description of the problem

I'm writting a function to simplify my use of rasterstat.

def zonal_stat(src_rst, mask_vector, out_vector, measurments=available_stats[:4], verbose=False):
    """Compute statistics over value of the source raster for each geometry defined in the mask. results are displayed in a vector file. The user can choose the statistics he want to inclued in the final file
    Args : 
        src_rst (str) : path to the source raster
        mask_vector (str) : path to the vector mask 
        out_vector (str) : path to the output vector 
        measurment ([str], optional) : list of the measurment you want to perform. available measurments are :
            - min
            - max
            - mean
            - count
            - sum
            - std
            - median
            - majority
            - minority
            - unique
            - range
            - nodata
            - percentile
        the first 4 are the defaulted values. 'all' key word can also be used to perform all measurments. ['hist'] will perform an histogram of the categorical value of the raster. it will be the only stat performed if it is found in the list
        verbose (bool) : wether or not to display text
    # apply the verbose option
    v_print = custom_print(verbose)
    # get the file parameters
    with rio.open(src_rst) as f:
        crs = f.crs
        nodata = f.nodata
    # open the mask and project it into the raster crs 
    gdf = gpd.read_file(mask_vector).to_crs(crs)
    categorical = False
    stats = None
    # filter the measurments
    if measurments: 
        if 'hist' in measurments:
            categorical = True
        elif 'all' in measurments:
            stats = available_stats[:len(available_stats) - 2 - 1]
            tmp = [stat for stat in measurments if stat in available_stats]
            stats = None if len(tmp)==0 else tmp
    # if categorical I need to fetch the potential values 
    if categorical:
        with rio.open(src_rst) as f:
            count = np.bincount(f.read(1).flatten())
            features = np.where(count!=0)[0]
            # remove the nodata value from the feature
            features = features[~np.isin(features, nodata)]
            del count
    # loop over the different geometry 
    # longer than using just rasterstats but then I cannot display any evolution informations
    tmp_res = {i:[] for i in features} if categorical else {i: [] for i in stats}

    with tqdm(total=len(gdf), disable= not verbose) as pbar:
        for index, row in gdf.iterrows():
            #update tqdm 
            # get the geometry coordinates 
            left, bottom, right, top = row.geometry.bounds
            with rio.open(src_rst) as f:
                # window inside the extends of the raster
                left = max(left, f.bounds.left)
                top = min(top, f.bounds.top)
                right = min(right, f.bounds.right)
                bottom = max(bottom, f.bounds.bottom)
                # creat the window over the geometry 
                tlx, tly = f.index(left, top)
                brx, bry = f.index(right, bottom)
                win = Window.from_slices((tlx, brx), (tly, bry))
                win_transform = f.window_transform(win)
                # read the data in the window
                data = f.read(1, window=win)
                # do a zonal stat on 1 geometry
                zs = rstats.zonal_stats(
                    [row.geometry], # there is only one geometry 
                    affine = win_transform, 
                    nodata = nodata, 
                    all_touched = True,
                    stats = stats,
                    categorical = categorical)[0]
                # release memory
                del data
            # for categorical measurments I need to transform the index in int 
            if categorical:
                zs = {int(i): int(zs[i]) for i in zs}
            # fill the dictionary
            for idx in tmp_res:
                val = zs[idx] if idx in zs.keys() else 0
    df = pd.DataFrame(tmp_res)
    # create the result geodataframe
    stat_gdf = gpd.GeoDataFrame(
        geometry = gdf.geometry,
        crs = crs
    # save it 
    return df, stat_gdf

I tested this function on 2 small files and I get the following error when I run df, stat_gdf = sgt.zonal_stat(source, shapes, results, ['hist'])

AttributeError                            Traceback (most recent call last)
<ipython-input-9-c5098434ebe0> in <module>
      1 import sgt
----> 3 df, stat_gdf = sgt.zonal_stat(source, shapes, results, ['hist'])
      4 #df, stat_gdf = sgt.zonal_stat(source, shapes, results)
      5 df

~/.local/lib/python3.6/site-packages/sgt/sgt_zonal.py in zonal_stat(src_rst, mask_vector, out_vector, measurments, verbose)
    159     # save it
--> 160     stat_gdf.to_file(out_vector)
    162     return df, stat_gdf

/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py in to_file(self, filename, driver, schema, index, **kwargs)
    744         from geopandas.io.file import _to_file
--> 746         _to_file(self, filename, driver, schema, index, **kwargs)
    748     def set_crs(self, crs=None, epsg=None, inplace=False, allow_override=False):

/usr/local/lib/python3.6/dist-packages/geopandas/io/file.py in _to_file(df, filename, driver, schema, index, mode, crs, **kwargs)
    253             crs_wkt = crs.to_wkt("WKT1_GDAL")
    254         with fiona.open(
--> 255             filename, mode=mode, driver=driver, crs_wkt=crs_wkt, schema=schema, **kwargs
    256         ) as colxn:
    257             colxn.writerecords(df.iterfeatures())

/usr/local/lib/python3.6/dist-packages/fiona/env.py in wrapper(*args, **kwargs)
    398     def wrapper(*args, **kwargs):
    399         if local._env:
--> 400             return f(*args, **kwargs)
    401         else:
    402             if isinstance(args[0], str):

/usr/local/lib/python3.6/dist-packages/fiona/__init__.py in open(fp, mode, driver, schema, crs, encoding, layer, vfs, enabled_drivers, crs_wkt, **kwargs)
    272             c = Collection(path, mode, crs=crs, driver=driver, schema=this_schema,
    273                            encoding=encoding, layer=layer, enabled_drivers=enabled_drivers, crs_wkt=crs_wkt,
--> 274                            **kwargs)
    275         else:
    276             raise ValueError(

/usr/local/lib/python3.6/dist-packages/fiona/collection.py in __init__(self, path, mode, driver, schema, crs, encoding, layer, vsi, archive, enabled_drivers, crs_wkt, ignore_fields, ignore_geometry, **kwargs)
    163             elif self.mode in ('a', 'w'):
    164                 self.session = WritingSession()
--> 165                 self.session.start(self, **kwargs)
    166         except IOError:
    167             self.session = None

fiona/ogrext.pyx in fiona.ogrext.WritingSession.start()

AttributeError: 'int' object has no attribute 'encode'

Can someone explain me what this error means and how to write a workaround ?

what I've already tested

I've tried on my side to create the geodataframe from the df var outside of the function (in a notebook) and there were no problem.

truc = gpd.GeoDataFrame(df, geometry=geo.geometry, crs=crs)

I've also tried to modify the code in my function with :

# create the result geodataframe
stat_gdf = gpd.GeoDataFrame(
    geometry = gdf.geometry,
    columns = [str(i) for i in tmp_res] + ['geometry'], # geopandas refuse int as column key
    crs = crs

But then all the value in the geoDataFrame are replaced by Nan (None in the saved .shp)

to reproduce my sources files

def fake_tif():
    # initialte parameters 
    crs = rio.crs.CRS.from_epsg(4326)
    res = 0.00026949458523585647 # ~30m in deg
    bands, rows, cols = shape = (1, 6, 6)
    west, south, east, north = -cols*res/2, -rows*res/2, cols*res/2, rows*res/2
    transform = rio.transform.from_bounds(west, south, east, north, cols, rows)

    # create the dataset 
    data = np.zeros(shape, dtype=np.uint8)
    data[0, 2:4, 2:4] = 10
    data[0, 4, 4] = 10
    data[0, :2, :3] = 22
    data[0, 0, 5] = 36
    plt.imshow(data[0], extent=[west,east,south,north])
    # burn it into a file 
    file = tmp_dir.joinpath('source.tif')
    kwargs = {
        'driver': 'GTiff', 
        'dtype': 'uint8', 
        'width': shape[1], 
        'height': shape[2], 
        'count': bands, 
        'crs': crs, 
        'tiled': False, 
        'compress': 'lzw', 
        'interleave': 'band',
        'transform': transform,
        'nodata': 0
    with rio.open(file, 'w', **kwargs) as dst:
    return file

source = fake_tif()
def fake_shape():
    """create a shahpaefile composed of 4 squares, that cross the tif file"""
    crs = rio.crs.CRS.from_epsg(4326)
    res = 0.00026949458523585647 # ~30m in deg
    cols = 6
    rows = 6
    west, south, east, north = -cols*res/2, -rows*res/2, cols*res/2, rows*res/2
    height = cols*res
    length = cols*res
    data = {
        'fixed_id' : [i+1 for i in range(4)],
        'geometry' : [
            sg.Point(west + length/4,south + 3*height/4),
            sg.Point(west + 3*length/4,south + 3*height/4),
            sg.Point(west + length/4,south + height/4),
            sg.Point(west + 3*length/4,south + height/4)
    gdf = gpd.GeoDataFrame(data, crs='EPSG:4326')
    gdf.geometry = gdf.geometry.buffer(cols*res/4, cap_style=3)
    dst = tmp_dir.joinpath('shapes.shp')
    return dst

shapes = fake_shape()
  • Good reproducible example! An improvement would be to explicitly specify the imports. import rasterio as rio; import numpy as np; from matplotlib import pyplot as plt; import shapely.geometry as sg; import geopandas as gpd; from tqdm import tqdm; from rasterio.windows import Window; import rasterstats as rstats; import pandas as pd; – Stefan Jan 22 at 11:19

Reading this SO answer and fiona/ogrext.pyx it seems that you are passing wrong parameters to the gdf.to_file() method. According to ogrext.pyx, e.g. path, driver and crs are encoded using utf-8.

In your specific case, the problem is that your GeoDataFrame has ints as column names:

Index([10, 22, 36, 'geometry'], dtype='object')

fiona doesn't like that. A solution would be to cast the column names to str:

tmp_res = {str(i):[] for i in features} if categorical else {i: [] for i in stats}

The column names would be like this then:

Index(['10', '22', '36', 'geometry'], dtype='object')

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