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Resolve Pandas FutureWarnings #1766

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merged 2 commits into from
Dec 10, 2023
Merged

Resolve Pandas FutureWarnings #1766

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9b91580
Pandas future proofing (tested)
JuliaLWang8 Dec 8, 2023
0bcd2dc
Removed unnecessary iloc
JuliaLWang8 Dec 10, 2023
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9 changes: 9 additions & 0 deletions tests/ticker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -223,6 +223,15 @@ 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
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12 changes: 6 additions & 6 deletions yfinance/base.py
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Expand Up @@ -706,33 +706,33 @@ 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]
f_close_bad = df_block_calib['Close'] == tag
df_new['Adj Close'] = df_block['Close'] * div_adjusts
if f_close_bad.any():
df_new.loc[f_close_bad, 'Adj Close'] = df_new['Close'][f_close_bad] * div_adjusts[f_close_bad]
df_new.loc[f_close_bad, 'Adj Close'] = df_new['Close'].iloc[f_close_bad] * div_adjusts[f_close_bad]
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# Check whether 'df_fine' has different split-adjustment.
# If different, then adjust to match 'df'
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