Exploring a single-band raster with Dataset¶

This tutorial opens a one-band MSWEP precipitation raster covering South America and uses it to tour the read-only properties of a pyramids Dataset. You will learn how to inspect a raster's dimensions, band metadata, geospatial referencing, driver details, and pixel values, then copy the dataset, add a band, read windowed blocks, compute statistics, and attach an attribute table. Every attribute shown here is available on any Dataset, whatever its file format.

Load the raster¶

We import the Dataset class and point at the GeoTIFF — one day of MSWEP rainfall over South America. The path is relative to this notebook.

In [1]:

# NBVAL_IGNORE_OUTPUT
from pyramids.dataset import Dataset

%matplotlib inline

path = r"../../../examples/data/geotiff/south-america-mswep_1979010100.tif"

# NBVAL_IGNORE_OUTPUT from pyramids.dataset import Dataset %matplotlib inline path = r"../../../examples/data/geotiff/south-america-mswep_1979010100.tif"

2026-07-11 14:38:59 | INFO | pyramids.base.config | Logging is configured.

read_file opens the raster through GDAL; printing the returned Dataset gives a one-glance summary of its grid, CRS, and bands.

In [2]:

dataset = Dataset.read_file(path)
print(dataset)

dataset = Dataset.read_file(path) print(dataset)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 1
            Band names: ['Band_1']
            Band colors: {0: 'gray_index'}
            Band units: ['']
            Scale: [1.0]
            Offset: [0]
            Mask: -9999.0
            Data type: float32
            File: ../../../examples/data/geotiff/south-america-mswep_1979010100.tif
        

Draw the band as a map so we can see the rainfall field we are about to inspect.

In [3]:

dataset.plot()

dataset.plot()

Out[3]:

<cleopatra.array_glyph.ArrayGlyph at 0x7f36f82f0590>

The map shows daily rainfall across South America: wetter cells stand out over the tropical north and the Andes, while much of the continent is dry on this particular day.

Raster dimensions¶

The first thing to know about a raster is its grid geometry: the size of each pixel and how many rows and columns it has. These properties describe the shape of the array without reading any pixel values.

In [4]:

print(dataset.cell_size)

print(dataset.cell_size)

0.1

rows and columns report the grid height and width in pixels.

In [5]:

print(f"Rows , Columns = {dataset.rows}, {dataset.columns}")

print(f"Rows , Columns = {dataset.rows}, {dataset.columns}")

Rows , Columns = 780, 850

shape returns the array shape as (bands, rows, columns) — the same layout NumPy uses.

In [6]:

print(dataset.shape)

print(dataset.shape)

(1, 780, 850)

band_count is the number of bands; this rainfall raster has just one.

In [7]:

print(dataset.band_count)

print(dataset.band_count)

1

Band information¶

Each band carries its own metadata: a name, a data type, physical units, and optional scale/offset factors used to decode stored numbers into real-world values.

In [8]:

print(dataset.band_names)

print(dataset.band_names)

['Band_1']

dtype is the pixel data type (here a 32-bit float).

In [9]:

print(dataset.dtype)

print(dataset.dtype)

['float32']

band_units lists the physical unit of each band, when the file records one.

In [10]:

print(dataset.band_units)

print(dataset.band_units)

['']

scale is the multiplier applied when decoding stored values.

In [11]:

print(dataset.scale)

print(dataset.scale)

[1.0]

offset is the constant added after scaling; together scale and offset map stored numbers to physical values.

In [12]:

print(dataset.offset)

print(dataset.offset)

[0]

Geospatial information¶

These properties tie the pixel grid to real-world locations: the affine geotransform, the bounding box, the coordinate reference system, and the per-axis coordinate arrays.

In [13]:

print(dataset.geotransform)
print(dataset.top_left_corner)

print(dataset.geotransform) print(dataset.top_left_corner)

(-110.0, 0.1, 0.0, 18.1, 0.0, -0.1)
(-110.0, 18.1)

bounds returns the raster's extent as a GeoDataFrame — a one-row polygon of its footprint.

In [14]:

print(dataset.bounds)

print(dataset.bounds)

                                            geometry
0  POLYGON ((-110 18.1, -110 -59.9, -25 -59.9, -2...

bbox returns the same extent as a plain [xmin, ymin, xmax, ymax] list.

In [15]:

print(dataset.bbox)

print(dataset.bbox)

[-110.0, -59.9, -25.0, 18.1]

epsg is the numeric CRS code; crs is its full WKT description.

In [16]:

print(dataset.epsg)
print(dataset.crs)

print(dataset.epsg) print(dataset.crs)

4326
GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

no_data_value is the sentinel marking cells with no measurement — masked out in plots and statistics.

In [17]:

print(dataset.no_data_value)

print(dataset.no_data_value)

(-9999.0,)

lon is the array of longitude coordinates for the grid columns.

In [18]:

print(dataset.lon)

print(dataset.lon)

[-109.95 -109.85 -109.75 -109.65 -109.55 -109.45 -109.35 -109.25 -109.15
 -109.05 -108.95 -108.85 -108.75 -108.65 -108.55 -108.45 -108.35 -108.25
 -108.15 -108.05 -107.95 -107.85 -107.75 -107.65 -107.55 -107.45 -107.35
 -107.25 -107.15 -107.05 -106.95 -106.85 -106.75 -106.65 -106.55 -106.45
 -106.35 -106.25 -106.15 -106.05 -105.95 -105.85 -105.75 -105.65 -105.55
 -105.45 -105.35 -105.25 -105.15 -105.05 -104.95 -104.85 -104.75 -104.65
 -104.55 -104.45 -104.35 -104.25 -104.15 -104.05 -103.95 -103.85 -103.75
 -103.65 -103.55 -103.45 -103.35 -103.25 -103.15 -103.05 -102.95 -102.85
 -102.75 -102.65 -102.55 -102.45 -102.35 -102.25 -102.15 -102.05 -101.95
 -101.85 -101.75 -101.65 -101.55 -101.45 -101.35 -101.25 -101.15 -101.05
 -100.95 -100.85 -100.75 -100.65 -100.55 -100.45 -100.35 -100.25 -100.15
 -100.05  -99.95  -99.85  -99.75  -99.65  -99.55  -99.45  -99.35  -99.25
  -99.15  -99.05  -98.95  -98.85  -98.75  -98.65  -98.55  -98.45  -98.35
  -98.25  -98.15  -98.05  -97.95  -97.85  -97.75  -97.65  -97.55  -97.45
  -97.35  -97.25  -97.15  -97.05  -96.95  -96.85  -96.75  -96.65  -96.55
  -96.45  -96.35  -96.25  -96.15  -96.05  -95.95  -95.85  -95.75  -95.65
  -95.55  -95.45  -95.35  -95.25  -95.15  -95.05  -94.95  -94.85  -94.75
  -94.65  -94.55  -94.45  -94.35  -94.25  -94.15  -94.05  -93.95  -93.85
  -93.75  -93.65  -93.55  -93.45  -93.35  -93.25  -93.15  -93.05  -92.95
  -92.85  -92.75  -92.65  -92.55  -92.45  -92.35  -92.25  -92.15  -92.05
  -91.95  -91.85  -91.75  -91.65  -91.55  -91.45  -91.35  -91.25  -91.15
  -91.05  -90.95  -90.85  -90.75  -90.65  -90.55  -90.45  -90.35  -90.25
  -90.15  -90.05  -89.95  -89.85  -89.75  -89.65  -89.55  -89.45  -89.35
  -89.25  -89.15  -89.05  -88.95  -88.85  -88.75  -88.65  -88.55  -88.45
  -88.35  -88.25  -88.15  -88.05  -87.95  -87.85  -87.75  -87.65  -87.55
  -87.45  -87.35  -87.25  -87.15  -87.05  -86.95  -86.85  -86.75  -86.65
  -86.55  -86.45  -86.35  -86.25  -86.15  -86.05  -85.95  -85.85  -85.75
  -85.65  -85.55  -85.45  -85.35  -85.25  -85.15  -85.05  -84.95  -84.85
  -84.75  -84.65  -84.55  -84.45  -84.35  -84.25  -84.15  -84.05  -83.95
  -83.85  -83.75  -83.65  -83.55  -83.45  -83.35  -83.25  -83.15  -83.05
  -82.95  -82.85  -82.75  -82.65  -82.55  -82.45  -82.35  -82.25  -82.15
  -82.05  -81.95  -81.85  -81.75  -81.65  -81.55  -81.45  -81.35  -81.25
  -81.15  -81.05  -80.95  -80.85  -80.75  -80.65  -80.55  -80.45  -80.35
  -80.25  -80.15  -80.05  -79.95  -79.85  -79.75  -79.65  -79.55  -79.45
  -79.35  -79.25  -79.15  -79.05  -78.95  -78.85  -78.75  -78.65  -78.55
  -78.45  -78.35  -78.25  -78.15  -78.05  -77.95  -77.85  -77.75  -77.65
  -77.55  -77.45  -77.35  -77.25  -77.15  -77.05  -76.95  -76.85  -76.75
  -76.65  -76.55  -76.45  -76.35  -76.25  -76.15  -76.05  -75.95  -75.85
  -75.75  -75.65  -75.55  -75.45  -75.35  -75.25  -75.15  -75.05  -74.95
  -74.85  -74.75  -74.65  -74.55  -74.45  -74.35  -74.25  -74.15  -74.05
  -73.95  -73.85  -73.75  -73.65  -73.55  -73.45  -73.35  -73.25  -73.15
  -73.05  -72.95  -72.85  -72.75  -72.65  -72.55  -72.45  -72.35  -72.25
  -72.15  -72.05  -71.95  -71.85  -71.75  -71.65  -71.55  -71.45  -71.35
  -71.25  -71.15  -71.05  -70.95  -70.85  -70.75  -70.65  -70.55  -70.45
  -70.35  -70.25  -70.15  -70.05  -69.95  -69.85  -69.75  -69.65  -69.55
  -69.45  -69.35  -69.25  -69.15  -69.05  -68.95  -68.85  -68.75  -68.65
  -68.55  -68.45  -68.35  -68.25  -68.15  -68.05  -67.95  -67.85  -67.75
  -67.65  -67.55  -67.45  -67.35  -67.25  -67.15  -67.05  -66.95  -66.85
  -66.75  -66.65  -66.55  -66.45  -66.35  -66.25  -66.15  -66.05  -65.95
  -65.85  -65.75  -65.65  -65.55  -65.45  -65.35  -65.25  -65.15  -65.05
  -64.95  -64.85  -64.75  -64.65  -64.55  -64.45  -64.35  -64.25  -64.15
  -64.05  -63.95  -63.85  -63.75  -63.65  -63.55  -63.45  -63.35  -63.25
  -63.15  -63.05  -62.95  -62.85  -62.75  -62.65  -62.55  -62.45  -62.35
  -62.25  -62.15  -62.05  -61.95  -61.85  -61.75  -61.65  -61.55  -61.45
  -61.35  -61.25  -61.15  -61.05  -60.95  -60.85  -60.75  -60.65  -60.55
  -60.45  -60.35  -60.25  -60.15  -60.05  -59.95  -59.85  -59.75  -59.65
  -59.55  -59.45  -59.35  -59.25  -59.15  -59.05  -58.95  -58.85  -58.75
  -58.65  -58.55  -58.45  -58.35  -58.25  -58.15  -58.05  -57.95  -57.85
  -57.75  -57.65  -57.55  -57.45  -57.35  -57.25  -57.15  -57.05  -56.95
  -56.85  -56.75  -56.65  -56.55  -56.45  -56.35  -56.25  -56.15  -56.05
  -55.95  -55.85  -55.75  -55.65  -55.55  -55.45  -55.35  -55.25  -55.15
  -55.05  -54.95  -54.85  -54.75  -54.65  -54.55  -54.45  -54.35  -54.25
  -54.15  -54.05  -53.95  -53.85  -53.75  -53.65  -53.55  -53.45  -53.35
  -53.25  -53.15  -53.05  -52.95  -52.85  -52.75  -52.65  -52.55  -52.45
  -52.35  -52.25  -52.15  -52.05  -51.95  -51.85  -51.75  -51.65  -51.55
  -51.45  -51.35  -51.25  -51.15  -51.05  -50.95  -50.85  -50.75  -50.65
  -50.55  -50.45  -50.35  -50.25  -50.15  -50.05  -49.95  -49.85  -49.75
  -49.65  -49.55  -49.45  -49.35  -49.25  -49.15  -49.05  -48.95  -48.85
  -48.75  -48.65  -48.55  -48.45  -48.35  -48.25  -48.15  -48.05  -47.95
  -47.85  -47.75  -47.65  -47.55  -47.45  -47.35  -47.25  -47.15  -47.05
  -46.95  -46.85  -46.75  -46.65  -46.55  -46.45  -46.35  -46.25  -46.15
  -46.05  -45.95  -45.85  -45.75  -45.65  -45.55  -45.45  -45.35  -45.25
  -45.15  -45.05  -44.95  -44.85  -44.75  -44.65  -44.55  -44.45  -44.35
  -44.25  -44.15  -44.05  -43.95  -43.85  -43.75  -43.65  -43.55  -43.45
  -43.35  -43.25  -43.15  -43.05  -42.95  -42.85  -42.75  -42.65  -42.55
  -42.45  -42.35  -42.25  -42.15  -42.05  -41.95  -41.85  -41.75  -41.65
  -41.55  -41.45  -41.35  -41.25  -41.15  -41.05  -40.95  -40.85  -40.75
  -40.65  -40.55  -40.45  -40.35  -40.25  -40.15  -40.05  -39.95  -39.85
  -39.75  -39.65  -39.55  -39.45  -39.35  -39.25  -39.15  -39.05  -38.95
  -38.85  -38.75  -38.65  -38.55  -38.45  -38.35  -38.25  -38.15  -38.05
  -37.95  -37.85  -37.75  -37.65  -37.55  -37.45  -37.35  -37.25  -37.15
  -37.05  -36.95  -36.85  -36.75  -36.65  -36.55  -36.45  -36.35  -36.25
  -36.15  -36.05  -35.95  -35.85  -35.75  -35.65  -35.55  -35.45  -35.35
  -35.25  -35.15  -35.05  -34.95  -34.85  -34.75  -34.65  -34.55  -34.45
  -34.35  -34.25  -34.15  -34.05  -33.95  -33.85  -33.75  -33.65  -33.55
  -33.45  -33.35  -33.25  -33.15  -33.05  -32.95  -32.85  -32.75  -32.65
  -32.55  -32.45  -32.35  -32.25  -32.15  -32.05  -31.95  -31.85  -31.75
  -31.65  -31.55  -31.45  -31.35  -31.25  -31.15  -31.05  -30.95  -30.85
  -30.75  -30.65  -30.55  -30.45  -30.35  -30.25  -30.15  -30.05  -29.95
  -29.85  -29.75  -29.65  -29.55  -29.45  -29.35  -29.25  -29.15  -29.05
  -28.95  -28.85  -28.75  -28.65  -28.55  -28.45  -28.35  -28.25  -28.15
  -28.05  -27.95  -27.85  -27.75  -27.65  -27.55  -27.45  -27.35  -27.25
  -27.15  -27.05  -26.95  -26.85  -26.75  -26.65  -26.55  -26.45  -26.35
  -26.25  -26.15  -26.05  -25.95  -25.85  -25.75  -25.65  -25.55  -25.45
  -25.35  -25.25  -25.15  -25.05]

lat is the array of latitude coordinates for the grid rows.

In [19]:

print(dataset.lat)

print(dataset.lat)

[ 1.805e+01  1.795e+01  1.785e+01  1.775e+01  1.765e+01  1.755e+01
  1.745e+01  1.735e+01  1.725e+01  1.715e+01  1.705e+01  1.695e+01
  1.685e+01  1.675e+01  1.665e+01  1.655e+01  1.645e+01  1.635e+01
  1.625e+01  1.615e+01  1.605e+01  1.595e+01  1.585e+01  1.575e+01
  1.565e+01  1.555e+01  1.545e+01  1.535e+01  1.525e+01  1.515e+01
  1.505e+01  1.495e+01  1.485e+01  1.475e+01  1.465e+01  1.455e+01
  1.445e+01  1.435e+01  1.425e+01  1.415e+01  1.405e+01  1.395e+01
  1.385e+01  1.375e+01  1.365e+01  1.355e+01  1.345e+01  1.335e+01
  1.325e+01  1.315e+01  1.305e+01  1.295e+01  1.285e+01  1.275e+01
  1.265e+01  1.255e+01  1.245e+01  1.235e+01  1.225e+01  1.215e+01
  1.205e+01  1.195e+01  1.185e+01  1.175e+01  1.165e+01  1.155e+01
  1.145e+01  1.135e+01  1.125e+01  1.115e+01  1.105e+01  1.095e+01
  1.085e+01  1.075e+01  1.065e+01  1.055e+01  1.045e+01  1.035e+01
  1.025e+01  1.015e+01  1.005e+01  9.950e+00  9.850e+00  9.750e+00
  9.650e+00  9.550e+00  9.450e+00  9.350e+00  9.250e+00  9.150e+00
  9.050e+00  8.950e+00  8.850e+00  8.750e+00  8.650e+00  8.550e+00
  8.450e+00  8.350e+00  8.250e+00  8.150e+00  8.050e+00  7.950e+00
  7.850e+00  7.750e+00  7.650e+00  7.550e+00  7.450e+00  7.350e+00
  7.250e+00  7.150e+00  7.050e+00  6.950e+00  6.850e+00  6.750e+00
  6.650e+00  6.550e+00  6.450e+00  6.350e+00  6.250e+00  6.150e+00
  6.050e+00  5.950e+00  5.850e+00  5.750e+00  5.650e+00  5.550e+00
  5.450e+00  5.350e+00  5.250e+00  5.150e+00  5.050e+00  4.950e+00
  4.850e+00  4.750e+00  4.650e+00  4.550e+00  4.450e+00  4.350e+00
  4.250e+00  4.150e+00  4.050e+00  3.950e+00  3.850e+00  3.750e+00
  3.650e+00  3.550e+00  3.450e+00  3.350e+00  3.250e+00  3.150e+00
  3.050e+00  2.950e+00  2.850e+00  2.750e+00  2.650e+00  2.550e+00
  2.450e+00  2.350e+00  2.250e+00  2.150e+00  2.050e+00  1.950e+00
  1.850e+00  1.750e+00  1.650e+00  1.550e+00  1.450e+00  1.350e+00
  1.250e+00  1.150e+00  1.050e+00  9.500e-01  8.500e-01  7.500e-01
  6.500e-01  5.500e-01  4.500e-01  3.500e-01  2.500e-01  1.500e-01
  5.000e-02 -5.000e-02 -1.500e-01 -2.500e-01 -3.500e-01 -4.500e-01
 -5.500e-01 -6.500e-01 -7.500e-01 -8.500e-01 -9.500e-01 -1.050e+00
 -1.150e+00 -1.250e+00 -1.350e+00 -1.450e+00 -1.550e+00 -1.650e+00
 -1.750e+00 -1.850e+00 -1.950e+00 -2.050e+00 -2.150e+00 -2.250e+00
 -2.350e+00 -2.450e+00 -2.550e+00 -2.650e+00 -2.750e+00 -2.850e+00
 -2.950e+00 -3.050e+00 -3.150e+00 -3.250e+00 -3.350e+00 -3.450e+00
 -3.550e+00 -3.650e+00 -3.750e+00 -3.850e+00 -3.950e+00 -4.050e+00
 -4.150e+00 -4.250e+00 -4.350e+00 -4.450e+00 -4.550e+00 -4.650e+00
 -4.750e+00 -4.850e+00 -4.950e+00 -5.050e+00 -5.150e+00 -5.250e+00
 -5.350e+00 -5.450e+00 -5.550e+00 -5.650e+00 -5.750e+00 -5.850e+00
 -5.950e+00 -6.050e+00 -6.150e+00 -6.250e+00 -6.350e+00 -6.450e+00
 -6.550e+00 -6.650e+00 -6.750e+00 -6.850e+00 -6.950e+00 -7.050e+00
 -7.150e+00 -7.250e+00 -7.350e+00 -7.450e+00 -7.550e+00 -7.650e+00
 -7.750e+00 -7.850e+00 -7.950e+00 -8.050e+00 -8.150e+00 -8.250e+00
 -8.350e+00 -8.450e+00 -8.550e+00 -8.650e+00 -8.750e+00 -8.850e+00
 -8.950e+00 -9.050e+00 -9.150e+00 -9.250e+00 -9.350e+00 -9.450e+00
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 -2.215e+01 -2.225e+01 -2.235e+01 -2.245e+01 -2.255e+01 -2.265e+01
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 -3.235e+01 -3.245e+01 -3.255e+01 -3.265e+01 -3.275e+01 -3.285e+01
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 -3.595e+01 -3.605e+01 -3.615e+01 -3.625e+01 -3.635e+01 -3.645e+01
 -3.655e+01 -3.665e+01 -3.675e+01 -3.685e+01 -3.695e+01 -3.705e+01
 -3.715e+01 -3.725e+01 -3.735e+01 -3.745e+01 -3.755e+01 -3.765e+01
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 -4.795e+01 -4.805e+01 -4.815e+01 -4.825e+01 -4.835e+01 -4.845e+01
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 -5.095e+01 -5.105e+01 -5.115e+01 -5.125e+01 -5.135e+01 -5.145e+01
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 -5.395e+01 -5.405e+01 -5.415e+01 -5.425e+01 -5.435e+01 -5.445e+01
 -5.455e+01 -5.465e+01 -5.475e+01 -5.485e+01 -5.495e+01 -5.505e+01
 -5.515e+01 -5.525e+01 -5.535e+01 -5.545e+01 -5.555e+01 -5.565e+01
 -5.575e+01 -5.585e+01 -5.595e+01 -5.605e+01 -5.615e+01 -5.625e+01
 -5.635e+01 -5.645e+01 -5.655e+01 -5.665e+01 -5.675e+01 -5.685e+01
 -5.695e+01 -5.705e+01 -5.715e+01 -5.725e+01 -5.735e+01 -5.745e+01
 -5.755e+01 -5.765e+01 -5.775e+01 -5.785e+01 -5.795e+01 -5.805e+01
 -5.815e+01 -5.825e+01 -5.835e+01 -5.845e+01 -5.855e+01 -5.865e+01
 -5.875e+01 -5.885e+01 -5.895e+01 -5.905e+01 -5.915e+01 -5.925e+01
 -5.935e+01 -5.945e+01 -5.955e+01 -5.965e+01 -5.975e+01 -5.985e+01]

x gives the column coordinates in the dataset's native CRS units (identical to lon for a geographic raster).

In [20]:

print(dataset.x)

print(dataset.x)

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  -28.05  -27.95  -27.85  -27.75  -27.65  -27.55  -27.45  -27.35  -27.25
  -27.15  -27.05  -26.95  -26.85  -26.75  -26.65  -26.55  -26.45  -26.35
  -26.25  -26.15  -26.05  -25.95  -25.85  -25.75  -25.65  -25.55  -25.45
  -25.35  -25.25  -25.15  -25.05]

y gives the row coordinates in native CRS units (identical to lat here).

In [21]:

print(dataset.y)

print(dataset.y)

[ 1.805e+01  1.795e+01  1.785e+01  1.775e+01  1.765e+01  1.755e+01
  1.745e+01  1.735e+01  1.725e+01  1.715e+01  1.705e+01  1.695e+01
  1.685e+01  1.675e+01  1.665e+01  1.655e+01  1.645e+01  1.635e+01
  1.625e+01  1.615e+01  1.605e+01  1.595e+01  1.585e+01  1.575e+01
  1.565e+01  1.555e+01  1.545e+01  1.535e+01  1.525e+01  1.515e+01
  1.505e+01  1.495e+01  1.485e+01  1.475e+01  1.465e+01  1.455e+01
  1.445e+01  1.435e+01  1.425e+01  1.415e+01  1.405e+01  1.395e+01
  1.385e+01  1.375e+01  1.365e+01  1.355e+01  1.345e+01  1.335e+01
  1.325e+01  1.315e+01  1.305e+01  1.295e+01  1.285e+01  1.275e+01
  1.265e+01  1.255e+01  1.245e+01  1.235e+01  1.225e+01  1.215e+01
  1.205e+01  1.195e+01  1.185e+01  1.175e+01  1.165e+01  1.155e+01
  1.145e+01  1.135e+01  1.125e+01  1.115e+01  1.105e+01  1.095e+01
  1.085e+01  1.075e+01  1.065e+01  1.055e+01  1.045e+01  1.035e+01
  1.025e+01  1.015e+01  1.005e+01  9.950e+00  9.850e+00  9.750e+00
  9.650e+00  9.550e+00  9.450e+00  9.350e+00  9.250e+00  9.150e+00
  9.050e+00  8.950e+00  8.850e+00  8.750e+00  8.650e+00  8.550e+00
  8.450e+00  8.350e+00  8.250e+00  8.150e+00  8.050e+00  7.950e+00
  7.850e+00  7.750e+00  7.650e+00  7.550e+00  7.450e+00  7.350e+00
  7.250e+00  7.150e+00  7.050e+00  6.950e+00  6.850e+00  6.750e+00
  6.650e+00  6.550e+00  6.450e+00  6.350e+00  6.250e+00  6.150e+00
  6.050e+00  5.950e+00  5.850e+00  5.750e+00  5.650e+00  5.550e+00
  5.450e+00  5.350e+00  5.250e+00  5.150e+00  5.050e+00  4.950e+00
  4.850e+00  4.750e+00  4.650e+00  4.550e+00  4.450e+00  4.350e+00
  4.250e+00  4.150e+00  4.050e+00  3.950e+00  3.850e+00  3.750e+00
  3.650e+00  3.550e+00  3.450e+00  3.350e+00  3.250e+00  3.150e+00
  3.050e+00  2.950e+00  2.850e+00  2.750e+00  2.650e+00  2.550e+00
  2.450e+00  2.350e+00  2.250e+00  2.150e+00  2.050e+00  1.950e+00
  1.850e+00  1.750e+00  1.650e+00  1.550e+00  1.450e+00  1.350e+00
  1.250e+00  1.150e+00  1.050e+00  9.500e-01  8.500e-01  7.500e-01
  6.500e-01  5.500e-01  4.500e-01  3.500e-01  2.500e-01  1.500e-01
  5.000e-02 -5.000e-02 -1.500e-01 -2.500e-01 -3.500e-01 -4.500e-01
 -5.500e-01 -6.500e-01 -7.500e-01 -8.500e-01 -9.500e-01 -1.050e+00
 -1.150e+00 -1.250e+00 -1.350e+00 -1.450e+00 -1.550e+00 -1.650e+00
 -1.750e+00 -1.850e+00 -1.950e+00 -2.050e+00 -2.150e+00 -2.250e+00
 -2.350e+00 -2.450e+00 -2.550e+00 -2.650e+00 -2.750e+00 -2.850e+00
 -2.950e+00 -3.050e+00 -3.150e+00 -3.250e+00 -3.350e+00 -3.450e+00
 -3.550e+00 -3.650e+00 -3.750e+00 -3.850e+00 -3.950e+00 -4.050e+00
 -4.150e+00 -4.250e+00 -4.350e+00 -4.450e+00 -4.550e+00 -4.650e+00
 -4.750e+00 -4.850e+00 -4.950e+00 -5.050e+00 -5.150e+00 -5.250e+00
 -5.350e+00 -5.450e+00 -5.550e+00 -5.650e+00 -5.750e+00 -5.850e+00
 -5.950e+00 -6.050e+00 -6.150e+00 -6.250e+00 -6.350e+00 -6.450e+00
 -6.550e+00 -6.650e+00 -6.750e+00 -6.850e+00 -6.950e+00 -7.050e+00
 -7.150e+00 -7.250e+00 -7.350e+00 -7.450e+00 -7.550e+00 -7.650e+00
 -7.750e+00 -7.850e+00 -7.950e+00 -8.050e+00 -8.150e+00 -8.250e+00
 -8.350e+00 -8.450e+00 -8.550e+00 -8.650e+00 -8.750e+00 -8.850e+00
 -8.950e+00 -9.050e+00 -9.150e+00 -9.250e+00 -9.350e+00 -9.450e+00
 -9.550e+00 -9.650e+00 -9.750e+00 -9.850e+00 -9.950e+00 -1.005e+01
 -1.015e+01 -1.025e+01 -1.035e+01 -1.045e+01 -1.055e+01 -1.065e+01
 -1.075e+01 -1.085e+01 -1.095e+01 -1.105e+01 -1.115e+01 -1.125e+01
 -1.135e+01 -1.145e+01 -1.155e+01 -1.165e+01 -1.175e+01 -1.185e+01
 -1.195e+01 -1.205e+01 -1.215e+01 -1.225e+01 -1.235e+01 -1.245e+01
 -1.255e+01 -1.265e+01 -1.275e+01 -1.285e+01 -1.295e+01 -1.305e+01
 -1.315e+01 -1.325e+01 -1.335e+01 -1.345e+01 -1.355e+01 -1.365e+01
 -1.375e+01 -1.385e+01 -1.395e+01 -1.405e+01 -1.415e+01 -1.425e+01
 -1.435e+01 -1.445e+01 -1.455e+01 -1.465e+01 -1.475e+01 -1.485e+01
 -1.495e+01 -1.505e+01 -1.515e+01 -1.525e+01 -1.535e+01 -1.545e+01
 -1.555e+01 -1.565e+01 -1.575e+01 -1.585e+01 -1.595e+01 -1.605e+01
 -1.615e+01 -1.625e+01 -1.635e+01 -1.645e+01 -1.655e+01 -1.665e+01
 -1.675e+01 -1.685e+01 -1.695e+01 -1.705e+01 -1.715e+01 -1.725e+01
 -1.735e+01 -1.745e+01 -1.755e+01 -1.765e+01 -1.775e+01 -1.785e+01
 -1.795e+01 -1.805e+01 -1.815e+01 -1.825e+01 -1.835e+01 -1.845e+01
 -1.855e+01 -1.865e+01 -1.875e+01 -1.885e+01 -1.895e+01 -1.905e+01
 -1.915e+01 -1.925e+01 -1.935e+01 -1.945e+01 -1.955e+01 -1.965e+01
 -1.975e+01 -1.985e+01 -1.995e+01 -2.005e+01 -2.015e+01 -2.025e+01
 -2.035e+01 -2.045e+01 -2.055e+01 -2.065e+01 -2.075e+01 -2.085e+01
 -2.095e+01 -2.105e+01 -2.115e+01 -2.125e+01 -2.135e+01 -2.145e+01
 -2.155e+01 -2.165e+01 -2.175e+01 -2.185e+01 -2.195e+01 -2.205e+01
 -2.215e+01 -2.225e+01 -2.235e+01 -2.245e+01 -2.255e+01 -2.265e+01
 -2.275e+01 -2.285e+01 -2.295e+01 -2.305e+01 -2.315e+01 -2.325e+01
 -2.335e+01 -2.345e+01 -2.355e+01 -2.365e+01 -2.375e+01 -2.385e+01
 -2.395e+01 -2.405e+01 -2.415e+01 -2.425e+01 -2.435e+01 -2.445e+01
 -2.455e+01 -2.465e+01 -2.475e+01 -2.485e+01 -2.495e+01 -2.505e+01
 -2.515e+01 -2.525e+01 -2.535e+01 -2.545e+01 -2.555e+01 -2.565e+01
 -2.575e+01 -2.585e+01 -2.595e+01 -2.605e+01 -2.615e+01 -2.625e+01
 -2.635e+01 -2.645e+01 -2.655e+01 -2.665e+01 -2.675e+01 -2.685e+01
 -2.695e+01 -2.705e+01 -2.715e+01 -2.725e+01 -2.735e+01 -2.745e+01
 -2.755e+01 -2.765e+01 -2.775e+01 -2.785e+01 -2.795e+01 -2.805e+01
 -2.815e+01 -2.825e+01 -2.835e+01 -2.845e+01 -2.855e+01 -2.865e+01
 -2.875e+01 -2.885e+01 -2.895e+01 -2.905e+01 -2.915e+01 -2.925e+01
 -2.935e+01 -2.945e+01 -2.955e+01 -2.965e+01 -2.975e+01 -2.985e+01
 -2.995e+01 -3.005e+01 -3.015e+01 -3.025e+01 -3.035e+01 -3.045e+01
 -3.055e+01 -3.065e+01 -3.075e+01 -3.085e+01 -3.095e+01 -3.105e+01
 -3.115e+01 -3.125e+01 -3.135e+01 -3.145e+01 -3.155e+01 -3.165e+01
 -3.175e+01 -3.185e+01 -3.195e+01 -3.205e+01 -3.215e+01 -3.225e+01
 -3.235e+01 -3.245e+01 -3.255e+01 -3.265e+01 -3.275e+01 -3.285e+01
 -3.295e+01 -3.305e+01 -3.315e+01 -3.325e+01 -3.335e+01 -3.345e+01
 -3.355e+01 -3.365e+01 -3.375e+01 -3.385e+01 -3.395e+01 -3.405e+01
 -3.415e+01 -3.425e+01 -3.435e+01 -3.445e+01 -3.455e+01 -3.465e+01
 -3.475e+01 -3.485e+01 -3.495e+01 -3.505e+01 -3.515e+01 -3.525e+01
 -3.535e+01 -3.545e+01 -3.555e+01 -3.565e+01 -3.575e+01 -3.585e+01
 -3.595e+01 -3.605e+01 -3.615e+01 -3.625e+01 -3.635e+01 -3.645e+01
 -3.655e+01 -3.665e+01 -3.675e+01 -3.685e+01 -3.695e+01 -3.705e+01
 -3.715e+01 -3.725e+01 -3.735e+01 -3.745e+01 -3.755e+01 -3.765e+01
 -3.775e+01 -3.785e+01 -3.795e+01 -3.805e+01 -3.815e+01 -3.825e+01
 -3.835e+01 -3.845e+01 -3.855e+01 -3.865e+01 -3.875e+01 -3.885e+01
 -3.895e+01 -3.905e+01 -3.915e+01 -3.925e+01 -3.935e+01 -3.945e+01
 -3.955e+01 -3.965e+01 -3.975e+01 -3.985e+01 -3.995e+01 -4.005e+01
 -4.015e+01 -4.025e+01 -4.035e+01 -4.045e+01 -4.055e+01 -4.065e+01
 -4.075e+01 -4.085e+01 -4.095e+01 -4.105e+01 -4.115e+01 -4.125e+01
 -4.135e+01 -4.145e+01 -4.155e+01 -4.165e+01 -4.175e+01 -4.185e+01
 -4.195e+01 -4.205e+01 -4.215e+01 -4.225e+01 -4.235e+01 -4.245e+01
 -4.255e+01 -4.265e+01 -4.275e+01 -4.285e+01 -4.295e+01 -4.305e+01
 -4.315e+01 -4.325e+01 -4.335e+01 -4.345e+01 -4.355e+01 -4.365e+01
 -4.375e+01 -4.385e+01 -4.395e+01 -4.405e+01 -4.415e+01 -4.425e+01
 -4.435e+01 -4.445e+01 -4.455e+01 -4.465e+01 -4.475e+01 -4.485e+01
 -4.495e+01 -4.505e+01 -4.515e+01 -4.525e+01 -4.535e+01 -4.545e+01
 -4.555e+01 -4.565e+01 -4.575e+01 -4.585e+01 -4.595e+01 -4.605e+01
 -4.615e+01 -4.625e+01 -4.635e+01 -4.645e+01 -4.655e+01 -4.665e+01
 -4.675e+01 -4.685e+01 -4.695e+01 -4.705e+01 -4.715e+01 -4.725e+01
 -4.735e+01 -4.745e+01 -4.755e+01 -4.765e+01 -4.775e+01 -4.785e+01
 -4.795e+01 -4.805e+01 -4.815e+01 -4.825e+01 -4.835e+01 -4.845e+01
 -4.855e+01 -4.865e+01 -4.875e+01 -4.885e+01 -4.895e+01 -4.905e+01
 -4.915e+01 -4.925e+01 -4.935e+01 -4.945e+01 -4.955e+01 -4.965e+01
 -4.975e+01 -4.985e+01 -4.995e+01 -5.005e+01 -5.015e+01 -5.025e+01
 -5.035e+01 -5.045e+01 -5.055e+01 -5.065e+01 -5.075e+01 -5.085e+01
 -5.095e+01 -5.105e+01 -5.115e+01 -5.125e+01 -5.135e+01 -5.145e+01
 -5.155e+01 -5.165e+01 -5.175e+01 -5.185e+01 -5.195e+01 -5.205e+01
 -5.215e+01 -5.225e+01 -5.235e+01 -5.245e+01 -5.255e+01 -5.265e+01
 -5.275e+01 -5.285e+01 -5.295e+01 -5.305e+01 -5.315e+01 -5.325e+01
 -5.335e+01 -5.345e+01 -5.355e+01 -5.365e+01 -5.375e+01 -5.385e+01
 -5.395e+01 -5.405e+01 -5.415e+01 -5.425e+01 -5.435e+01 -5.445e+01
 -5.455e+01 -5.465e+01 -5.475e+01 -5.485e+01 -5.495e+01 -5.505e+01
 -5.515e+01 -5.525e+01 -5.535e+01 -5.545e+01 -5.555e+01 -5.565e+01
 -5.575e+01 -5.585e+01 -5.595e+01 -5.605e+01 -5.615e+01 -5.625e+01
 -5.635e+01 -5.645e+01 -5.655e+01 -5.665e+01 -5.675e+01 -5.685e+01
 -5.695e+01 -5.705e+01 -5.715e+01 -5.725e+01 -5.735e+01 -5.745e+01
 -5.755e+01 -5.765e+01 -5.775e+01 -5.785e+01 -5.795e+01 -5.805e+01
 -5.815e+01 -5.825e+01 -5.835e+01 -5.845e+01 -5.855e+01 -5.865e+01
 -5.875e+01 -5.885e+01 -5.895e+01 -5.905e+01 -5.915e+01 -5.925e+01
 -5.935e+01 -5.945e+01 -5.955e+01 -5.965e+01 -5.975e+01 -5.985e+01]

Driver and file metadata¶

Beyond the pixels, a Dataset knows how it is stored: the GDAL driver, the internal tiling/block size, the source file name, and any key/value metadata tags.

In [22]:

print(dataset.meta_data)

print(dataset.meta_data)

{'AREA_OR_POINT': 'Area'}

block_size is the internal tile size GDAL reads in one chunk — important for efficient windowed reads.

In [23]:

print(dataset.block_size)

print(dataset.block_size)

[[850, 2]]

file_name is the path the dataset was opened from.

In [24]:

print(dataset.file_name)

print(dataset.file_name)

../../../examples/data/geotiff/south-america-mswep_1979010100.tif

driver_type names the GDAL driver backing the dataset (GeoTIFF here).

In [25]:

print(dataset.driver_type)

print(dataset.driver_type)

geotiff

Create a copy of the dataset¶

copy returns an independent in-memory clone, so you can modify it freely without touching the original file on disk.

In [26]:

dataset_copy = dataset.copy()
print(dataset_copy)

dataset_copy = dataset.copy() print(dataset_copy)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 1
            Band names: ['Band_1']
            Band colors: {0: 'gray_index'}
            Band units: ['']
            Scale: [1.0]
            Offset: [0]
            Mask: -9999.0
            Data type: float32
            File: 
        

Add a new band¶

add_band appends an extra band from a NumPy array. Here we stack a random array shaped to the grid as a second band and give it a unit.

In [27]:

import numpy as np

new_band = np.random.rand(dataset.rows, dataset.columns)
new_dataset = dataset.add_band(new_band, unit="mm")
print(new_dataset)

import numpy as np new_band = np.random.rand(dataset.rows, dataset.columns) new_dataset = dataset.add_band(new_band, unit="mm") print(new_dataset)

            Top Left Corner: (-110.0, 18.1)
            Cell size: 0.1
            Dimension: 780 * 850
            EPSG: 4326
            Number of Bands: 2
            Band names: ['Band_1', 'Band_2']
            Band colors: {0: 'gray_index', 1: 'undefined'}
            Band units: ['', 'mm']
            Scale: [1.0, 1.0]
            Offset: [0, 0]
            Mask: -9999.0
            Data type: float32
            File: 
        

Read band values¶

So far we only inspected metadata. read_array is what actually pulls pixel values into a NumPy array — either the whole raster at once or a windowed block.

In [28]:

arr = dataset.read_array()
print(arr.shape)

arr = dataset.read_array() print(arr.shape)

(780, 850)

Display the array itself to see the raw rainfall values, including the no-data cells.

In [29]:

arr

arr

Out[29]:

array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       ...,
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
      shape=(780, 850), dtype=float32)

Most cells hold small floating-point rainfall values; the large negative entries are the no-data sentinel seen above.

read_array with a window=(x, y, width, height) reads only a sub-block — here the top-left 100x100 pixels — instead of loading the whole raster into memory.

In [30]:

block = dataset.read_array(band=0, window=(0, 0, 100, 100))
print(block.shape)
print(block)

block = dataset.read_array(band=0, window=(0, 0, 100, 100)) print(block.shape) print(block)

(100, 100)
[[-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 ...
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]
 [-9999. -9999. -9999. ... -9999. -9999. -9999.]]

get_block_arrangement lists how the raster would be tiled into blocks of the given size, handy for chunked, memory-safe processing of large rasters.

In [31]:

dataset.get_block_arrangement(x_block_size=100, y_block_size=100)

dataset.get_block_arrangement(x_block_size=100, y_block_size=100)

Out[31]:

	x_offset	y_offset	window_xsize	window_ysize
0	0	0	100	100
1	100	0	100	100
2	200	0	100	100
3	300	0	100	100
4	400	0	100	100
...	...	...	...	...
67	400	700	100	80
68	500	700	100	80
69	600	700	100	80
70	700	700	100	80
71	800	700	50	80

72 rows × 4 columns

Band statistics¶

stats computes summary statistics (min, max, mean, standard deviation) for a band, skipping no-data cells.

In [32]:

stats = dataset.stats(band=0)
stats

stats = dataset.stats(band=0) stats

Out[32]:

	min	max	mean	std
Band_1	-9998.992188	65.326111	-1949.050171	3413.121826

The statistics summarise the whole band in one row — a quick way to read the value range and spot outliers before analysis.

Attribute table¶

A raster band can carry a raster attribute table (RAT) — a small lookup table that gives meaning to pixel values, much like a map legend. Below we read the current table, then set our own.

Read the band attribute table¶

In [33]:

df = dataset.get_attribute_table(band=0)
print(df)

df = dataset.get_attribute_table(band=0) print(df)

  Precipitation Range (mm)   Category              Description
0                     0-50        Low   Very low precipitation
1                   51-100   Moderate   Moderate precipitation
2                  101-200       High       High precipitation
3                  201-500  Very High  Very high precipitation
4                     >500    Extreme    Extreme precipitation

The raster ships without a predefined attribute table, so the returned frame is empty — we populate it next.

Set the band attribute table¶

set_attribute_table attaches a pandas DataFrame to the band, labelling ranges of pixel values with a category and description — turning bare numbers into an interpretable legend.

In [34]:

import pandas as pd

attribute_table = {
    'Precipitation Range (mm)': ['0-50', '51-100', '101-200', '201-500', '>500'],
    'Category': ['Low', 'Moderate', 'High', 'Very High', 'Extreme'],
    'Description': [
        'Very low precipitation',
        'Moderate precipitation',
        'High precipitation',
        'Very high precipitation',
        'Extreme precipitation',
    ],
}
df = pd.DataFrame(attribute_table)
dataset.set_attribute_table(df, band=0)

import pandas as pd attribute_table = { 'Precipitation Range (mm)': ['0-50', '51-100', '101-200', '201-500', '>500'], 'Category': ['Low', 'Moderate', 'High', 'Very High', 'Extreme'], 'Description': [ 'Very low precipitation', 'Moderate precipitation', 'High precipitation', 'Very high precipitation', 'Extreme precipitation', ], } df = pd.DataFrame(attribute_table) dataset.set_attribute_table(df, band=0)