import swisslandstats as sls

ldf = sls.read_csv('data/AREA_NOAS04_72_171129.csv')

# Transform the 4-category land statistics of 2009 `AS09_4` to a numpy array

ldf.to_ndarray('AS09R_4')

# Export the same column to a GeoTIFF

ldf.to_geotiff('landstats.tiff', 'AS09R_4')

# Or plot it

ldf.plot('AS09R_4', cmap=sls.noas04_4_cmap, legend=True)

# Clip the dataframe by a place query to OSM's Nominatim

vaud_ldf = sls.clip_by_nominatim(ldf, 'Vaud, Switzerland')
vaud_ldf.plot('AS09R_4', cmap=sls.noas04_4_cmap, legend=True)

import matplotlib.pyplot as plt

year_columns = ['85', '97', '09']
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for year_column, ax in zip(year_columns, axes):
    ax = vaud_ldf.plot('AS%sR_4' % year_column, cmap=sls.noas04_4_cmap, ax=ax)
    ax.set_title(year_column)