# Python Gotchas in the GDAL and OGR Python Bindings

This page lists aspects of GDAL’s and OGR’s Python bindings that may catch Python programmers by surprise. If you find something new, feel free to add it to the list, but consider discussing it on the gdal-dev mailing list first, to make sure you fully understand the issue and that others agree that it is unexpected, “non-Pythonic”, or something that would catch many Python programmers by surprise. Be sure to reference email threads, GitHub tickets, and other sources of additional information.

This list is not the place to report bugs. If you believe something is a bug, please open a ticket and report the problem to gdal-dev. Then consider listing it here if it is something related to Python specifically. Do not list it here if it relates to GDAL or OGR generally, and not the Python bindings specifically.

Not all items listed here are bugs. Some of these are just how GDAL and OGR work and cannot be fixed easily without breaking existing code. If you don’t like how something works and think it should be changed, feel free to discuss it on gdal-dev and see what can be done.

## Gotchas that are by design… or per history

These are unexpected behaviors that are not considered by the GDAL and OGR teams to be bugs and are unlikely to be changed due to effort required, or whose fixing might affect backward compatibility, etc.

### Python bindings do not raise exceptions unless you explicitly call `UseExceptions()`

By default, the GDAL and OGR Python bindings do not raise exceptions when errors occur. Instead they return an error value such as `None` and write an error message to `sys.stdout`. For example, when you try to open a non-existing dataset with GDAL:

    >>> from osgeo import gdal
    >>> gdal.Open('C:\\foo.img')
    ERROR 4: 'C:\foo.img does not exist in the file system,
    and is not recognized as a supported dataset name.

    >>>

In Python, it is traditional to report errors by raising exceptions. You can enable this behavior in GDAL and OGR by calling the `UseExceptions()` function:

    >>> from osgeo import gdal
    >>> gdal.UseExceptions()    # Enable exceptions
    >>> gdal.open('C:\\foo.img')
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    RuntimeError: 'C:\foo.img' does not exist in the file system,
    and is not recognized as a supported dataset name.

    >>>

The GDAL team acknowledges that Python programmers expect exceptions to be enabled by default, but says that exceptions are disabled by default to preserve backward compatibility.

### Python crashes if you use an object after deleting an object it has a relationship with

Consider this example:

    >>> from osgeo import gdal
    >>> dataset = gdal.Open('C:\\RandomData.img')
    >>> band = dataset.GetRasterBand(1)
    >>> print(band.Checksum())
    31212

In this example, `band` has a relationship with `dataset` that requires `dataset` to remain allocated in order for `band` to work. If we delete `dataset` and then try to use `band`, Python will crash:

    >>> from osgeo import gdal
    >>> dataset = gdal.Open('C:\\RandomData.img')
    >>> band = dataset.GetRasterBand(1)
    >>> del dataset           # This will cause the Python garbage collector to deallocate dataset
    >>> band.GetChecksum()    # This will now crash Python because the band's dataset is gone
    < Python crashes >

This problem can manifest itself in subtle ways. For example, it can occur if you try to instantiate a temporary dataset instance within a single line of code:

    >>> from osgeo import gdal
    >>> print(gdal.Open('C:\\RandomData.img').GetRasterBand(1).Checksum())
    < Python crashes >

In this example, the dataset instance was no longer needed after the call to `GetRasterBand()` so Python deallocated it before calling `Checksum()`.

    >>> from osgeo import gdal
    >>>
    >>> def load_band(data_filename):
    >>>     dataset = gdal.Open(data_filename)
    >>>     return dataset.GetRasterBand(1)
    >>>
    >>> band = load_band('c:\\RandomData.img')
    >>> print(band.Checksum())
    < Python crashes >

this example is the same case with above but it looks different. the dataset object is only available in the `load_band` function and it’ll be deleted right after leaving the function.

This problem occurs because the GDAL and OGR objects are implemented in C++ and the relationships between them are maintained in C++ using pointers. When you delete the dataset instance in Python it causes the C++ object behind it to be deallocated. But the C++ object behind the band instance does not know that this happened, so it contains a pointer to the C++ dataset object that no longer exists. When the band tries to access the non-existing object, the process crashes.

The GDAL team knows that this design is not what Python programmers expect. Unfortunately the design is difficult to correct so it is likely to remain for some time. Please consult the GDAL team for more information.

The problem is not restricted to GDAL band and dataset objects. It happens in other areas where objects have relationships with each other. Unfortunately there is no complete list, so you have to watch for it yourself. One other known place involves the OGR `GetGeometryRef()` function:

    >>> feat = lyr.GetNextFeature()
    >>> geom = feat.GetGeometryRef()     # geom contains a reference into the C++ geometry object maintained by the C++ feature object
    >>> del feat                         # This deallocates the C++ feature object, and its C++ geometry
    >>> print(geom.ExportToWkt())        # Crash here. The C++ geometry no longer exists
    < Python crashes >

If you read the GDAL and OGR API documentation carefully, you will see that the functions that end in “Ref” obtain references to internal objects, rather than making new copies. This is a clue that the problem could occur. Be careful when using the “Ref” functions. Also watch out for functions that end in “Directly”, such as `SetGeometryDirectly()`, which transfer ownership of internal objects:

    >>> point = ogr.Geometry(ogr.wkbPoint)
    >>> feature = ogr.Feature(layer_defn)
    >>> feature.SetGeometryDirectly(point)    # Transfers ownership of the C++ geometry from point to feature
    >>> del feature                           # point becomes implicitly invalid, because feature owns the C++ geometry
    >>> print(point.ExportToWkt())            # Crash here
    < Python crashes >

The advantage of the “Ref” and “Directly” functions is they provide faster performance because a duplicate object does not need to be created. The disadvantage is that you have to watch out for this problem.

### Python crashes if you add a new field to an OGR layer when features deriving from this layer definition are still active

For example:

    >>> feature = lyr.GetNextFeature()
    >>> field_defn = ogr.FieldDefn("foo", ogr.OFTString)
    >>> lyr.CreateField(field_defn)                       # now, existing features deriving from this layer are invalid
    >>> feature.DumpReadable()                            # segfault
    < Python crashes >

For more information, please see #3552.

### Layers with attribute filters (`SetAttributeFilter()`) will only return filtered features when using `GetNextFeature()`

If you read the documentation for `SetAttributeFilter()` carefully you will see the caveat about `OGR_L_GetNextFeature()`. This means that if you use `GetFeature()`, instead of `GetNextFeature()`, then you can still access and work with features from the layer that are not covered by the filter. `GetFeatureCount()` will respect the filter and show the correct number of features filtered. However, working with `GetFeatureCount()` in a loop can lead to some subtle confusion. Iterating over the Layer object or using `GetNextFeature()` should be the default method for accessing features:

    >>> lyr = inDataSource.GetLayer()
    >>> lyr.SetAttributeFilter("PIN = '0000200001'")      # this is a unique filter for only one record
    >>> for i in range( 0, lyr.GetFeatureCount() ):
    ...    feat = lyr.GetFeature( i )
    ...    print(feat)                                    # this will print one feat, but it's the first feat in the Layer and not the filtered feat
    ...

### Certain objects contain a `Destroy()` method, but you should never use it

You may come across examples that call the `Destroy()` method. This tutorial even gives specific advice on page 12 about when to call `Destroy`. But according to email from Even Rouault, `Destroy()` never need to be called:

    >I have some Python code that uses OGR geometry objects internally, creating
    > them like this:
    >
    > point = ogr.Geometry(ogr.wkbPoint)
    >
    > Does this code need to explicitly destroy these geometries, like the
    > following, to avoid leaks, or can it simply allow them to go out of scope
    > and have Python's reference counting and garbage collector clean them up?
    >
    > point.Destroy()

    There's no reason to call Destroy(), at all. Native object gets destroyed when
    Python object goes out of scope, or when they are assigned to None. So replace
    foo.Destroy() by foo = None if you really want to control when the underlying
    C++ object is destroyed.

    > I'm sorry for my ignorance here. I found a nice GDAL tutorial that seems to
    > say they *should* be explicitly destroyed in certain circumstances (see
    > http://www.gis.usu.edu/~chrisg/python/2009/lectures/ospy_slides2.pdf, page
    > 12). But I have not really seen any other examples of this.
    >

    Destroy() was perhaps necessary with old-gen bindings, but I'm not even sure
    of that... Perhaps this shouldn't have been exposed at all... But, as
    mentioned in the slides, it is true that there are situations where you
    shouldn't call Destroy() at all.

### Saving and closing datasets/datasources

To save and close GDAL raster datasets or OGR vector datasources, the object needs to be dereferenced, such as setting it to `None`, a different value, or deleting the object. If there are more than one copies of the dataset or datasource object, then each copy needs to be dereferenced.

For example, creating and saving a raster dataset:

    >>> from osgeo import gdal
    >>> driver = gdal.GetDriverByName('GTiff')
    >>> dst_ds = driver.Create('new.tif', 10, 15)
    >>> band = dst_ds.GetRasterBand(1)
    >>> arr = band.ReadAsArray()  # raster values are all zero
    >>> arr[2, 4:] = 50  # modify some data
    >>> band.WriteArray(arr)  # raster file still unmodified
    >>> band = None  # dereference band to avoid gotcha described previously
    >>> dst_ds = None  # save, close

The last dereference to the raster dataset writes the data modifications and closes the raster file. `WriteArray(arr)` does not write the array to disk, unless the GDAL block cache is full (typically 40 MB).

With some drivers, raster datasets can be intermittently saved without closing using `FlushCache()`. Similarly, vector datasets can be saved using `SyncToDisk()`. However, neither of these methods guarantee that the data are written to disk, so the preferred method is to deallocate as shown above.

### Exceptions raised in custom error handlers do not get caught

The python bindings allow you to specify a python callable as an error handler (#4993). However, these error handlers appear to be called in a separate thread and any exceptions raised do not propagate back to the main thread (#5186).

So if you want to catch warnings as well as errors, something like this won’t work:

    from osgeo import gdal

    def error_handler(err_level, err_no, err_msg):
        if err_level >= gdal.CE_Warning:
            raise RuntimeError(err_level, err_no, err_msg)  # this exception does not propagate back to main thread!

    if __name__ == '__main__':
        # Test custom error handler
        gdal.PushErrorHandler(error_handler)
        gdal.Error(gdal.CE_Warning, 2, 'test warning message')
        gdal.PopErrorHandler()

But you can do something like this instead:

    from osgeo import gdal

    class GdalErrorHandler(object):
        def __init__(self):
            self.err_level = gdal.CE_None
            self.err_no = 0
            self.err_msg = ''

        def handler(self, err_level, err_no, err_msg):
            self.err_level = err_level
            self.err_no = err_no
            self.err_msg = err_msg

    if __name__ == '__main__':
        err = GdalErrorHandler()
        gdal.PushErrorHandler(err.handler)
        gdal.UseExceptions()  # Exceptions will get raised on anything >= gdal.CE_Failure

        assert err.err_level == gdal.CE_None, 'the error level starts at 0'

        try:
            # Demonstrate handling of a warning message
            try:
                gdal.Error(gdal.CE_Warning, 8675309, 'Test warning message')
            except Exception:
                raise AssertionError('Operation raised an exception, this should not happen')
            else:
                assert err.err_level == gdal.CE_Warning, (
                    'The handler error level should now be at warning')
                print('Handled error: level={}, no={}, msg={}'.format(
                    err.err_level, err.err_no, err.err_msg))

            # Demonstrate handling of an error message
            try:
                gdal.Error(gdal.CE_Failure, 42, 'Test error message')
            except Exception as e:
                assert err.err_level == gdal.CE_Failure, (
                    'The handler error level should now be at failure')
                assert err.err_msg == e.args[0], 'raised exception should contain the message'
                print('Handled warning: level={}, no={}, msg={}'.format(
                    err.err_level, err.err_no, err.err_msg))
            else:
                raise AssertionError('Error message was not raised, this should not happen')

        finally:
            gdal.PopErrorHandler()

## Gotchas that result from bugs or behaviors of other software

### Python crashes in GDAL functions when you upgrade or downgrade numpy

Much of GDAL’s Python bindings are implemented in C++. Much of the core of numpy is implemented in C. The C++ part of GDAL’s Python bindings interacts with the C part of numpy through numpy’s ABI (application binary interface). This requires GDAL’s Python bindings to be compiled using numpy header files that define numpy C data structures. Those data structures sometimes change between numpy versions. When this happens, the new version of numpy is not be compatible at the binary level with the old version, and the GDAL Python bindings must be recompiled before they will work with the new version of numpy. And when they are recompiled, they probably won’t work with the old version.

If you obtained a precompiled version of GDAL’s Python bindings, such as the Windows packages from http://gisinternals.com/sdk.php be sure you look up what version of numpy was used to compile them, and install that version of numpy on your machine.

### Python bindings cannot be used successfully from ArcGIS in-process geoprocessing tools (ArcGIS 9.3 and later)

ArcGIS allows the creation of custom, Python-based geoprocessing tools. Until ArcGIS 10, there was no easy way to read raster data into memory. GDAL provides such a mechanism.

Starting with ArcGIS 9.3, geoprocessing tools can either run in the ArcGIS process itself (ArcCatalog.exe or ArcMap.exe) or run in a separate python.exe worker process. Unfortunately ArcGIS contains a bug in how it runs in-process tools. Thus, if you use GDAL from an in-process tool, it will run fine the first time but after that it may fail with `TypeError` exceptions until you restart the ArcGIS process. For example, band.ReadAsArray() fails with:

`TypeError: in method 'BandRasterIONumpy', argument 1 of type 'GDALRasterBandShadow *`’

This is a bug in ArcGIS. Please see #3672 for complete details and advice on workarounds.