eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I am trying to learn python and came across this Collatz's hypothesis where I need to take any non-negative and non-zero integer number as input and if it's even, evaluate a condition otherwise, if it's odd, evaluate another condition if number ≠ 1, go back to even condition. I am checking same condition multiple times. Please guide me how to reuse the conditions without repeating them. Thank you in advance and my code is below. "was trying to reduce input intakes and minimize condition check for one" c0 = int(input("Enter a non-negative and non-zero integer number: ")) steps = 0 if c0 < 0: c0 = input("Enter a non-negative and non-zero integer number: ") else: while c0 != 1: while c0 % 2 == 0 and c0 != 0: c0 /= 2 steps += 1 print(int(c0)) if c0 == 1: break else: c0 = 3*c0 + 1 steps += 1 print(int(c0)) else: print("steps = ", steps) 

Answer: Because you loop through it and regularly check for c0 being 1 and c0 not being 0, why not just use that as the while condition? Using it would make the break check unnecessary. Additionally, you don't need to use a while loop to check for even or odd numbers. Instead, you can use one while loop and conditionals inside. Also, you check for < 0 instead of <= 0 in your first conditional. Additionally since c0 can never equal 0, you don't need to check for it in the loop. c0 = int(input("Enter a non-negative and non-zero integer number: ")) steps = 0 if c0 <= 0: #Checks for negative or 0 c0 = input("Enter a non-negative and non-zero integer number: ") else: while c0 != 1: #Checks if c0 is 1. If so, print the steps and terminate if c0 % 2 == 0: #If c0 is even c0 /= 2 steps += 1 print(int(c0)) else: #If c0 is odd c0 = 3*c0 + 1 steps += 1 print(int(c0)) else: print("steps = ", steps)
Question: I'm struggling to make a Many-to-one relationship reusable. Simplified, let's say I have: class Car(models.Model): ... class Wheel(models.Model): car = models.ForeignKey(Car) ...  Pretty straight forward. What, however, if I'd like to use my Wheel model also on another model, Bike? Can I define the relationship in reverse, on the "One" side of the relationship? Defining this as a Many-to-many on the Vehicles would mean the same Wheel could belong to multiple Vehicles, which is not what I want. Would I have to subclass my Wheel to CarWheel and BikeWheel only to be able to differentiate the Foreignkey for each relationship? Seems like there should be a cleaner solution.

Answer: If I understand correctly, you will need to "subclass" from a parent model. For instance, Vehicle: class Vehicle(models.Model): pass class Bike(Vehicle): pass class Wheel(models.Model): vehicle = models.ForeignKey( Vehicle, on_delete=models.CASCADE, related_name='wheels', )  Now to access the Wheel instances from a Bike instance: bike = Bike.objects.first() bike_wheels = bike.wheels.all()
Question: At my shcool we have a interactive white boards and we can export them to a website with a provided link. Only problem is that the links expire (which is stupid), so I want to make a simple python script that gets the images and downloads them. Here is the link to the website: https://air.ifpshare.com/documentPreview.html?s_id=8ec97e16-51c4-4a77-9f64-7d5dccd9bb41#/detail/561f0184-384c-4ca1-91a4-b2e687865408/record When I open chrome and inspect the website, I see that the images are contained in a main divider with sub divider and image elements which encode the image in base 64. This is thus easy to decode them in python. This is the simple script i wrote to get the html: import requests page = requests.get("https://air.ifpshare.com/documentPreview.html?s_id=8ec97e16-51c4-4a77-9f64-7d5dccd9bb41#/detail/561f0184-384c-4ca1-91a4-b2e687865408/record") print(page.text)  Only problem is, when I try to get the html, I don't get any of the content... The content seems to be coming from the javascript that is in the website. The same thing happens when I use Selenium Here is what I get: <!DOCTYPE html><html><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=no"><link rel=stylesheet href=//at.alicdn.com/t/font_833191_27456hr9ow5.css><title id=PageTitle></title><style>html, body { max-width: 480px; height: 100%; margin: auto; background-size: 100% 100%; background: #F8F8F8; }</style><link href=/static/css/documentPreview.01a0856b7f615fdfd7f4b853e047bcd0.css rel=stylesheet></head><body><div id=app></div><script type=text/javascript src=/static/js/manifest.a3f705024b2774dd271e.js></script><script type=text/javascript src=/static/js/vendor.03a8b2ef6819d9eaa4e7.js></script><script type=text/javascript src=/static/js/documentPreview.a9f6fe7b5c4d6f073050.js></script></body></html>  Does anyone know a workaround?

Answer: Note: this answer contains different methods to reach your goal. I saw your target web app fetching image download URLs from an API endpoint and it is easy to fetch those images using the requests library with a little bit of code (no need to use bs4 if you want). here is the API endpoint https://air.ifpshare.com/api/pub/files/UUID So what is the file UUID in your target URL?  Your provided URL: https://air.ifpshare.com/documentPreview.html?s_id=8ec97e16-51c4-4a77-9f64-7d5dccd9bb41#/detail/561f0184-384c-4ca1-91a4-b2e687865408/record File UUID: after the /detail/ path you will see a UUID value, well, this is your file UUID,  now merge this file UUID with the API endpoint you will get the downloadUrl value from the JSON response and this is your complete download URL, here is the code: import requests def fetchResp(UUID): url = f"https://air.ifpshare.com/api/pub/files/{UUID}" response = requests.get(url) items = response.json()['items'] for n, urls in enumerate(items): image = urls['downloadUrl'] image_url = f"https:{image}" #missing HTTP in the response value so added this manually image_data = requests.get(image_url).content with open(f"image-{n}.png", 'wb') as image_d: image_d.write(image_data) fetchResp('561f0184-384c-4ca1-91a4-b2e687865408') #file UUID is here
Question: My dataframe looks like this: | col1 | col2 | col3 | | ---- | ---- | ---- | | 1 | abc | txt1 | | 1 | abc | txt2 | | 2 | abc | txt3 | | 1 | xyz | txt4 | | 2 | xyz | txt5 |  I want to merge the text in col3 between rows only if the rows have the same value in col1 AND the rows have same value in col2. Expected result: | col1 | col2 | col3 | | ---- | ---- | ---------- | | 1 | abc | txt1, txt2 | | 2 | abc | txt3 | | 1 | xyz | txt4 | | 2 | xyz | txt5 |  I have used this: df = df.groupby([df[col1], df[col2]]).aggregate({'col3': ', '.join})  Which joins the col3 correctly, but it also merges col1 and col2 into one column (list). How can I achieve the expected result while keeping 3 separate columns (col1, col2, col3)?

Answer: A possible solution, which:  Performs a group-by operation using two columns, col1 and col2, as the grouping keys.  It then aggregates the values in col3 for each group by applying a lambda function that concatenates the values into a single string, with each value separated by a comma.   (df.groupby(['col1', 'col2'], as_index=False) .agg({'col3': lambda x: ', '. join(x)}))  Output:  col1 col2 col3 0 1 abc txt1, txt2 1 1 xyz txt4 2 2 abc txt3 3 2 xyz txt5
Question: I have var DICTIONARY, which is a dictionary where the keys are English letters and the values are words that start with the corresponding letter. The initial filling of DICTIONARY looks like this: DICTIONARY = { 'a': 'apple', 'b': 'banana', 'c': 'cat', 'd': 'dog', }  My code has 2 while loops, since higher try-except would end the entire generator loop: def alphabet(): while True: try: letter = yield #waiting for first input from send while True: try: letter = yield DICTIONARY[letter] except KeyError: letter = yield 'default' # return 'default', if key is not found except Exception: letter = yield 'default' except KeyError: letter = yield 'default' except Exception: letter = yield 'default'  However for the input: coro = alphabet() next(coro) print(coro.send('apple')) print(coro.send('banana')) print(coro.throw(KeyError)) print(coro.send('dog')) print(coro.send('d'))  expected output: default default default default dog  But I don't catch last default - it's None: default default default None dog  What is wrong?

Answer: I believe that there is two errors. First - is that you are using while True: block twice. I would simplify the code into smth like this: def alphabet(): while True: try: letter = yield #waiting for first input from send try: word = DICTIONARY[letter] except KeyError: word = 'default' # return 'default', if key is not found except Exception: word = 'default' yield word except KeyError: yield 'default' except Exception: yield 'default'  Second issue is that generator stack on previous yield function. If you'll add more next calls the issue will be fixed: coro = alphabet() next(coro) print(coro.send('apple')) next(coro) print(coro.send('banana')) next(coro) print(coro.throw(KeyError)) next(coro) print(coro.send('dog')) next(coro) print(coro.send('d'))  Output: default default default default dog
Question: I have the following class, and MyPy is reporting the problem NamedTuple type as an attribute is not supported for the self.Data attribute. from collections import namedtuple from collections.abc import MutableSequence class Record(MutableSequence): def __init__(self, recordname: str, fields: list, records=None): if records is None: records = [] self.fields = fields defaults = [""] * len(fields) self.Data = namedtuple(recordname, self.fields, defaults=defaults) self._records = [self.Data(**record) for record in records] self.valid_fieldnames = set(self.fields) self.lookup_tables: dict = {} def __getitem__(self, idx): return self._records[idx] def __setitem__(self, idx, val): self._records[idx] = self.Data(**val) def __delitem__(self, idx): del self._records[idx] def __len__(self): return len(self._records) def insert(self, idx, val): a = self._records[:idx] b = self._records[idx:] if isinstance(val, self.Data): self._records = a + [val] + b else: self._records = a + [self.Data(**val)] + b  I don't understand what the issue is that MyPy is reporting on, and haven't had any luck looking online. The code actually runs as expected without any errors; it's only MyPy that reports an issue and I'm just curious to understand what it is. Some additional info: I am using Python-3.12 in VS Code with the official MyPy Type Checker extension, which is what's reporting the problem. I tried a few things to see if the error would go away, but none of them worked. For example, I removed MutableSequence as a parent class, I deleted the defaults from the namedtuple constructor, and I removed all the dunder methods, but the error persisted.

Answer: This makes a lot of sense if you consider how namedtuple works. It's a code-generator, and it creates a new type. Therefore, if you use it during the class init like this, you will be generating a new type per-instance, and different instances of Record will have different types for self.Data, even if they have the same typename and fields. A static type checker such as Mypy can not do anything useful with attribute value types being created dynamically at runtime. Further, the if isinstance(val, self.Data) logic in the insert method is likely to have some unpleasant surprises, because the types associated with different record instances will have separate identities.
Question: I'm working inside a conda environment and I'm trying to downgrade numpy to version 1.16, but when running pip install numpy==1.16 I keep getting the following error: $ pip install numpy==1.16 Collecting numpy==1.16 Downloading numpy-1.16.0.zip (5.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.1/5.1 MB 10.8 MB/s eta 0:00:00 Preparing metadata (setup.py) ... done Building wheels for collected packages: numpy Building wheel for numpy (setup.py) ... error error: subprocess-exited-with-error × python setup.py bdist_wheel did not run successfully. │ exit code: 1 ╰─> [17 lines of output] Running from numpy source directory. /tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/numpy/distutils/misc_util.py:476: SyntaxWarning: "is" with a literal. Did you mean "=="? return is_string(s) and ('*' in s or '?' is s) Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 34, in <module> File "/tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/setup.py", line 415, in <module> setup_package() File "/tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/setup.py", line 394, in setup_package from numpy.distutils.core import setup File "/tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/numpy/distutils/core.py", line 26, in <module> from numpy.distutils.command import config, config_compiler, \ File "/tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/numpy/distutils/command/config.py", line 19, in <module> from numpy.distutils.mingw32ccompiler import generate_manifest File "/tmp/pip-install-jdof0z8r/numpy_4597057bbb504aa18b7bda112f0aa37f/numpy/distutils/mingw32ccompiler.py", line 34, in <module> from distutils.msvccompiler import get_build_version as get_build_msvc_version ModuleNotFoundError: No module named 'distutils.msvccompiler' [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for numpy Running setup.py clean for numpy error: subprocess-exited-with-error × python setup.py clean did not run successfully. │ exit code: 1 ╰─> [10 lines of output] Running from numpy source directory. `setup.py clean` is not supported, use one of the following instead: - `git clean -xdf` (cleans all files) - `git clean -Xdf` (cleans all versioned files, doesn't touch files that aren't checked into the git repo) Add `--force` to your command to use it anyway if you must (unsupported). [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed cleaning build dir for numpy Failed to build numpy ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (numpy)  How can I resolve this?

Answer: What seemed to solve this issue was installing a specific version of setuptools (<65) into the conda environment: conda install "setuptools <65"
Question: I have a virtual Conda environment named dev that was created using the following YAML file: # *** dev.yml *** name: dev channels: - defaults # Check this channel first - conda-forge # Fallback to conda-forge if packages are not available in defaults dependencies: - python==3.12 - numpy>=2.0.1 # ...more libraries without fixed versions  The environment was created without any dependency issues, and I successfully have NumPy at version 2.0.1. However, when I try to update the packages using the command: conda update --all  I get the following output suggesting that NumPy will be downgraded: The following packages will be DOWNGRADED: numpy 2.0.1-py312h2809609_1 --> 1.26.4-py312h2809609_0 numpy-base 2.0.1-py312he1a6c75_1 --> 1.26.4-py312he1a6c75_0  Why is Conda trying to downgrade NumPy during the update? I understand that dependencies might conflict during an update, but I thought this would be caught during the initial environment creation which runs without dependency warnings. Is there a way to conditionally update packages where dependencies are solvable while keeping the current NumPy version?

Answer: It is likely that an update to one of the packages might have pinned the newest version to use numpy less that v2. To override this behavior, you can pin a version during the update: conda update --all numpy=2.*  Or you can pin the package by adding the package spec to the file at <environment>/conda-meta/pinned before you do the update echo numpy=2.* >>%CONDA_PREFIX%/conda-meta/pinned conda update --all
Question: I am looking for an optimized way of checking for any duplicates in a Panda dataframe column, but excluding a given position in every element of that column. In the example there is a duplication in 'id1_ver1_ready' if we exclude the version number ('id1_ver1_ready' <-> 'id1_ver3_ready'). Same for ( 'id5_ver1_unknown' <-> 'id5_ver6_unknown') from numpy import nan df = pd.DataFrame({'ID': ['id1_ver1_ready', 'id2_ver1_unknown', 'id3_ver1_processed', 'id1_ver3_ready', 'id4_ver1_ready', 'id5_ver1_unknown', 'id5_ver6_unknown', 'id6_ver1_processed']})enter code here 

Answer: Another possible solution, which filters df by removing duplicate entries based on the first and third parts of the ID column:  The str.split('_', expand=True) method splits the ID strings at the underscores into separate columns, resulting in a dataframe where each part of the ID is in its own column.  The duplicated([0, 2]) method then identifies rows that have the same values in the first (index 0) and third (index 2) columns, marking duplicates as True.  The tilde ~ operator negates this boolean mask, so the entire expression returns only the rows of df that have unique combinations of values in the first and third parts of the ID.   df[~df['ID'].str.split('_', expand=True).duplicated([0,2])]  Output:  ID 0 id1_ver1_ready 1 id2_ver1_unknown 2 id3_ver1_processed 4 id4_ver1_ready 5 id5_ver1_unknown 7 id6_ver1_processed
Question: I'm using SymPy version 1.13.3 to integrate an expression involving trigonometric functions. My goal is to linearize trigonometric products using the TR8 function from sympy.simplify.fu. However, the results with and without linearization should be identical, but they are not. Here's a simplified version of my code: import sympy as sp import numpy as np import matplotlib.pyplot as plt from sympy.simplify.fu import TR8 as linearize_trigo_expr from sympy import cos, sin # Definitions JMax = 3 nb_vals_t = 100 t_vals = np.linspace(0, 0.2, nb_vals_t) s, t = sp.symbols('s t') u = -12.33203125*sin(t) + 5.6064453125*sin(4*t) - 2.2708740234375*sin(9*t) + sin(12*t) - 0.103253126144409*sin(16*t) - 1.39863395690918*cos(t) + 1.81103515625*cos(4*t) - 0.4464111328125*cos(9*t) + 0.0336227416992188*cos(16*t) integrand = u * (u.subs(t, s) * sum(sp.sin(j**2 * (t - s)) / j**4 for j in range(1, JMax + 1))) # Without trigonometric linearization integral = sp.integrate(integrand, (s, 0, t)) integral_values = [] for t_val in t_vals: integral_values.append(integral.subs(t, t_val)) plt.plot(t_vals, integral_values) plt.xlabel('t') plt.ylabel('Integral') plt.title("Test without trigonometric linearization") plt.grid(True) plt.show() # With trigonometric linearization integrand_simple = linearize_trigo_expr(integrand) integral_simple = sp.integrate(integrand_simple, (s, 0, t)) integral_simple_values = [] for t_val in t_vals: integral_simple_values.append(integral_simple.subs(t, t_val)) plt.plot(t_vals, integral_simple_values) plt.xlabel('t') plt.ylabel('Integral') plt.title("Test with trigonometric linearization") plt.grid(True) plt.show()  Both graphs should be identical, but they differ. Does anyone know why the trigonometric linearization isn't working as expected, and if there is a solution to make it work properly? NB: this is probably not a MWE, but I have tried to find a simple example showing the mistakes that the TR8 function does. Here are the graphs:  

Answer: The problem is that you are using float numbers with symbolic integration, which sometimes leads to wrong results. When doing symbolic integration, always use exact numbers. Use sympy's nsimplify to convert floats to rational. integrand_2 = integrand.nsimplify() integrand_3 = linearize_trigo_expr(integrand_2) r2 = integrand_2.integrate((s, 0, t)) r3 = integrand_3.integrate((s, 0, t)) sp.plot(r2, r3, (t, 0, 0.2))   The results are the same.
Question: I'm working on a script to convert a data file from one format to another. I need to remove the special characters from the column headers. I am using Pandas to read a CSV file with the below structure. I'm looking for a tidy way to remove the [units] form the column name. Data File: Date ,Time ,app1_sum [Ml] ,app1_q [l/s] ,app1_h [m] ,app1_a [m²] ,app1_v [m/s] ,app1_t_water [°C] 02.10.2024 ,19:05:00 ,57293.336 ,620.78 ,0.436 ,0.586 ,1.059 ,18.2  My goal is to have column names stripped down to their simplest form: Date,Time,app1_sum,app1_q,app1_h,app1_a,app1_v,app1_t_water My current approach is to remove the brackers first, then remove the remining units one at a time. df.columns = df.columns.str.replace('[', '') df.columns = df.columns.str.replace(']', '') df.columns = df.columns.str.replace(' Ml', '')  I also tried to use regex to remove one unit at a time, this works but doesn't feel right. df.rename(columns=lambda x: re.sub(r'\[Ml\]', '', x), inplace=True) df.rename(columns=lambda x: re.sub(r'\[l/s\]', '', x), inplace=True) df.rename(columns=lambda x: re.sub(r'\[m\]', '', x), inplace=True) df.rename(columns=lambda x: re.sub(r'\[m²\]', '', x), inplace=True) df.rename(columns=lambda x: re.sub(r'\[m/s\]', '', x), inplace=True) df.rename(columns=lambda x: re.sub(r'\[°C\]', '', x), inplace=True) 

Answer: You can use the following code to replace all brackets and their content in the columns names: df.rename(columns=lambda x: re.sub(r'\[[^\]]+\]', '', x), inplace=True)  \[ will match the opening bracket [ [^\]]+ will match an abtritrary number of every character except ] \] will match the closing bracket The \ are needed to escape the brackets since they are part of regex syntax
Question: This is quite a simple ask but I can't seem to find any clear simplistic solution to this, feels like I'm missing something. Let's say I have a DataFrame of type df = pl.from_repr(""" ┌───────┬───────┬───────┐ │ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ bool ┆ bool ┆ bool │ ╞═══════╪═══════╪═══════╡ │ false ┆ true ┆ false │ │ false ┆ false ┆ false │ │ false ┆ false ┆ false │ └───────┴───────┴───────┘ """)  How do I do a simple check if any of the values in the DataFrame is True? Some solutions I have found is selection = df.select(pl.all().any(ignore_nulls=True))  or selection = df.filter(pl.any_horizontal())  and then check in that row any(selection.row(0))  Is just seems like so many steps for a single check

Answer: These two options are a bit shorter and stay in pure Polars. # Unpivot all the booleans into a single "value" column # Pull the "value column out as a Series any do the any df.unpivot()["value"].any() # pl.all().any() checks for any True values per column # pl.any_horizontal() checks horizontally per row, reducing to a single value df.select(pl.any_horizontal(pl.all().any())).item()  To your question  This is quite a simple ask but I can't seem to find any clear simplistic solution to this, feels like I'm missing something. It just seems like so many steps for a single check  You are not missing anything. The reason it feels like a bit more work is because a DataFrame can be thought of more like a (database) table. Generally you have different columns of potentially different types, and you want to different calculations with different columns. So reducing both dimensions into a single value in a single step is just not something typically offered by DataFrame libraries. Numpy is much better suited if you have matrices and does offer this in a single step. arr = df.to_numpy() arr.any() # True