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DOC: add warning to append about inefficiency #16956

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DOC: add warning to append about inefficiency #16956

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DOC: add warning to append about inefficiency
shanral Jul 15, 2017
ea4cc30
DOC: fix wording in frame and series
shanral Jul 16, 2017
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11 changes: 7 additions & 4 deletions doc/make.py
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Expand Up @@ -150,10 +150,13 @@ def _remove_notebooks():
print("Warning: Pandoc is not installed. Skipping notebooks.")
_remove_notebooks()

yield
for nb, content in contents.items():
with open(nb, 'wt') as f:
f.write(content)
try:
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Why this try-except here?

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This was to fix an issue if a doc build failed halfway through due to a missing dependency or ^C. I worked on this fix with @TomAugspurger, but am no longer to reproduce it's success. I am removing it from further pull requests.

yield
except BaseException:
for nb, content in contents.items():
with open(nb, 'wt') as f:
f.write(content)
raise


def html():
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5 changes: 5 additions & 0 deletions pandas/core/frame.py
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Expand Up @@ -4618,6 +4618,11 @@ def append(self, other, ignore_index=False, verify_integrity=False):
the DataFrame's index, the order of the columns in the resulting
DataFrame will be unchanged.

Iteratively appending rows to a Dataframe can be more computationally
intense than a single concatenate. A better solution is to append those
rows to a list then concatenate the list with the original Dataframe
all at once.
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intense --> intensive
"list then" --> "list and then"

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can you show some pseudo code


See also
--------
pandas.concat : General function to concatenate DataFrame, Series
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11 changes: 11 additions & 0 deletions pandas/core/series.py
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Expand Up @@ -1522,6 +1522,17 @@ def append(self, to_append, ignore_index=False, verify_integrity=False):
verify_integrity : boolean, default False
If True, raise Exception on creating index with duplicates

Notes
-----
Iteratively appending to a series can be more computationally intense
than a single concatenate. A better solution is to append values to a
list then concatenate the list with the original series all at once.
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series --> Series
intense --> intensive
"list then" --> "list and then"


See also
--------
pandas.concat : General function to concatenate DataFrame, Series
or Panel objects

Returns
-------
appended : Series
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