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Upgrade to Pandas 2.x #94

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Sep 11, 2023
Merged

Upgrade to Pandas 2.x #94

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25 changes: 13 additions & 12 deletions climada_petals/entity/exposures/openstreetmap/osm_dataloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
import urllib.request

import geopandas as gpd
import numpy as np
import pandas as pd
from osgeo import ogr, gdal
import overpy
import shapely
Expand Down Expand Up @@ -417,28 +417,29 @@ def retrieve_cis(self, ci_type):
"""
# features consisting in points and multipolygon results:
if ci_type in ['healthcare','education','food']:
gdf = self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query'])
gdf = gdf.append(
gdf = pd.concat([
self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query']),
self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query']))
DICT_CIS_OSM[ci_type]['osm_query']),
])

# features consisting in multipolygon results:
elif ci_type in ['air']:
gdf = self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query'])
DICT_CIS_OSM[ci_type]['osm_query'])

# features consisting in points, multipolygons and lines:
elif ci_type in ['gas','oil','telecom','water','wastewater','power',
'rail','road']:
gdf = self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query'])
gdf = gdf.append(
gdf = pd.concat([
self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query']),
self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query']))
gdf = gdf.append(
DICT_CIS_OSM[ci_type]['osm_query']),
self.retrieve('lines', DICT_CIS_OSM[ci_type]['osm_keys'],
DICT_CIS_OSM[ci_type]['osm_query']))
DICT_CIS_OSM[ci_type]['osm_query']),
])
else:
LOGGER.warning('feature not in DICT_CIS_OSM. Returning empty gdf')
gdf = gpd.GeoDataFrame()
Expand Down
2 changes: 1 addition & 1 deletion climada_petals/entity/exposures/test/test_spamagrar_unit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ def init_testdata():
@staticmethod
def assert_pd_frame_equal(df1, df2, **kwds):
"""Assert that two dataframes are equal, ignoring ordering of columns"""
from pandas.util.testing import assert_frame_equal
from pandas.testing import assert_frame_equal
return assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1),
check_names=True, **kwds)

Expand Down
2 changes: 1 addition & 1 deletion climada_petals/hazard/emulator/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,7 @@ def seasonal_average(data, season):
data = data[(data['year'] > year_min) & (data['year'] <= year_max)]
data = data.reset_index(drop=True)
data = data.groupby('year').mean().reset_index()
return data.drop('month', 1)
return data.drop('month', axis=1)


def seasonal_statistics(events, season):
Expand Down
6 changes: 4 additions & 2 deletions climada_petals/hazard/low_flow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,10 +27,12 @@
import datetime as dt
from pathlib import Path
import cftime
import xarray as xr
import geopandas as gpd
import numpy as np
import numba
import pandas as pd
import xarray as xr


from sklearn.cluster import DBSCAN
from sklearn.neighbors import BallTree
Expand Down Expand Up @@ -754,7 +756,7 @@ def data_preprocessing_percentile(percentile, yearrange, yearrange_ref,
dataf = _xarray_to_geopandas(data_chunk)
first_file = False
else:
dataf = dataf.append(_xarray_to_geopandas(data_chunk))
dataf = pd.concat([dataf, _xarray_to_geopandas(data_chunk)])
del data_chunk
dataf = dataf.sort_values(['lat', 'lon', 'dtime'], ascending=[True, True, True])
return dataf.reset_index(drop=True), centroids
Expand Down
2 changes: 1 addition & 1 deletion climada_petals/hazard/wildfire.py
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Original file line number Diff line number Diff line change
Expand Up @@ -606,7 +606,7 @@ def _clean_firms_df(self, df_firms):
df_firms_viirs = df_firms_viirs.drop(df_firms_viirs[ \
df_firms_viirs.confidence == 'l'].index)
df_firms_viirs = df_firms_viirs.rename(columns={'bright_ti4':'brightness'})
temp = temp.append(df_firms_viirs, sort=True)
temp = pd.concat([temp, df_firms_viirs], sort=True)
temp = temp.drop(columns=['bright_ti4'])

df_firms = temp
Expand Down