add parquet-support

docs/changelog.rst

@@ -13,6 +13,7 @@ Added
 -----
 - docs now include a dropdown for selecting older versions of the docs. (#457)
 - Support for loading the same data source but from different places (e.g. local & aws)
+- Add support for reading and writing tabular data in ``parquet`` format. (PR #445)
 
 Changed
 -------

docs/user_guide/data_types.rst

@@ -270,7 +270,7 @@ Vector data (GeoDataFrame)
      - :py:meth:`~hydromt.io.open_vector`
      - Point, Line and Polygon geometries. Uses :py:func:`geopandas.read_file`
    * - ``vector_table``
-     - CSV, XY, and EXCEL.
+     - CSV, XY, PARQUET and EXCEL.
      - :py:meth:`~hydromt.io.open_vector`
      - Point geometries only. Uses :py:meth:`~hydromt.io.open_vector_from_table`
 
@@ -350,6 +350,15 @@ options.
       driver_kwargs:
         driver: csv
 
+.. _binary_vector:
+
+HydroMT also supports reading and writing verctor data in binary format. Currently only parquet is
+supported, but others could be added if desried. The structure of the files should be the same as
+the text format files described above but writting according to the parquet file spec. Since this is
+a binary format, not exmaples are provided, but for example pandas can wrtie the same data structure
+to parquet as it can csv.
+
+
 .. _GeoDataset:
 
 Geospatial point time-series (GeoDataset)
@@ -442,6 +451,7 @@ separate (text) files are parsed to **GeoDataset** using the **vector** driver.
 The GeoDataset must at least contain a location index with point geometries which is referred to by the ``path`` argument
 The path may refer to both GIS vector data such as GeoJSON with only Point geometries
 or tabulated point vector data such as csv files, see earlier examples for GeoDataFrame datasets.
+Finally, certain binary formats such as parquet are also supported.
 In addition a tabulated time-series text file can be passed to be used as a variable of the GeoDataset.
 This data is added by a second file which is referred to using the ``fn_data`` key-word argument.
 The index of the time-series (in the columns header) and point locations must match.
@@ -492,6 +502,9 @@ read the time stamps the :py:func:`pandas.to_datetime` method is used.
      - Excel files
      - :py:func:`pandas.read_excel`
      - If required, provide a sheet name through driver_kwargs
+   * - ``parquet``
+     - Binary encoded comunar dataformat
+     - :py:func:`pandas.read_parquet`
    * - ``fwf``
      - Fixed width delimited text files
      - :py:func:`pandas.read_fwf`

hydromt/data_adapter/dataframe.py

@@ -54,10 +54,10 @@ def __init__(
             Path to data source. If the dataset consists of multiple files, the path may
             contain {variable}, {year}, {month} placeholders as well as path
             search pattern using a '*' wildcard.
-        driver: {'csv', 'xlsx', 'xls', 'fwf'}, optional
+        driver: {'csv', 'parquet', 'xlsx', 'xls', 'fwf'}, optional
             Driver to read files with, for 'csv' :py:func:`~pandas.read_csv`,
-            for {'xlsx', 'xls'} :py:func:`~pandas.read_excel`, and for 'fwf'
-            :py:func:`~pandas.read_fwf`.
+            for 'parquet' :py:func:`~pandas.read_parquet`, for {'xlsx', 'xls'}
+            :py:func:`~pandas.read_excel`, and for 'fwf' :py:func:`~pandas.read_fwf`.
             By default the driver is inferred from the file extension and falls back to
             'csv' if unknown.
         filesystem: {'local', 'gcs', 's3'}, optional
@@ -131,7 +131,7 @@ def to_file(
         data_name : str
             Name of the output file without extension.
         driver : str, optional
-            Driver to write the file, e.g., 'csv', 'excel'. If None,
+            Driver to write the file, e.g., 'csv','parquet', 'excel'. If None,
             the default behavior is used.
         variables : list of str, optional
             Names of DataFrame columns to include in the output. By default,
@@ -169,6 +169,9 @@ def to_file(
             driver = "csv"
             fn_out = join(data_root, f"{data_name}.csv")
             obj.to_csv(fn_out, **kwargs)
+        elif driver == "parquet":
+            fn_out = join(data_root, f"{data_name}.parquet")
+            obj.to_parquet(fn_out, **kwargs)
         elif driver == "excel":
             fn_out = join(data_root, f"{data_name}.xlsx")
             obj.to_excel(fn_out, **kwargs)
@@ -208,6 +211,8 @@ def get_data(
 
         if self.driver in ["csv"]:
             df = pd.read_csv(self.path, **kwargs)
+        elif self.driver == "parquet":
+            df = pd.read_parquet(self.path, **kwargs)
         elif self.driver in ["xls", "xlsx", "excel"]:
             df = pd.read_excel(self.path, engine="openpyxl", **kwargs)
         elif self.driver in ["fwf"]:

hydromt/data_adapter/geodataframe.py

@@ -27,6 +27,7 @@ class GeoDataFrameAdapter(DataAdapter):
     _DRIVERS = {
         "xy": "xy",
         "csv": "csv",
+        "parquet": "parquet",
         "xls": "xls",
         "xlsx": "xlsx",
     }
@@ -169,7 +170,7 @@ def to_file(
             return None, None
 
         if driver is None:
-            _lst = ["csv", "xls", "xlsx", "xy", "vector_table"]
+            _lst = ["csv", "parquet", "xls", "xlsx", "xy", "vector_table"]
             driver = "csv" if self.driver in _lst else "GPKG"
         # always write netcdf
         if driver == "csv":
@@ -181,6 +182,15 @@ def to_file(
                 )
             gdf["x"], gdf["y"] = gdf.geometry.x, gdf.geometry.y
             gdf.drop(columns="geometry").to_csv(fn_out, **kwargs)
+        elif driver == "parquet":
+            fn_out = join(data_root, f"{data_name}.parquet")
+            if not np.all(gdf.geometry.type == "Point"):
+                raise ValueError(
+                    f"{data_name} contains other geometries than 'Point' "
+                    "which cannot be written to parquet."
+                )
+            gdf["x"], gdf["y"] = gdf.geometry.x, gdf.geometry.y
+            gdf.drop(columns="geometry").to_parquet(fn_out, **kwargs)
         else:
             driver_extensions = {
                 "ESRI Shapefile": ".shp",
@@ -240,10 +250,23 @@ def get_data(
 
         # read and clip
         logger.info(f"GeoDataFrame: Read {self.driver} data{clip_str}.")
-        if self.driver in ["csv", "xls", "xlsx", "xy", "vector", "vector_table"]:
+        if self.driver in [
+            "csv",
+            "parquet",
+            "xls",
+            "xlsx",
+            "xy",
+            "vector",
+            "vector_table",
+        ]:
             # "csv", "xls", "xlsx", "xy" deprecated use vector_table instead.
             # specific driver should be added to open_vector kwargs
             if "driver" not in kwargs and self.driver in ["csv", "xls", "xlsx", "xy"]:
+                warnings.warn(
+                    "using the driver setting is deprecated. Please use"
+                    "vector_table instead."
+                )
+
                 kwargs.update(driver=self.driver)
             # Check if file-object is required because of additional options
             gdf = io.open_vector(

hydromt/io.py

@@ -2,7 +2,7 @@
 import glob
 import io
 import logging
-from os.path import abspath, basename, dirname, isfile, join
+from os.path import abspath, basename, dirname, isfile, join, splitext
 from pathlib import Path
 
 import dask
@@ -309,11 +309,11 @@ def open_geodataset(
     fn_data: path, str
         Path to data file of which the index dimension which should match the geospatial
         coordinates index.
-        This can either be a csv with datetime in the first column and the location
-        index in the header row, or a netcdf with a time and index dimensions.
+        This can either be a csv, or parquet with datetime in the first column and the
+        location index in the header row, or a netcdf with a time and index dimensions.
     var_name: str, optional
-        Name of the variable in case of a csv fn_data file. By default, None and
-        infered from basename.
+        Name of the variable in case of a csv, or parquet fn_data file. By default,
+        None and infered from basename.
     crs: str, `pyproj.CRS`, or dict
         Source coordinate reference system, ignored for files with a native crs.
     bbox : array of float, default None
@@ -361,7 +361,7 @@ def open_geodataset(
 def open_timeseries_from_table(
     fn, name=None, index_dim="index", logger=logger, **kwargs
 ):
-    """Open timeseries csv file and parse to xarray.DataArray.
+    """Open timeseries csv or parquet file and parse to xarray.DataArray.
 
     Accepts files with time index on one dimension and numeric location index on the
     other dimension. In case of string location indices, non-numeric parts are
@@ -387,9 +387,16 @@ def open_timeseries_from_table(
     da: xarray.DataArray
         DataArray
     """
-    kwargs0 = dict(index_col=0, parse_dates=True)
-    kwargs0.update(**kwargs)
-    df = pd.read_csv(fn, **kwargs0)
+    _, ext = splitext(fn)
+    if ext == ".csv":
+        kwargs0 = dict(index_col=0, parse_dates=True)
+        kwargs0.update(**kwargs)
+        df = pd.read_csv(fn, **kwargs0)
+    elif ext in [".parquet", ".pq"]:
+        df = pd.read_parquet(fn, **kwargs)
+    else:
+        raise ValueError(f"Unknown table file format: {ext}")
+
     # check if time index
     if np.dtype(df.index).type != np.datetime64:
         try:
@@ -422,20 +429,21 @@ def open_vector(
     logger=logger,
     **kwargs,
 ):
-    """Open fiona-compatible geometry, csv, excel or xy file and parse it.
+    """Open fiona-compatible geometry, csv, parquet, excel or xy file and parse it.
 
-    Construct a :py:meth:`geopandas.GeoDataFrame` CSV or XLS file are
+    Construct a :py:meth:`geopandas.GeoDataFrame` CSV, parquet, or XLS file are
     converted to point geometries based on default columns names
     for the x- and y-coordinates, or if given, the x_dim and y_dim arguments.
 
     Parameters
     ----------
     fn : str
         path to geometry file
-    driver: {'csv', 'xls', 'xy', 'vector'}, optional
+    driver: {'csv', 'xls', 'xy', 'vector', 'parquet'}, optional
         driver used to read the file: :py:meth:`geopandas.open_file` for gdal vector
         files, :py:meth:`hydromt.io.open_vector_from_table`
-        for csv, xls(x) and xy files. By default None, and infered from file extention.
+        for csv, parquet, xls(x) and xy files. By default None, and infered from
+        file extention.
     crs: str, `pyproj.CRS`, or dict
         Source coordinate reference system, ignored for files with a native crs.
     dst_crs: str, `pyproj.CRS`, or dict
@@ -452,7 +460,7 @@ def open_vector(
         the predicate function against each item. Requires bbox or mask.
         By default 'intersects'
     x_dim, y_dim : str
-        Name of x, y-coordinate columns, only applicable for csv or xls tables
+        Name of x, y-coordinate columns, only applicable for parquet, csv or xls tables
     assert_gtype : {Point, LineString, Polygon}, optional
         If given, assert geometry type
     mode: {'r', 'a', 'w'}
@@ -470,7 +478,7 @@ def open_vector(
     """
     filtered = False
     driver = driver if driver is not None else str(fn).split(".")[-1].lower()
-    if driver in ["csv", "xls", "xlsx", "xy"]:
+    if driver in ["csv", "parquet", "xls", "xlsx", "xy"]:
         gdf = open_vector_from_table(fn, driver=driver, **kwargs)
     else:
         gdf = gpd.read_file(fn, bbox=bbox, mask=geom, mode=mode, **kwargs)
@@ -509,12 +517,13 @@ def open_vector_from_table(
     crs=None,
     **kwargs,
 ):
-    """Read point geometry files from csv, xy or excel table files.
+    """Read point geometry files from csv, parquet, xy or excel table files.
 
     Parameters
     ----------
-    driver: {'csv', 'xls', 'xlsx', 'xy'}
+    driver: {'csv', 'parquet', 'xls', 'xlsx', 'xy'}
         If 'csv' use :py:meth:`pandas.read_csv` to read the data;
+        If 'parquet' use :py:meth:`pandas.read_parquet` to read the data;
         If 'xls' or 'xlsx' use :py:meth:`pandas.read_excel` with `engine=openpyxl`
         If 'xy' use :py:meth:`pandas.read_csv` with `index_col=False`, `header=None`,
         `delim_whitespace=True`.
@@ -537,13 +546,15 @@ def open_vector_from_table(
         Parsed and filtered point geometries
     """
     driver = driver.lower() if driver is not None else str(fn).split(".")[-1].lower()
-    if "index_col" not in kwargs:
+    if "index_col" not in kwargs and driver != "parquet":
         kwargs.update(index_col=0)
-    if driver in ["csv"]:
+    if driver == "csv":
         df = pd.read_csv(fn, **kwargs)
+    elif driver == "parquet":
+        df = pd.read_parquet(fn, **kwargs)
     elif driver in ["xls", "xlsx"]:
         df = pd.read_excel(fn, engine="openpyxl", **kwargs)
-    elif driver in ["xy"]:
+    elif driver == "xy":
         x_dim = x_dim if x_dim is not None else "x"
         y_dim = y_dim if y_dim is not None else "y"
         kwargs.update(index_col=False, header=None, delim_whitespace=True)

hydromt/stats/extremes.py

@@ -898,7 +898,7 @@ def get_lmom(x, nmom=4):
         vector of (nmom) L-moments
     """
     n = len(x)
-    xs = np.msort(x)
+    xs = np.sort(x, axis=0)
     bb = np.zeros(nmom - 1)
     ll = np.zeros(nmom - 1)
     b0 = xs.mean(axis=0)

tests/data/parameters_data.yml

@@ -9,3 +9,11 @@ vito_mapping:
     source_info: landuse parameters based on vito classification (https://land.copernicus.eu/global/products/lc)
     source_version: 1.0
   path: vito_mapping.csv
+vito_mapping_parquet:
+  data_type: DataFrame
+  driver: parquet
+  meta:
+    category: landuse
+    source_info: landuse parameters based on vito classification (https://land.copernicus.eu/global/products/lc)
+    source_version: 1.0
+  path: vito_mapping.parquet

tests/test_data_adapter.py

@@ -219,13 +219,13 @@ def test_geodataset(geoda, geodf, ts, tmpdir):
     assert da3.vector.crs.to_epsg() == 4326
     with pytest.raises(FileNotFoundError, match="No such file or catalog source"):
         data_catalog.get_geodataset("no_file.geojson")
-    # Test nc file writing to file
     with tempfile.TemporaryDirectory() as td:
+        # Test nc file writing to file
         GeoDatasetAdapter(fn_nc).to_file(
             data_root=td, data_name="test", driver="netcdf"
         )
         GeoDatasetAdapter(fn_nc).to_file(
-            data_root=td, data_name="test1", driver="netcdf", variables="test1"
+            data_root=tmpdir, data_name="test1", driver="netcdf", variables="test1"
         )
         GeoDatasetAdapter(fn_nc).to_file(data_root=td, data_name="test", driver="zarr")
 
@@ -297,6 +297,14 @@ def test_dataframe(df, tmpdir):
     assert isinstance(df1, pd.DataFrame)
     pd.testing.assert_frame_equal(df, df1)
 
+    # test reading parquet
+    fn_df_parquet = str(tmpdir.join("test.parquet"))
+    df.to_parquet(fn_df_parquet)
+    data_catalog = DataCatalog()
+    df2 = data_catalog.get_dataframe(fn_df_parquet, driver="parquet")
+    assert isinstance(df2, pd.DataFrame)
+    pd.testing.assert_frame_equal(df, df2)
+
     # Test FWF support
     fn_fwf = str(tmpdir.join("test.txt"))
     df.to_string(fn_fwf, index=False)
@@ -309,9 +317,9 @@ def test_dataframe(df, tmpdir):
     if compat.HAS_OPENPYXL:
         fn_xlsx = str(tmpdir.join("test.xlsx"))
         df.to_excel(fn_xlsx)
-        df2 = data_catalog.get_dataframe(fn_xlsx, driver_kwargs=dict(index_col=0))
-        assert isinstance(df2, pd.DataFrame)
-        assert np.all(df2 == df)
+        df3 = data_catalog.get_dataframe(fn_xlsx, driver_kwargs=dict(index_col=0))
+        assert isinstance(df3, pd.DataFrame)
+        assert np.all(df3 == df)
 
 
 def test_dataframe_unit_attrs(df: pd.DataFrame, tmpdir):

tests/test_data_catalog.py

@@ -322,9 +322,13 @@ def test_export_global_datasets(tmpdir):
 def test_export_dataframe(tmpdir, df, df_time):
     # Write two csv files
     fn_df = str(tmpdir.join("test.csv"))
+    fn_df_parquet = str(tmpdir.join("test.parquet"))
     df.to_csv(fn_df)
+    df.to_parquet(fn_df_parquet)
     fn_df_time = str(tmpdir.join("test_ts.csv"))
+    fn_df_time_parquet = str(tmpdir.join("test_ts.parquet"))
     df_time.to_csv(fn_df_time)
+    df_time.to_parquet(fn_df_time_parquet)
 
     # Test to_file method (needs reading)
     data_dict = {
@@ -345,6 +349,16 @@ def test_export_dataframe(tmpdir, df, df_time):
                 "parse_dates": True,
             },
         },
+        "test_df_parquet": {
+            "path": fn_df_parquet,
+            "driver": "parquet",
+            "data_type": "DataFrame",
+        },
+        "test_df_ts_parquet": {
+            "path": fn_df_time_parquet,
+            "driver": "parquet",
+            "data_type": "DataFrame",
+        },
     }
 
     data_catalog = DataCatalog()
@@ -356,11 +370,11 @@ def test_export_dataframe(tmpdir, df, df_time):
         bbox=[11.70, 45.35, 12.95, 46.70],
     )
     data_catalog1 = DataCatalog(str(tmpdir.join("data_catalog.yml")))
-    assert len(data_catalog1.iter_sources()) == 1
+    assert len(data_catalog1.iter_sources()) == 2
 
     data_catalog.export_data(str(tmpdir))
     data_catalog1 = DataCatalog(str(tmpdir.join("data_catalog.yml")))
-    assert len(data_catalog1.iter_sources()) == 2
+    assert len(data_catalog1.iter_sources()) == 4
     for key, source in data_catalog1.iter_sources():
         dtypes = pd.DataFrame
         obj = source.get_data()