eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I am utilizing robot framework's KubeLibrary to interact with my k8s cluster. By default, any function and response is automatically logged to DEBUG in robot framework, meaning that it's response will be visible in the log.html that robot framework generates. I want to disable this logging for some parts of my code or remove it afterwards somehow, e.g. I don't want the secrets to be part of the log.html regardless of log level. Current implementation which includes response in log.html when viewing DEBUG.  from KubeLibrary import KubeLibrary ... def some_keyword(): instance.kube = KubeLibrary(kube_config=instance.kubeconfig) secrets_matching_secret_name = instance.kube.get_secrets_in_namespace( name_pattern=f"^{secret_name}$", namespace=namespace )  Wishful skeleton code for how it could be done  instance.kube = KubeLibrary(kube_config=instance.kubeconfig) instance.kube.disable_debug_log() secrets_matching_secret_name = instance.kube.get_secrets_in_namespace( name_pattern=f"^{secret_name}$", namespace=namespace ) instance.kube.enable_debug_log()  Is there any way I can disable it? Or somehow filter it out from log.html using rebot or similar?

Answer: I've been dealing with this exact issue in Robot Framework + KubeLibrary. After trying several approaches, here's what actually worked for me: import logging from contextlib import contextmanager @contextmanager def suppress_debug_logging(): """Temporarily suppress DEBUG level logging to keep secrets out of Robot logs.""" # Store original log levels here original_levels = {} for name, logger in logging.root.manager.loggerDict.items(): if isinstance(logger, logging.Logger): original_levels[name] = logger.level logger.setLevel(logging.INFO) # Handle root logger too root_level = logging.root.level logging.root.setLevel(logging.INFO) try: yield # Run here the code that shouldn't be debug-logged finally: for name, level in original_levels.items(): logging.getLogger(name).setLevel(level) logging.root.setLevel(root_level) # Usage example: def some_keyword(): instance.kube = KubeLibrary(kube_config=instance.kubeconfig) with suppress_debug_logging(): secrets_matching_secret_name = instance.kube.get_secrets_in_namespace( name_pattern=f"^{secret_name}$", namespace=namespace )  This approach works because KubeLibrary uses Python's standard logging module before Robot Framework gets a chance to capture the logs. By temporarily bumping the log level from DEBUG to INFO for all loggers, you prevent the sensitive data from being logged. I tried robot's Set Log Level keyword first, but that didn't work since it only affects Robot's own logging not the underlying Python logging. The rebot approach is too blunt / you lose the entire keyword entry rather than just hiding the sensitive parts. One thing to watch out for: if your KubeLibrary is using a logger with a custom name, you might need to adjust the logging level directly for that logger. If you're running into issues with this approach, let me know your specific version of KubeLibrary and I can suggest maybe more
Question: I want to add color to cells of Status Pass and Fail. I tried following code it will print a colored data frame but exported csv was not colored. And when I applied two rules (green and red), only green shows. df = pd.DataFrame([{'Status':'Pass', 'Value': '0'}, {'Status':'Pass', 'Value': '1'}, {'Status':'FAIL', 'Value': '2'}]) # add background color green to all lines with status 'PASS' df.to_csv('test.csv') df.style.map(lambda x: 'background-color: green' if x == 'Pass' else '', subset=['Status']) 

Answer: You can't export colors to a text/CSV file. Furthermore, you ran your command after to_csv, which wouldn't affect it if this was supported. You can export as xlsx by chaining the style.map to to_excel: ( df.style.map( lambda x: 'background-color: green' if x == 'Pass' else '', subset=['Status'], ).to_excel('color_test.xlsx') )  Output:
Question: I have a Polars DataFrame below. import polars as pl df = pl.DataFrame({"a":["1.2", "2.3", "5.4"], "b":["0.4", "0.03", "0.12"], "c":["AA", "BB", "CC"]}) >>> df a b c str str str ------------------------- "1.2" "0.04" "AA" "2.3" "0.3" "BB" "3.5" "0.12" "CC"  How can I convert the columns to specific types? In this case, I want to convert columns a and b into floats. I expect below. >>> df a b c f64 f64 str ------------------------ 1.2 0.04 "AA" 2.3 0.3 "BB" 3.5 0.12 "CC" 

Answer: There are multiple approaches in Polars to convert column type. Using the cast method with with_columns is one of the most efficient approach: df = df.with_columns([ pl.col("a").cast(pl.Float64), pl.col("b").cast(pl.Float64) ])  Another approach: df = df.select([ pl.col("a").cast(pl.Float64), pl.col("b").cast(pl.Float64), pl.col("c") ])  And: df = df.cast({"a": pl.Float64, "b": pl.Float64})  For large dataset, you may try: df = df.lazy().with_columns([ pl.col("a").cast(pl.Float64), pl.col("b").cast(pl.Float64) ]) df_result = df.collect()  For more information: https://docs.pola.rs/user-guide/expressions/casting/ Output: ┌─────┬──────┬─────┐ │ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ f64 ┆ f64 ┆ str │ ╞═════╪══════╪═════╡ │ 1.2 ┆ 0.4 ┆ AA │ │ 2.3 ┆ 0.03 ┆ BB │ │ 5.4 ┆ 0.12 ┆ CC │ └─────┴──────┴─────┘
Question: Would it be frowned upon if the index variable i were not used inside a for loop? I have never come across a code that didn't use the value of the index while it iterates through the loop. def questionable(): for i in range(3): print('Is this OK?') # (or do something more complicated) # as opposed to: def proper(): for i in range(3): print(i) # (or do something that the value of 'i' is necessary)  What's a more Pythonic way to rewrite the function questionable, that is, to repeatedly do something without using the iteration variable?

Answer: When you want to perform an action a certain number of times without caring about the loop variable, the convention is to use an underscore (_) instead of a named variable like i. This signals to readers of your code: "I'm not going to use this variable." So your questionable function can be rewritten in a more Pythonic way like this: def more_pythonic(): for _ in range(3): print('Is this OK?')
Question: executing a polars.read_database() resulted in columns with the Decimal data type, which I'd like to cast to either Int or Float, depending on the value of the scale parameter in Decimal. Alternatively, I'd be happy if there is a way to instruct polars to not use the Decimal data type as an option and during schema inference to let it assign the appropriate Float or Int. Is there a way to use polars.selectors to conditionally target Decimal based on whether scale is zero or not? Or to instruct polars.read_database to not use Decimal? Ideally, I'd like to be able to do something like:  df.with_columns( pl.selectors.decimal(scale="1+").cast(pl.Float64()), pl.selectors.decimal(scale="0").cast(pl.Int64()) )  Of course, pl.selectors.decimal() doesn't have any arguments that it can take. An alternative would be some sort of pl.when ... but I would need to extract the value for scale first, and not sure how to do that. Or attack this at the read_database level. Any ideas?

Answer: A fairly explicit solution that works is: int_dec_cols = [c for c, dt in df.schema.items() if isinstance(dt, pl.Decimal) and dt.scale == 0] flt_dec_cols = [c for c, dt in df.schema.items() if isinstance(dt, pl.Decimal) and dt.scale > 0] df = df.with_columns( pl.col(int_dec_cols).cast(pl.Int64), pl.col(flt_dec_cols).cast(pl.Float64), )
Question: I'm using --mount=type=cache in my Dockerfile to cache pip packages between builds, but it doesn't seem to have any effect on build speed. I expected pip to reuse cached packages and avoid re-downloading them, but every build takes the same amount of time. Here is the relevant part of my Dockerfile: FROM python:3.13-slim AS compile-image RUN python -m venv /opt/venv ENV PATH="/opt/venv/bin:$PATH" RUN pip install --no-cache-dir --upgrade pip # Final image FROM python:3.13-slim ENV PATH="/opt/venv/bin:$PATH" COPY --from=compile-image /opt/venv /opt/venv WORKDIR /app ENV HOME=/app COPY . . RUN --mount=type=cache,mode=0755,target=/root/.cache/pip \ pip install -e .  Shouldn't this cache downloaded packages and reuse them in subsequent builds? What could be the reason it's not working as expected?

Answer: The pip cache is relative to the user's home directory. Just above the RUN pip install line you set ENV HOME=/app, and so the pip cache is in /app/.cache/pip (you also see that directory in the error message). Use that directory as the cache location RUN --mount=type=cache,target=/app/.cache/pip pip ... # ^^^^^ the directory you set as $HOME  For most purposes in a container, though, "home directory" isn't an especially meaningful concept, and there's no reason to change it. If you don't set $HOME and you are running as root then the default home directory will be /root and the invocation you had before should work. # ENV HOME=/home # default value, don't need to set this explicitly RUN --mount=type=cache,target=/root/.cache/pip pip ... # ^^^^^^ default value of $HOME for root  You can also use this caching in combination with Docker's normal layer caching. You probably want to run the pip install command in the first stage if you do have a multi-stage build (the setup you show gains little from it), and if you can do it only copying in the pyproject.toml file first, then you'll avoid re-running the installation sequence if source files but not application dependencies change.
Question: I have a collection of user data as follows:    user start end     John Doe 2025-03-21 11:30:35 2025-03-21 13:05:26   ... ... ...   Jane Doe 2023-12-31 01:02:03 2024-01-02 03:04:05    Each user has a start and end datetime of some activity. I would like to place this temporal range in the index so I can quickly query the dataframe to see which users were active during a certain date/time range like so: df['2024-01-01:2024-01-31']  Pandas has Period objects, but these seem to only support a specific year, day, or minute, not an arbitrary start and end datetime. Pandas also has MultiIndex indices, but these seem to be designed for hierarchical categorical labels, not for time ranges. Any other ideas for how to represent this time range in an index?

Answer: Here is your solution: import pandas as pd data = { 'user': ['John Doe', 'Jane Doe'], 'start': [pd.Timestamp('2025-03-21 11:30:35'), pd.Timestamp('2023-12-31 01:02:03')], 'end': [pd.Timestamp('2025-03-21 13:05:26'), pd.Timestamp('2024-01-02 03:04:05')], } df = pd.DataFrame(data) interval_index = pd.IntervalIndex.from_arrays(df['start'], df['end'], closed='both') df.set_index(interval_index, inplace=True) df.drop(columns=['start', 'end'], inplace=True) # check user query_time = pd.Timestamp("2024-01-01 12:00:00") active_users = df[df.index.contains(query_time)] print(active_users)  Output: D:\python>python test.py user [2023-12-31 01:02:03, 2024-01-02 03:04:05] Jane Doe
Question: I'm working on a side project to make my git commit message better and make the process faster, but I cant seem to figure out how to trigger the scripts after git add, without manually doing it. This is the flow I want:  I run git add. My script kicks in immediately. It fetches the staged diff. AI generates a commit message It asks for confirmation If confirmed, it commits.  I've tried looking into hooks like pre-commit, but those seem to run before git commit, not git add. Ideally, I'd love to run the message generator right after I run git add, possibly via a Git alias or a custom CLI.

Answer: If you don't want to use a prepare-commit-msg hook, you can use a CLI script like this:  Add specified files git add "$@"   Get diff diff=$(git diff --cached)   Generate commit message commit_msg=$(echo "$diff" | your_ai_commit_generator)   Ask for confirmation echo "Suggested commit message:" echo "$commit_msg" read -p "Use this commit message? (y/n): " confirm if [[ "$confirm" == "y" ]]; then git commit -m "$commit_msg" else echo "Commit canceled." fi   Save it as gitadd in your $PATH Give it executable permissions chmod +x gitadd   Run gitadd myfile.js    Full script: #!/bin/bash git add "$@" diff=$(git diff --cached) commit_msg=$(echo "$diff" | your_ai_commit_generator) # <- you plug in your AI here echo "Suggested commit message:" echo "$commit_msg" read -p "Use this commit message? (y/n): " confirm if [[ "$confirm" == "y" ]]; then git commit -m "$commit_msg" else echo "Commit canceled." fi
Question: I am trying to create and save KeePass entries using pykeepass and saving a .txt file as an attachment. However I get a type-error: Traceback (most recent call last): File "c:\Users\Simplicissimus\Documents\coding\directory\attachment.py", line 15, in <module> entry.add_attachment('attachment.txt', f.read()) ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Simplicissimus\AppData\Local\Programs\Python\Python313\Lib\site-packages\pykeepass\entry.py", line 163, in add_attachment E.Key(filename), ~~~~~^^^^^^^^^^ File "src\\lxml\\builder.py", line 219, in lxml.builder.ElementMaker.__call__ TypeError: bad argument type: bytes(b'Lorem Ipsum bla bla bla\r\n') (base) PS C:\Users\Simplicissimus\Documents\coding\directory>  My minimal reproducible example is: from pykeepass import PyKeePass kp = PyKeePass('testDatabase.kdbx', password='passwort') generalGroup = kp.find_groups(name='General',first=True) entry = kp.add_entry( generalGroup, title='title', username='username', password= 'password', ) with open('loremipsum.txt', 'rb') as f: entry.add_attachment('attachment.txt', f.read()) kp.save()  (All files are in the same directory, the file loremipsum.txt contains the line: "Lorem Impsum bla bla bla") How do I have to convert the file content of a .txt-file to Bytes? I am using the pykeepass Version: 4.1.0.post1, Keepass Version 2.58 and Python 3.13.

Answer: I can't test it but examples on page pykeepass show # add attachment data to the db >>> binary_id = kp.add_binary(b'Hello world') >>> kp.binaries [b'Hello world'] # add attachment reference to entry >>> a = e.add_attachment(binary_id, 'hello.txt')  So maybe it needs first add it as add_binary() and later attache it to entry with open('loremipsum.txt', 'rb') as f: binary_id = kp.add_binary(f.read()) a = entry.add_attachment(binary_id, 'attachment.txt')
Question: I'm playing around with the pipe | and ampersand & operators, as well as the .gt() and .lt() built-in functions to see how they work together. I'm looking at a column in a DataFrame with values from 0.00 to 1.00. I can use the >, <, and & operators together and find no problem, same with using .gt(), .lt(), and &. However, if I try to chain .gt().lt() it gives another result. In my example I'm using .gt(0.7).lt(0.9), but this yields values <=0.7. If I change the order to .lt(0.9).gt(0.7), I get values <=0.9. I can always just write it like this df['column'].gt(0.7)&df['column'].lt(0.9), just wondering if there's a way of chaining .gt().lt()

Answer: The misunderstanding is that in Python True == 1 and False == 0 (see bool). Suppose we have: import pandas as pd data = {'col': [0.5, 0.8, 1]} df = pd.DataFrame(data) df['col'].gt(0.7)  When we chain .lt(0.9), this check takes place on the result of .gt(0.7): 0 False # 0 < 0.9 (True) 1 True # 1 < 0.9 (False) 2 True # 1 < 0.9 (False) Name: col, dtype: bool  Use Series.between instead, with inclusive to control the comparison operators: df['col'].between(0.7, 0.9, inclusive='neither') 0 False # 0.5 1 True # 0.8 2 False # 1 Name: col, dtype: bool
Question: I have a tensor p of shape (B, 3, N) in PyTorch: # 2 batches, 3 channels (x, y, z), 5 points p = torch.rand(2, 3, 5, requires_grad=True) """ p: tensor([[[0.8365, 0.0505, 0.4208, 0.7465, 0.6843], [0.9922, 0.2684, 0.6898, 0.3983, 0.4227], [0.3188, 0.2471, 0.9552, 0.5181, 0.6877]], [[0.1079, 0.7694, 0.2194, 0.7801, 0.8043], [0.8554, 0.3505, 0.4622, 0.0339, 0.7909], [0.5806, 0.7593, 0.0193, 0.5191, 0.1589]]], requires_grad=True) """  And then another z_shift of shape [B, 1]: z_shift = torch.tensor([[1.0], [10.0]], requires_grad=True) """ z_shift: tensor([[1.], [10.]], requires_grad=True) """  I want to apply the appropriate z-shift of all points in each batch, leaving x and y unchanged: """ p: tensor([[[0.8365, 0.0505, 0.4208, 0.7465, 0.6843], [0.9922, 0.2684, 0.6898, 0.3983, 0.4227], [1.3188, 1.2471, 1.9552, 1.5181, 1.6877]], [[0.1079, 0.7694, 0.2194, 0.7801, 0.8043], [0.8554, 0.3505, 0.4622, 0.0339, 0.7909], [10.5806, 10.7593, 10.0193, 10.5191, 10.1589]]]) """  I managed to do it like: p[:, 2, :] += z_shift  for the case where requires_grad=False, but this fails inside the forward of my nn.Module (which I assume is equivalent to requires_grad=True) with: RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation. 

Answer: In PyTorch, tensors directly created by users are termed leaf tensors, and their views share the same underlying storage. Performing in-place assignments on a view can modify the storage of the original tensor midst in the computational graph, leading to undefined behavior. Thus, directly assigning values to views should be avoided. To achieve this safely, replace in-place operations with an out-of-place approach. For example: p_shifted = torch.stack([ p[:, 0, :], p[:, 1, :], p[:, 2, :] + z_shift, ], dim=1)  This constructs a new tensor via torch.stack instead of modifying the original storage in-place, ensuring computational graph integrity while fulfilling the intended functionality.
Question: I'm working on a Python project with a multi-stage Docker build and running into an issue where pydantic (just example) isn't installed, even though pip is present and working in the final image. Here's my project structure: project-root/ ├── docker-compose.yml ├── vector_db_service/ │ ├── app/ │ │ └── __init__.py │ ├── Dockerfile │ ├── pyproject.toml │ ├── .env  docker-compose.yml: services: vector_db_service: container_name: vector_db_service build: context: ./vector_db_service dockerfile: Dockerfile command: tail -f /dev/null env_file: - ./vector_db_service/.env  Dockerfile: # Build stage FROM python:3.13-slim AS compile-image RUN python -m venv /opt/venv ENV PATH="/opt/venv/bin:$PATH" RUN pip install --no-cache-dir --upgrade pip # Final image FROM python:3.13-slim COPY --from=compile-image /opt/venv /opt/venv WORKDIR /app ENV HOME=/app ENV PATH="/opt/venv/bin:$PATH" RUN addgroup --system app && adduser --system --group app COPY . . RUN chown -R app:app $HOME RUN chown -R app:app "/opt/venv/" USER app RUN pip install -e pydantic  The last line, RUN pip install -e pydantic, doesn't install anything. The build finishes successfully, but the package isn’t installed. I confirmed that pip is installed in the final image. I’ve tried other variations like RUN pip install pydantic or RUN pip install -e ., but they didn't change the outcome. My pyproject.toml does list pydantic as a dependency. Do I need to install from the project root, or am I missing something in the build process? Any help would be greatly appreciated. Thank you in advance!

Answer: Try to copy project files which you need to install  FROM python:3.13-slim AS compile-image RUN python -m venv /opt/venv ENV PATH="/opt/venv/bin:$PATH" RUN pip install --no-cache-dir --upgrade pip setuptools wheel WORKDIR /app COPY pyproject.toml ./ COPY vector_db_service ./vector_db_service RUN pip install --no-cache-dir . FROM python:3.13-slim COPY --from=compile-image /opt/venv /opt/venv WORKDIR /app ENV HOME=/app ENV PATH="/opt/venv/bin:$PATH" RUN addgroup --system app && adduser --system --group app COPY . . RUN chown -R app:app $HOME RUN chown -R app:app "/opt/venv/" USER app
Question: I am trying to plot the expression 4*sin(2*x)/cos3(2*x) in Sympy: from sympy import * from sympy.plotting import plot x = symbols('x') expr = 4*sin(2*x)/cos(2*x)**3 plot(expr)  But all I get is:  when it should look a bit like a horizontally-squished tangent graph: 

Answer: SymPy's plotting module doesn't handle very well functions with poles. But SymPy Plotting Backends (which is a more advanced plotting module) is up for the task: from spb import plot plot(expr, (x, -2*pi, 2*pi), detect_poles=True, ylim=(-3, 3))   In particular:  detect_poles=True: run the algorithm to detect poles (singularities). ylim=(-3, 3): limit the visualization on the y-axis in the specified range. Failing to set this keywork argument will get you a plot similar to what you shown.  If you are interested in using that module, here is the installation page. The documentation is also full with examples. EDIT to reply to comment: Yes, it is necessary to manually activate the algorithm, otherwise false positive might be introduced, depending on the function. Since I'm here, I'm going to better explain this algorithms:  detect_poles=False: used by default, no detection will be used. You will end up with something like this:  Here, the vertical lines should not be present.  detect_poles=True (the result of this algorithm is shown above): run a gradient-based algorithm on the numerical data. In short, if the gradient of the function computed between two consecutive points along the x-axis, is greater than some threshold, then a singularity is found and the algorithm insert a NaN point between them, which forces the vertical line to disappear. However, this strategy can be fooled by some functions that exhibits very high gradients: in this cases, the algorithm will insert NaN values even though there might not be any singularity. There are some parameters that can be used to fine tune the results of the visualization. The easiest one is to increasing the number of discretization points (n=1000 by default).  detect_poles="symbolic": it runs both a gradient-based algorithm on the numerical data, as well as a symbolic algorithm on the expression to be plotted, which involves the use of SymPy's solve. Depending on the symbolic expression, solve might not be able to solve for all singularities. Or worse, it could take forever to complete the task. Here is the output:  Here, the vertical dotted lines indicate where the singularities are found by the symbolic algorithm.   Given the limitations of both the gradient-based and symbolic algorithms, I (developer of that module) decided that it is best to leave detect_poles=False by default, and let the user enable the algorithm that better suits its needs. :) More examples of plotting discountinuities can be found on this tutorial page.