Merge pull request #1766 from JuliaLWang8/pandas-future-proofing

Pandas future proofing
ranaroussi · Dec 10, 2023 · 5805029 · 5805029
2 parents c60e590 + 0bcd2dc
commit 5805029
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Showing 2 changed files with 14 additions and 5 deletions.
diff --git a/tests/ticker.py b/tests/ticker.py
@@ -222,6 +222,15 @@ def test_actions(self):
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
+    def test_reconstruct_intervals_batch(self):
+        data = self.ticker.history(period="3mo", interval="1d", prepost=True, repair=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        reconstructed = self.ticker._reconstruct_intervals_batch(data, "1wk", True)
+        self.assertIsInstance(reconstructed, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
 
 class TestTickerEarnings(unittest.TestCase):
     session = None

diff --git a/yfinance/base.py b/yfinance/base.py
@@ -699,26 +699,26 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                     # But in case are repairing a chunk of bad 1d data, back/forward-fill the
                     # good div-adjustments - not perfect, but a good backup.
                     div_adjusts[f_tag] = np.nan
-                    div_adjusts = div_adjusts.fillna(method='bfill').fillna(method='ffill')
+                    div_adjusts = div_adjusts.ffill().bfill()
                     for idx in np.where(f_tag)[0]:
                         dt = df_new_calib.index[idx]
                         n = len(div_adjusts)
                         if df_new.loc[dt, "Dividends"] != 0:
                             if idx < n - 1:
                                 # Easy, take div-adjustment from next-day
-                                div_adjusts[idx] = div_adjusts[idx + 1]
+                                div_adjusts[idx] = div_adjusts.iloc[idx + 1]
                             else:
                                 # Take previous-day div-adjustment and reverse todays adjustment
                                 div_adj = 1.0 - df_new_calib["Dividends"].iloc[idx] / df_new_calib['Close'].iloc[
                                     idx - 1]
-                                div_adjusts[idx] = div_adjusts[idx - 1] / div_adj
+                                div_adjusts[idx] = div_adjusts.iloc[idx - 1] / div_adj
                         else:
                             if idx > 0:
                                 # Easy, take div-adjustment from previous-day
-                                div_adjusts[idx] = div_adjusts[idx - 1]
+                                div_adjusts[idx] = div_adjusts.iloc[idx - 1]
                             else:
                                 # Must take next-day div-adjustment
-                                div_adjusts[idx] = div_adjusts[idx + 1]
+                                div_adjusts[idx] = div_adjusts.iloc[idx + 1]
                                 if df_new_calib["Dividends"].iloc[idx + 1] != 0:
                                     div_adjusts[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
                                                         df_new_calib['Close'].iloc[idx]