eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I'm trying to install PyTorch with CUDA support on my Windows 11 machine, which has CUDA 12 installed and python 3.10. When I run nvcc --version, I get the following output: nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Tue_Aug_15_22:09:35_Pacific_Daylight_Time_2023 Cuda compilation tools, release 12.2, V12.2.140 Build cuda_12.2.r12.2/compiler.33191640_0  I'd like to install PyTorch version 2.0.0 with CUDA support, so I attempted to run the following command: python -m pip install torch==2.0.0+cu117  However, I encountered the following error: ERROR: Could not find a version that satisfies the requirement torch==2.0.0+cu117 (from versions: 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 2.0.0, 2.0.1) ERROR: No matching distribution found for torch==2.0.0+cu117  Does anyone have any suggestions?

Answer: To install PyTorch using pip or conda, it's not mandatory to have an nvcc (CUDA runtime toolkit) locally installed in your system; you just need a CUDA-compatible device. To install PyTorch (2.0.1 with CUDA 11.7), you can run: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117  For CUDA 11.8, run: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  If you want to use CUDA 12.1, you can install PyTorch Nightly using: (FYI: as of today (Sep 9, 2023), cu12.1 is not available for a stable release.) pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121  You can find more details on PyTorch's official page: https://pytorch.org/get-started/locally/. I hope it helps you. Thank you!
Question: I need to convert a markdown table into a pandas DataFrame. I've managed to do this using the pd.read_csv function with '|' as the separator, but it seems like there's some additional cleanup required. Specifically, I need to remove the row containing '-----', which is used for table separation, and I also want to get rid of the last column. Here's a simplified example of what I'm doing: import pandas as pd from io import StringIO # The text containing the table text = """ | Some Title | Some Description | Some Number | |------------|------------------------------|-------------| | Dark Souls | This is a fun game | 5 | | Bloodborne | This one is even better | 2 | | Sekiro | This one is also pretty good | 110101 | """ # Use StringIO to create a file-like object from the text text_file = StringIO(text) # Read the table using pandas read_csv with '|' as the separator df = pd.read_csv(text_file, sep='|', skipinitialspace=True) # Remove leading/trailing whitespace from column names df.columns = df.columns.str.strip() # Remove the index column df = df.iloc[:, 1:]  Is there a more elegant and efficient way to convert a markdown table into a DataFrame without needing to perform these additional cleanup steps? I'd appreciate any suggestions or insights on improving this process.

Answer: Like this import re import pandas as pd text = """ | Some Title | Some Description | Some Number | |------------|------------------------------|-------------| | Dark Souls | This is a fun game | 5 | | Bloodborne | This one is even better | 2 | | Sekiro | This one is also pretty good | 110101 | """ pattern = r"\| ([\w\s]+) \| ([\w\s]+) \| ([\w\s]+) \|" # Use the findall function to extract all rows that match the pattern matches = re.findall(pattern, text) # Extract the header and data rows header = matches[0] data = matches[1:] # Create a pandas DataFrame using the extracted header and data rows df = pd.DataFrame(data, columns=header) # Optionally, convert numerical columns to appropriate types df['Some Number'] = df['Some Number'].astype(int) print(df)
Question: I'm newly working with a MultiIndex, and am struggling with how to get certain cross-sections of data. Specifically, when I want to specify more than one category within an index level, but not all categories at that level. Borrowing from pandas documentation for the data: d = {'num_legs': [4, 4, 2, 2], 'num_wings': [0, 0, 2, 2], 'class': ['mammal', 'mammal', 'mammal', 'bird'], 'animal': ['cat', 'dog', 'bat', 'penguin'], 'locomotion': ['walks', 'walks', 'flies', 'walks']} df = pd.DataFrame(data=d) df = df.set_index(['class', 'animal', 'locomotion']) df num_legs num_wings class animal locomotion mammal cat walks 4 0 dog walks 4 0 bat flies 2 2 bird penguin walks 2 2  I'm using pandas.DataFrame.xs() to grab the data I want. I'm able to get crossection based on one citerion like so: df.xs('mammal',level = 'class'), and can get refine on multiple levels like so: df.xs(('mammal','flies'),level = ['class','locomotion']) Where things break down is when I want to specify, say, bat and penguin, instead of just bat. A statement like df.xs((('mammal','bird')),level = ['class','class']) technically works, but it's looking for data that is both 'mammal' and 'bird', returning an empty dataframe. Statements like df.xs((('mammal','bird')),level = 'class') return errors. The .xs documentation doesn't cover this case. Is what I want possible? Is there a better way altogether? Any light you can shine on the subject is appreciated. EDIT: Answers by both respondents very helpful. I should have mentioned that some of that data I am working with is multi-indexed at the column level, which is part of why I was looking at df.xs(). Is there a similar simple way of specifying multiple criteria at the same level for multi-indexed columns?

Answer: For these cases, I recommend just to use .loc: out = df.loc[(["mammal", "bird"], slice(None), slice(None))] print(out)  Prints:  num_legs num_wings class animal locomotion mammal cat walks 4 0 dog walks 4 0 bat flies 2 2 bird penguin walks 2 2   EDIT: For multi-index columns: Initial df:  num_legs num_wings A B A B class animal locomotion mammal cat walks 4 0 1 2 dog walks 4 0 1 2 bat flies 2 2 1 2 bird penguin walks 2 2 2 2 bacteria coli swims -1 -1 -1 -1  Then: out = df.loc[(["mammal", "bird"], slice(None), "walks"), ((slice(None), "A"))] print(out)  Prints:  num_legs num_wings A A class animal locomotion mammal cat walks 4 1 dog walks 4 1 bird penguin walks 2 2
Question: My jax code runs fine but when I try to insert a breakpoint with jax.debug.breakpoint I get the error: jax.errors.UnexpectedTracerError. I would expect this error to show up also without setting a breakpoint. Is this intended behavior or is something weird happening? When using jax_checking_leaks none of the reported tracers seem to actually be leaked.

Answer: There is currently a bug in jax.debug.breakpoint that can lead to spurious tracer leaks in some situations: see https://github.com/google/jax/issues/16732. There's not any easy workaround at the moment, unfortunately, but hopefully the issue will be addressed soon.
Question: I read that linprog from scipy returns minimal solutions and one could get the optimal by multiply the objective function by -1. I read it here: https://realpython.com/linear-programming-python/ And I've tested the example they provided to see if i could get the minimal solution too -- I could.  Regarding the problem im trying to solve I would expect the solutions to be: opt_sol1.x = [0.61538462 0.38461538] opt_sol2.x = [0.0, 1.0]  but i get the same result [0.61538462 0.38461538] in both cases why? -- my guess is it relates to the values in my objective function to be close to one another but just a guess And is there a way i can get the second solution im looking for? from scipy.optimize import linprog obj_fct1 = [0.5, 0.5] obj_fct2 = [-0.5, -0.5] lhs_ineq = [[1.5, 0.2]] rhs_ineq = [1] lhs_eq = [[1,1]] rhs_eq = [1] bnds = [(0, 1), (0, 1)] opt_sol1 = linprog(c=obj_fct1, A_ub=lhs_ineq, b_ub=rhs_ineq, A_eq=lhs_eq, b_eq=rhs_eq, bounds=bnds) print(opt_sol1.x) print("------------------------------------------") opt_sol2 = linprog(c=obj_fct2, A_ub=lhs_ineq, b_ub=rhs_ineq, A_eq=lhs_eq, b_eq=rhs_eq, bounds=bnds) print(opt_sol2.x) >>> [0.61538462 0.38461538] >>> ------------------------------------------ >>> [0.61538462 0.38461538] 

Answer: The "issue" here is that there are an infinite number of minima and maxima for this problem, with the objective function equal to the same value for all optimal solutions (ignoring the sign flip for maximization). This can be seen by examining your equality constraint relative to your objective function and noting that they are linear combinations of each other. In the case of linear programs, the optimal solution lies on the boundary, either a line or vertex. If it is at a vertex and not a line, then there is a unique solution, but if it lies on a line, then there are infinite solutions. Because your objective function and equality constraint are linear combinations of each other, the consequence is that there are an infinite number of solutions and the minima and maxima are the same. In your case, your objective is f(x1, x2) = 0.5*x1 + 0.5*x2 or, equivalently, f(x1, x2) = 0.5*(x1 + x2). Your equality constraint says that x1 + x2 = 1, so we see that f(x1, x2) = 0.5 is the result. (If we were maximizing, we would change the sign of the objective function as you did and get that f(x1,x2) = -0.5 for all feasible solutions.) Considering there are an infinite number of solutions for both the maximization and minimization problems, why does the solver always return [0.61538462 0.38461538]? The default solver uses a form of the SIMPLEX algorithm, which always returns a vertex value (even when there is a line of solutions, a vertex value is still a solution, and this narrows down the number of possible solutions that need to be checked). In this case, there is a vertex when x1 + x2 = 1 and 1.5*x1 + 0.2*x2 = 1 intersect, which is at x1 = 8/13 and x2 = 5/13.
Question: I am a newbie using DuckDb library in python and while going through docs I stumbled upon 2 functions to execute sql instructions, namely execute() and sql(). What's the difference between the 2? I am really scratching my head with this.

Answer: While sql and execute can be used to achieve the same results, they have slight differences which may impact which function you use. The sql function runs the query as-is. It will return a DuckDBPyRelation which allows "constructing relationships".  duckdb.sql(query: str, alias: str = 'query_relation', connection: duckdb.DuckDBPyConnection = None) → duckdb.DuckDBPyRelation Run a SQL query. If it is a SELECT statement, create a relation object from the given SQL query, otherwise run the query as-is.  The execute function will also run queries, but can handle prepared statements that accepts parameters and returns the connection DuckDBPyConnection instead of a relationship.  duckdb.execute(query: str, parameters: object = None, multiple_parameter_sets: bool = False, connection: duckdb.DuckDBPyConnection = None) → duckdb.DuckDBPyConnection Execute the given SQL query, optionally using prepared statements with parameters set
Question: I am trying to process the contents of a file. I need ideas as to how to interact with the user and to wait for a press of a key (s) to write the current line into another file. Thank you in advance. with open(srcFile) as f: line = f.readline().strip() #- do something with line #- part where I need help if s is press, write the current line to file if s is not pressed after 5 seconds, continue with the next line in srcFile 

Answer: you can use the time and keyboard package for this case: import time import keyboard start_time = time.time() # Start the timer key_pressed = False while True: if keyboard.is_pressed("s"): key_pressed = True break if time.time() - start_time >= 5: break if key_pressed: # write the current line to file  You might test with the above code to see the effect.
Question: I want to use the Association Rule Mining package from PyCaret. I installed the same using: pip install pycaret[full]  However, when I try to import the arules module, I get the ModuleNotFoundError: >>> from pycaret.arules import * Traceback (most recent call last): File "<stdin>", line 1, in <module> ModuleNotFoundError: No module named 'pycaret.arules'  What should I do to be able to use the association rule mining module?

Answer: Arules module was removed from Pycaret versions 3.x. If you really need to use Arules, then you need to downgrade your Pycaret to at least 2.3.10. I tested with Pycaret 2.3.5 and it works fine: pip install pycaret==2.3.5
Question: I have two key/value inputs, both in the form of nested lists. Any given key may have multiple values, as with K1 and K2 here: key_in = [['K1', 'K1', 'K2', 'K3'], ['K1', 'K2', 'K2', 'K3']] val_in = [['V1', 'V2', 'V3', 'V4'], ['V5', 'V6', 'V7', 'V8']]  I would like to merge / consolidate these into a list of nested dicts: out = [{'K1': {'V1', 'V2'}, 'K2': {'V3'}, 'K3': {'V4'}}, {'K1': {'V5'}, 'K2': {'V6', 'V7'}, 'K3': {'V8'}}]  The closest thing I know how to do is dict(zip(k, v)) for k, v in zip(key_in, val_in), but that doesn't work for one-to-many key:vals. In case this is something that could be done more efficiently in pandas, my ultimate goal is the creation of a dataframe like below. (I mention efficiency since my source data is many millions of rows.)  K1 K2 K3 0 {V1, V2} {V3} {V4} 1 {V5} {V6, V7} {V8} 

Answer: You can use a double zip-loop to feed the nested dicts with sets and form your list : from collections import defaultdict out = [] for ink, inv in zip(key_in, val_in): d = defaultdict(set) for k, v in zip(ink, inv): d[k].add(v) out.append(dict(d))  Another variant, as suggest by @mozway, you can use : out = [] for ink, inv in zip(key_in, val_in): d = {} for k, v in zip(ink, inv): d.setdefault(k, set()).add(v) out.append(d)  Output : >>> print(out) [{'K1': {'V1', 'V2'}, 'K2': {'V3'}, 'K3': {'V4'}}, {'K1': {'V5'}, 'K2': {'V6', 'V7'}, 'K3': {'V8'}}]  To make a DataFrame, you simply call the constructor : >>> print(pd.DataFrame(out)) K1 K2 K3 0 {V2, V1} {V3} {V4} 1 {V5} {V7, V6} {V8}
Question: I am working with a data frame that has one column, values, with a list of items within it. Below is data frame I have: | uniqId | DeptId | Date | values | | -------- | ------- | ---------- | ---------- | | 1234 | BKNG | 2023-09-05 | [VGM, FJK] | | 2534 | FINA | 2023-09-04 | [GTD, WEH] | | 3469 | ASKG | 2023-09-05 | [MUG, PIS] |  And I want to achieve the output as: | values_1 | uniqId | DeptId | Date | values | | -------- | -------- | ------- | ---------- | ---------- | | VGM | 1234 | BKNG | 2023-09-05 | [VGM, FJK] | | FJK | 1234 | BKNG | 2023-09-05 | [VGM, FJK] | | GTD | 2534 | FINA | 2023-09-04 | [GTD, WEH] | | WEH | 2534 | FINA | 2023-09-04 | [GTD, WEH] | | MUG | 3469 | ASKG | 2023-09-05 | [MUG, PIS] | | PIS | 3469 | ASKG | 2023-09-05 | [MUG, PIS] |  The original values column stays with the data frame and create new column called values_1, with each item from the list in Values listed as its own column with rest of the data columns as is. Can someone please help me with this? I know that pandas melt or pandas long function can be utilized but I was not sure how to apply to a column that contains list. https://www.geeksforgeeks.org/python-pandas-melt/ Thanks!

Answer: Try: cp = df["values"] df = df.explode("values") df = df.rename(columns={"values": "values_1"}).assign(values=cp) print(df)  Prints:  uniqId DeptId Date values_1 values 0 1234 BKNG 2023-09-05 VGM [VGM, FJK] 0 1234 BKNG 2023-09-05 FJK [VGM, FJK] 1 2534 FINA 2023-09-04 GTD [GTD, WEH] 1 2534 FINA 2023-09-04 WEH [GTD, WEH] 2 3469 ASKG 2023-09-05 MUG [MUG, PIS] 2 3469 ASKG 2023-09-05 PIS [MUG, PIS]
Question: I have a list of some strings that looks like this: my_list = ['0123_abcd', '1234_bcde', '2345_cdef', '3456_defg', '4567_efgh'] I want to sort this list by using another list containing only substrings of the first list: ordering = ['3456', '2345', '0123'] Every Element which is in my_list but not in ordering shall be set to the end of the list. So my expected result would be: result_list = ['3456_defg', '2345_cdef', '0123_abcd', '1234_bcde', '4567_efgh'] My code so far: result_list = sorted(my_list, key=lambda e: (ordering.index(e),e) if e in ordering else (len(ordering),e)) This works fine, as long as my_list looks like this: my_list = ['0123', '1234', '2345', '3456', '4567']. If I add the underscore and some letters after the numbers in my_list it does not work. I don't get any error message but the ordering is wrong. So I would guess I have to use something like .split('_')[0] somewhere in my line of code. But result_list = sorted(my_list, key=lambda e: e.split("_")[0] (ordering.index(e),e) if e in ordering else (len(ordering),e)) also does not work. How do I get this list sorting right?

Answer: If the strings in ordering are unique, then this is a possible solution: ordering_dict = {s: i for i, s in enumerate(ordering)} sorted_list = sorted( my_list, key=lambda x: ordering_dict.get(x.split("_")[0], len(ordering_dict)), )  This has the advantage over other methods to not have quadratic complexity!
Question: I'm struggling a bit trying to read/set the fps for my webcam and to read the timestamp for specific frames captured from my webcam. Specifically when I try to use vc.get(cv2.CAP_PROP_POS_MSEC), vc.get(cv2.CAP_PROP_FPS), vc.get(cv2.CAP_PROP_FRAME_COUNT) they return respectively -1, 0, -1. Clearly there's something that I'm missing. Can someone help? The code looks like this: import os import time import cv2 import numpy as np [...] # Create a new VideoCapture object vc = cv2.VideoCapture(0, cv2.CAP_DSHOW) vc.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) vc.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) # Initialise variables to store current time difference as well as previous time call value previous = time.time() delta = 0 n = len(os.listdir("directory")) # Keep looping while True: timem = vc.get(cv2.CAP_PROP_POS_MSEC) fps = vc.get(cv2.CAP_PROP_FPS) total_frames = vc.get(cv2.CAP_PROP_FRAME_COUNT) print(timem, fps, total_frames) # Get the current time, increase delta and update the previous variable current = time.time() delta += current - previous previous = current # Check if 3 (or some other value) seconds passed if delta > 3: # Operations on image # Reset the time counter delta = 0 _, img = vc.read() [...] # press esc to exit if cv2.waitKey(20) == 27: break  edit: if I remove cv2.CAP_DSHOW, it works, but then I cannot use CAP_PROP_FRAME_WIDTH

Answer: Apparently the OpenCV backend for cameras on your operating system (DSHOW) does not keep track of frame timestamps. After a read(), just use time.perf_counter() or a sibling function. It'll be close enough, unless you throttle the reading, in which case the frames would be stale. You could open an issue on OpenCV's github and request such a feature for DSHOW/MSMF. One would expect such a timestamp to represent the time the frame was taken, not the time it was finally read by the user program.
Question: I have a nested array called element_text in the form of for example: [[1, 'the'], [1, 'quick brown'], [2, 'fox jumped'], [2, 'over'], [2, 'the'], [3, 'lazy goat']]  And would like to concatenate the elements in the array and return a new array called page_text as so: [[1, 'the quick brown'], [2, 'fox jumped over the'], [3, 'lazy goat']]  So, if the first number is the same, join the second text strings together with a space in between. I've tried: page_text = [] for i in element_text: #join the list of strings together if the page number is the same if i[0] == i[0]: text = " ".join(i[1]) page_text.append([i[0], text])  But this just returns the same array as what was there in the first place. Any help appreciated! Thanks, Carolina

Answer: Solution: You can use pandas by grouping the records by your number and joining all the strings together into a new column. import pandas as pd data = [[1, 'the'], [1, 'quick brown'], [2, 'fox jumped'], [2, 'over'], [2, 'the'], [3, 'lazy goat']] df = pd.DataFrame(data, columns=['num','text']) df['full_text'] = df.groupby(['num'])['text'].transform(lambda x : ' '.join(x)) df = df[['num','full_text']].drop_duplicates(subset='num') df.head() # num full_text #0 1 the quick brown #2 2 fox jumped over the #5 3 lazy goat
Question: Without further ado, my input (s1) & expected-output (df) are below : #INPUT s1 = pd.Series(['a', np.nan, 'b', 'c', np.nan, np.nan, 'd', np.nan]).rename('col1') #EXPECTED-OUTPUT s2 = pd.Series([1, 2, 3, 3, 4, 4, 5, 6]).rename('col2') # flag the transition null>notnull or vice-versa s3 = pd.Series([0, 1, 0, 0, 2, 3, 0, 4]).rename('col3') # counter of the null values df = pd.concat([s1, s2, s3], axis=1) col1 col2 col3 0 a 1 0 1 NaN 2 1 2 b 3 0 3 c 3 0 4 NaN 4 2 5 NaN 4 3 6 d 5 0 7 NaN 6 4  I tried plenty of weird combinations of cumsum and masks but without any success. Likely that's because I don't have the basics of the logical thinking. What questions do I need to ask myself before starting to build the chain that will give me my series ? Any help would be greately appreciated, guys !

Answer: You can use isna with cumsum and where for "col3". For "col2" a classical ne+shift/cumsum: m = df['col1'].isna() # if the flag is different from the previous one, increment df['col2'] = m.ne(m.shift()).cumsum() # increment on each True, mask the False df['col3'] = m.cumsum().where(m, 0)  Output:  col1 col2 col3 0 a 1 0 1 NaN 2 1 2 b 3 0 3 c 3 0 4 NaN 4 2 5 NaN 4 3 6 d 5 0 7 NaN 6 4  Intermediates:  col1 col2 col3 m m.shift() m.ne(m.shift()) m.cumsum() 0 a 1 0 False NaN True 0 1 NaN 2 1 True False True 1 2 b 3 0 False True True 1 3 c 3 0 False False False 1 4 NaN 4 2 True False True 2 5 NaN 4 3 True True False 3 6 d 5 0 False True True 3 7 NaN 6 4 True False True 4