eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I'm wondering if it's possible to use webbrowser.open() method, open the browser, get its handler (?), and use it in driver = webdriver.Chrome() command somehow (webdriver is from selenium)? Is it feasible at all? I'm using Python.

Answer: No, webbrowser.open() is from another package unrealted to Selenium. To interact with Selenium you have to use a Selenium driven WebDriver initiated Browsing Context.
Question: I am trying to web scrape a site with tis code: import requests import pandas as pd import numpy as np from bs4 import BeautifulSoup import json import numpy as np url = 'https://www.quantalys.com/Categories' soup = BeautifulSoup(requests.get(url).content, "html.parser") data = soup.select_one("#DataCatsWithPerfs")["value"] data = json.loads(data) df = pd.DataFrame(data) df  but when I tried to print the df (just the last 2 columns), I get: dtRetMonth perfCalendaire 0 2023-07-01 [{'nYear': 2015, 'nRet': 0.1204}, {'nYear': 20... 1 2023-07-01 [{'nYear': 2015, 'nRet': 0.143}, {'nYear': 201... 2 2023-07-01 [{'nYear': 2015, 'nRet': 0.2041}, {'nYear': 20... 3 2023-07-01 [{'nYear': 2015, 'nRet': 0.0155}, {'nYear': 20... 4 2023-07-01 [{'nYear': 2015, 'nRet': -0.1601}, {'nYear': 2...  How to expand the perfCalendaire columns into '2015', '2016',...columns ? I would like to expand columns after web scraping operation.

Answer: import requests import pandas as pd from bs4 import BeautifulSoup import json url = 'https://www.quantalys.com/Categories' soup = BeautifulSoup(requests.get(url).content, "html.parser") data = soup.select_one("#DataCatsWithPerfs")["value"] data = json.loads(data) df = pd.DataFrame(data) for index, row in df.iterrows(): for item in row['perfCalendaire']: year = str(item['nYear']) df.loc[index, year] = item['nRet'] df = df.drop(columns=['perfCalendaire']) df = df.dropna(axis=1, how='all') print(df)   years_columns = [str(year) for year in range(2015, 2022)] print(df[years_columns])
Question: I have two data frames df1:  Name Month Amount Status 0 Bill Apr 0 1 Bill May 0 2 Bill Jun 100 member 3 Sally Apr 0 4 Sally May 0 5 Sally Jun 200 member 6 Tom Apr 0 7 Tom May 300 member 8 Tom Jun 0  and df2:  Name Month 0 Bill Jun 1 Tom May  I am looking to update df1 whenever there is a match in df2 on Name and Month and move the Amount and Status to the previous Month. It can be assumed there is always a previous Month to move to for each person and that the rows are already in the correct order by Name and Month. The intended result would be as follows:  Name Month Amount Status 0 Bill Apr 0 1 Bill May 100 member 2 Bill Jun 0 3 Sally Apr 0 4 Sally May 0 5 Sally Jun 200 member 6 Tom Apr 300 member 7 Tom May 0 8 Tom Jun 0  I believe there is a way to do this with itterows() but is there a more direct approach to use here?

Answer: Try: # merge to find rows with Name, Month in df2 exist = df1[['Name','Month']].merge(df2.assign(exist=1), how='left')['exist'].notna() # find the previous rows prev_rows = exist.groupby(df1['Name']).shift(-1, fill_value=False) # fill the previous rows df1.loc[prev_rows, ['Amount','Status']] = df1.loc[exist, ['Amount','Status']].to_numpy() # remove the given rows df1.loc[exist, ['Amount','Status']] = [0, None]  Output:  Name Month Amount Status 0 Bill Apr 0 None 1 Bill May 100 member 2 Bill Jun 0 None 3 Sally Apr 0 None 4 Sally May 0 None 5 Sally Jun 200 member 6 Tom Apr 300 member 7 Tom May 0 None 8 Tom Jun 0 None
Question: I am pulling some JSON formatted log data out of my SEIM and into a pandas dataframe. I am able to easily convert the JSON into multiple columns within the dataframe, but there is a "message" field in the JSON that contains a quoted CSV, like this. # dummy data dfMyData = pd.DataFrame({"_raw": [\ """{"timestamp":1691096387000,"message":"20230803 20:59:47,ip-123-123-123-123,mickey,321.321.321.321,111111,10673010,type,,'I am a, quoted, string, with commas,',0,,","logstream":"Blah1","loggroup":"group 1"}""", """{"timestamp":1691096386000,"message":"20230803 21:00:47,ip-456-456-456-456,mouse,654.654.654.654,222222,10673010,type,,'I am another quoted string',0,,","logstream":"Blah2","loggroup":"group 2"}""" ]}) # Column names for the _raw.message field that is generated. MessageColumnNames = ["Timestamp","dest_host","username","src_ip","port","number","type","who_knows","message_string","another_number","who_knows2","who_knows3"] # Convert column to json object/dict dfMyData['_raw'] = dfMyData['_raw'].map(json.loads) # convert JSON into columns within the dataframe dfMyData = pd.json_normalize(dfMyData.to_dict(orient='records'))  I've seen this done before with str.split() to split on columns and then concat it back to the original dataframe, however the str.split method doesn't handle quoted values within the CSV. pd.read_csv can handle the quoted CSV correctly, but I can't figure out how to apply it across the dataframe and expand the output of that into new dataframe columns. Additionally, when I split dfMyData['_raw.message'] out into new columns, I'd also like to supply a list of column names for the data and have the new columns be created with those names. Anyone know of an easy way to split a quoted CSV string in a dataframe column into new named columns within the dataframe?

Answer: Update Actually all you need is to pass your data into a csv-reader, which in turn is an appropriate data type for pandas.DataFrame: pd.DataFrame(csv.reader(dfMyData['_raw.message'], quotechar="'"), columns=columns)   Previous answer We can try to convert the data into a csv and read them back with appropriate parameters: import csv from tempfile import TemporaryFile seq = dfMyData.iloc[:,1] # column of interest in the original data columns = [*'ABCDEFGHIJKL'] # custom names of future data columns with TemporaryFile() as file: seq.to_csv( file, sep='\N{unit separator}', header=False, index=False, quoting=csv.QUOTE_NONE ) file.seek(0) # read data from the start df = pd.read_csv( file, header=None, names=columns, quotechar="\'" ) print(df)  Notes:  quoting=csv.QUOTE_NONE to avoid \" at the ends of each line sep='\N{unit separator}' to avoid confusion with commas quotechar="\'" when reading back because of a specific quoting inside the lines since we are dumping a sequence without indexes, the '\N{unit separator}' delimiter will never make it into the final data  Transformed data:
Question: When working with PyTorch/numpy and similar packages, is there a good way to view matrices (or, in general, arrays with two or more dimensions) in debug mode, similar to the way Matlab (or even pyCharm if I remember correctly) present it? This is, for example, a PyTorch tensor, which is very confusing -- opening H here gives me the same thing again and again.  As opposed to Matlab, where I can watch it like that:  Would appreciate any help with that!

Answer: Yes, make sure you have the Jupyter extension installed and then simply right click the variable in the Debug menu and select the View Value in Data Viewer option.
Question: I am using Skfuzzy to interpret two arrays: 1. distances to a stream [dist]; 2. Strahler order[order]. I then want to calculate the consequent (a vulnerability value) for each stream distance and Strahler order pairs using a set of custom membership values I have created for the antecedents and consequent. I have the program working for a single input of values, but I don't understand how to get skfuzzy to loop through an array of input values and output the consequent for each value pair. The control system: # Creation of control system using the defined rules vulnerability_ctrl = ctrl.ControlSystem([rule1, rule2, rule3]) vulnerability = ctrl.ControlSystemSimulation(vulnerability_ctrl)  The inputs and computation: # input of distance and Strahler order arrays vulnerability.input[dist] vulnerability.input[order] vulnerability.compute()  I get this error because I don't know how skfuzzy deals with an array of inputs: TypeError: '_InputAcceptor' object is not subscriptable 

Answer: One can iterate down an array through the use of a for loop as shown below. First ensure that your array is converted to float type using 'numpy.asfarray'. # Convert to float array dist_x = np.asfarray(strm_dist) order_x = np.asfarray(strm_order) # iterate down an array of input values strm_vul = [] for i in range(len(dist)): vulnerability.input['dist'] = dist_x[i] vulnerability.input['order'] = order_x[i] vulnerability.compute() strm_vul.append(vulnerability.output['vul'])
Question: I am using Jupyter Notebook to run python code. I already did the following: !pip install pyldavis  I can successfully import pyLDAvis via the following codes: import pyLDAvis pyLDAvis.enable_notebook()  However, I cannot import pyLDAvis.sklearn via the following codes: import pyLDAvis.sklearn  It returns:  ModuleNotFoundError Traceback (most recent call last) Cell In[52], line 1 ----> 1 import pyLDAvis.sklearn_models ModuleNotFoundError: No module named 'pyLDAvis.sklearn_models'  Why is that and what should I do to deal with it?

Answer: It looks like there has been a change in how the software handles this pattern. This issue posted here in May of this year (2023) that looks to be the same as yours. It links over to a solution that details how the use of the software has recently developed:  "pyLDAvis v 3.4.0 no longer has the file sklearn.py in the pip package." Replace any logic involving:  import pyLDAvis.sklearn ... pyLDAvis.sklearn.prepare   "with"  import pyLDAvis.lda_model ... pyLDAvis.lda_model.prepare   How this was found via troubleshooting: I went to the page for the package in the Python Package Index (PyPI) and clicked on 'GitHub statistics:' on the left side under 'GitHub statistics:'. Then in the 'filters' slot I entered 'pyLDAvis.sklearn'. The four that came up as open didn't look too similar to the OP, and so I clicked on the '7 closed' tag above the listing. The most recent one listed 'ModuleNotFoundError: No module named 'pyLDAvis.sklearn' looked to be a good match to this post, and so I examined it.
Question: I have the following structur of a dataframe: df = pd.DataFrame({'Level': ["a","b", "c"], 'Kontogruppe': ["a", "a", "b"], 'model': ["alpha", "beta", "alpha"], 'MSE': [0, 1 ,1], 'actual_value': [1,2,3], 'forecast_value': [2,2,2]})  For this dataframe I run severel functions, for example: def metrics(df): df_map= pd.DataFrame({'Level': ["a"], 'Kontogruppe': ["a"], 'model': ["alpha"], 'MSE': [0]}) for i in df['Level'].unique(): for j in df['Kontogruppe'].unique(): for k in df['model'].unique(): df_lkm = df.loc[(df['Level'] == i) & (df['Kontogruppe'] == j) & (df['model'] == k)] if df_lkm.empty: out_MSE = 10000000000 else: out_MSE = sum(df_lkm['actual_value'])/sum(df_lkm['forecast_value']) df_map_map = pd.DataFrame({'Level': [i], 'Kontogruppe': [j], 'model': [k], 'out_MSE': [out_MSE]}) df_map = pd.concat([df_map, df_map_map]) df = pd.merge(df, df_map, how='left', on=['Level', 'Kontogruppe', 'model']) return df df = metrics(df)  so basically I loop over the unique column values and filter the dataframe based on this. In this case I get for every Level, Kontogruppe and model the value 'out_MSE' gets calculated over all entries of actual_values and forecast_values. And is appended as a value for every row in a new column. For this problem is there are more efficient way to this? Is there any pythonic way in general to avoid this for loops, my dataframe is big and this costs a lot of performance.

Answer: If I understand correctly, you might just want a simple groupby.sum with a bit of post-processing. Because you only care about the existing combinations, there is no need to loop over all of them and assign a large value. (df.groupby(['Level', 'Kontogruppe', 'model'], as_index=False) [['actual_value', 'forecast_value']].sum() .eval('out_MSE = actual_value/forecast_value') )  Output:  Level Kontogruppe model actual_value forecast_value out_MSE 0 a a alpha 1 2 0.5 1 b a beta 2 2 1.0 2 c b alpha 3 2 1.5  Output of your code for comparison:  Level Kontogruppe model MSE_x actual_value forecast_value MSE_y out_MSE 0 a a alpha 0 1 2 0.0 NaN 1 a a alpha 0 1 2 NaN 0.5 2 b a beta 1 2 2 NaN 1.0 3 c b alpha 1 3 2 NaN 1.5
Question: I have a dataclass with (kind of) a getter method. This code works as expected: from dataclasses import dataclass @dataclass() class A: def get_data(self): # get some values from object's fields # do some calculations return "a calculated value" @dataclass() class B(A): def get_data(self): data = super().get_data() return data + " (modified)" b = B() print(b.get_data()) # a calculated value (modified)  However, if I add slots=True, I get a TypeError: from dataclasses import dataclass @dataclass(slots=True) class A: def get_data(self): return "a calculated value" @dataclass(slots=True) class B(A): def get_data(self): data = super().get_data() return data + " (modified)" b = B() print(b.get_data()) # TypeError: super(type, obj): obj must be an instance or subtype of type  The error vanishes if I use the old-style super(), contrary to pep-3135: from dataclasses import dataclass @dataclass(slots=True) class A: def get_data(self): return "a calculated value" @dataclass(slots=True) class B(A): def get_data(self): data = super(B, self).get_data() return data + " (modified)" b = B() print(b.get_data()) # a calculated value (modified)  Why does this happen and how to fix it the right way?

Answer:  slots: If true (the default is False), __slots__ attribute will be generated and new class will be returned instead of the original one. If __slots__ is already defined in the class, then TypeError is raised. https://docs.python.org/3/library/dataclasses.html#dataclasses.dataclass  Taking this as an example: @dataclass(slots=True) class Foo: pass  This means this works something like this: class Foo: pass Foo = dataclass(slots=True)(Foo)  You define a class Foo, and then it gets replaced with a different, altered class. Now, your method: def get_data(self): data = super().get_data() ...  This super() was written in the original Foo class and will assume it's supposed to look up the parent of this original Foo class; but the instance it currently has is not actually an instance of that class, it's an instance of the other, altered class. When you do this instead: data = super(Foo, self).get_data()  This looks up what currently refers to the name Foo, which again matches what self also refers to at this moment.
Question: How can I find out where Python was installed in a Windows 11 machine, so that I can use the address to add Python to the PATH variable? The documentation I have found on this assumes that the user can already use the python command in the cli. But in this case, the cli cannot find python yet because python has not been added to the PATH yet. Also, I looked closely in Windows file explorer and was not able to find Python in Program Files, Program Files (x86), the root of the C drive, or any of the many other places that I looked. Here are the commands that first install python and then try to check the resulting Python version. PS C:\Users\Administrator> C:\temp\python-3.11.4-amd64.exe /passive InstallAllUsers=0 InstallLauncherAllUsers=0 PrependPath=1 Include_test=0 PS C:\Users\Administrator> python --version python : The term 'python' is not recognized as the name of a cmdlet, function, script file, or operable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. At line:1 char:1 + python --version + ~~~~~~ + CategoryInfo : ObjectNotFound: (python:String) [], CommandNotFoundException + FullyQualifiedErrorId : CommandNotFoundException PS C:\Users\Administrator>  The /passive flag above results in the python install GUI launching while the install happens, and then closing without giving any error message, so it seems clear that the install command is indeed running. ... If you can suggest any alterations to the command to elucidate any possible error message, I would be happy to try your suggested fully automated commands that might leave better logs. This is on an Amazon EC2 instance being accessed remotely using an RDP file, if that makes any difference. This provisioning process must be completely automated. Powershell is running as an administrator while invoking the above commands.

Answer: The default install location for user installations on Windows uses the LOCALAPPDATA variable. This typically points towards C:\users\<username>\Appdata\Local\Programs\Python\Python<XY>\ where <XY> are the major and minor versions. In your case this would be C:\users\Administrator\Appdata\Local\Programs\Python\Python311\. Source, specifically, here. You might note that there are PythonXY-32 and PythonXY-64 alternatives for 32 and 64 bit installations respectively, however, I believe these are no longer used in Windows 11 since the installer is now 64 bit by default. If you are using the same installer for all installs you should be able to rely on %LOCALAPPDATA%\Programs\Python\Python311\ as the path. (Or "$($env:LOCALAPPDATA)\Programs\Python\Python311\" in Powershell. Python typically adds both "$($env:LOCALAPPDATA)\Programs\Python\Python311\" and "$($env:LOCALAPPDATA)\Programs\Python\Python311\Scripts\" to PATH since the Scripts directory will include helpful tools like pip, mypy, and pylint (if installed). These could be run as subcommands (python -m mypy my_file.py) if Scripts isn't on the PATH, but most documentation will demonstrate them as being run directly (mypy my_file.py).
Question: I have a requirement where i need to generate a list of all the methods and its parameters belonging to the Python class passed as argument. I want something like this class MyClass: def __init__(self,attr1): pass def method1(self,param1,param2): pass def method2(self,param3): pass def method3(self): pass def get_all_methods_details(className): # gets all the methods associated to the class along with its method.  When I do: get_all_methods_details(myClass)  it should return something like this: [(method1, param1,param2),(method2,param3),(method3)]  This is what I have tried: def get_all_method_details(className): return [ m for m in dir(className) if not m.startswith('__')] print(get_all_method_details(MyClass))  this prints: [method1, method2, method3]  I saw solutions for getting parameters from normal function but those does not work with methods.

Answer: You can use the .co_varnames attribute of the code object: def get_all_methods_details(class_name): return [ (m, getattr(class_name, m).__code__.co_varnames) for m in dir(class_name) if not m.startswith("__") ]  This should return: [('method1', ('self', 'param1', 'param2')), ('method2', ('self', 'param3')), ('method3', ('self',))]  As a side note, those are not class methods. They are instance methods.
Question: I'm trying to create some labels manually which should align exactly with the tick locations. However, when plotting text ha='center' aligns the bounding box of the text in the center, but the text itself within the bounding box is shifted to the left. How can I align the text itself in the center? I found this question but it doesn't help as it shifts the bounding box, while I need to shift the text.  import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt print(matplotlib.__version__) # 3.5.3 fig, ax = plt.subplots() ax.plot([.5, .5], [0, 1], transform=ax.transAxes) ax.text(.5, .5, 'This text needs to be center-aligned'.upper(), ha='center', va='center', rotation='vertical', transform=ax.transAxes, bbox=dict(fc='blue', alpha=.5)) ax.set_title('The box is center-aligned but the text is too much to the left') plt.show()  

Answer: You can set the transform parameter of the text object with an actual matplotlib transform, e.g.: import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.transforms as transforms mpl.rcParams['figure.dpi'] = 300 # mpl.use("TkAgg") print(mpl.__version__) # 3.5.3 fig, ax = plt.subplots() dx, dy = 5.5 / 300.0, 0 / 300.0 offset = transforms.ScaledTranslation(dx, dy, fig.dpi_scale_trans) text_transform = ax.transData + offset ax.plot( [0.5, 0.5], [0, 1], ) ax.text( 0.5, 0.5, "This text needs to be center-aligned".upper(), ha="center", va="center", rotation="vertical", transform=text_transform, bbox=dict(fc="blue", alpha=0.5), ) ax.set_title("The box is center-aligned but the text is too much to the left") plt.show()  produced:  Note: The exact setting of dx (the amount to shift the plotted text in the x-dimension of the Axes coordinate system of plot) will depend on the value set for the figure DPI (here I've set it to 300 and was also running this inside a jupyter notebook).
Question: I'm using Python's SpeechRecognition to geenerate captions for a livestream. I noticed that when I listen to mic input, recognizer would need a couple of seconds of silence in order to stop capturing audio. Is there a way to reduce that amount of silence needed to say .5 seconds? I'm open to using other methods/libraries, as long as it's not something too low level. Here's my code so far: import speech_recognition as sr from googletrans import Translator import threading # Config OUTPUT_FILE_NAME = "transcription.txt" def listen(recognizer, microphone): with microphone as source: audio = recognizer.listen(source) return audio def transcribe(audio, recognizer, translator): try: uk_text = recognizer.recognize_google(audio, language="uk-UA") translated_text = translator.translate(uk_text, src="uk", dest="en") write_to_file(OUTPUT_FILE_NAME, translated_text.text) except sr.UnknownValueError: print("Could not understand audio.") write_to_file(OUTPUT_FILE_NAME, "") except sr.RequestError as e: print(f"Error occurred during recognition: {e}") def write_to_file(file_path, text): with open(file_path, "w", encoding="utf-8") as file: file.write(text) def get_mic(): for index, source in enumerate(sr.Microphone.list_microphone_names()): print(f"{index}: {source}") while True: index = input("Select an index from the list above: ") try: return int(index) except ValueError: print("Invalid index") if __name__ == "__main__": mic_index = get_mic() translator = Translator() recognizer = sr.Recognizer() microphone = sr.Microphone(device_index=mic_index) print("Adjusting for ambient noise, please don't say anything...") with microphone as source: recognizer.adjust_for_ambient_noise(source) print("Listening...") try: while True: audio = listen(recognizer, microphone) transcription_thread = threading.Thread( target=transcribe, kwargs={"audio":audio, "recognizer":recognizer, "translator":translator} ) transcription_thread.setDaemon(True) transcription_thread.start() except KeyboardInterrupt: print("\nShutting down recognition service...") write_to_file("transcription.txt", "Recognition service inactive. This is sample text.")  I've tried using phrase_time_limit on a .listen() function, but that's not what I'm looking for, as it would sometimes cut me off in the middle of the word.

Answer: From the source code of the SpeechRecognition library, the parameter you need is pause_threshold, which is a parameter taken by the Recognizer object. self.pause_threshold = 0.8 # seconds of non-speaking audio before a phrase is considered complete  In your code above, it would be passed like: recognizer = sr.Recognizer(pause_threshold=0.5) # or other value  Try experimenting with the pause_threshold value.
Question: I am trying to remove all non-matching items (values with different keys) in two dicts dict_a and dict_b. What is a better way of achieving this? Example: dict_a = {key1: x1, key2: y1, key4: z1} dict_b = {key1: x2, key3: y2, key4: w1} # becomes: # dict_a = {key1: x1, key4: z1} # dict_b = {key1: x2, key4: w1}  My attempt: keep_keys = [keyA for keyA in dict_a.keys() if keyA in dict_b.keys()] for i in dict_b.copy().keys(): if i not in keep_keys: del dict_b[i] for i in dict_a.copy().keys(): if i not in keep_keys: del dict_a[i]  I am looking for shorter (pythonic) way of doing this.

Answer: You can do: d1 = {"key1": "x1", "key2": "y1", "key4": "z1"} d2 = {"key1": "x2", "key3": "y2", "key4": "w1"} common_keys = d1.keys() & d2.keys() d1 = {k: d1[k] for k in common_keys} d2 = {k: d2[k] for k in common_keys} print(d1) print(d2)  Prints: {'key1': 'x1', 'key4': 'z1'} {'key1': 'x2', 'key4': 'w1'}