Source code for hpipy.datasets._base
import importlib.resources as pkg_resources
import pandas as pd
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def load_ex_sales() -> pd.DataFrame:
"""Load example home sales dataset.
Returns:
pandas.DataFrame: A DataFrame with house sales.
Example:
>>> from hpipy.datasets import load_ex_sales
>>> df = load_ex_sales()
>>> df.head()
pinx sale_id sale_price ... eff_age longitude latitude
0 ..0007600046 2011..2621 308900 ... 12 -122.302032 47.603913
1 ..0007600054 2010..16414 369950 ... 103 -122.302030 47.603044
2 ..0007600057 2014..23738 520000 ... 112 -122.302114 47.602875
3 ..0007600057 2016..28612 625000 ... 114 -122.302114 47.602875
4 ..0007600065 2014..15956 465000 ... 0 -122.297278 47.601812
"""
with pkg_resources.files("hpipy.datasets.data").joinpath("ex_sales.csv").open("r") as f:
return pd.read_csv(f, parse_dates=["sale_date"])
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def load_seattle_sales() -> pd.DataFrame:
"""Load Seattle home sales dataset.
Returns:
pandas.DataFrame: A DataFrame with house sales.
Example:
>>> from hpipy.datasets import load_seattle_sales
>>> df = load_seattle_sales()
>>> df.head()
pinx sale_id sale_price ... eff_age longitude latitude
0 ..0001800010 2013..2432 289000 ... 6 -122.312491 47.561380
1 ..0001800066 2013..21560 356000 ... 87 -122.322007 47.550353
2 ..0001800075 2010..24221 333500 ... 80 -122.311654 47.561470
3 ..0001800075 2016..6629 577200 ... 86 -122.311654 47.561470
4 ..0001800080 2012..9521 237000 ... 72 -122.309695 47.561472
"""
with pkg_resources.files("hpipy.datasets.data").joinpath("seattle_sales.csv").open("r") as f:
return pd.read_csv(f, parse_dates=["sale_date"])
