eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I have a pandas DataFrame like this:     id unit step phase start_or_end_of_phase op_name occurence     1 A 50l LOAD start P12load5 2   2 A 50l LOAD end P12load5 2   3 A 50l STIR start P12s5 4   4 A 50l STIR end P13s5 3   5 A 50l COLLECT start F7_col1 1   6 A 50l COLLECT end H325_col1 1   7 A 1000l SET_TEMP start xyz 2   8 A 1000l SET_TEMP end qwe 3   9 A 1000l SET_TEMP2 start asf 4   10 A 1000l SET_TEMP2 end fdsa 5   11 A 1000l FILTER start 4fags 1   11 A 1000l FILTER end mllsgrs_1 1   12 B MACHINE1 ... ... ... ...     ...and want to create nested dictionaries like this: A = {50l : { 'LOAD' : {'start':{'op_name' : 'p12load5', 'occurrence': 2}, 'end':{'op_name': 'P12load5', 'occurrence': 2}}, 'STIR': {'start':{'op_name' : 'P12s5', 'occurrence': 4}, 'end':{'op_name': 'P13s5', 'occurrence': 3}}, 'COLLECT': {'start':{'op_name' : 'F7_col1', 'occurrence': 1}, 'end':{'op_name': 'H325_col1', 'occurrence': 1}} }, 1000l : { 'SET_TEMP' : ....  I have been trying to combine groupby() with to_dict() but couldn't wrap my head around it. My last attempt was this (based on How to convert pandas dataframe to nested dictionary): populated_dict = process_steps_table.groupby(['unit', 'step', 'phase', 'start_or_end_phase']).apply(lambda x: x.set_index('start_or_end_phase').to_dict(orient='index')).to_dict()  and got his error: DataFrame index must be unique for orient='index'. I am not sure if I have to apply the set_index() lambda function to the groups and why.

Answer: You have to reshape your dataframe before export as dictionary: nested_cols = ['step', 'phase', 'start_or_end_of_phase'] value_cols = ['op_name', 'occurence'] # Reshape your dataframe df1 = df.set_index(nested_cols)[value_cols].stack() # Export nested dict d = {} # items(): # t -> flatten index to convert to nested dict # v -> last level of your nested dict (values) for t, v in df1.items(): e = d.setdefault(t[0], {}) # create a new entry with an empty dict for k in t[1:-1]: e = e.setdefault(k, {}) # create a nested sub entry with an empty dict e[t[-1]] = v # finally add values when you reach the end of the index  Output import json # just for a best representation print(json.dumps(d, indent=4)) # Output { "50l": { "LOAD": { "start": { "op_name": "P12load5", "occurence": 2 }, "end": { "op_name": "P12load5", "occurence": 2 } }, "STIR": { "start": { "op_name": "P12s5", "occurence": 4 }, "end": { "op_name": "P13s5", "occurence": 3 } }, "COLLECT": { "start": { "op_name": "F7_col1", "occurence": 1 }, "end": { "op_name": "H325_col1", "occurence": 1 } } }, "1000l": { "SET_TEMP": { "start": { "op_name": "xyz", "occurence": 2 }, "end": { "op_name": "qwe", "occurence": 3 } }, "SET_TEMP2": { "start": { "op_name": "asf", "occurence": 4 }, "end": { "op_name": "fdsa", "occurence": 5 } }, "FILTER": { "start": { "op_name": "4fags", "occurence": 1 }, "end": { "op_name": "mllsgrs_1", "occurence": 1 } } } }
Question: How does mypy apply the Liskov substitution principle to *args, **kwargs parameters? I thought the following code should fail a mypy check since some calls to f allowed by the Base class are not allowed by C, but it actually passed. Are there any reasons for this? from abc import ABC, abstractmethod from typing import Any class Base(ABC): @abstractmethod def f(self, *args: Any, **kwargs: Any) -> int: pass class C(Base): def f(self, batch: int, train: bool) -> int: return 1  I also tried to remove either *args or **kwargs, both failed.

Answer: Unlike Daniil said in currently accepted answer, the reason is exactly (*args: Any, **kwargs: Any) signature part. Please check the corresponding discussion on mypy issue tracker:  I actually like this idea, I have seen this confusion several times, and although it is a bit unsafe, most of the time when people write (*args, **kwargs) it means "don't care", rather than "should work for all calls".   [GVR] Agreed, this is a case where practicality beats purity.  So, mypy gives a special treatment to functions of form # _T is arbitrary type class _: def _(self, *args, **kwargs) -> _T: ...  and considers them fully equivalent to Callable[..., _T]. Yes, this actually violates LSP, of course, but this was designed specially to allow declaring functions with signature "just ignore my parameters". To declare the broadest possible function that really accepts arbitrary positional and keyword arguments, you should use object in signature instead.
Question: I have a problem starting Playwright in Python maximized. I found some articles for other languages but doesn't work in Python, also nothing is written about maximizing window in Python in the official documentation. I tried browser = p.chromium.launch(headless=False, args=["--start-maximized"]) And it starts maximized but then automatically restores back to the default small window size. Any ideas? Thanks

Answer: I just found the answer: I need to set also the following and it works: browser.new_context(no_viewport=True)
Question: I was able to save the data to a Django model without any errors, but data not reflected in db. But after a sleep time I was able to save the data again with same method. What might be causing this ? I suspect use of the Google API, but was able to print the data before performing the save operation. def update_channel(): client = Client.objects.get(name="name") print(f"Existing channel: {data['id']}") # 123 # fetch channel data from google api data = google_drive.subscribe_new_channel() client.channel_id = data["id"] client.channel_resource_id = data["resourceId"] client.save() client.refresh_from_db() print(f"New channel: {data['id']}") # 456 print(f"New channel in db: {client.channel_id}") # 456 time.sleep(5) client.refresh_from_db() print(f"channel in db: {client.channel_id}") # 123  Sample Output: Existing channel: 123 New channel: 456 New channel in db: 456 channel in db: 123 

Answer: This can happen if another process has already fetched the same client object and saved the object after your save operation. In this case, the data in the second process still be the old one and overwrites your change when it saves.
Question: I am looking to create a python function that will take a long SQL script that I have to create a table and place the session variables into the script so it can be used as a view within Snowflake. For example, SET TABLE_NAME = MY_TABLE_NAME; CREATE OR REPLACE VIEW MY_VIEW AS ( SELECT * FROM IDENTIFIER($TABLE_NAME) )  With the python script, the previous block becomes CREATE OR REPLACE VIEW MY_VIEW AS( SELECT * FROM MY_TABLE )  However, during testing, I realized that the if the variable name is within another function, the last parenthesis is captured and is removed. Is there a way that I can replace the string with the variable value only if it is wrapped in the identifier function? I would like this code: IDENTIFIER($VAR_NAME) identifier($VAR_NAME) SELECT * FROM $VAR_NAME DATEADD('DAY',+1,$VAR_NAME)  To become: VAR_NAME VAR_NAME SELECT * FROM VAR_NAME DATEADD('DAY',+1,VAR_NAME)  This is what I have tried so far. https://regex101.com/r/2SriK9/2 Thanks. P.S. In the last example, if var_name were a function, it would need to have the function and then close with a closing parenthesis: DATEADD('DAY',+1,MY_FUNC()) [Currently, my output makes it DATEADD('DAY',+1,MY_FUNC()] with no closing parenthesis on the dateadd function.

Answer: There are two patterns you're looking for here: one enclosed in the identifier function, and the other with just a preceding $ character, so you can use an alternation pattern to search for both of them, capture the variable names of each, if any, and replace the match with what's captured. Find (with the case-insensitive flag): identifier\(\$(\w+)\)|\$(\w+)  Replace with: \1\2  Demo: https://regex101.com/r/2SriK9/3
Question: Once in a while I get to the point where I need to run the following line: DF['is_flagged'] = DF['id'].isin(DF2[DF2['flag']==1]['id'])  Lately I started using polars, and I wonder how to convert it easily to polars. For example: df1 = pl.DataFrame({ 'Animal_id': [1, 2, 3, 4, 5, 6, 7], 'age': [4, 6, 3, 8, 3, 8, 9] }) df2 = pl.DataFrame({ 'Animal_id': [1, 2, 3, 4, 5, 6, 7], 'Animal_type': ['cat', 'dog', 'cat', 'cat', 'dog', 'dog', 'cat'] })  Expected output: shape: (7, 3) ┌───────────┬─────┬────────┐ │ animal_id ┆ age ┆ is_dog │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 │ ╞═══════════╪═════╪════════╡ │ 1 ┆ 4 ┆ 0 │ │ 2 ┆ 6 ┆ 1 │ │ 3 ┆ 3 ┆ 0 │ │ 4 ┆ 8 ┆ 0 │ │ 5 ┆ 3 ┆ 1 │ │ 6 ┆ 8 ┆ 1 │ │ 7 ┆ 9 ┆ 0 │ └───────────┴─────┴────────┘  Without using flag and then join I tried to use the is_in() function but this didn’t worked.

Answer: So to be honest I am not quite sure from your question, your pandas snippet and your example what your desired solution is, but here are my three takes. import polars as pl df1 = pl.DataFrame( {"Animal_id": [1, 2, 3, 4, 5, 6, 7], "age": [4, 6, 3, 8, 3, 8, 9]} ).lazy() df2 = pl.DataFrame( { "Animal_id": [1, 2, 3, 4, 5, 6, 7], "Animal_type": ["cat", "dog", "cat", "cat", "dog", "dog", "cat"], } ).lazy()  1 Solution So this one is a small adaptation of the solution of @ignoring_gravity. So the assumption in his solution is that the DataFrames have the same length and the Animal_id matches in both tables. If that was your goal I want to give you another solution because by subsetting (["is_dog"]) you lose the possibility to use the lazy api. df1.with_context( df2.select(pl.col("Animal_type").is_in(["dog"]).cast(int).alias("is_dog")) ).select(["Animal_id", "age", "is_dog"]).collect()  2 Solution So in case you want something more similar to your pandas snippet and because you wrote you don't want to have a join. df1.with_context( df2.filter(pl.col("Animal_type") == "dog").select( pl.col("Animal_id").alias("valid_ids") ) ).with_columns( [pl.col("Animal_id").is_in(pl.col("valid_ids")).cast(int).alias("is_dog")] ).collect()  3 Solution So this would be the solution with a join. In my opinion the best solution regarding your example and example output, but maybe there are other reasons that speak against a join, which aren't apparent from your example. df1.join( df2.select( ["Animal_id", pl.col("Animal_type").is_in(["dog"]).cast(int).alias("is_dog")] ), on=["Animal_id"], ).collect()
Question: I am trying to add the numpy package to my lambda function, but I can't import it. I have followed several tutorials, but they all have the same problem. On my last attempt, I executed the following step by step:  I created a lambda function in AWS and tested it Installed the numpy package on my local machine and zipped it Created lambda layer and loaded the numpy package Fixed the layer to lambda and tested  When I run my lambda function without importing numpy, it works perfectly, however when I import it I get this error: { "errorMessage": "Unable to import module 'lambda_function': No module named 'numpy'", "errorType": "Runtime.ImportModuleError", "requestId": "0153834a-6b28-44d1-889f-3e2e3ead9c4a", "stackTrace": [] }  A very common error on the forum, however everything is fine with lambda_function, because as I said it works fine if I'm not importing any module.  lambda_function.py  import json import numpy def lambda_handler(event, context): # TODO implement return { 'statusCode': 200, 'body': json.dumps('Hello from Lambda!') }  I would like to learn how to use Layers inside Lambda.

Answer: There are at least two ways to add NumPy to your AWS Lambda as layer:  Add an AWS (Managed) Layer: From your Lambda console, choose AWS Layers and AWSSDKPandas-Python39 (Choose a layer accordingly for your Python version). Since Pandas is built on top of NumPy, you should be able to use NumPy once you add this Pandas layer.  Add a custom layer: Create a virtual environment on your local machine, install NumPy, create a zip file, and upload to AWS as a layer. Ensure the version of Python in your local Virtual env is the same as the Lambda. Step by step instruction here. This is a general approach which you can use to add any external Python libraries as Lambda layers.
Question: class MyValues(enum.Enum): value1 = 1 value2 = 2 value3 = 1 print(MyValues._member_names_)  Output would be a list with only the first two members [value1,value2] I would also like to have value3 in that list, i tried with aenum with NoAlias setting but did not work. Is there any way to get all members even if they have duplicate values?

Answer: _member_names_ is not part of the Enum documented public API. You should not use it. __members__ is what you're looking for. It's a mapping from member names to members, including all aliases. You can use list(MyValues.__members__) if you just want a list of the names.
Question: I have a python script in which logs messages to the console. But I also want those messages to be sent to the telegram using my bot. Any hint and suggestion will be helpful. Thanks in advance. I haven't tried anything yet, just got thought if that possible or not and if it is then how?

Answer:  Make A Telegram Bot Using BotFather for Telegram Search for BotFather in your Telegram client by opening it. A pre-installed Telegram bot that assists users in making original Telegram bots To create a new bot, enter /newbot. Name and username your bot. Copy the token for your new Telegram bot. Please take note that anyone who obtains your token has complete control over your Telegram bot, so avoid uploading it online.  Obtaining a chat ID To send Telegram messages using Python, we require the conversation ID that each chat in Telegram holds.   Send a message to your Telegram bot (any random message) Use this Python program to locate your chat ID. import requests TOKEN = "YOUR TELEGRAM BOT TOKEN" url = f"https://api.telegram.org/bot{TOKEN}/getUpdates" print(requests.get(url).json())  The getUpdates function, which is invoked by this script, sort of checks for new messages. Our chat ID can be located in the JSON that was returned (the one in red) Please take note that your results can be empty if you don't message your Telegram bot. Copy the chat ID and paste it in the following step.  Python-based Telegram message delivery The following Python script requires you to enter 1) your Telegram bot token and 2) your chat ID from the previous two steps. (Be sure to include modify your message.)  import requests TOKEN = "YOUR TELEGRAM BOT TOKEN" chat_id = "YOUR CHAT ID" message = "hello from your telegram bot" url = f"https://api.telegram.org/bot{TOKEN}/sendMessage?chat_id={chat_id}&text={message}" print(requests.get(url).json()) # this sends the message  Run the Python script and check your Telegram!
Question: In python we can add lists to each other with the extend() method but it adds the second list at the end of the first list. lst1 = [1, 4, 5] lst2 = [2, 3] lst1.extend(lst2) Output: [1, 4, 5, 2, 3]  How would I add the second list to be apart of the 1st element? Such that the result is this; [1, 2, 3, 4, 5 ]  I've tried using lst1.insert(1, *lst2) and got an error; TypeError: insert expected 2 arguments, got 3 

Answer: For those who don't like reading comments: lst1 = [1, 4, 5] lst2 = [2, 3] lst1[1:1] = lst2 print(lst1)  Output: [1, 2, 3, 4, 5]
Question: I want to generate a statistical sample from a multidimensional normal distribution. For this I need to generate one specific kind of covariance matrix: 1 0.99 0.98 0.97 ... 0.99 1 0.99 0.98 ... 0.98 0.99 1 0.99 ... 0.97 0.98 0.99 1 ... ... ... ... ...  Is there a way to generate this kind of matrix for multiple dimensions easily, without writing it by hand? (I need to have matrices with 50-100 dimensions, so doing it by hand is very tedious.)

Answer: You can use the scipy.linalg.toeplitz function, as it is made for exactly this kind of matrix: >>> import numpy as np >>> from scipy import linalg >>> linalg.toeplitz(np.arange(1,0,-0.1), np.arange(1,0,-0.1)) array([[1. , 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1], [0.9, 1. , 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2], [0.8, 0.9, 1. , 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3], [0.7, 0.8, 0.9, 1. , 0.9, 0.8, 0.7, 0.6, 0.5, 0.4], [0.6, 0.7, 0.8, 0.9, 1. , 0.9, 0.8, 0.7, 0.6, 0.5], [0.5, 0.6, 0.7, 0.8, 0.9, 1. , 0.9, 0.8, 0.7, 0.6], [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 0.9, 0.8, 0.7], [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 0.9, 0.8], [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 0.9], [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]])
Question: The following code generates two DataFrames: frame1=pd.DataFrame({'dates':['2023-01-01','2023-01-07','2023-01-09'],'values':[0,18,28]}) frame1['dates']=pd.to_datetime(frame1['dates']) frame1=frame1.set_index('dates') frame2=pd.DataFrame({'dates':['2023-01-08','2023-01-12'],'values':[8,12]}) frame2['dates']=pd.to_datetime(frame2['dates']) frame2=frame2.set_index('dates')  Using frame1.asfreq('D').interpolate() frame2.asfreq('D').interpolate()  we can interpolate their values between the days to obtain  and  However, consider now the concatenation table: frame1['frame']='f1' frame2['frame']='f2' concat=pd.concat([frame1,frame2]) concat=concat.set_index('frame',append=True) concat=concat.reorder_levels(['frame','dates']) concat   I want to do the interpolation using one command like concat.groupby('frame').apply(lambda g:g.asfreq('D').interpolate())  direktly in the concatenation table. Unfortunately, my above command does not work but raises a TypeError: TypeError: Cannot convert input [('f1', Timestamp('2023-01-01 00:00:00'))] of type <class 'tuple'> to Timestamp  How do I fix that command to work?

Answer: You have to drop the first level index (the group key) before use asfreq like your initial dataframes: >>> concat.groupby('frame').apply(lambda g: g.loc[g.name].asfreq('D').interpolate()) values frame dates f1 2023-01-01 0.0 2023-01-02 3.0 2023-01-03 6.0 2023-01-04 9.0 2023-01-05 12.0 2023-01-06 15.0 2023-01-07 18.0 2023-01-08 23.0 2023-01-09 28.0 f2 2023-01-08 8.0 2023-01-09 9.0 2023-01-10 10.0 2023-01-11 11.0 2023-01-12 12.0  To debug use a named function instead of a lambda function: def interpolate(g): print(f'[Group {g.name}]') print(g.loc[g.name]) print() return g.loc[g.name].asfreq('D').interpolate() out = concat.groupby('frame').apply(interpolate)  Output: [Group f1] values dates 2023-01-01 0 2023-01-07 18 2023-01-09 28 [Group f2] values dates 2023-01-08 8 2023-01-12 12
Question: How to load toml file into a python file that my code python file: import toml toml.get("first").name  toml file : [first] name = "Mark Wasfy" age = 22 [second] name = "John Wasfy" age = 25 

Answer: it can be used without open as a file import toml data = toml.load("./config.toml") print(data["first"]["name"])
Question: Trying to go from a script kiddie to a semi-respectable software engineer and need to learn how to write clean, digestible code. The book I'm reading pointed me towards PEP 8 - I know this is the foundational styling guide for Python. What I can't seem to figure out is if all the guidelines are still valid today in 2022 and nothing has changed since its last update in 2013 OR if there are supplemental PEPs I should be reading. Visited https://peps.python.org/pep-0000/ and started browsing through the different releases but got confused and unsure which PEPs besides 8 have to do with style guidelines. I found this previous question from 9 years ago and wanted to see if any of the answers have changed.

Answer: At the top, below the authors, you can see it says  Status: Active  As the tooltip explains, that means PEP 8 is  Currently valid informational guidance, or an in-use process  If the PEP is ever replaced it will say "Status: Superseded". At the bottom of the page it says:  Last modified: 2022-05-11 17:45:05 GMT  You can check the link to see what changes have been made to PEP 8 when.
Question: The full error: NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].  I get this when attempting to train a YOLOv8 model on a Windows 11 machine, everything works for the first epoch then this occurs.  I also get this error immediately after the first epoch ends but I don't think it is relevant. Error executing job with overrides: ['task=detect', 'mode=train', 'model=yolov8n.pt', 'data=custom.yaml', 'epochs=300', 'imgsz=160', 'workers=8', 'batch=4']  I was trying to train a YOLOv8 image detection model utilizing CUDA GPU.

Answer: This error occurs when you have Torch and Torchaudio for CUDA but not Torchvision for CUDA installed. Uninstall Torch and Torchvision, I used pip: pip uninstall torch torchvision  Then go here to install the proper versions of both using the nice interface. I got the following command: pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
Question: I'm trying to install guesslang with pip but it seems that the last version (which was released on August 2021) depends on an obsolete version of Tensorflow (2.5.0). The problem is that I can't find this version anywhere. So, how can I install it? Or is there any other python library that does language detection? However here's the error I get when trying to install it, maybe I misunderstood... > pip install guesslang Collecting guesslang Using cached guesslang-2.2.1-py3-none-any.whl (2.5 MB) Using cached guesslang-2.2.0-py3-none-any.whl (2.5 MB) Using cached guesslang-2.0.3-py3-none-any.whl (2.1 MB) Using cached guesslang-2.0.1-py3-none-any.whl (2.1 MB) Using cached guesslang-2.0.0-py3-none-any.whl (13.0 MB) Using cached guesslang-0.9.3-py3-none-any.whl (3.2 MB) Collecting numpy Using cached numpy-1.24.1-cp310-cp310-win_amd64.whl (14.8 MB) Collecting guesslang Using cached guesslang-0.9.1-py3-none-any.whl (3.2 MB) ERROR: Cannot install guesslang==0.9.1, guesslang==0.9.3, guesslang==2.0.0, guesslang==2.0.1, guesslang==2.0.3, guesslang==2.2.0 and guesslang==2.2.1 because these package versions have conflicting dependencies. The conflict is caused by: guesslang 2.2.1 depends on tensorflow==2.5.0 guesslang 2.2.0 depends on tensorflow==2.5.0 guesslang 2.0.3 depends on tensorflow==2.5.0 guesslang 2.0.1 depends on tensorflow==2.2.0 guesslang 2.0.0 depends on tensorflow==2.2.0 guesslang 0.9.3 depends on tensorflow==1.7.0rc1 guesslang 0.9.1 depends on tensorflow==1.1.0 To fix this you could try to: 1. loosen the range of package versions you've specified 2. remove package versions to allow pip attempt to solve the dependency conflict ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts 

Answer: tensorflow 2.5.0 released wheels for Python 3.6-3.9. Downgrade to Python 3.9 to install guesslang with tensorflow 2.5.0.
Question: Currently I do it this way: import pandas as pd dt = pd.DataFrame({ '1st':[1,0,1,0,1], '2nd':[2,1,2,1,2], '3rd':[3,0,3,2,3], '4th':[4,3,4,3,4], '5th':[5,0,5,4,5], 'minute_traded':[6,5,6,5,6] }) dt = dt[ (dt['1st'] < dt['2nd']) & (dt['2nd'] < dt['3rd']) & (dt['3rd'] < dt['4th']) & (dt['4th'] < dt['5th']) & (dt['5th'] < dt['minute_traded']) ] print(dt)  Result:  1st 2nd 3rd 4th 5th minute_traded 0 1 2 3 4 5 6 2 1 2 3 4 5 6 3 0 1 2 3 4 5 4 1 2 3 4 5 6  Is there a more correct method for an analysis like this that always uses the same pattern and only changes the columns to be analyzed?

Answer: Using shift to perform the comparison and all to aggregate as single boolean for boolean indexing: out = dt[dt.shift(axis=1).lt(dt).iloc[:, 1:].all(axis=1)]  Output:  1st 2nd 3rd 4th 5th minute_traded 0 1 2 3 4 5 6 2 1 2 3 4 5 6 3 0 1 2 3 4 5 4 1 2 3 4 5 6