Fix several deprecation warnings in pandas 2.1 which became actual errors in 2.2

AUTHORS.rst

@@ -29,3 +29,4 @@ Contributions
 - `Anton Ian Sipos <https://github.com/aisipos>`_
 - `Chuan-Jhe Hwong <https://github.com/CJHwong>`_
 - `Thomas Grainger <https://github.com/graingert/>`_
+- `Ryan Smith <https://github.com/bixbyr/>`_

django_pandas/io.py

@@ -84,7 +84,7 @@ def read_frame(qs, fieldnames=(), index_col=None, coerce_float=False,
     """
 
     if fieldnames:
-        fieldnames = pd.unique(fieldnames)
+        fieldnames = pd.unique(pd.Series(fieldnames))
         if index_col is not None and index_col not in fieldnames:
             # Add it to the field names if not already there
             fieldnames = tuple(fieldnames) + (index_col,)
@@ -151,7 +151,7 @@ def read_frame(qs, fieldnames=(), index_col=None, coerce_float=False,
         df.set_index(index_col, inplace=True)
 
     if datetime_index:
-        df.index = pd.to_datetime(df.index, errors="ignore")
+        df.index = pd.to_datetime(df.index)
     return df
 
 

django_pandas/managers.py

@@ -241,9 +241,9 @@ def to_timeseries(self, fieldnames=(), verbose=True,
 
         if freq is not None:
             if agg_kwargs is None:
-                agg_kwargs=dict()
+                agg_kwargs = dict()
             if agg_args is None:
-                agg_args=[]
+                agg_args = []
             df = df.resample(freq, **rs_kwargs).agg(*agg_args, **agg_kwargs)
 
         return df
@@ -253,7 +253,7 @@ def to_dataframe(self, fieldnames=(), verbose=True, index=None,
         """
         Returns a DataFrame from the queryset
 
-        Paramaters
+        Parameters
         -----------
 
         fieldnames:  The model field names(columns) to utilise in creating

django_pandas/tests/test_manager.py

@@ -1,8 +1,12 @@
+from datetime import datetime
+
 from django.test import TestCase
 import pandas as pd
 import numpy as np
 import pickle
 import django
+from pandas.core.indexes.datetimes import bdate_range
+
 from .models import (
     DataFrame, WideTimeSeries, WideTimeSeriesDateField,
     LongTimeSeries, PivotData, Dude, Car, Spot
@@ -68,8 +72,28 @@ def unpivot(self, frame):
                 'date': np.tile(np.array(frame.index), K)}
         return pd.DataFrame(data, columns=['date', 'variable', 'value'])
 
+    def _makeTimeDataFrame(self, n_rows: int) -> pd.DataFrame:
+        # Beginning in 2.2 pandas._testing.makeTimeDataFrame was removed, however all that is required for the tests
+        # in this module is a dataframe with columns A, B, C, D of random values indexed by a DatetimeIndex.
+        data = {}
+        for c in ['A', 'B', 'C', 'D']:
+            dt = datetime(2000, 1, 1)
+            dr = bdate_range(dt, periods=n_rows, freq='B', name=c)
+            pd.DatetimeIndex(dr, name=c)
+
+            data[c] = pd.Series(
+                np.random.default_rng(2).standard_normal(n_rows),
+                index=pd.DatetimeIndex(dr, name=c),
+                name=c,
+            )
+        return pd.DataFrame(data)
+
     def setUp(self):
-        self.ts = tm.makeTimeDataFrame(100)
+        if PANDAS_VERSIONINFO >= '2.2.0':
+            self.ts = self._makeTimeDataFrame(100)
+        else:
+            self.ts = tm.makeTimeDataFrame(100)
+
         self.ts2 = self.unpivot(self.ts).set_index('date')
         self.ts.columns = ['col1', 'col2', 'col3', 'col4']
         create_list = []
@@ -87,9 +111,9 @@ def setUp(self):
                                                        col4=cols['col4']))
         WideTimeSeriesDateField.objects.bulk_create(create_list)
 
-        create_list = [LongTimeSeries(date_ix=r[0], series_name=r[1][0],
-                                      value=r[1][1])
-                       for r in self.ts2.iterrows()]
+        create_list = [LongTimeSeries(date_ix=timestamp, series_name=s.iloc[0],
+                                      value=s.iloc[1])
+                       for timestamp, s in self.ts2.iterrows()]
 
         LongTimeSeries.objects.bulk_create(create_list)
 
@@ -125,18 +149,24 @@ def test_longstorage(self):
 
     def test_resampling(self):
         qs = LongTimeSeries.objects.all()
-        rs_kwargs = {'kind': 'period'}
         agg_args = None
         agg_kwargs = None
         if PANDAS_VERSIONINFO >= '0.25.0':
             agg_kwargs = {'func': 'sum'}
         else:
-            agg_args= ['sum']
+            agg_args = ['sum']
+
+        if PANDAS_VERSIONINFO >= '2.2.0':
+            freq = 'ME'
+        else:
+            freq = 'M'
+
         df = qs.to_timeseries(index='date_ix', pivot_columns='series_name',
                               values='value', storage='long',
-                              freq='M', rs_kwargs=rs_kwargs,
+                              freq=freq,
                               agg_args=agg_args,
                               agg_kwargs=agg_kwargs)
+        df.index = pd.PeriodIndex(df.index)
 
         self.assertEqual([d.month for d in qs.dates('date_ix', 'month')],
                          df.index.month.tolist())
@@ -147,9 +177,10 @@ def test_resampling(self):
         qs2 = WideTimeSeries.objects.all()
 
         df1 = qs2.to_timeseries(index='date_ix', storage='wide',
-                                freq='M', rs_kwargs=rs_kwargs,
+                                freq=freq,
                                 agg_args=agg_args,
-                                agg_kwargs = agg_kwargs)
+                                agg_kwargs=agg_kwargs)
+        df1.index = pd.PeriodIndex(df1.index)
 
         self.assertEqual([d.month for d in qs.dates('date_ix', 'month')],
                          df1.index.month.tolist())
@@ -222,11 +253,10 @@ def setUp(self):
                                   'value_col_d': np.random.randn(11),
                                   'value_col_e': np.random.randn(11),
                                   'value_col_f': np.random.randn(11)})
-
-        create_list = [PivotData(row_col_a=r[1][0], row_col_b=r[1][1],
-                                 row_col_c=r[1][2], value_col_d=r[1][3],
-                                 value_col_e=r[1][4], value_col_f=r[1][5])
-                       for r in self.data.iterrows()]
+        create_list = [PivotData(row_col_a=r.iloc[0], row_col_b=r.iloc[1],
+                                 row_col_c=r.iloc[2], value_col_d=r.iloc[3],
+                                 value_col_e=r.iloc[4], value_col_f=r.iloc[5])
+                       for _, r in self.data.iterrows()]
 
         PivotData.objects.bulk_create(create_list)
 

django_pandas/utils.py

@@ -48,8 +48,7 @@ def get_cache_key_from_pk(pk):
 
     def inner(pk_series):
         pk_series = pk_series.astype(object).where(pk_series.notnull(), None)
-        cache_keys = pk_series.apply(
-            get_cache_key_from_pk, convert_dtype=False)
+        cache_keys = pk_series.apply(get_cache_key_from_pk)
         unique_cache_keys = list(filter(None, cache_keys.unique()))
 
         if not unique_cache_keys: