From d2a8d085befccaf6d097ecda2853315dbd1460da Mon Sep 17 00:00:00 2001 From: harshitha1201 Date: Fri, 10 Mar 2023 16:23:57 +0530 Subject: [PATCH 01/17] Faq pull request --- doc/getting-started-guide/faq.rst | 130 ++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 0eeb09c432c..e05a2e846f9 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -186,6 +186,136 @@ What other projects leverage xarray? See section :ref:`ecosystem`. +How can I read format X in xarray? +---------------------------------- + +To read format X in xarray, you need to know the `format of the data `_ you want to read. If the format is supported, you can use the appropriate IO function provided by xarray. The following table provides links to IO functions for different file formats in xarray, as well as links to other packages that can be used to read these formats: + +.. csv-table:: + :header: "File Format", "xarray IO Function", " Other Packages" + :widths: 15, 35, 15 + + "NetCDF (.nc, .nc4, .cdf)","xarray.open_dataset() OR xarray.open_mfdataset()", "`netCDF4 `_, `netcdf `_ , `cdms2 `_" + "HDF5 (.h5, .hdf5)","xarray.open_dataset() OR xarray.open_mfdataset()", "`h5py `_, `pytables `_ " + "GRIB1/GRIB2 (.grb, .grib)", "xarray.open_dataset()", "`cfgrib `_, `pygrib `_" + "Zarr","xarray.open_zarr()","`zarr `_ , `fsspec `_" + "CSV (.csv)","xarray.open_dataset()
xarray.open_mfdataset()","`pandas `_ , `dask `_ " + "Excel (.xls, .xlsx)","xarray.open_dataset()","`pandas `_, `openpyxl `_ " + "JSON (.json)","xarray.open_dataset()","`json `_, `pandas `_" + +To use these IO functions in xarray, you can simply call them with the path to the file(s) you want to read as an argument. + +NetCDF +------ +:: + + import xarray as xr + + # Open a NetCDF file using xarray + ds = xr.open_dataset('/path/to/my/file.nc') + + # Open multiple NetCDF files as a single dataset using xarray + ds = xr.open_mfdataset('/path/to/my/files/*.nc') + + # Open a NetCDF file using netCDF4 package + from netCDF4 import Dataset + nc = Dataset('/path/to/my/file.nc', 'r') + + # Open a NetCDF file using cdms2 package + import cdms2 + f = cdms2.open('/path/to/my/file.nc') + +HDF5 +---- +:: + + import xarray as xr + + # Open a HDF5 file using xarray + ds = xr.open_dataset('/path/to/my/file.h5') + + # Open a HDF5 file using h5py package + import h5py + f = h5py.File('/path/to/my/file.h5', 'r') + + # Open a HDF5 file using pytables package + import tables + f = tables.open_file('/path/to/my/file.h5', 'r') + +GRIB1/GRIB2 +----------- +:: + + import xarray as xr + + # Open a GRIB file using xarray + ds = xr.open_dataset('/path/to/my/file.grb') + + # Open a GRIB file using cfgrib package + import cfgrib + ds = cfgrib.open_dataset('/path/to/my/file.grb') + + # Open a GRIB file using pygrib package + import pygrib + grbs = pygrib.open('/path/to/my/file.grb') + + +Zarr +---- +:: + + import xarray as xr + + # Open a Zarr store using xarray + store = xr.open_zarr('/path/to/my/store.zarr') + ds = xr.open_zarr(store) + + # Open a Zarr store using zarr package + import zarr + store = zarr.open('/path/to/my/store.zarr') + +CSV +--- +:: + + import xarray as xr + + # Open a CSV file using xarray + ds = xr.open_dataset('/path/to/my/file.csv') + + # Open a CSV file using pandas package + import pandas as pd + df = pd.read_csv('/path/to/my/file.csv') + +Excel +----- +:: + + import xarray as xr + + # Open an Excel file using xarray + ds = xr.open_dataset('/path/to/my/file.xlsx', engine='openpyxl') + + # Open an Excel file using pandas package + import pandas as pd + df = pd.read_excel('/path/to/my/file.xlsx') + +JSON +---- +:: + + import xarray as xr + + # Open a JSON file using xarray + ds = xr.open_dataset('/path/to/my/file.json') + + # Open a JSON file using json package + import json + with open('/path/to/my/file.json', 'r') as f: + data = json.load(f) + +These are just examples and may not cover all possible use cases. Some packages may have additional functionality beyond what is shown here. You can refer to the documentation for each package for more information. + How should I cite xarray? ------------------------- From f8d4346ea8ade9614bc39f8fe84f24b6b1ea6463 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 10 Mar 2023 10:59:27 +0000 Subject: [PATCH 02/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index e05a2e846f9..29f530a1974 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -193,8 +193,8 @@ To read format X in xarray, you need to know the `format of the data `_, `netcdf `_ , `cdms2 `_" "HDF5 (.h5, .hdf5)","xarray.open_dataset() OR xarray.open_mfdataset()", "`h5py `_, `pytables `_ " "GRIB1/GRIB2 (.grb, .grib)", "xarray.open_dataset()", "`cfgrib `_, `pygrib `_" From 946d37342a80a6787ad6698a38ea3f35d84a0503 Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 01:50:00 +0530 Subject: [PATCH 03/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 29f530a1974..d531ef9ea60 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -186,13 +186,13 @@ What other projects leverage xarray? See section :ref:`ecosystem`. -How can I read format X in xarray? ----------------------------------- +How do I open X file format as an xarray.Dataset? +------------------------------------------------- -To read format X in xarray, you need to know the `format of the data `_ you want to read. If the format is supported, you can use the appropriate IO function provided by xarray. The following table provides links to IO functions for different file formats in xarray, as well as links to other packages that can be used to read these formats: +To open format X file in xarray, you need to know the `format of the data `_ you want to read. If the format is supported, you can use the appropriate function provided by xarray. The following table provides links to the functions for different file formats in xarray, as well as links to other packages that can be used: .. csv-table:: - :header: "File Format", "xarray IO Function", " Other Packages" + :header: "File Format", "xarray Backend", " Other Packages" :widths: 15, 35, 15 "NetCDF (.nc, .nc4, .cdf)","xarray.open_dataset() OR xarray.open_mfdataset()", "`netCDF4 `_, `netcdf `_ , `cdms2 `_" From 9c43778455bbaab9487c834bcfdac6ada7d1f10e Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 02:14:51 +0530 Subject: [PATCH 04/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index d531ef9ea60..6da3b74312e 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -207,6 +207,7 @@ To use these IO functions in xarray, you can simply call them with the path to t NetCDF ------ +Use xarray.open_dataset() to open a NetCDF file and return an xarray.Dataset object. :: import xarray as xr @@ -227,10 +228,20 @@ NetCDF HDF5 ---- +Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset object. :: import xarray as xr + # specify the file path + file_path = "path/to/your/hdf5/file.h5" + + # use xarray to open the file and return an xarray.Dataset object + dataset = xr.open_dataset(file_path, engine="h5netcdf") + + # print the contents of the dataset + print(dataset) + # Open a HDF5 file using xarray ds = xr.open_dataset('/path/to/my/file.h5') @@ -244,6 +255,7 @@ HDF5 GRIB1/GRIB2 ----------- +To open a GRIB1 or GRIB2 file and return an xarray.Dataset object using pygrib.open(), you can use the following code: :: import xarray as xr @@ -270,10 +282,6 @@ Zarr store = xr.open_zarr('/path/to/my/store.zarr') ds = xr.open_zarr(store) - # Open a Zarr store using zarr package - import zarr - store = zarr.open('/path/to/my/store.zarr') - CSV --- :: From 2457a9e2c2e419033292a1a21cc2dbd7a0f800c6 Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 02:34:27 +0530 Subject: [PATCH 05/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 6da3b74312e..eddeb097998 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -186,7 +186,7 @@ What other projects leverage xarray? See section :ref:`ecosystem`. -How do I open X file format as an xarray.Dataset? +How do I open format X file as an xarray.Dataset? ------------------------------------------------- To open format X file in xarray, you need to know the `format of the data `_ you want to read. If the format is supported, you can use the appropriate function provided by xarray. The following table provides links to the functions for different file formats in xarray, as well as links to other packages that can be used: @@ -203,7 +203,7 @@ To open format X file in xarray, you need to know the `format of the data `_, `openpyxl `_ " "JSON (.json)","xarray.open_dataset()","`json `_, `pandas `_" -To use these IO functions in xarray, you can simply call them with the path to the file(s) you want to read as an argument. +To use these backend functions in xarray, you can simply call them with the path to the file(s) you want to read as an argument. NetCDF ------ @@ -212,15 +212,27 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray.Dataset obj import xarray as xr - # Open a NetCDF file using xarray - ds = xr.open_dataset('/path/to/my/file.nc') + # use xarray to open the file and return an xarray.Dataset object + dataset = xr.open_dataset('/path/to/my/file.nc') + + # print the contents of the dataset + print(dataset) # Open multiple NetCDF files as a single dataset using xarray ds = xr.open_mfdataset('/path/to/my/files/*.nc') # Open a NetCDF file using netCDF4 package - from netCDF4 import Dataset - nc = Dataset('/path/to/my/file.nc', 'r') + import xarray as xr + import netCDF4 as nc + + # use netCDF4 to open the file + nc_file = nc.Dataset("path/to/your/netcdf/file.nc") + + # use xarray to convert the netCDF4.Dataset to an xarray.Dataset + dataset = xr.open_dataset(xr.backends.NetCDF4DataStore(nc_file)) + + # print the contents of the dataset + print(dataset) # Open a NetCDF file using cdms2 package import cdms2 From decfd6bb7787b82c5f1a1295b2b4b6db3bec142e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 10 Mar 2023 21:05:13 +0000 Subject: [PATCH 06/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index eddeb097998..aa95e321754 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -224,7 +224,7 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray.Dataset obj # Open a NetCDF file using netCDF4 package import xarray as xr import netCDF4 as nc - + # use netCDF4 to open the file nc_file = nc.Dataset("path/to/your/netcdf/file.nc") From 9e9694873a09656fb250547a4e76c932fffdd12f Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 12:54:24 +0530 Subject: [PATCH 07/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 47 ++++++++++++++++++------------- 1 file changed, 27 insertions(+), 20 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index aa95e321754..115b5bcab5b 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -231,9 +231,6 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray.Dataset obj # use xarray to convert the netCDF4.Dataset to an xarray.Dataset dataset = xr.open_dataset(xr.backends.NetCDF4DataStore(nc_file)) - # print the contents of the dataset - print(dataset) - # Open a NetCDF file using cdms2 package import cdms2 f = cdms2.open('/path/to/my/file.nc') @@ -245,18 +242,12 @@ Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset obje import xarray as xr - # specify the file path - file_path = "path/to/your/hdf5/file.h5" - - # use xarray to open the file and return an xarray.Dataset object - dataset = xr.open_dataset(file_path, engine="h5netcdf") - - # print the contents of the dataset - print(dataset) - - # Open a HDF5 file using xarray - ds = xr.open_dataset('/path/to/my/file.h5') + # Open HDF5 file as an xarray Dataset + ds = xr.open_dataset('path/to/hdf5/file.h5', engine='h5netcdf') + # Print Dataset object + print(ds) + # Open a HDF5 file using h5py package import h5py f = h5py.File('/path/to/my/file.h5', 'r') @@ -267,17 +258,33 @@ Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset obje GRIB1/GRIB2 ----------- -To open a GRIB1 or GRIB2 file and return an xarray.Dataset object using pygrib.open(), you can use the following code: +use the cfgrib.open_dataset() function from the cfgrib package to open a GRIB1 file as an xarray Dataset. :: import xarray as xr + import cfgrib - # Open a GRIB file using xarray - ds = xr.open_dataset('/path/to/my/file.grb') + # Open GRIB1 file as an xarray Dataset + ds = xr.open_dataset('path/to/grib1/file.grb', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) - # Open a GRIB file using cfgrib package - import cfgrib - ds = cfgrib.open_dataset('/path/to/my/file.grb') + # Print Dataset object + print(ds) + # path/to/grib1/file.grb should be replaced with the actual file path to your GRIB1 file.The engine parameter is set to cfgrib, which is required for reading GRIB1 files as xarray Datasets +The open_dataset() function reads the GRIB1 file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB1 file. + + +import xarray as xr +import cfgrib + +# Open GRIB2 file as an xarray Dataset +ds = xr.open_dataset('path/to/grib2/file.grb2', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) + +# Print Dataset object +print(ds) + +path/to/grib2/file.grb2 should be replaced with the actual file path to your GRIB2 file. Additionally, note that the engine parameter is set to cfgrib, which is required for reading GRIB2 files as xarray Datasets. + +The open_dataset() function reads the GRIB2 file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB2 file. # Open a GRIB file using pygrib package import pygrib From a2234b89358ea6dc49f082bef27b7a299b6c20bc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 11 Mar 2023 07:25:00 +0000 Subject: [PATCH 08/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 115b5bcab5b..332036240e9 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -247,7 +247,7 @@ Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset obje # Print Dataset object print(ds) - + # Open a HDF5 file using h5py package import h5py f = h5py.File('/path/to/my/file.h5', 'r') From c1110e28c3f516ac1ccdaa954ac6f16f3d336087 Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 13:08:07 +0530 Subject: [PATCH 09/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 332036240e9..a7ea63764b9 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -207,15 +207,15 @@ To use these backend functions in xarray, you can simply call them with the path NetCDF ------ -Use xarray.open_dataset() to open a NetCDF file and return an xarray.Dataset object. +Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset object. :: import xarray as xr # use xarray to open the file and return an xarray.Dataset object dataset = xr.open_dataset('/path/to/my/file.nc') - - # print the contents of the dataset + + # Print Dataset object print(dataset) # Open multiple NetCDF files as a single dataset using xarray From ca07ceec5cf33082783df0111c2eabad4041d2e6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 11 Mar 2023 07:38:41 +0000 Subject: [PATCH 10/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index a7ea63764b9..1713ab75bef 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -214,7 +214,7 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset obj # use xarray to open the file and return an xarray.Dataset object dataset = xr.open_dataset('/path/to/my/file.nc') - + # Print Dataset object print(dataset) From 733a17eb777a64cd8a614ac543d695af7c84b70c Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 14:03:23 +0530 Subject: [PATCH 11/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 58 ++++++++++++------------------- 1 file changed, 23 insertions(+), 35 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 1713ab75bef..d5a577a0a87 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -213,28 +213,14 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset obj import xarray as xr # use xarray to open the file and return an xarray.Dataset object - dataset = xr.open_dataset('/path/to/my/file.nc') + ds = xr.open_dataset('/path/to/my/file.nc') # Print Dataset object - print(dataset) + print(ds) # Open multiple NetCDF files as a single dataset using xarray ds = xr.open_mfdataset('/path/to/my/files/*.nc') - # Open a NetCDF file using netCDF4 package - import xarray as xr - import netCDF4 as nc - - # use netCDF4 to open the file - nc_file = nc.Dataset("path/to/your/netcdf/file.nc") - - # use xarray to convert the netCDF4.Dataset to an xarray.Dataset - dataset = xr.open_dataset(xr.backends.NetCDF4DataStore(nc_file)) - - # Open a NetCDF file using cdms2 package - import cdms2 - f = cdms2.open('/path/to/my/file.nc') - HDF5 ---- Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset object. @@ -266,29 +252,18 @@ use the cfgrib.open_dataset() function from the cfgrib package to open a GRIB1 f # Open GRIB1 file as an xarray Dataset ds = xr.open_dataset('path/to/grib1/file.grb', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) + + # OR open GRIB2 file as an xarray Dataset + ds = xr.open_dataset('path/to/grib2/file.grb2', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) # Print Dataset object print(ds) - # path/to/grib1/file.grb should be replaced with the actual file path to your GRIB1 file.The engine parameter is set to cfgrib, which is required for reading GRIB1 files as xarray Datasets -The open_dataset() function reads the GRIB1 file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB1 file. - - -import xarray as xr -import cfgrib - -# Open GRIB2 file as an xarray Dataset -ds = xr.open_dataset('path/to/grib2/file.grb2', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) + +The open_dataset() function reads the GRIB file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB file. -# Print Dataset object -print(ds) - -path/to/grib2/file.grb2 should be replaced with the actual file path to your GRIB2 file. Additionally, note that the engine parameter is set to cfgrib, which is required for reading GRIB2 files as xarray Datasets. - -The open_dataset() function reads the GRIB2 file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB2 file. - - # Open a GRIB file using pygrib package - import pygrib - grbs = pygrib.open('/path/to/my/file.grb') +We recommend installing cfgrib via conda: +:: + conda install -c conda-forge cfgrib Zarr @@ -330,7 +305,20 @@ Excel JSON ---- :: + import pandas as pd + import xarray as xr + + # Load JSON file as a pandas DataFrame + df = pd.read_json('path/to/json/file.json') + # Convert pandas DataFrame to xarray Dataset + ds = df.to_xarray() + + # Print xarray Dataset object + print(ds) + + + import xarray as xr # Open a JSON file using xarray From 70b835d9b9537d3766dad57172f8792ab3f5a4b3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 11 Mar 2023 08:34:06 +0000 Subject: [PATCH 12/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index d5a577a0a87..ce0ef238185 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -252,13 +252,13 @@ use the cfgrib.open_dataset() function from the cfgrib package to open a GRIB1 f # Open GRIB1 file as an xarray Dataset ds = xr.open_dataset('path/to/grib1/file.grb', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) - + # OR open GRIB2 file as an xarray Dataset ds = xr.open_dataset('path/to/grib2/file.grb2', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) # Print Dataset object print(ds) - + The open_dataset() function reads the GRIB file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB file. We recommend installing cfgrib via conda: @@ -316,9 +316,9 @@ JSON # Print xarray Dataset object print(ds) - - - + + + import xarray as xr # Open a JSON file using xarray From 4685d338c18c0dd1ee773da39c0262aef23822ce Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sat, 11 Mar 2023 14:22:00 +0530 Subject: [PATCH 13/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 50 ++++++++++++++++++------------- 1 file changed, 30 insertions(+), 20 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index ce0ef238185..ed865d36cec 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -272,10 +272,12 @@ Zarr import xarray as xr - # Open a Zarr store using xarray - store = xr.open_zarr('/path/to/my/store.zarr') - ds = xr.open_zarr(store) + # Open the Zarr file and load it into an xarray dataset + dataset = xr.open_dataset('/path/to/file.zarr', engine='zarr') + # Print the dataset to see its contents + print(dataset) + CSV --- :: @@ -291,20 +293,38 @@ CSV Excel ----- +Excel files are not typically used for scientific data storage, and xarray does not have a built-in method to read Excel files. However, if your Excel file contains data that is organized in a way that can be converted to an xarray dataset, you can use the pandas and xarray packages in Python to read the file and convert it to an xarray object. :: + import pandas as pd import xarray as xr - # Open an Excel file using xarray - ds = xr.open_dataset('/path/to/my/file.xlsx', engine='openpyxl') + # Open the Excel file and read the data into a pandas dataframe using the openpyxl engine + df = pd.read_excel('/path/to/your/file.xlsx', engine='openpyxl', sheet_name='Sheet1') - # Open an Excel file using pandas package - import pandas as pd - df = pd.read_excel('/path/to/my/file.xlsx') + # Convert the pandas dataframe to an xarray dataset + dataset = xr.Dataset.from_dataframe(df) + + # Print the dataset to see its contents + print(dataset) JSON ---- +JSON is not a file format that is commonly used for scientific data, and xarray does not have a built-in method to read JSON files. However, if your JSON file contains data that is organized in a way that can be converted to an xarray dataset, you can use the json and xarray packages in Python to read the file and convert it to an xarray object. :: + import json + import xarray as xr + + # Open the JSON file and read its contents + with open('/path/to/your/file.json', 'r') as f: + data_dict = json.load(f) + + # Convert the JSON data to an xarray dataset + dataset = xr.Dataset.from_dict(data_dict) + + # Print the dataset to see its contents + print(dataset) + import pandas as pd import xarray as xr @@ -316,18 +336,8 @@ JSON # Print xarray Dataset object print(ds) - - - - import xarray as xr - - # Open a JSON file using xarray - ds = xr.open_dataset('/path/to/my/file.json') - - # Open a JSON file using json package - import json - with open('/path/to/my/file.json', 'r') as f: - data = json.load(f) + +Note that the structure of your JSON file needs to be compatible with the xarray data model for this approach to work. Specifically, your JSON data needs to be organized as a dictionary of arrays, where each key in the dictionary corresponds to a variable name and each value is an array of data. These are just examples and may not cover all possible use cases. Some packages may have additional functionality beyond what is shown here. You can refer to the documentation for each package for more information. From 0b0f905272a7caf1b52d39461eac3ed1f9753441 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 11 Mar 2023 08:52:35 +0000 Subject: [PATCH 14/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index ed865d36cec..a33dfd06686 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -277,7 +277,7 @@ Zarr # Print the dataset to see its contents print(dataset) - + CSV --- :: @@ -336,7 +336,7 @@ JSON is not a file format that is commonly used for scientific data, and xarray # Print xarray Dataset object print(ds) - + Note that the structure of your JSON file needs to be compatible with the xarray data model for this approach to work. Specifically, your JSON data needs to be organized as a dictionary of arrays, where each key in the dictionary corresponds to a variable name and each value is an array of data. These are just examples and may not cover all possible use cases. Some packages may have additional functionality beyond what is shown here. You can refer to the documentation for each package for more information. From b70b6e6ee0dc960c2ddc881371f40f68288385c6 Mon Sep 17 00:00:00 2001 From: harshitha1201 <97012127+harshitha1201@users.noreply.github.com> Date: Sun, 12 Mar 2023 23:42:08 +0530 Subject: [PATCH 15/17] Update faq.rst --- doc/getting-started-guide/faq.rst | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index a33dfd06686..7517ad61962 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -208,7 +208,8 @@ To use these backend functions in xarray, you can simply call them with the path NetCDF ------ Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset object. -:: + +.. code:: python import xarray as xr @@ -224,7 +225,8 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset obj HDF5 ---- Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset object. -:: + +.. code:: python import xarray as xr @@ -245,7 +247,8 @@ Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset obje GRIB1/GRIB2 ----------- use the cfgrib.open_dataset() function from the cfgrib package to open a GRIB1 file as an xarray Dataset. -:: + +.. code:: python import xarray as xr import cfgrib @@ -268,7 +271,8 @@ We recommend installing cfgrib via conda: Zarr ---- -:: + +.. code:: python import xarray as xr @@ -280,7 +284,7 @@ Zarr CSV --- -:: +.. code:: python import xarray as xr @@ -294,7 +298,8 @@ CSV Excel ----- Excel files are not typically used for scientific data storage, and xarray does not have a built-in method to read Excel files. However, if your Excel file contains data that is organized in a way that can be converted to an xarray dataset, you can use the pandas and xarray packages in Python to read the file and convert it to an xarray object. -:: + +.. code:: python import pandas as pd import xarray as xr @@ -311,7 +316,8 @@ Excel files are not typically used for scientific data storage, and xarray does JSON ---- JSON is not a file format that is commonly used for scientific data, and xarray does not have a built-in method to read JSON files. However, if your JSON file contains data that is organized in a way that can be converted to an xarray dataset, you can use the json and xarray packages in Python to read the file and convert it to an xarray object. -:: + +.. code:: python import json import xarray as xr From f280c79c11e3ee91aa18c45694e58a54b75651c1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 12 Mar 2023 18:12:42 +0000 Subject: [PATCH 16/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 136 ++++++++++++++++-------------- 1 file changed, 74 insertions(+), 62 deletions(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 7517ad61962..769aaff5700 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -211,16 +211,16 @@ Use xarray.open_dataset() to open a NetCDF file and return an xarray Dataset obj .. code:: python - import xarray as xr + import xarray as xr - # use xarray to open the file and return an xarray.Dataset object - ds = xr.open_dataset('/path/to/my/file.nc') + # use xarray to open the file and return an xarray.Dataset object + ds = xr.open_dataset("/path/to/my/file.nc") - # Print Dataset object - print(ds) + # Print Dataset object + print(ds) - # Open multiple NetCDF files as a single dataset using xarray - ds = xr.open_mfdataset('/path/to/my/files/*.nc') + # Open multiple NetCDF files as a single dataset using xarray + ds = xr.open_mfdataset("/path/to/my/files/*.nc") HDF5 ---- @@ -228,21 +228,23 @@ Use xarray.open_dataset() to open an HDF5 file and return an xarray.Dataset obje .. code:: python - import xarray as xr + import xarray as xr + + # Open HDF5 file as an xarray Dataset + ds = xr.open_dataset("path/to/hdf5/file.h5", engine="h5netcdf") + + # Print Dataset object + print(ds) - # Open HDF5 file as an xarray Dataset - ds = xr.open_dataset('path/to/hdf5/file.h5', engine='h5netcdf') + # Open a HDF5 file using h5py package + import h5py - # Print Dataset object - print(ds) + f = h5py.File("/path/to/my/file.h5", "r") - # Open a HDF5 file using h5py package - import h5py - f = h5py.File('/path/to/my/file.h5', 'r') + # Open a HDF5 file using pytables package + import tables - # Open a HDF5 file using pytables package - import tables - f = tables.open_file('/path/to/my/file.h5', 'r') + f = tables.open_file("/path/to/my/file.h5", "r") GRIB1/GRIB2 ----------- @@ -250,17 +252,25 @@ use the cfgrib.open_dataset() function from the cfgrib package to open a GRIB1 f .. code:: python - import xarray as xr - import cfgrib + import xarray as xr + import cfgrib - # Open GRIB1 file as an xarray Dataset - ds = xr.open_dataset('path/to/grib1/file.grb', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) + # Open GRIB1 file as an xarray Dataset + ds = xr.open_dataset( + "path/to/grib1/file.grb", + engine="cfgrib", + backend_kwargs={"filter_by_keys": {"typeOfLevel": "surface"}}, + ) - # OR open GRIB2 file as an xarray Dataset - ds = xr.open_dataset('path/to/grib2/file.grb2', engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}}) + # OR open GRIB2 file as an xarray Dataset + ds = xr.open_dataset( + "path/to/grib2/file.grb2", + engine="cfgrib", + backend_kwargs={"filter_by_keys": {"typeOfLevel": "surface"}}, + ) - # Print Dataset object - print(ds) + # Print Dataset object + print(ds) The open_dataset() function reads the GRIB file and returns an xarray Dataset object, which can be used to access and manipulate the data in the file. Note that the backend_kwargs parameter is used to filter the GRIB messages in the file by their keys. In this example, only surface-level data is read from the GRIB file. @@ -274,26 +284,27 @@ Zarr .. code:: python - import xarray as xr + import xarray as xr - # Open the Zarr file and load it into an xarray dataset - dataset = xr.open_dataset('/path/to/file.zarr', engine='zarr') + # Open the Zarr file and load it into an xarray dataset + dataset = xr.open_dataset("/path/to/file.zarr", engine="zarr") - # Print the dataset to see its contents - print(dataset) + # Print the dataset to see its contents + print(dataset) CSV --- .. code:: python - import xarray as xr + import xarray as xr - # Open a CSV file using xarray - ds = xr.open_dataset('/path/to/my/file.csv') + # Open a CSV file using xarray + ds = xr.open_dataset("/path/to/my/file.csv") - # Open a CSV file using pandas package - import pandas as pd - df = pd.read_csv('/path/to/my/file.csv') + # Open a CSV file using pandas package + import pandas as pd + + df = pd.read_csv("/path/to/my/file.csv") Excel ----- @@ -301,47 +312,48 @@ Excel files are not typically used for scientific data storage, and xarray does .. code:: python - import pandas as pd - import xarray as xr + import pandas as pd + import xarray as xr - # Open the Excel file and read the data into a pandas dataframe using the openpyxl engine - df = pd.read_excel('/path/to/your/file.xlsx', engine='openpyxl', sheet_name='Sheet1') + # Open the Excel file and read the data into a pandas dataframe using the openpyxl engine + df = pd.read_excel("/path/to/your/file.xlsx", engine="openpyxl", sheet_name="Sheet1") - # Convert the pandas dataframe to an xarray dataset - dataset = xr.Dataset.from_dataframe(df) + # Convert the pandas dataframe to an xarray dataset + dataset = xr.Dataset.from_dataframe(df) - # Print the dataset to see its contents - print(dataset) + # Print the dataset to see its contents + print(dataset) JSON ---- JSON is not a file format that is commonly used for scientific data, and xarray does not have a built-in method to read JSON files. However, if your JSON file contains data that is organized in a way that can be converted to an xarray dataset, you can use the json and xarray packages in Python to read the file and convert it to an xarray object. .. code:: python - import json - import xarray as xr - # Open the JSON file and read its contents - with open('/path/to/your/file.json', 'r') as f: - data_dict = json.load(f) + import json + import xarray as xr - # Convert the JSON data to an xarray dataset - dataset = xr.Dataset.from_dict(data_dict) + # Open the JSON file and read its contents + with open("/path/to/your/file.json", "r") as f: + data_dict = json.load(f) - # Print the dataset to see its contents - print(dataset) + # Convert the JSON data to an xarray dataset + dataset = xr.Dataset.from_dict(data_dict) - import pandas as pd - import xarray as xr + # Print the dataset to see its contents + print(dataset) + + import pandas as pd + import xarray as xr - # Load JSON file as a pandas DataFrame - df = pd.read_json('path/to/json/file.json') + # Load JSON file as a pandas DataFrame + df = pd.read_json("path/to/json/file.json") - # Convert pandas DataFrame to xarray Dataset - ds = df.to_xarray() + # Convert pandas DataFrame to xarray Dataset + ds = df.to_xarray() - # Print xarray Dataset object - print(ds) + # Print xarray Dataset object + print(ds) Note that the structure of your JSON file needs to be compatible with the xarray data model for this approach to work. Specifically, your JSON data needs to be organized as a dictionary of arrays, where each key in the dictionary corresponds to a variable name and each value is an array of data. From 312c602be5a809c5b05f28b6cab1a7a8d1a087e4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Mar 2023 13:07:36 +0000 Subject: [PATCH 17/17] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- doc/getting-started-guide/faq.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/getting-started-guide/faq.rst b/doc/getting-started-guide/faq.rst index 769aaff5700..9ebccc60ef4 100644 --- a/doc/getting-started-guide/faq.rst +++ b/doc/getting-started-guide/faq.rst @@ -316,7 +316,9 @@ Excel files are not typically used for scientific data storage, and xarray does import xarray as xr # Open the Excel file and read the data into a pandas dataframe using the openpyxl engine - df = pd.read_excel("/path/to/your/file.xlsx", engine="openpyxl", sheet_name="Sheet1") + df = pd.read_excel( + "/path/to/your/file.xlsx", engine="openpyxl", sheet_name="Sheet1" + ) # Convert the pandas dataframe to an xarray dataset dataset = xr.Dataset.from_dataframe(df)