Re: ast.parse, ast.dump, but with comment preservation?
> On 16 Dec 2021, at 03:49, samue...@gmail.com wrote: > > I wrote a little open-source tool to expose internal constructs in OpenAPI. > Along the way, I added related functionality to: > - Generate/update a function prototype to/from a class > - JSON schema > - Automatically add type annotations to all function arguments, class > attributes, declarations, and assignments > > alongside a bunch of other features. All implemented using just the builtin > modules (plus astor on Python < 3.9; and optionally black). > > Now I'm almost at the point where I can run it—without issue—against, e.g., > the entire TensorFlow codebase. Unfortunately this is causing huge `diff`s > because the comments aren't preserved (and there are some whitespace issues… > but I should be able to resolve the latter). > > Is the only viable solution available to rewrite around redbaron | libcst? - > I don't need to parse the comments just dump them out unedited whence they're > found… > > Thanks for any suggestions Have a look at the code that is used by https://github.com/asottile/pyupgrade There are a couple of libraries that it uses that does what I think you want to do. Barry > > PS: Library is https://github.com/SamuelMarks/cdd-python (might relicense > with CC0… anyway too early for others to use; wait for the 0.1.0 release ;]) > -- > https://mail.python.org/mailman/listinfo/python-list -- https://mail.python.org/mailman/listinfo/python-list
Re: ast.parse, ast.dump, but with comment preservation?
Hi ! Maybe RedBaron may help you ? https://github.com/PyCQA/redbaron IIRC, it aims to conserve the exact same representation of the source code, including comments and empty lines. --lucas On 16/12/2021 04:37, samue...@gmail.com wrote: I wrote a little open-source tool to expose internal constructs in OpenAPI. Along the way, I added related functionality to: - Generate/update a function prototype to/from a class - JSON schema - Automatically add type annotations to all function arguments, class attributes, declarations, and assignments alongside a bunch of other features. All implemented using just the builtin modules (plus astor on Python < 3.9; and optionally black). Now I'm almost at the point where I can run it—without issue—against, e.g., the entire TensorFlow codebase. Unfortunately this is causing huge `diff`s because the comments aren't preserved (and there are some whitespace issues… but I should be able to resolve the latter). Is the only viable solution available to rewrite around redbaron | libcst? - I don't need to parse the comments just dump them out unedited whence they're found… Thanks for any suggestions PS: Library is https://github.com/SamuelMarks/cdd-python (might relicense with CC0… anyway too early for others to use; wait for the 0.1.0 release ;]) -- https://mail.python.org/mailman/listinfo/python-list
Update a specific element in all a list of N lists
Dear All,
I really need your assistance,
I have a dataset with 1005000 rows and 25 columns,
The main column that I repeatedly use are Time, ID, and Reputation
First I sliced the data based on the time, and I append the sliced data in
a list called "df_list". So I get 201 lists with 25 columns
The main code is starting for here:
for elem in df_list:
{do something.}
{Here I'm trying to calculate the outliers}
Out.append(outliers)
Now my problem is that I need to locate those outliers in the df_list and
then update another column with is the "Reputation"
Note that the there is a duplicated IDs but at different time slot
example is ID = 1 is outliers, I need to select all ID = 1 in the list and
update their reputation column
I tried those solutions:
1)
grp = data11.groupby(['ID'])
for i in GlobalNotOutliers.ID:
data11.loc[grp.get_group(i).index, 'Reput'] += 1
for j in GlobalOutliers.ID:
data11.loc[grp.get_group(j).index, 'Reput'] -= 1
It works for a dataframe but not for a list
2)
for elem in df_list:
elem.loc[elem['ID'].isin(Outlier['ID'])]
It doesn't select the right IDs, it gives the whole values in elem
3) Here I set the index using IDs:
for i in Outlier.index:
for elem in df_list:
print(elem.Reput)
if i in elem.index:
# elem.loc[elem[i] , 'Reput'] += 1
m = elem.iloc[i, :]
print(m)
It gives this error:
IndexError: single positional indexer is out-of-bounds
I'm greatly thankful to anyone who can help me,
--
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