors accept a names argument which stores string names for the levels themselves.

    If no names are provided, None will be assigned:. This index can back any axis of a pandas object, and the number of levels of the index is up to you:. The reason that the MultiIndex matters is that it can allow you to do grouping, selection, and reshaping operations as we will describe below and in subsequent areas of the documentation. As you will see in later sections, you can find yourself working with hierarchically-indexed data without creating a MultiIndex explicitly yourself.

    However, when loading data from a file, you may wish to generate your own MultiIndex when preparing the data set. See Cross-section with hierarchical index for how to select on a deeper level. The MultiIndex keeps all the defined levels of an index, even if they are not actually used. When slicing an index, you may notice this.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

    Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. The goal is to re-index using a 10 year series of daily dates.

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    A brute force Python loop method is outlined below. Is there a better way? Let the DataArray have 3 Coordinates: location: locations sample: 10 samples 0,1,2,3,4,5,6,7,8,9 attributes: 2 attributes date, temp.

    Each location has a different set of 'date' values. Using brute force, one could Python loop through the locations:. DataArray pd. The brute force solution is to execute the following command for each of potentially s of locations, and then reassemble into one DataArray containing all of the locations. Note that the above dict ordering for the Panels is random. To recreate, 'time' and 'temp' attributes may need to be reversed. In [5]: da.

    In [6]: da. In [7]: da. My first suggestion is to put your two attributes temp and time in different variables in an xarray. More generally, I'm not sure this data is well suited for xarray, because your time labels can't be easily expressed in 1D. It might make more sense to flatten the data into a tabular structure, e.

    xarray reindex

    DataFrame with columns ['temp', 'time', 'period', 'location'] and do a multi-dimensional pandas groupby, something like df.

    Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

    Sign up. New issue. Jump to bottom. Copy link Quote reply. Let the DataArray have 3 Coordinates: location: locations sample: 10 samples 0,1,2,3,4,5,6,7,8,9 attributes: 2 attributes date, temp Each location has a different set of 'date' values. Thank you, Doug. This comment has been minimized. Sign in to view. I don't quite understand. Can you clarify the result you are looking for? An example might help. DataFrame np. OK, this makes more more sense now.

    xarray reindex

    Thank you for xarray, and for offering helpful suggestions. Sign up for free to join this conversation on GitHub. Already have an account?


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