@@ -10,7 +10,7 @@ You can display the GDAL version that Pyogrio was compiled against by
1010
1111## List available drivers
1212
13- Use ` pyogrio.list_drivers() ` to list all available drivers in your installation
13+ Use {func} ` ~ pyogrio.list_drivers` to list all available drivers in your installation
1414of GDAL. However, just because a driver is listed does not mean that it is
1515currently compatible with Pyogrio.
1616
@@ -53,7 +53,7 @@ The following drivers are known to be well-supported and tested in Pyogrio:
5353
5454## List available layers
5555
56- To list layers available in a data source:
56+ To list layers available in a data source, use {func} ` ~pyogrio.list_layers ` :
5757
5858``` python
5959>> > from pyogrio import list_layers
@@ -68,9 +68,9 @@ be nonspatial. In this case, the geometry type will be `None`.
6868
6969## Read basic information about a data layer
7070
71- To list information about a data layer in a data source, use the name of the layer
72- or its index (0-based) within the data source. By default, this reads from the
73- first layer.
71+ To list information about a data layer in a data source, use
72+ {func} ` ~pyogrio.read_info ` . You can specify the name of the layer or its index
73+ (0-based) within the data source. By default, this reads from the first layer.
7474
7575``` python
7676>> > from pyogrio import read_info
@@ -102,8 +102,9 @@ To read from a layer using name or index (the following are equivalent):
102102
103103## Read a data layer into a GeoPandas GeoDataFrame
104104
105- To read all features from a spatial data layer. By default, this operates on
106- the first layer unless ` layer ` is specified using layer name or index.
105+ To read all features from a spatial data layer, use {func}` ~pyogrio.read_dataframe ` .
106+ By default, this operates on the first layer unless ` layer ` is specified using layer
107+ name or index.
107108
108109``` python
109110>> > from pyogrio import read_dataframe
@@ -212,7 +213,7 @@ Note: the `bbox` values must be in the same CRS as the dataset.
212213Note: if GEOS is present and used by GDAL , only geometries that intersect `bbox`
213214will be returned; if GEOS is not available or not used by GDAL , all geometries
214215with bounding boxes that intersect this bbox will be returned.
215- `pyogrio.__gdal_geos_version__` will be `None ` if GEOS is not detected.
216+ {attr} `pyogrio.__gdal_geos_version__` will be `None ` if GEOS is not detected.
216217
217218# # Filter records by a geometry
218219
@@ -238,7 +239,7 @@ need to convert it to a Shapely geometry before using `mask`.
238239Note: if GEOS is present and used by GDAL , only geometries that intersect `mask`
239240will be returned; if GEOS is not available or not used by GDAL , all geometries
240241with bounding boxes that intersect the bounding box of `mask` will be returned.
241- `pyogrio.__gdal_geos_version__` will be `None ` if GEOS is not detected.
242+ {attr} `pyogrio.__gdal_geos_version__` will be `None ` if GEOS is not detected.
242243
243244# # Execute a sql query
244245
@@ -345,7 +346,8 @@ or a DBF file, directly into a Pandas `DataFrame`.
345346# # Read feature bounds
346347
347348You can read the bounds of all or a subset of features in the dataset in order
348- to create a spatial index of features without reading all underlying geometries.
349+ to create a spatial index of features without reading all underlying geometries
350+ with {func}`~ pyogrio.read_bounds` .
349351This is typically 2 - 3x faster than reading full feature data, but the main
350352benefit is to avoid reading all feature data into memory for very large datasets.
351353
@@ -368,7 +370,7 @@ This function supports options to subset features from the dataset:
368370
369371# # Write a GeoPandas GeoDataFrame
370372
371- You can write a `GeoDataFrame` `df` to a file as follows :
373+ You can write a `GeoDataFrame` `df` to a file with {func} ` ~ pyogrio.write_dataframe` :
372374
373375```python
374376>> > from pyogrio import write_dataframe
@@ -472,7 +474,7 @@ You can also read from a URL with this syntax:
472474
473475GDAL only supports datetimes at a millisecond resolution. Reading data will thus
474476give at most millisecond resolution (`datetime64[ms]` data type ). With pandas 2.0
475- ` pyogrio.read_dataframe() ` will return datetime data as `datetime64[ms]`
477+ {func} ` ~ pyogrio.read_dataframe` will return datetime data as `datetime64[ms]`
476478correspondingly. For previous versions of pandas, `datetime64[ns]` is used as
477479ms precision was not supported. When writing, only precision up to
478480ms is retained.
@@ -489,7 +491,7 @@ in mixed time zone offsets being written for time zones where daylight saving
489491time is used (e.g. + 01 :00 and + 02 :00 offsets for time zone Europe/ Brussels).
490492When roundtripping through GDAL , the information about the original time zone
491493is lost, only the offsets can be preserved. By default,
492- {func}`pyogrio.read_dataframe() ` will convert columns with mixed offsets to UTC
494+ {func}`pyogrio.read_dataframe` will convert columns with mixed offsets to UTC
493495to return a datetime64 column. If you want to preserve the original offsets,
494496you can use `datetime_as_string=True ` or `mixed_offsets_as_utc=False ` .
495497
@@ -498,7 +500,7 @@ you can use `datetime_as_string=True` or `mixed_offsets_as_utc=False`.
498500It is possible to use dataset and layer creation options available for a given
499501driver in GDAL (see the relevant
500502[GDAL driver page](https:// gdal.org/ drivers/ vector/ index.html)). These
501- can be passed in as additional `kwargs` to ` write_dataframe` or using
503+ can be passed in as additional `kwargs` to {func} ` ~ pyogrio. write_dataframe` or using
502504dictionaries for dataset or layer- level options.
503505
504506Where possible, Pyogrio uses the metadata of the driver to determine if a
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