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ENH: Remove for loop from DataFrame.update() #56303

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ENH: Remove for loop from DataFrame.update() #56303

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38 changes: 19 additions & 19 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8923,29 +8923,29 @@ def update(

other = other.reindex(self.index)

for col in self.columns.intersection(other.columns):
this = self[col]._values
that = other[col]._values
cols = self.columns.intersection(other.columns)
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Calling ._values on multiple columns converts to a common dtype for all columns, probably object as soon as you have non-numeric columns. This is terrible from a performance perspective, so doing this at once is actually a lot slower in real world use cases than doing one column at a time

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Ah, understood. Thank you for taking the time to explain.

this = self[cols]._values
that = other[cols]._values

if filter_func is not None:
mask = ~filter_func(this) | isna(that)
if filter_func is not None:
mask = ~filter_func(this) | isna(that)
else:
if errors == "raise":
mask_this = notna(that)
mask_that = notna(this)
if (mask_this & mask_that).any():
raise ValueError("Data overlaps.")

if overwrite:
mask = isna(that)
else:
if errors == "raise":
mask_this = notna(that)
mask_that = notna(this)
if any(mask_this & mask_that):
raise ValueError("Data overlaps.")

if overwrite:
mask = isna(that)
else:
mask = notna(this)
mask = notna(this)

# don't overwrite columns unnecessarily
if mask.all():
continue
# no update necessary
if mask.all():
return

self.loc[:, col] = self[col].where(mask, that)
self.loc[:, cols] = self[cols].where(mask, that)

# ----------------------------------------------------------------------
# Data reshaping
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