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DataFrameGroupBy.agg with nan results error Fixed #59190

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DataFrameGroupBy.agg with nan results error Fixed #59190

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46 changes: 20 additions & 26 deletions pandas/core/_numba/kernels/min_max_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,8 +31,6 @@ def sliding_min_max(
nobs = 0
output = np.empty(N, dtype=result_dtype)
na_pos = []
# Use deque once numba supports it
# https://github.com/numba/numba/issues/7417
Q: list = []
W: list = []
for i in range(N):
Expand All @@ -46,36 +44,28 @@ def sliding_min_max(
ai = values[k]
if not np.isnan(ai):
nobs += 1
elif is_max:
ai = -np.inf
else:
ai = np.inf
# Discard previous entries if we find new min or max
ai = -np.inf if is_max else np.inf
if is_max:
while Q and ((ai >= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
while Q and ((ai >= values[Q[-1]]) or np.isnan(values[Q[-1]])):
Q.pop()
else:
while Q and ((ai <= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
while Q and ((ai <= values[Q[-1]]) or np.isnan(values[Q[-1]])):
Q.pop()
Q.append(k)
W.append(k)

# Discard entries outside and left of current window
while Q and Q[0] <= start[i] - 1:
Q.pop(0)
while W and W[0] <= start[i] - 1:
if not np.isnan(values[W[0]]):
nobs -= 1
W.pop(0)

# Save output based on index in input value array
if Q and curr_win_size > 0 and nobs >= min_periods:
output[i] = values[Q[0]]
else:
if values.dtype.kind != "i":
output[i] = np.nan
else:
na_pos.append(i)
output[i] = np.nan

return output, na_pos

Expand All @@ -100,27 +90,31 @@ def grouped_min_max(
if lab < 0:
continue

if values.dtype.kind == "i" or not np.isnan(val):
if not np.isnan(val):
nobs[lab] += 1
else:
# NaN value cannot be a min/max value
continue

if nobs[lab] == 1:
# First element in group, set output equal to this
output[lab] = val
continue

if is_max:
if val > output[lab]:
output[lab] = val
else:
if val < output[lab]:
output[lab] = val
if is_max:
if val > output[lab]:
output[lab] = val
else:
if val < output[lab]:
output[lab] = val

# Set labels that don't satisfy min_periods as np.nan
for lab, count in enumerate(nobs):
if count < min_periods:
na_pos.append(lab)
output[lab] = np.nan

return output, na_pos

# Example usage:
if __name__ == "__main__":
import pandas as pd

s = pd.Series([np.nan, 0, 1], dtype="Float64")
print((s / s).max()) # <NA>
print((s / s).groupby([9, 9, 9]).max().iat[0]) # <NA>
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