Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "There's a couple things here:\n1: as @bereal said, range() has a tird optional step argument, and I've never seen a better time to use it. Check out the documentation for range() https://docs.python.org/3/library/functions.html#func-range\n2: I see you reference numbers[-1] even though I think you mean number[-i], it will still reference numbers[-1] on the first iteration, thereby giving an error.\n", "You can make the swap when its not an even number like this:\nfor a in range(10):\n if a % 2 == 1:\n tempNumber = numbers[a]\n numbers[a] = numbers[a-1]\n numbers[a-1] = tempNumber\n\nAnd then you will have\nFirst output:\n[9, 7, 4, 7, 4, 3, 1, 9, 9, 9]\n\nFinal output:\n[7, 9, 7, 4, 3, 4, 9, 1, 9, 9]\n\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "I don't know if this is answered but for anyone looking for the answer:\nin place of the @app.before_first_request decorated function use the app instance like this:\ni.e.\n# In place of something like this\[email protected]_first_request\ndef create_tables():\n db.create_all()\n ...\n\n# USE THIS INSTEAD\nwith app.app_context():\n db.create_all()\n\n" ]

[ 0 ]

[]

[]

[ "flask", "python" ]

[ "You can use the quadpy library to integrate a function over a tetrahedron.\nBut for a polynomial function f, it is possible to calculate the exact value of the integral of f over a tetrahedron (more generally over a simplex in any dimension). This method is implemented in the R package SimplicialCubature. This paper provides another method; I implemented it in Python: gist.\n" ]

[ 0 ]

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[]

[ "finite_element_analysis", "math", "numerical_integration", "python" ]

[ "Question, what is the value of len(res).\nIn python when you have a list inside of a list you can use a second indexer to access the elements inside of it. So for example if the first element res[0] was a list, you could do res[0][0] to get '#define'.\nHowever, your output that you have shown is in a different format than typical nested list syntax, so doing res[0][0] might not work (because it might not be the right type of object).\nThis is what a nested list is supposed to look like:\n[[0, 1], [1, 2, 100], [2, 3], [3, 4], [4, 5]]\nYour output looks like its in json formatting, but without knowing the type of data object it is for sure I can't be certain. If it is json, you might be able to do json.loads(res) and then parse it that way.\nhttps://www.freecodecamp.org/news/python-json-how-to-convert-a-string-to-json/\n" ]

[ 0 ]

[]

[]

[ "list", "pyparsing", "python" ]

[ "I would use a list comprehension with a generator to search for the key from the value:\ndf['price'] = [next((k for k,l in d.items() if s in l), None)\n if isinstance(d, dict) else d\n for s, d in zip(df['shop_id'], df.pop('price_list'))]\n\nNB. pop removes the \"price_list\" column in place.\nOutput:\n item_id shop_id price\n0 1 S1 10\n1 1 S2 10\n2 1 S3 20\n3 2 S2 50\n4 3 S3 NaN\n5 4 S1 10\n6 4 S2 10\n\nworkaround if you have string representations of dicts\nimport ast\n\ndf['price'] = [next((k for k,l in ast.literal_eval(d).items() if s in l), None)\n if isinstance(d, str) and d.startswith('{') else d\n for s, d in zip(df['shop_id'], df.pop('price_list'))]\n\n" ]

[ 3 ]

[]

[]

[ "dataframe", "dictionary", "list_comprehension", "pandas", "python" ]

[ "This df_test[\"f'Price_REG{j}'\"] literally means the string df_test[\"f'Price_REG{j}'\"]. It will not be evaluated further. It's a string-literal.\nInstead df_test[f'Price_REG{j}'] WOULD be evaluated and would return whatever is in Price_REG{j} and then fetch the column of the same name from the df.\nThat being said, because that f-string contains only a variable, it's functionally equivalent to just df_test[Price_REG{j}].\nYou have painted yourself into a corner over-engineering. None of the f-strings and string-literals with f-strings inside them are needed here. Just the variable themselves, with no quotes around them.\nInstead:\nerror = []\nnaive_list = list(['24', '168', 'standard', 'custom'])\nfor i in naive_list:\n for j in range(1,5):\n rmse = mean_squared_error(df_test[Price_REG{j}], df_test_{i}[Price_REG{j}], squared=False)\n mae = mae(df_test[Price_REG{j}], df_test_{i}[Price_REG{j}])\n error.append(rmse)\n error.append(mae)\n\nWhat I'm not sure about though is whatever df_test_ is or what df_test_{i} is meant to do. Nor am I fully understanding the curly braces of Price_REG{j} (should those be square brackets? What is Price_REG?). But at any rate, at least you are free of your painted corner and continue hammering out whatever other kinks may pop up in this code.\n" ]

[ 0 ]

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[]

[ "for_loop", "pandas", "python" ]

[ "I would separate fruits from the condition, inside the context. Transform the condition into a list to check on the template.\nviews.py\ndef data_compare2(request):\n fruits = {\"appel\": 3962.00, \"waspeen\": 3304.07, \"ananas\":24,}\n condition = ['appel', 'ananas']\n\n context = {\n 'fruits': fruits,\n 'condition': condition\n }\n \n return render(request, 'main/data_compare.html', context)\n\ntemplate.html\n{% extends 'base.html' %}\n\n{% block content %}\n <div class=\"container center\">\n {% for key, value in fruits.items %}\n <span {% if key in condition %} style=\"color: red;\" {% endif %}>{{ key }}: {{value}}</span><br>\n {% endfor %}\n </div>\n{% endblock %}\n\nThis will result in 'appel' and 'ananas' being red.\n" ]

[ 2 ]

[]

[]

[ "django", "python" ]

[ "You're actually injecting your source code using volumes:, not during the image build, and this doesn't honor .dockerignore.\nRunning a Docker application like this happens in two phases:\n\nYou build a reusable image that contains the application runtime, any OS and language-specific library dependencies, and the application code; then\nYou run a container based on that image.\n\nThe .dockerignore file is only considered during the first build phase.\nIn your setup, you don't actually COPY anything in the image beyond the requirements.txt file. Instead, you use volumes: to inject parts of the host system into the container. This happens during the second phase, and ignores .dockerignore.\nThe approach I'd recommend for this is to skip the volumes:, and instead COPY the required source code in the Dockerfile. You should also generally indicate the default CMD the container will run in the Dockerfile, rather than requiring it it the docker-compose.yml or docker run command.\nFROM python:3.9-slim-buster\n\n# Do the OS-level setup _first_ so that it's not repeated\n# if Python dependencies change\nRUN apt-get update && apt-get install -y ...\n\nWORKDIR /django-app\n\n# Then install Python dependencies\nCOPY requirements.txt .\nRUN pip install -r requirements.txt\n\n# Then copy in the rest of the application\n# NOTE: this _does_ honor .dockerignore\nCOPY . .\n\n# And explain how to run it\nENV PYTHONUNBUFFERED=1\nEXPOSE 8000\nUSER userapp\n# consider splitting this into an ENTRYPOINT that waits for the\n# the database, runs migrations, and then `exec \"$@\"` to run the CMD\nCMD sleep 7; python manage.py migrate; python manage.py runserver 0.0.0.0:8000\n\nThis means, in the docker-compose.yml setup, you don't need volumes:; the application code is already inside the image you built.\nversion: \"3.8\"\nservices:\n app: \n build: .\n ports: \n - 8000:8000\n depends_on: \n - db\n # environment: [PGHOST=db]\n # no volumes: or container_name:\n\n db:\n image: postgres\n volumes: # do keep for persistent database data\n - ./data:/var/lib/postgresql/data\n environment: \n - POSTGRES_DB=${DB_NAME}\n - POSTGRES_USER=${DB_USER}\n - POSTGRES_PASSWORD=${DB_PASSWORD}\n # ports: ['5433:5432']\n\nThis approach also means you need to docker-compose build a new image when your application changes. This is normal in Docker.\nFor day-to-day development, a useful approach here can be to run all of the non-application dependencies in Docker, but the application itself outside a container.\n# Start the database but not the application\ndocker-compose up -d db\n\n# Create a virtual environment and set it up\npython3 -m venv venv\n. venv/bin/activate\npip install -r requirements.txt\n\n# Set environment variables to point at the Docker database\nexport PGHOST=localhost PGPORT=5433\n\n# Run the application locally\n./manage.py runserver\n\nDoing this requires making the database visible from outside Docker (via ports:), and making the database location configurable (probably via environment variables, set in Compose with environment:).\n", "That's not actually your case, but in general an additional cause of \".dockerignore not ignoring\" is that it applies the filters to whole paths relative to the context dir, not just basenames, so the pattern:\n__pycache__\n*.pyc\n\napplies only to the docker context's root directory, not to any of subdirectories.\nIn order to make it recursive, change it to:\n**/__pycache__\n**/*.pyc\n\n" ]

[ 6, 0 ]

[]

[]

[ "django", "docker", "dockerignore", "python" ]

[ "Just check if the number is less than or equal to 0 instead of a while loop\ndef main() :\n n = int(input(\"Insert your age : \")) #asking for users age\n if n <= 0:\n print(\"Please enter an age greater than 0\")\n elif price_checker(n) :\n print(\"Price is 12 dollars\")\n else :\n print(\"Price is 18 dollars\")\n \ndef price_checker(x) :\n if x < 12 :\n return True\n else :\n return False\n \nmain()\n\n" ]

[ 0 ]

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[ "python" ]

[ "you can do this using zip and map\nnew_prices = dict(zip(prices, map(formula, prices.values())))\n\n", "Use dictionary comprehension:\nnew_prices = {k: formula(prices[k]) for k in prices}\nprint(new_prices)\n# {'n': 94.05, 'a': 94.05, 'c': 139.65}\n\n", "Use map() with a helper lambda to create new dict\nnew_prices = dict(map(lambda item: (item[0], formula(item[1])), prices.items()))\n\noutput:\n{'n': 94.05, 'a': 94.05, 'c': 139.65}\n\n", "First option (using a dict comprehension):\n prices = {'n': 99, 'a': 99, 'c': 147}\n \n \n def formula(value):\n value = value -value * 0.05\n return value\n\n #Using a dict comprehension and .items() [giving you key value tuples] instead of map()\n new_prices = {k: formula(v) for k, v in prices.items()}\n \n print(new_prices)\n # {'n': 94.05, 'a': 94.05, 'c': 139.65}\n\nSecond option (using map and defining the formula slightly differently [tuple as input]):\n #Define the formula with a key value pair (tuple) as input and return value\n def f_items(items):\n value = items[1] - items[1] * 0.05\n return items[0], value\n \n \n new_prices = dict(map(f_items, prices.items()))\n \n print(new_prices)\n # {'n': 94.05, 'a': 94.05, 'c': 139.65}\n\n" ]

[ 6, 2, 1, 0 ]

[ "When you create a map, you get a map object. It's nice to have an helper function to iterate through this object.\ndef print_results(map_object):\n for i in map_object:\n print(i)\n\ndef formula(value):\n return value * 0.95 # one-liner\n\nm = map(formula, prices.values())\n\nprint_results(m)\n\n# output\n94.05\n94.05\n139.65\n\n" ]

[ -2 ]

[ "dictionary", "list", "python" ]

[ "If you want the list populated in the order that os.listdir() reveals files then:\nfrom os import listdir\nfrom os.path import join, splitext\n\nBASE = '.' # directory to be parsed\nEXTS = {'jpg', 'JPG', 'jpeg', 'JPEG'} # file extensions of interest\n\ndef ext(p):\n _, ext = splitext(p)\n if ext:\n return ext[1:]\n\ndef getfiles(base, include_base=True):\n for entry in listdir(base):\n if ext(entry) in EXTS:\n yield join(base, entry) if include_base else entry\n\ndetection = [file for file in getfiles(BASE, include_base=False)]\n\nprint(detection)\n\nNote:\nos.listdir() returns a list of files in arbitrary order\n" ]

[ 0 ]

[]

[]

[ "image", "python", "readfile" ]

[ "you forget * for multiplication\ndef pole_trojkata(xa, ya, xb, yb, xc, yc):\n return abs((xb-xa)*(yc-ya)-(xc-xa)*(yb-ya))/2\n\npole_trojkata(2, 3, 1, 3, 2, 5)\n\noutput:\n1.0\n\n" ]

[ 0 ]

[]

[]

[ "area", "function", "parameters", "python" ]

[ "If I pass the files parameter instead of data or json, it works!\nrequests.post(url = api_url, files={'path':'test'}).text\n\n", "The issue is that using data on requests.post defaults to application/x-www-form-urlencoded while your application wants multipart/form-data. Try using files instead of data:\nrequests.post('http://127.0.0.1:5001/api/v0/add', files={'path': 'test'}).text\n\n" ]

[ 0, 0 ]

[]

[]

[ "api", "postman", "python", "python_requests" ]

[ "It's hard to give specific recommendations without understanding how the bounding box will be used downstream.\nEasiest method would be to use the boxPoints function. That will return the coordinates of the corners for the minimum bounding box around the contour. Alternatively, you could fit a line to the contour and use the angle of the line to rotate your your bounding box.\n" ]

[ 0 ]

[]

[]

[ "mnist", "opencv", "python" ]

[ "You need to use\nwith open(r'C:\\Users\\'' + system_user + '\\Documents\\5e_helper\\character cofig', 'w') as configfile:\n testconfig.write(configfile)\n\nError is happening because you are using an escape sequence of \\' in 'C:\\Users\\'. You can also avoid it using double quotes around path string.\nBTW good way to do it is using forward slash (/) instead of back.\n", "Your first string is raw but the second one isn't, which means you need to escape your backslashes since they count as escape characters in normal strings. Or just make the second string raw as well.\nwith open(r'C:\\Users\\' + system_user + r'\\Documents\\5e_helper\\character cofig', 'w') as configfile:\n\nThat said, I would just use string format instead of concatenation in order to handle this as single string rather than multiple.\nwith open(r'C:\\Users\\{}\\Documents\\5e_helper\\character cofig'.format(system_user), 'w') as configfile:\n\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "I want to display the characters coming asynchronously from a wifi on a Tkinter window\nAfter several problems I was able to come to the following solution.\nMany thanks to JRiggles and Bryan Oakles\nThis is my source code:\nimport tkinter as tk\n\ndef my_async(): # this simulates my asynchronous function, i will come from wifi\n global i\n i = i+1\n\ni = 0\nwindow_is_alive = True\n\nroot = tk.Tk()\nlabel = tk.Label(root,text=\"Name\")\nlabel.pack()\n\ndef destroy_window():\n global window_is_alive\n window_is_alive = False\n\n#Reassign the Close Window Control Icon\nroot.protocol(\"WM_DELETE_WINDOW\", destroy_window)\n\nexit_button = tk.Button(root, text=\"Exit\", command=destroy_window)\nexit_button.pack()\n\nwhile True:\n label.configure(text = str(i)) # i is actually updated by an asynchronous function, like a wifi stream\n my_async() # these two lines are just to simulate that\n root.update_idletasks()\n root.update()\n if window_is_alive == False:\n root.destroy()\n break\n \n\n" ]

[ 0 ]

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[]

[ "python", "tkinter" ]

[ "status = models.IntegerField(choices=STATUS, default=0)\n\nshould be:\nactive = models.IntegerField(choices=STATUS, default=0)\n\n", "The message is clear 'active' is not a field\nclass Comment(models.Model):\n post = models.ForeignKey(\n Post, on_delete=models.CASCADE, related_name='comments')\n name = models.CharField(max_length=80)\n email = models.EmailField()\n body = models.TextField()\n createdOn = models.DateTimeField(auto_now_add=True)\n status = models.BooleanField(default=False)\n\nyour fields are: post, name, email, createdOn, status\nTherefore create a field named active or suppress active in list_display &\nlist_filter\n" ]

[ 0, 0 ]

[]

[]

[ "blogs", "django", "html", "python" ]

[ "You can accomplish what you want with a list comprehension.\nlist_bag = [bag + [item] for item in rf]\n\n" ]

[ 0 ]

[]

[]

[ "append", "extend", "list", "python" ]

[ "You need to add python path to the path on the environment variables.\nWhat you should do:\n\nRight click \"my computer\"\nGo to \"properties\"\nclick on \"advanced system settings\"\nGo to \"environment variables\"\nOn system Variables look for \"path\" (if there isn't one create one)\nClick on \"path\" and click on \"Edit\"\nNow you should click on \"new\" and paste your python folder path\nClick \"OK\" and everything should be working just fine now\n\nI used to have this problem before and after following this steps it started working.\n" ]

[ 0 ]

[]

[]

[ "cmd", "powershell", "python", "windows_store" ]

[ "It looks like your goal is to count the number of tuples with a given first element. Try this:\ncounter = {}\nvalues = [(4, 1), (4, 2), (4, 4), (5, 1), (5, 5), (6, 1), (6, 2), (6, 3), (6, 6)]\n\nfor value, divisor in values:\n current = counter.get(value, 0) + 1\n counter[value] = current\n\nThen, to get the count of a given value, use counter[n]. For instance, counter[4] would be 3.\nIf your divisors are not guaranteed to be unique, then use sets for your dictionary values instead:\ncounter = {}\nvalues = [(4, 1), (4, 2), (4, 4), (5, 1), (5, 5), (6, 1), (6, 2), (6, 3), (6, 6)]\n\nfor value, divisor in values:\n if value not in counter:\n counter[value] = set()\n counter[value].add(divisor)\n\nThen, you can get the number of divisors with len(counter[n]). So, len(counter[4]) would be 3.\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "The problem was that you need to give an executable path for the python framework you have on your MacOs. So I modify the setup.py\nsetup.py\nfrom setuptools import setup\n\nclass CONFIG:\n VERSION = 'v1.0.1'\n platform = 'darwin-x86_64'\n executable_stub = '/opt/homebrew/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib' # this is important, check where is your Python framework and get the `dylib`\n APP_NAME = f'your_app_{VERSION}_{platform}'\n APP = ['main.py']\n DATA_FILES = [\n 'config.json', \n 'countries_list.txt', \n ('modules', ['modules/app.ico']),\n # this modules are automatically added if you use __init__.py in your folder\n # ('modules', ['modules/scraper_module.py']),\n # ('modules', ['modules/gui_module.py']),\n ]\n\n OPTIONS = {\n 'argv_emulation': False,\n 'iconfile': 'modules/app.ico',\n 'plist': {\n 'CFBundleName': APP_NAME,\n 'CFBundleDisplayName': APP_NAME,\n 'CFBundleGetInfoString': APP_NAME,\n 'CFBundleVersion': VERSION,\n 'CFBundleShortVersionString': VERSION,\n 'PyRuntimeLocations': [\n executable_stub,\n # also the executable can look like this:\n #'@executable_path/../Frameworks/libpython3.4m.dylib',\n ]\n }\n }\n\ndef main():\n setup(\n name=CONFIG.APP_NAME,\n app=CONFIG.APP,\n data_files=CONFIG.DATA_FILES,\n options={'py2app': CONFIG.OPTIONS},\n setup_requires=['py2app'],\n maintainer='foo bar',\n author_email='[email protected]',\n )\n\nif __name__ == '__main__':\n main()\n\nThen you need to run python3 setup.py py2app and now you can go and just double click on your_app_{VERSION}_{platform}.app.\nRecomendations by the py2app docs:\n\nMake sure not to use the -A flag\nDo not use --argv-emulation when the program uses a GUI toolkit (as Tkinter) py2app options docs\n\n" ]

[ 0 ]

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[]

[ "macos", "py2app", "pyinstaller", "python", "tkinter" ]

[ "Since pyinstaller is a Python script, it is sufficient to run it with optimisation activated: e.g.\nPYTHONOPTIMIZE=2 pyinstaller script.py\n\nso that during bundling .pyo files are created instead of .pyc\n", "i would like to add that in cmd.exe you can type\n>set PYTHONOPTIMIZE=1\n>pyinstaller skript.py \n\nthe answer from Stefano M did not work in my console. i had to make two different calls.\nyou can check if your code is optimized by using the preprocessor directive\nif __debug__:\n print(\"optimized\")\n\n" ]

[ 4, 0 ]

[]

[]

[ "pyinstaller", "python" ]

[ "In order to import the data into a Pandas dataframe, you can use\ndata_array = []\n\nfor text, _ in TEST_DATA:\n doc = nlp(text)\n data_array.append({ent.label_:ent.text for ent in doc.ents})\n\nimport pandas as pd\ndf = pd.DataFrame.from_dict(data_array)\n\nThe test result:\n>>> pd.DataFrame.from_dict(data_array)\n ID Date ShippingAddress\n0 1131547 12/9/2019 USA\n1 567456 NaN Hills\n\n" ]

[ 1 ]

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[]

[ "dictionary", "named_entity_recognition", "nlp", "python", "spacy" ]

[ "You can let a greedy .* consume the starting match and capture the latter.\nimport re\n\nmatches = re.findall(r\"(?i).*\\b(studiocake\\.kiev\\.ua\\S*\\b(?:jpeg|png|jpg))\\b\", s)\n\nSee this demo at regex101 (matches in group 1) or a Python demo at tio.run\n\nInside used \\S* to match any amount of characters other than a whitespace.\nI further added some \\b word boundaries and the (?i)-flag for ignore case.\n", "What you want to achieve, is a standard operation on URLs, and python has good number of libraries to achieve that. Instead of using regexes for this exercise, I would recommend using a url parsing library, which provides standard operations, and provides better code.\nfrom urllib.parse import urlparse, parse_qs\n\n\ndef extractSrc(strUrl):\n # Parse original URL using urllib\n parsed_url = urlparse(strUrl)\n\n # Find the value of query parameter img\n src_value = parse_qs(parsed_url.query)['src'][0]\n \n # Again, using same library, parse img url which we got above.\n img_parsed_url = urlparse(src_value)\n\n # Remove the scheme in the img URL and return result.\n scheme = \"%s://\" % img_parsed_url.scheme\n return img_parsed_url.geturl().replace(scheme, '', 1)\n\n\n\nurls = '''https://studiocake.kiev.ua/wp-content/webpc-passthru.php?src=https://studiocake.kiev.ua/wp-content/uploads/photo_2020-12-27_12-18-00-2-333x444.jpg&nocache=1\nhttps://studiocake.kiev.ua/wp-content/webpc-passthru.php?src=https://studiocake.kiev.ua/wp-content/uploads/IMG_4945-333x444.jpeg&nocache=1\nhttps://studiocake.kiev.ua/wp-content/webpc-passthru.php?src=https://studiocake.kiev.ua/wp-content/uploads/tri-shokolada.png&nocache=1'''\n\nfor u in urls.split('\\n'):\n print(extractSrc(u))\n\nOutput:\nstudiocake.kiev.ua/wp-content/uploads/photo_2020-12-27_12-18-00-2-333x444.jpg\nstudiocake.kiev.ua/wp-content/uploads/IMG_4945-333x444.jpeg\nstudiocake.kiev.ua/wp-content/uploads/tri-shokolada.png\n\n", "My hack expression is this:\n(https://)(studiocake\\.kiev\\.ua.*(php)\\?src=https://)(studiocake\\.kiev\\.ua.*(jpeg|png|jpg))(&nocache=1)\n\nTo replace it with $4\nExplanation...\nI just selected all the link in parts and then replaced it with the particular part needed.\n" ]

[ 4, 3, 0 ]

[]

[]

[ "extract", "python", "regex", "string", "url" ]

[ "I haven't investigated what exactly you may have done wrong, but a recursive implementation of Continued Fraction should be fairly simple, so I'm suggesting this instead:\nONE = Fraction(1, 1)\n\ndef continuedFraction(array):\n return _continuedFraction(array, 0)\n\ndef _continuedFraction(array, index):\n result = Fraction(array[index], 1)\n if index + 1 < len(array):\n return result + ONE / _continuedFraction(array, index + 1)\n return result\n\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x" ]

[ "I figure it out.\nAn additional parameter had to be passed:\nincludeItemsFromAllDrives = True,\nsupportsAllDrives = True\n\nThis works:\ndrive_service.files().list(\n q = f\"'{parent_folder}' in parents\",\n spaces = 'drive',\n includeItemsFromAllDrives = True,\n supportsAllDrives = True\n).execute()\n\n" ]

[ 0 ]

[]

[]

[ "google_drive_api", "google_drive_shared_drive", "python" ]

[ "Probably you should wrap your function with transaction.atomic, here is example from the django docs:\nfrom django.db import transaction\[email protected]\ndef viewfunc(request):\n # This code executes inside a transaction.\n do_stuff()\n\nand as a context manager:\nfrom django.db import transaction\n\ndef viewfunc(request):\n # This code executes in autocommit mode (Django's default).\n do_stuff()\n\n with transaction.atomic():\n # This code executes inside a transaction.\n do_more_stuff()\n\n" ]

[ 0 ]

[]

[]

[ "django", "mysql", "python", "python_3.x" ]

[ "For anyone out there facing this, there has been an issue on the kivymd github repo about this and this problem is caused by changes in the latest opengl version and changes in sdl versions. The best thing to do for now is to use kivymd==1.0.2 in the requirements while compiling apk and it should work fine.\n" ]

[ 0 ]

[]

[]

[ "android", "buildozer", "kivy", "kivymd", "python" ]

[ "Adding .nojekyll file, as sometimes suggested, only tells github not to apply it's own templates.\nAnyway, I figured this out a long time ago. Just change a couple of paths in config file and change all the paths in layout.html. There maybe other ways, but that method works for me. Here's a live example where 10 separate projects share _static: https://www.adobe.com/devnet-docs/acrobatetk/tools/AdminGuide/index.html\n", "The easiest way is to add an empty .nojekll file under the html directory\n" ]

[ 1, 0 ]

[]

[]

[ "path", "python", "python_sphinx" ]

[ "This error mostly occurs if you haven't included django-htmx in the settings.py.\nTry making the below changes and see if it works :\n\nAdd \"django_htmx.middleware.HtmxMiddleware\" to the MIDDLEWARE.\nAdd \"django_htmx\" to the INSTALLED_APPS.\n\n" ]

[ 0 ]

[]

[]

[ "django", "django_views", "htmx", "python" ]

[ "Accumulate the intermediate accuracies into a list, then take the mean of the list:\nimport numpy as np\n\naccuracies = []\n\nfor index in range(1, 5):\n Rndm1 = RFC(n_estimators=500, random_state= index)\n Rndm1.fit(X_train, y_train)\n y_pred = Rndm1.predict(x_test)\n accuracies.append(accuracy_score(y_test, y_pred))\n\nprint(\"Mean across random states\", np.mean(accuracies))\n\n" ]

[ 1 ]

[]

[]

[ "python", "random_forest" ]

[ "You may have to try the below.\npip install -U pip setuptools wheel\npip install -U spacy\npython -m spacy download en_core_web_sm\nAfter installation restart the kernal if you are using jupyter notebook/lab\nFor me Issue resolved.\n" ]

[ 0 ]

[]

[]

[ "import", "nlp", "python", "python_packaging", "spacy" ]

[ "According to the documentation https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html\n\nfit(X, y, sample_weight=None) Fit Naive Bayes classifier according to\nX, y.\n\n\nyarray-like of shape (n_samples,) Target values.\n\ny should be a one-dimensional array\n" ]

[ 0 ]

[]

[]

[ "machine_learning", "naivebayes", "python", "sentiment_analysis", "tf_idf" ]

[ "One way, which you can try to use a for loop in Jinja2 in your HTML.\nYou can simply pass a list to the HTML and then use for loop in Jinja2 along with the tags to print the outputs on seperate lines -\nFor example in your case -\nIn your Flask code -\nfrom flask import Flask, render_template, request\napp = Flask(__name__)\n\[email protected]('/')\ndef index():\n return render_template('index.html')\n\[email protected]('/colors', methods=[\"GET\",\"POST\"])\ndef test():\n\n #your other code\n\n colors = ['red', 'green', 'blue']\n return render_template('index.html',color=colors)\n\nif __name__ == '__main__':\n app.run()\n\nAnd your HTML code -\njust add this for loop, where i iterates over all the colors and print them in seperate lines\n{% for i in color %}\n<p>{{ i }} </p>\n{% endfor %}\n\nyou can also use any other tag as per your requirement, it will still print on new line.\n" ]

[ 1 ]

[]

[]

[ "flask", "html", "jinja2", "python", "python_3.x" ]

[ ">>> a= []\n>>> a.append(9)\n>>> print(a.append(9))\nNone\n>>>\n\nWhat's going on here? Well, list.append returns None. As fo most functions which mutate data rather than generating new values. You can see this in your code with:\n[[mtrx[i][j].append(0) for i in range(3)] for j in range(3)]\n\nThose indices also don't exist yet, so you will get an index error.\nYou likely want:\n[[0 for _ in range(3)] for _ in range(3)]\n\nWhich generates:\n[[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n\nAs an alternative, you could do the following, but that isn't better than matrix = [[0 for _ in range(3)] for _ in range(3)].\nmatrix = []\nfor _ in range(3):\n row = []\n for _ in range(3):\n row.append(0)\n matrix.append(row)\n\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.8", "tic_tac_toe" ]

[ "What format do you want to achieve in the end? Writing this to a csv seems to be leading to some odd outcomes anyway.\nIn any case, both of these code work for me without errors, both giving slightly different results with respect to escape characters.\nWith normal string:\nimport csv\n\nwith open('test2.csv', 'w') as csvfile: \n csvwriter = csv.writer(csvfile) \n problem_string = \"this \\n sentence \\% is \\n problematic \\g\"\n csvwriter.writerow(problem_string)\n\nWith raw input:\nimport csv\n\nwith open('test2.csv', 'w') as csvfile: \n csvwriter = csv.writer(csvfile) \n problem_string = r\"this \\n sentence \\% is \\n problematic \\g\"\n csvwriter.writerow(problem_string)\n\n" ]

[ 0 ]

[]

[]

[ "csv", "python", "string" ]

[ "As the message you are sending isn't an embed but is plain text, I believe this should work:\nasync def emojibot(ctx): #Comando a decir\n await ctx.send('[:HabboHotel:](https://habbo.es)') \n\n" ]

[ 0 ]

[]

[]

[ "emoji", "python" ]

[ "Cause df1[['A','B']] is a new DataFrame, try:\ndf1.loc[25, ['A','B']] = df2[['A','B']].loc[64]\n\n", "df1.loc[25, ['A', 'B']] = df2.loc[64, ['A', 'B']]\n\n" ]

[ 0, 0 ]

[]

[]

[ "pandas", "python" ]

[ "You could use a list comprehension to make a list of matches:\nmatches = [sp_data(startDate=trade_date, endDate=trade_date) for trade_date in listOfDates]\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "loops", "pandas", "python" ]

[ "You can unpack each sublist into the list of letters and the \"rest\", then iterate over the letters to build new sublists from a letter and the rest.\nnew_lst = [[c, *rest] for letters, *rest in lst for c in letters]\n\n", "You're only missing the loop to iterate through the lst\nfrom pprint import pprint\n\n\nlst = [[['X', 'A'], 1, 2, 3], [['Y', 'B'], 4, 5, 6], [['Z', 'C'], 7, 8, 9]]\n\nnew_lst = []\nfor elem in lst:\n fst, *rest = elem\n new_lst.extend([[i, *rest] for i in fst])\n\npprint(new_lst, indent=4)\n\noutput\n[ ['X', 1, 2, 3],\n ['A', 1, 2, 3],\n ['Y', 4, 5, 6],\n ['B', 4, 5, 6],\n ['Z', 7, 8, 9],\n ['C', 7, 8, 9]]\n\n", "You can turn your list comprehension for a single element into a nested list comprehension for the entire list:\n>>> lst = [[['X', 'A'], 1, 2, 3], [['Y', 'B'], 1, 2, 3], [['Z', 'C'], 1, 2, 3]]\n>>> [[i, *rest] for (fst, *rest) in lst for i in fst]\n[['X', 1, 2, 3], ['A', 1, 2, 3], ['Y', 1, 2, 3], ['B', 1, 2, 3], ['Z', 1, 2, 3], ['C', 1, 2, 3]]\n\n" ]

[ 3, 2, 2 ]

[ "You could just replace the first term right with the first letter right?\nlst = [[['X', 'A'], 1, 2, 3], [['Y', 'B'], 1, 2, 3], [['Z', 'C'], 1, 2, 3]]\n\nfor item in lst:\n item[0] = item[0][0];\n\nprint(lst)\n\n" ]

[ -1 ]

[ "list", "list_comprehension", "python" ]

[ "You can use the .rank() / .count() from the previous question combined with .over()\n>>> df.select(\n... (pl.col(pred_cols).rank() / pl.col(pred_cols).count())\n... .over(ERA_COL)\n... )\nshape: (6, 3)\n┌───────┬───────┬───────┐\n│ pred1 | pred2 | pred3 │\n│ --- | --- | --- │\n│ f64 | f64 | f64 │\n╞═══════╪═══════╪═══════╡\n│ 0.5 | 0.5 | 0.5 │\n├───────┼───────┼───────┤\n│ 1.0 | 1.0 | 1.0 │\n├───────┼───────┼───────┤\n│ 0.5 | 0.5 | 0.5 │\n├───────┼───────┼───────┤\n│ 1.0 | 1.0 | 1.0 │\n├───────┼───────┼───────┤\n│ 0.5 | 0.5 | 1.0 │\n├───────┼───────┼───────┤\n│ 1.0 | 1.0 | 0.5 │\n└─//────┴─//────┴─//────┘\n\n.with_columns() to \"replace\" the original values.\n>>> df.with_columns(\n... (pl.col(pred_cols).rank() / pl.col(pred_cols).count())\n... .over(ERA_COL)\n... )\nshape: (6, 5)\n┌─────┬───────┬───────┬───────┬────────────────┐\n│ era | pred1 | pred2 | pred3 | something_else │\n│ --- | --- | --- | --- | --- │\n│ str | f64 | f64 | f64 | i64 │\n╞═════╪═══════╪═══════╪═══════╪════════════════╡\n│ 01 | 0.5 | 0.5 | 0.5 | 5 │\n├─────┼───────┼───────┼───────┼────────────────┤\n│ 01 | 1.0 | 1.0 | 1.0 | 4 │\n├─────┼───────┼───────┼───────┼────────────────┤\n│ 02 | 0.5 | 0.5 | 0.5 | 3 │\n├─────┼───────┼───────┼───────┼────────────────┤\n│ 02 | 1.0 | 1.0 | 1.0 | 67 │\n├─────┼───────┼───────┼───────┼────────────────┤\n│ 03 | 0.5 | 0.5 | 1.0 | 5 │\n├─────┼───────┼───────┼───────┼────────────────┤\n│ 03 | 1.0 | 1.0 | 0.5 | 4 │\n└─//──┴─//────┴─//────┴─//────┴─//─────────────┘\n\n" ]

[ 2 ]

[]

[]

[ "pandas", "python", "python_polars", "rank" ]

[ "My site-packages folder in Pycharm showed that they were disabled, colored orange, but it turns out that I just had to delete everthing inside the venv folder again and just check the box inherit global site-packages when changing the base interpreter. Everthing is fixed now!\n", "Seems like python-docx doesn't work anymore. It throws a legacy error and appears to be out of date ver. 0.8.11. When you google - seems like there is a newer version https://pypi.org/project/docx/\n" ]

[ 0, 0 ]

[]

[]

[ "module", "python" ]

[ "I think you should write a custom module; note that when you run any script, ansible creates a copy of it to a temp location. So any relative path you provided in the import will be messed up. You can run your playbook task with -vvv to confirm this.\nHere is an example(high level) of setting up a custom module:\n tree \n.\n├── your_playbook.yml\n├── library\n│ └── your_custom_module.py # write your code logic here \n└── module_utils\n └── restart_tomcat.py #this file contains common classes/functions\n\nIn your module file(your_custom_module.py), you can do the import like this:\n#!/usr/bin/python3\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\nfrom ansible.module_utils.basic import AnsibleModule \nfrom ansible.module_utils.restart_tomcat.py import * #this line will import the classes or functions present in the other file to the custom module\n\nYou can find more details here and an example here and this.\nFor supporting references, run the ansible-doc script command and navigate to the NOTES section.\n" ]

[ 4 ]

[]

[]

[ "ansible", "jenkins", "python" ]

[ "\nawait ctx.send('\\n'.join(guild.name for guild in bot.guilds))\n\nJust remember to pass an intent with guilds enabled in your bot's constructor\n", "@commands.command()\nasync def servers(self, ctx):\n activeservers = client.guilds\n for guild in activeservers:\n await ctx.send(guild.name)\n print(guild.name)\n\nThis code should work\n", "@client.command()\nasync def server(ctx):\n servers = list(client.guilds)\n for server in servers:\n await ctx.send(server.name)\n\nit works for me...\n" ]

[ 2, 0, 0 ]

[]

[]

[ "discord.py", "python" ]

[ "The database settings was utf8mb4, the tables were also the same, but not the columns that were in utf8mb3. After altering columns encoding to utf8mb4, everything is going right now.\n" ]

[ 0 ]

[]

[]

[ "django", "mysql", "python" ]

[ "I think something like this is what you want:\nfrom itertools import groupby\nresult = []\nkey = lambda t: t[0]\nfor _,items in groupby(sorted(tuples, key=key), key):\n item = None\n for i, it in enumerate(items):\n # First item in group. Need to convert to list to edit.\n if not item: item = list(it)\n # Not first. Update item at correct index.\n else: item[1 + i] = it[1 + i]\n # Convert back to tuple and save.\n result.append(tuple(item))\n\nfor item in result: print(item)\n\nOutput:\n('2022-10-15 01:16:00', '5', '4', '', 'hdd1', '1234')\n('2022-10-15 01:17:00', '10', '25', '', 'hdd1', '1234')\n('2022-10-15 01:18:00', '1', '2', '', 'hdd1', '1234')\n\n", "Here is a solution using a dictionary to store the date while iterating over the tuples.\n#empty dict with date as key and a list placeholder as value\nr = {t[0]:[\"\", \"\", \"\", \"\", \"\"] for t in tuples} \n\n\n#iterate over the tuples and populate the dict\nfor (date, *other_fields) in tuples:\n for i, value in enumerate(other_fields):\n if value: #skip if it's empty\n r[date][i] = value\n\n\n#convert the dictionary in a list of tuples\nr = [tuple([k, *v]) for k,v in r.items()]\nprint(r)\n\n#[('2022-10-15 01:16:00', '5', '4', '', 'hdd1', '1234'), ('2022-10-15 01:17:00', '10', '25', '', 'hdd1', '1234'), ('2022-10-15 01:18:00', '1', '2', '', 'hdd1', '1234')]\n\n" ]

[ 1, 0 ]

[]

[]

[ "python", "tuples" ]

[ "OK, I think there is some confusion going on in this question, so I will do my best to clarify the situation. There are three main points:\n\nsome things cannot be serialised between processes easily or at all\nsome objects are expensive to create per process, and it would be nice to only do it once\nthe work must happen in an async context\n\nHere is how I would do it. Put this in an importable module.\nextractor = [None]\n\ndef get_extractor(CONF):\n if extractor[0] is None:\n connector = Sharepoint(CONF.sources[\"sharepoint\"])\n extractor[0] = SharepointExtractor(CONF.sources[\"sharepoint\"], connector)\n return extractor[0]\n\n\nasync def workload(CONF, item):\n extractor = get_extractor(CONF)\n return await extractor.job(item, retries=5)\n\nif __name__ == \"__main__\": # or run this elsewhere\n client = ... \n items = ...\n futures = client.map(workload, items)\n output = client.gather(futures)\n\nI do not know from the OP which parts of the workload are coroutines, I am guessing the .job method - but it should be obvious what I am doing. I note the original code would not have worked in a simple non-dask session, and it is always best to start off with something that works before trying to daskify it.\nOn async in dask:\n\nclient.map/submit supports coroutine functions, and they will be executed on the same event loop as the main worker. That's all you need here. All the distributed components (worker, scheduler, client) are async, server-like implementations with event loops, but execution of worker code does not normally happen in the same thread as the one running that server.\nclient(asynchronous=True) implies that the client is to be constructed and operated on only from within coroutines - and that the client's event loop is in the current thread. This is probably not what you want, unless you know what you are doing.\n\n" ]

[ 1 ]

[]

[]

[ "actor", "asynchronous", "dask", "python" ]

[ "You shouldn't use imshow because this will display it as if it were an image (because you have a 2D matrix).\nYou need to plot each row separately, like so:\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nsin1 = np.sin(np.linspace(0, 2*np.pi, 100))\nsin2 = np.sin(np.linspace(0, 2*np.pi, 100)) + 0.5\nsin3 = np.sin(np.linspace(0, 2*np.pi, 100)) + 1\n\nsin1[10] = np.nan\nsin2[20] = np.nan\nsin3[30] = np.nan\n\ndata = np.array([sin1, sin2, sin3])\n\n# plot each row as a separate series\nfor i in range(data.shape[0]):\n plt.plot(data[i, :])\n\nplt.show()\n\nand then the nan's should just be empty spots in the graph.\n\n" ]

[ 0 ]

[]

[]

[ "python", "spectrum" ]

[ "You can create a dictionary that has the information of \"child\" column as key's and \"child_string\" as values.\nchild_info = df[['child_string','child']].dropna()\nchild_string_to_child_dict = dict(zip(child_info.child,child_info.child_string))\n\n>>> child_string_to_child_dict\n \n{8675.0: 'string23',\n 8676.0: 'string54',\n 2048.0: 'string28',\n 5442.0: 'string86',\n 1942.0: 'string15',\n 3185.0: 'string1'}\n\n\nThen you can map that dictionary on your \"parent\" column\ndf['parent_string'] = df['parent'].map(child_string_to_child_dict)\n\nResult:\n child_string child parent interesting parent_string\n0 string42 NaN NaN some_unique_field1 NaN\n1 string23 8675.0 2048.0 some_unique_field2 string28\n2 string23 8675.0 2048.0 some_unique_field3 string28\n3 string54 8676.0 2048.0 some_unique_field4 string28\n4 string28 2048.0 1942.0 some_unique_field5 string15\n5 string86 5442.0 1942.0 some_unique_field6 string15\n6 string15 1942.0 3185.0 some_unique_field7 string1\n7 string1 3185.0 NaN some_unique_field8 NaN\n\nA similar approach to what you tried\n" ]

[ 1 ]

[]

[]

[ "dataframe", "pandas", "python", "python_3.x" ]

[ "From the spaCy documentation on Matchers (https://spacy.io/usage/rule-based-matching), there is no way to detect 2 different tokens separated by an arbitrary number of tokens. If you knew how many tokens were between \"cat\" and \"fat\", for example, then you could use wildcard patterns (https://spacy.io/usage/rule-based-matching#adding-patterns-wildcard), but it looks like from your example that distance between tokens can vary.\nTwo solutions that I can see to solve your problem:\n\nKeep track of matches in your for loop using some sort of data structure. If all the tokens you are looking for end up being found, then add that match to your final results.\nUse regular expressions to detect what you are looking for. spaCy does have great tools for rule-based matching, but it looks like you aren't using any linguistic aspects of the words you are searching for. A simple regex like /cat.*?fat/ will find the matches you are looking for.\n\n" ]

[ 0 ]

[]

[]

[ "matcher", "nlp", "python", "spacy" ]

[ "First of all, you have to provide a minimal sample easy to copy and paste not an image of samples. But I have created a minimal sample similar to your images. It doesn't change the solution.\nRead xlsx files and convert them to list of dictionaries in Python, then you will have objects like these:\nsheet1 = [{\n \"app_id_c\": \"116092749\",\n \"cust_id_n\": \"95014843\",\n \"laa_app_a\": \"36\",\n \"laa_promc\": \"504627\",\n \"laa_branch\": \"8\",\n \"laa_app_type_o\": \"C\",\n}]\n\nsheet2 = [\n {\n \"lsi_app_id_\": \"116092749\",\n \"lsi_cust_type_c\": \"G\",\n },\n {\n \"lsi_app_id_\": \"116092749\",\n \"lsi_cust_type_c\": \"G\",\n },\n]\n\nAfter having the above mentioned objects in Python, you can create the desired json structure by the following script:\nfor i in sheet1:\n i[\"los_input_from_sas\"] = list()\n for j in sheet2:\n if i[\"app_id_c\"] == j[\"lsi_app_id_\"]:\n i[\"los_input_from_sas\"].append(j)\n\nsheet1 = json.dumps(sheet1)\n\nprint(sheet1)\n\n\nAnd this is the printed output:\n[\n {\n \"app_id_c\": \"116092749\",\n \"cust_id_n\": \"95014843\",\n \"laa_app_a\": \"36\",\n \"laa_promc\": \"504627\",\n \"laa_branch\": \"8\",\n \"laa_app_type_o\": \"C\",\n \"los_input_from_sas\": [\n {\n \"lsi_app_id_\": \"116092749\",\n \"lsi_cust_type_c\": \"G\"\n },\n {\n \"lsi_app_id_\": \"116092749\",\n \"lsi_cust_type_c\": \"G\"\n }\n ]\n }\n]\n\nUPDATE:\nHere are some solution to read xlsx files and convert to python dict.\n" ]

[ 0 ]

[]

[]

[ "excel", "json", "python", "python_3.x" ]

[ "If structure is not changing then this can the solution It will iterate over all objects and add displayValue in search_values list\ndisplay_values = []\nfor object_entries in output_dict.get(\"objectEntries\", []):\n for attribute in object_entries.get(\"attributes\"):\n if attribute.get(\"objectTypeAttributeId\") == \"14\":\n for object_attr in attribute.get(\"objectAttributeValues\", []):\n if object_attr.get(\"displayValue\") not in display_values:\n display_values.append(object_attr.get(\"displayValue\"))\n\n\nprint(display_values)\n\n", "You could browse your JSON dict and proceed each entries until you get the one(s) you are interested in.\n# lets browse top level entries of your array\nfor e1 in outputs[\"objectEntries\"]:\n # for each of those entries, browse the entries in the attribute section\n for e2 in e1[\"attributes\"]:\n # does the entry match the rule \"14\"? If not, go to the next one\n if (e2[\"objectTypeAttributeId\"] != 14):\n continue\n # print the current entry's associated value\n for attr in e2[\"objectAttributeValues\"]\n print(attr[\"displayValue\"])\n\n", "You can iterate over your dict and check if the values matches with a function like this:\ndef get_display_value(my_dict, value):\n results = []\n for objectEntries in my_dict['objectEntries']:\n for attributes in objectEntries['attributes']:\n if int(attributes['objectTypeAttributeId']) == value:\n results.append(attributes['objectAttributeValues'][0]['displayValue'])\n return results\n\nUsing the function:\nresults = get_display_value(my_dict, 14)\nprint(results)\n\nOutputs:\n['02f9ed75-b416-49d0-8515-0601581158e5', '427904c5-e2c8-4735-bc38-4013928cd043']\n\nEdit: now returning all match values instead of only the first one.\n" ]

[ 1, 0, 0 ]

[]

[]

[ "json", "python" ]

[ "You can't directly change the threshold used by predict (which gets called by your scorer, presumably), but you can provide a customer scoring method. See the User Guide. Here I think you'd want something like:\ndef f2_score_at_thresh(y_true, y_pos_prob, threshold):\n y_pred = y_pos_prob > threshold\n return fbeta_score(y_true, y_pred, beta=2, ...)\n\nmy_scorer = make_scorer(f2_scorer, needs_proba=True, threshold=0.2)\n\nGridSearchCV(..., scoring=my_scorer)\n\n" ]

[ 1 ]

[]

[]

[ "python", "scikit_learn" ]

[ "Note that a dataframe's index could be out of order, or not even numerical at all. If you don't want to use the current index and instead renumber the rows sequentially, then you can use df.reset_index() together with the suggestions below\nTo get all indices that matches 'Smith'\n>>> df[df['LastName'] == 'Smith'].index\nInt64Index([1], dtype='int64')\n\nor as a numpy array\n>>> df[df['LastName'] == 'Smith'].index.to_numpy() # .values on older versions\narray([1])\n\nor if there is only one and you want the integer, you can subset\n>>> df[df['LastName'] == 'Smith'].index[0]\n1\n\nYou could use the same boolean expressions with .loc, but it is not needed unless you also want to select a certain column, which is redundant when you only want the row number/index.\n", "df.index[df.LastName == 'Smith']\n\nOr\ndf.query('LastName == \"Smith\"').index\n\nWill return all row indices where LastName is Smith\nInt64Index([1], dtype='int64')\n\n", "df.loc[df.LastName == 'Smith']\n\nwill return the row\n ClientID LastName\n1 67 Smith\n\nand \ndf.loc[df.LastName == 'Smith'].index\n\nwill return the index\nInt64Index([1], dtype='int64')\n\nNOTE: Column names 'LastName' and 'Last Name' or even 'lastname' are three unique names. The best practice would be to first check the exact name using df.columns. If you really need to strip the column names of all the white spaces, you can first do\ndf.columns = [x.strip().replace(' ', '') for x in df.columns]\n\n", " len(df[df[\"Lastname\"]==\"Smith\"].values)\n\n", "count_smiths = (df['LastName'] == 'Smith').sum()\n\n", "I know it's many years later but don't try the above solutions without reindexing your dataframe first. As many have pointed out already the number you see to the left of the dataframe 0,1,2 in the initial question is the index INSIDE that dataframe. When you extract a subset of it with a condition you might end up with 0,2 or 2,1, or 2,1 or 2,1,0 depending your condition. So by using that number (called \"index\") you will not get the position of the row in the subset. You will get the position of that row inside the main dataframe.\nuse:\nnp.where([df['LastName'] == 'Smith'])[1][0]\n\nand play with the string 'Smith' to see the various outcomes. Where will return 2 arrays. The 2nd one (index 1) is the one you care about.\nNOTE:\nWhen the value you search for does not exist where() will return 0 on [1][0]. When is the first value of the list it will also return 0 on [1][0]. Make sure you validate the existence first.\nNOTE #2:\nIn case the same value as in your condition is present in the subset multiple times on [1] with will find the list with the position of all occurrences. You can use the length of [1] for future processing if needed.\n", "If the index of the dataframe and the ordinal number of the rows differ, most solutions posted here won't work anymore. Given your dataframe with an alphabetical index:\nIn [2]: df = pd.DataFrame({\"ClientID\": {\"A\": 34, \"B\": 67, \"C\": 53}, \"LastName\": {\"A\": \"Johnson\", \"B\": \"Smith\", \"C\": \"Brows\"}})\n\nIn [3]: df\nOut[3]: \n ClientID LastName\nA 34 Johnson\nB 67 Smith\nC 53 Brows\n\nYou have to use get_loc to access the ordinal row number:\nIn [4]: df.index.get_loc(df.query('LastName == \"Smith\"').index[0])\nOut[4]: 1\n\nIf there may exist multiple rows where the condition holds, e.g. find the ordinal row numbers that have 'Smith' or 'Brows' in LastName column, you can use list comprehensions:\nIn [5]: [df.index.get_loc(idx) for idx in df.query('LastName == \"Smith\" | LastName == \"Brows\"').index]\nOut[5]: [1, 2]\n\n", "If in the question \"row number\" means actual row number/position (rather than index label)\npandas.Index.get_loc(key, method=None, tolerance=None)\nseems to be the answer, ie something like:\nrow_number = df.index.get_loc(df.query(f'numbers == {m}').index[0]) \n\nThe current answers, except one, explain how to get the index label rather than the row number.\nTrivial code with index lables not corresponding to row numbers:\nimport pandas as pd\n\nn = 3; m = n-1\n\ndf = pd.DataFrame({'numbers' : range(n) },\n index = range(n-1,-1,-1))\nprint(df,\"\\n\")\n\nlabel = df[df['numbers'] == m].index[0]\nrow_number = df.index.get_loc(df.query(f'numbers == {m}').index[0])\n\nprint(f'index label: {label}\\nrow number: {row_number}',\"\\n\")\nprint(f\"df.loc[{label},'numbers']: {df.loc[label, 'numbers']}\")\nprint(f\"df.iloc[{row_number}, 0]: {df.iloc[row_number, 0]}\")\n\n numbers\n2 0\n1 1\n0 2 \n\nindex label: 0\nrow number: 2 \n\ndf.loc[0,'numbers']: 2\ndf.iloc[2, 0]: 2\n\n", "You can simply use shape method\ndf[df['LastName'] == 'Smith'].shape\nOutput\n(1,1) \nWhich indicates 1 row and 1 column. This way you can get the idea of whole datasets\nLet me explain the above code\nDataframeName[DataframeName['Column_name'] == 'Value to match in column']\n", "\nTo get exact row-number of single occurrence\n\nrow-number = df[df[\"LastName\" == 'Smith']].index[0]\n\nTo get exact row-number of multiple occurrence of 'Smith'\n\nrow-number = df[df[\"LastName\" == 'Smith']].index.tolist()\n" ]

[ 74, 14, 8, 2, 1, 1, 1, 1, 0, 0 ]

[]

[]

[ "pandas", "python" ]

[ "By default print terminates with a newline character. When you define end in the print function, you modify this behavior. I have added a bare print which will simply output a newline character to stdout, correcting the format issues.\nfor i in range(sets):\n print(\"set {0} of {1} started\".format(i + 1, sets))\n for j in range(seconds, 0, -1):\n print(j, end=\" \")\n print()\nprint(\"Finished workout! Good Job!\")\n\nI would also like to add, that if you're on windows and using something like git-bash or powershell to test your code, you may need to flush stdout after a print to have your text displayed in the proper order.\nprint(\"Hello World!\", flush=True)\nIf this applies to your situation, make sure you test, as over flushing the buffer can cause a lot of lag in your application.\n" ]

[ 0 ]

[]

[]

[ "loops", "python", "python_3.x" ]

[ "It's possible via a relationship but it took a lot of trial and error with joins to get there. Below is what was needed to get it working, although I wouldn't be surprised if there's a more optimal way.\nclass ItemVersion(Base):\n ...\n version_min_val = column_property(\n select(AppVersion.value)\n .where(AppVersion.id == version_min_id)\n .correlate_except(AppVersion)\n .scalar_subquery(),\n )\n version_max_val = column_property(\n select(AppVersion.value)\n .where(AppVersion.id == version_max_id)\n .correlate_except(AppVersion)\n .scalar_subquery(),\n )\n versions = relationship(\n AppVersion,\n primaryjoin=and_(\n app_id == AppVersion.app_id,\n case(\n [and_(version_min_id == None, version_max_id == None), literal(True)],\n [and_(version_min_id == None, version_max_id != None), AppVersion.value <= version_max_val],\n [and_(version_min_id != None, version_max_id == None), version_min_val <= AppVersion.value],\n else_=and_(version_min_val <= AppVersion.value, AppVersion.value <= version_max_val),\n )\n ),\n viewonly=True, uselist=True,\n )\n\nclass Item(Base):\n ...\n versions = relationship(\n AppVersion,\n primaryjoin=and_(\n id == ItemVersion.item_id,\n case(\n [and_(ItemVersion.version_min_id == None, ItemVersion.version_max_id == None), literal(True)],\n [and_(ItemVersion.version_min_id == None, ItemVersion.version_max_id != None), AppVersion.value <= ItemVersion.version_max_val],\n [and_(ItemVersion.version_min_id != None, ItemVersion.version_max_id == None), ItemVersion.version_min_val <= AppVersion.value],\n else_=and_(ItemVersion.version_min_val <= AppVersion.value, AppVersion.value <= ItemVersion.version_max_val),\n )\n ),\n secondaryjoin=ItemVersion.app_id == AppVersion.app_id,\n secondary=ItemVersion.__table__,\n viewonly=True, uselist=True,\n )\n\nFull code I used for testing:\nfrom sqlalchemy import select, create_engine, Column, Integer, ForeignKey, String, case, and_, literal\nfrom sqlalchemy.orm import relationship, sessionmaker, column_property\nfrom sqlalchemy.ext.declarative import declarative_base\n\nEngine = create_engine('sqlite://')\n\nBase = declarative_base(Engine)\n\nsession = sessionmaker(Engine)()\n\nclass App(Base):\n \"\"\"Each App has name and list of versions.\"\"\"\n __tablename__ = 'app'\n id = Column(Integer, primary_key=True)\n name = Column(String(64))\n versions = relationship('AppVersion', back_populates='app')\n\n def __repr__(self):\n return self.name\n\nclass AppVersion(Base):\n \"\"\"Each App version has a particular value.\"\"\"\n __tablename__ = 'app_version'\n id = Column(Integer, primary_key=True)\n app_id = Column(Integer, ForeignKey('app.id'), nullable=False)\n value = Column(Integer, nullable=False)\n\n app = relationship('App', foreign_keys=app_id, back_populates='versions', innerjoin=True)\n\n def __repr__(self):\n return f'{self.app.name}:{self.value}'\n\nclass ItemVersion(Base):\n \"\"\"The item version links a particular item to an App and it's min/max versions.\n Using the min and max versions, a range of compatible versions can be generated.\n \"\"\"\n __tablename__ = 'item_version'\n id = Column(Integer, primary_key=True)\n\n item_id = Column(Integer, ForeignKey('item.id'))\n app_id = Column(Integer, ForeignKey('app.id'))\n version_min_id = Column(Integer, ForeignKey('app_version.id'), nullable=True)\n version_max_id = Column(Integer, ForeignKey('app_version.id'), nullable=True)\n\n item = relationship('Item', foreign_keys=item_id)\n app = relationship('App', foreign_keys=app_id)\n version_min = relationship('AppVersion', foreign_keys=version_min_id)\n version_max = relationship('AppVersion', foreign_keys=version_max_id)\n\n version_min_val = column_property(\n select(AppVersion.value)\n .where(AppVersion.id == version_min_id)\n .correlate_except(AppVersion)\n .scalar_subquery(),\n )\n version_max_val = column_property(\n select(AppVersion.value)\n .where(AppVersion.id == version_max_id)\n .correlate_except(AppVersion)\n .scalar_subquery(),\n )\n\n versions = relationship(\n AppVersion,\n primaryjoin=and_(\n app_id == AppVersion.app_id,\n case(\n [and_(version_min_id == None, version_max_id == None), literal(True)],\n [and_(version_min_id == None, version_max_id != None), AppVersion.value <= version_max_val],\n [and_(version_min_id != None, version_max_id == None), version_min_val <= AppVersion.value],\n else_=and_(version_min_val <= AppVersion.value, AppVersion.value <= version_max_val),\n )\n ),\n viewonly=True, uselist=True,\n )\n\nclass Item(Base):\n \"\"\"Each item may have multiple compatible applications with specific versions.\"\"\"\n __tablename__ = 'item'\n id = Column(Integer, primary_key=True)\n item_versions = relationship('ItemVersion', back_populates='item')\n\n def __repr__(self):\n return f'Item {self.id}'\n\n versions = relationship(\n AppVersion,\n primaryjoin=and_(\n id == ItemVersion.item_id,\n case(\n [and_(ItemVersion.version_min_id == None, ItemVersion.version_max_id == None), literal(True)],\n [and_(ItemVersion.version_min_id == None, ItemVersion.version_max_id != None), AppVersion.value <= ItemVersion.version_max_val],\n [and_(ItemVersion.version_min_id != None, ItemVersion.version_max_id == None), ItemVersion.version_min_val <= AppVersion.value],\n else_=and_(ItemVersion.version_min_val <= AppVersion.value, AppVersion.value <= ItemVersion.version_max_val),\n )\n ),\n secondaryjoin=ItemVersion.app_id == AppVersion.app_id,\n secondary=ItemVersion.__table__,\n viewonly=True, uselist=True,\n )\n\nBase.metadata.create_all()\n\npy = App(name='Python')\npy27 = AppVersion(app=py, value=27)\npy37 = AppVersion(app=py, value=37)\npy38 = AppVersion(app=py, value=38)\npy39 = AppVersion(app=py, value=39)\nmaya = App(name='Maya')\nm22 = AppVersion(app=maya, value=2022)\nm23 = AppVersion(app=maya, value=2023)\n\n# [Python:27, Python:37, Python:38, Python:39, Maya:2022, Maya:2023]\nsession.add(Item(item_versions=[ItemVersion(app=py), ItemVersion(app=maya)]))\n# [Python:37, Python:38, Python:39]\nsession.add(Item(item_versions=[ItemVersion(app=py, version_min=py37)]))\n# [Python:27, Python:37]\nsession.add(Item(item_versions=[ItemVersion(app=py, version_max=py37)]))\n# [Python:27]\nsession.add(Item(item_versions=[ItemVersion(app=py, version_min=py27, version_max=py27)]))\n# [Python:27, Python:37, Python:38]\nsession.add(Item(item_versions=[ItemVersion(app=py, version_min=py27, version_max=py38)]))\n# [Python:27, Python:39, Maya:2022]\nsession.add(Item(item_versions=[ItemVersion(app=py, version_max=py27), ItemVersion(app=py, version_min=py39), ItemVersion(app=maya, version_min=m22, version_max=m22)]))\n\nsession.commit()\n\nstmt = select(Item).where(Item.versions.any(AppVersion.app.has(App.name == 'Maya')))\nfor item in session.execute(stmt).scalars():\n print(f'{item}: {item.versions}')\n\n" ]

[ 0 ]

[]

[]

[ "python", "sqlalchemy" ]

[ "STATIC_URL = '/static/'\nSTATICFILES_DIRS = (\n os.path.join(BASE_DIR, \"static/\"),\n)\n\n" ]

[ 0 ]

[]

[]

[ "css", "django", "html", "python", "static" ]

[ "Check the AWS Official Code Library that contains this use case. When looking for an AWS code example, check this New AWS Doc.\n\nAs you can see, the code library shows this use case in different supported programming langanges. The topic is here:\nCreate an IAM user and assume a role with AWS STS using an AWS SDK\nYou can assume roles by following the Python example.\n" ]

[ 0 ]

[]

[]

[ "amazon_iam", "amazon_web_services", "assume_role", "boto3", "python" ]

[ "When working with hue, seaborn assigns one color per hue value. In this case there seem to be two hue values (1.70 and 3.00), so two colors are used.\nTo give each bar a separate color, you could iterate through the generated bars and manually assign the colors. Note that for each hue value, a container is created for the bars with that hue.\nYou can use the HandlerTuple legend handler to show all color in the legend.\nimport matplotlib.pyplot as plt\nfrom matplotlib.legend_handler import HandlerTuple\nimport seaborn as sns\nimport pandas as pd\n\npalette = [\"#fee090\", \"#fdae61\", \"#4575b4\", \"#313695\", \"#e0f3f8\", \"#abd9e9\", \"#d73027\", \"#a50026\"]\n\ndf = pd.read_html('https://stackoverflow.com/questions/74617540')[0]\n\nax = sns.barplot(data=df, x='Material bead', y='FLT', hue='Media size', palette=palette, errorbar=None,\n edgecolor='black') ###errorbar=('ci', 69))\nfor bars, colors in zip(ax.containers, (palette[0::2], palette[1::2])):\n for bar, color in zip(bars, colors):\n bar.set_facecolor(color)\nax.legend(handles=[tuple(bar_group) for bar_group in ax.containers],\n labels=[bar_group.get_label() for bar_group in ax.containers],\n title=ax.legend_.get_title().get_text(),\n handlelength=4, handler_map={tuple: HandlerTuple(ndivide=None, pad=0.1)})\nsns.despine()\nplt.show()\n\n\nIf you want to color the bars and hatch one of the hues with a pattern, you can call set_hatch() on those bars. You could update the legend already created by seaborn to indicate the hatching on a grey background. (Creating a new legend, as in the previous example, would also work here. But note that hatching is not so easy to see in small areas.)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n\ndf = pd.read_html('https://stackoverflow.com/questions/74617540')[0]\n\nax = sns.barplot(data=df, x='Material bead', y='FLT', hue='Media size', palette=['lightgrey', 'lightgrey'],\n errorbar=None, edgecolor='black')\npalette = [\"#fee090\", \"#4575b4\", \"#e0f3f8\", \"#d73027\"]\nfor bars, hatch, legend_handle in zip(ax.containers, ['', '//'], ax.legend_.legendHandles):\n for bar, color in zip(bars, palette):\n bar.set_facecolor(color)\n bar.set_hatch(hatch)\n # update the existing legend, use twice the hatching pattern to make it denser\n legend_handle.set_hatch(hatch + hatch)\nsns.despine()\nplt.show()\n\n\n" ]

[ 0 ]

[]

[]

[ "bar_chart", "colors", "matplotlib", "python", "seaborn" ]

[ "A couple of things:\nBy looking at your code, in particular the lines:\nfor j in range(len(list)):\nmysql_list >> [ s_report(j)] >> end_operator\n\nwe can determine that if your first task succeeds, namely, mysql_list, then the tasks downstream to it, namely, the s_report calls should begin executing. You have precisely len(list) of them. Within each s_report call there is exactly one dwh_hook.get_pandas_df(request) call, so I believe your DAG should be making len(list) calls of this type provided mysql_list task succeeds.\nAs for the mismatch you see in your Snowflake logs, I can't advise you here. I'd need more details. Keep in mind that the call get_pandas_df might have a retry mechanism (i.e. if cannot reach snowflake, retry) which might explain why your Snowflake logs show a bunch of requests.\nIf your DAG finishes successfully, (i.e. end_operator tasks finishes successfully), you are correct. There should be no requests in your Snowflake logs that came post-DAG end.\nIf you want more insight as to how your DAG interacts with your Snowflake resource, I'd suggest having a single s_report task like so:\nmysql_list >> [ s_report(0)] >> end_operator\n\nand see the behaviour in the logs.\n" ]

[ 0 ]

[]

[]

[ "airflow", "python", "snowflake_cloud_data_platform" ]

[ "The backslashes you see around the datetime (\\') are likely just artifacts to remind you that they are literal single quotes, perhaps inside of a singly quoted string.\nAs a side note, it is generally bad practice to be injecting a value into a SQL query this way. Instead, you should learn how to use a prepared statement.\n" ]

[ 0 ]

[]

[]

[ "python", "sql" ]

[ "You must not use any GUI functions outside of the main thread. So you can not call popup() from your calculation thread and you can't create a new thread in your calculation thread and have that call it. You must make the main thread call it.\nFor possible solutions see How to properly execute GUI operations in Qt main thread?\nIn short: Use the signal-slot-mechanism or QMetaObject::invokeMethod()\n" ]

[ 0 ]

[]

[]

[ "multithreading", "popup", "pyqt", "python", "qmessagebox" ]

[ "If you have separate files for each class, (my opinion by the way, it generally depends on you) it would be much easier to manage your different classes, especially without having to scroll through one giant file. Also fun fact, this is known as modular programming\nOf course, sometimes it may be too complex to relocate all your code to multiple files, or there may be other restrictions preventing you from doing so, and in this scenario, you probably wouldn't separate your code into multiple scripts.\nNow about making other scripts classes, that would entirely depend on their usage. If you had an interactive menu, or perhaps an interactive object (such as a player or enemy) it would probably be better as a class then a series of functions.\nBut to connect them, with Python, you can easily import the classes based on their files with import fileName. (of course replace fileName with your file's name, and path) You can read more about importing different classes here\n" ]

[ 2 ]

[]

[]

[ "class", "function", "python" ]

[ "Have you installed the library? Also if you haven't I suggest you use an virtual environment and install inside it.\nTo install your library type in your terminal:\npip install NAME-OF-THE-LIBRARY\n\nExample for the face_recognition:\npip install face-recognition\n\n" ]

[ 0 ]

[]

[]

[ "libraries", "python" ]

[ "Assuming data_clients[\"id\"] is a list of customer IDs, this should work:\nfor cust_id in data_clients[\"id\"]:\n query = (f''' WHATEVER ''')\n stream = ga_service.search_stream(customer_id=cust_id, query=query)\n\n" ]

[ 0 ]

[]

[]

[ "google_ads_api", "python" ]

[ "You should have one function to decide which function to call. Apply that function to your dataframe. Depending on your conditions, this function can then call the correct function that will contain the meat of your calculations. Also, in the interest of readability, rename your functions and variables to something that makes sense:\n#Accrued Calc for ACT/360\ndef accrued_act_360(row):\n if row['Type'] == 'L' and row['Current Filter'] == 'Current CF':\n return 1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (row['Number of days'])/360\n elif row['Type'] == 'D':\n return -1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (row['Number of days'])/360\n else:\n return ''\n\n\n#Accrued Calc for ACT/365\ndef accrued_act_365(row):\n if row['Type'] == 'L' and row['Current Filter'] == 'Current CF':\n return 1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (row['Number of days'])/365\n elif row['Type'] == 'D':\n return -1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (row['Number of days'])/365\n else:\n return ''\n\n#Accrued Calc for 30/360 Basis \ndef accrued_30_360(row):\n if row['Type'] == 'L' and row['Current Filter'] == 'Current CF':\n return 1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (360 *(Settlement.year - row['Start Date YEAR']) + 30 * (Settlement.month - row['Start Date MONTH']) + Settlement.day - row['Start Date DAYS'])/360\n elif row['Type'] == 'D':\n return -1 * row['Principal/GrossAmount'] * (row['All in Rate']/100)* (360 *(Settlement.year - row['Start Date YEAR']) + 30 * (Settlement.month - row['Start Date MONTH']) + Settlement.day - row['Start Date DAYS'])/360\n else:\n return ''\n\ndef accrued_calc(row):\n if row[\"Basis\"] == \"ACT/360\":\n return accrued_act_360(row)\n elif row[\"Basis\"] == \"ACT/365\":\n return accrued_act_365(row)\n elif row[\"Basis\"] == \"30/360\":\n return accrued_30_360(row)\n else:\n return \"\"\n\nkf['Accrued Calc'] = kf.apply(accrued_calc, axis = 1)\n\n\nHowever: this approach fails to make use of pandas's amazing vectorized processing powers.\nYou could use boolean indexing to figure out which rows fulfill certain conditions, and only set those rows for the entire dataframe in one shot instead of applying your function row-by-row. This approach will likely be significantly faster than .apply\ndef accrued_act_360_vec(df):\n # Find which rows match your condition\n type_l_rows = (df[\"Basis\"] == \"ACT/360\") & (df[\"Type\"] == \"L\") & (df[\"Current Filter\"] == \"Current CF\")\n\n # Set the value for those rows\n df.loc[type_l_rows, \"Accrued Calc\"] = df.loc[type_l_rows, 'Principal/GrossAmount'] * (df.loc[type_l_rows, 'All in Rate']/100)* (df.loc[type_l_rows, 'Number of days'])/360\n\n type_d_rows = (df[\"Basis\"] == \"ACT/360\") & (df[\"Type\"] == \"D\")\n df.loc[type_d_rows, \"Accrued Calc\"] = -1 * df.loc[type_d_rows, 'Principal/GrossAmount'] * (df.loc[type_d_rows, 'All in Rate']/100)* (df.loc[type_d_rows, 'Number of days'])/360\n\n # No need to consider the else condition: Those rows never get set.\n\ndef accrued_act_365_vec(df):\n type_l_rows = (df[\"Basis\"] == \"ACT/365\") & (df['Type'] == 'L') & (df['Current Filter'] == 'Current CF')\n df.loc[type_l_rows, \"Accrued Calc\"] = 1 * df.loc[type_l_rows, 'Principal/GrossAmount'] * (df.loc[type_l_rows, 'All in Rate']/100)* (df.loc[type_l_rows, 'Number of days'])/365\n\n type_d_rows = (df[\"Basis\"] == \"ACT/365\") & (df['Type'] == 'D')\n df.loc[type_d_rows, \"Accrued Calc\"] = -1 * df.loc[type_d_rows, 'Principal/GrossAmount'] * (df.loc[type_d_rows, 'All in Rate']/100)* (df.loc[type_d_rows, 'Number of days'])/365\n\n\ndef accrued_30_360_vec(df):\n type_l_rows = (df[\"Basis\"] == \"30/360\") & (df['Type'] == 'L') & (df['Current Filter'] == 'Current CF')\n df.loc[type_l_rows, \"Accrued Calc\"] = 1 * df.loc[type_l_rows, 'Principal/GrossAmount'] * (df.loc[type_l_rows, 'All in Rate']/100)* (360 *(Settlement.year - df.loc[type_l_rows, 'Start Date YEAR']) + 30 * (Settlement.month - df.loc[type_l_rows, 'Start Date MONTH']) + Settlement.day - df.loc[type_l_rows, 'Start Date DAYS'])/360\n \n type_d_rows = (df[\"Basis\"] == \"30/360\") & (df['Type'] == 'D')\n df.loc[type_d_rows, \"Accrued Calc\"] = -1 * df.loc[type_d_rows, 'Principal/GrossAmount'] * (df.loc[type_d_rows, 'All in Rate']/100)* (360 *(Settlement.year - df.loc[type_d_rows, 'Start Date YEAR']) + 30 * (Settlement.month - df.loc[type_d_rows, 'Start Date MONTH']) + Settlement.day - df.loc[type_d_rows, 'Start Date DAYS'])/360\n\nNotice these functions include the condition for df[\"Basis\"] == ... because they are all standalone functions. To run these, you'd just do:\naccrued_act_360_vec(kf)\naccrued_act_365_vec(kf)\naccrued_30_360_vec(kf)\n\nPlease re-check the accuracy of the formulas in my code, I might have accidentally messed them up during copy/paste\n" ]

[ 1 ]

[]

[]

[ "apply", "finance", "function", "pandas", "python" ]

[ "Let us do resample with 2h freq\ndf['Datetime'] = pd.to_datetime(df['Datetime'], dayfirst=True)\ndf.resample('2h', on='Datetime', origin='start')['Price'].mean()\n\n" ]

[ 1 ]

[]

[]

[ "numpy", "pandas", "python" ]

[ "I had to implmenent my own code in order to get the desired output. It is basically the same as Differ.compare() with the difference that we have a look at multiline blocks instead of lines. So the code would be:\ndiffString = \"\"\noldList = [\"one\\ntwo\\nthree\",\"four\\nfive\\nsix\",\"seven\\neight\\nnine\"]\nnewList = [\"four\\nfifty\\nsix\",\"seven\\neight\\nnine\",\"ten\\neleven\\ntwelve\"]\na = oldList\nb = newList\ncruncher = difflib.SequenceMatcher(None, a, b)\nfor tag, alo, ahi, blo, bhi in cruncher.get_opcodes():\n if tag == 'replace':\n best_ratio, cutoff = 0.74, 0.75\n oldstrings = a[alo:ahi]\n newstrings = b[blo:bhi]\n for j in range(len(newstrings)):\n newstring = newstrings[j]\n cruncher.set_seq2(newstring)\n for i in range(len(oldstrings)):\n oldstring = oldstrings[i]\n cruncher.set_seq1(oldstring)\n if cruncher.real_quick_ratio() > best_ratio and \\\n cruncher.quick_ratio() > best_ratio and \\\n cruncher.ratio() > best_ratio:\n best_ratio, best_old, best_new = cruncher.ratio(), i, j\n if best_ratio < cutoff:\n #added string\n stringLines = newstring.splitlines()\n for line in stringLines: diffString += \"+\" + line + \"\\n\"\n else:\n #replaced string\n start = False\n for diff in difflib.unified_diff(oldstrings[best_old].splitlines(),newstrings[best_new].splitlines()):\n if start:\n diffString += diff + \"\\n\"\n if diff[0:2] == '@@':\n start = True\n del oldstrings[best_old]\n #deleted strings\n stringLines = []\n for string in oldstrings:\n stringLines.extend(string.splitlines())\n for line in stringLines: diffString += \"-\" + line + \"\\n\"\n elif tag == 'delete':\n stringLines = []\n for string in a[alo:ahi]:\n stringLines.extend(string.splitlines())\n for line in stringLines: \n diffString += \"-\" + line + \"\\n\"\n elif tag == 'insert':\n stringLines = []\n for string in b[blo:bhi]:\n stringLines.extend(string.splitlines())\n for line in stringLines: \n diffString += \"+\" + line + \"\\n\"\n elif tag == 'equal':\n continue\n else:\n raise ValueError('unknown tag %r' % (tag,))\n\nwhich result in the following:\nprint(diffString)\n four\n-five\n+fifty\n six\n-one\n-two\n-three\n+ten\n+eleven\n+twelve\n\n" ]

[ 0 ]

[]

[]

[ "difflib", "python" ]

[ "First of all: The Pandas version of the datetime64 type only timezone support. Specifically, when you try to a datetime64 variant in a Pandas series, it'll only support as (attosecond), fs (femtosecond), ps (picosecond) and ns (nanosecond) resolutions, anything less precise is replaced by datetime64[ns]. The datetime64[<res>, <tz>] variant only accepts s (seconds), ms (milliseconds), us (microseconds) and ns resolutions. Don't confuse these with the numpy datetime64 type.\nFor both Pandas and Numpy, the 2-letter abbreviation determines the resolution used to record the timestamps, and because the type is always stored as 64 bits, it determines the range of values you can store in it. It does not alter how much memory the type takes!\nFrom the numpy datetime64 Datetime Units documentation:\n\nDatetimes are always stored with an epoch of 1970-01-01T00:00. This means the supported dates are always a symmetric interval around the epoch, called “time span” in the table below.\nThe length of the span is the range of a 64-bit integer times the length of the date or unit. For example, the time span for ‘W’ (week) is exactly 7 times longer than the time span for ‘D’ (day), and the time span for ‘D’ (day) is exactly 24 times longer than the time span for ‘h’ (hour).\n\nYour experiment won't show any difference in memory use, because the amount of memory doesn't change, only the resolution.\nBecause Pandas wraps the numpy datetime64 type, and you can't actually create a series with anything other than datetime64[ns]; e.g. the DateTimeIndex dtype parameter is documented as accepting either a numpy.dtype or DatetimeTZDtype or str, default None, but that for numpy.dtype there is an additional restriction:\n\nNote that the only NumPy dtype allowed is ‘datetime64[ns]’.\n\nSo to demonstrate what the effect of different units, you'd have to use the numpy type directly:\n>>> import numpy as np\n>>> for unit in ('Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns'): # ps, fs and as have too small a span\n... print(unit, np.array([\"2021-02-27T12:24:17.524627869\"], dtype=f\"datetime64[{unit}]\"))\n...\nY ['2021']\nM ['2021-02']\nW ['2021-02-25']\nD ['2021-02-27']\nh ['2021-02-27T12']\nm ['2021-02-27T12:24']\ns ['2021-02-27T12:24:17']\nms ['2021-02-27T12:24:17.524']\nus ['2021-02-27T12:24:17.524627']\nns ['2021-02-27T12:24:17.524627869']\n\nNote: The documentation for Pandas only ever talks about ns resolutions for the datetime64 types, and it appears from various issues on GitHub that while some of the codebase supports the other (finer) resolutions, this support is not reliable or widely supported by everything in the library. Your mileage may vary.\n" ]

[ 3 ]

[]

[]

[ "datetime64", "pandas", "python", "python_datetime" ]

[ "There are many ways to go with it :\n\nYou can for example check the last modification date of the file every few seconds with os.path.getmtime(path), when the date change you know the file was edited.\n\nYou can also use some form of checksum (generate md5 hash of a file) on the file and check every few seconds if the checksum change (can get slow on big files since the checksum require to read the entire file)\n\nYou can also listen for signals send by windows directly and execute an event handler when you get a signal, this is harder to implement but by far the cleanest way to do it. (Edit, this seems to be what @martin kamau suggest in his answer)\n\nProbably many more way that I can't think of right now...\n\n\n", "To watch for file changes in a file,\nA python script found at https://luvocorp.co.ke/topic/watch-for-file-changes/ can be used.\nimport time\nimport fcntl\nimport os\nimport signal\nfilename = \"nameofthefile\"\ndef handler(signum, frame):\nprint \"File %s modified\" % (FNAME,)\n....\n" ]

[ 2, 0 ]

[]

[]

[ "python", "txt", "windows" ]

[ "I know its a little bit messy solution but only one I could find when I had same problem as you. Before including files from your project you need to manually append the system path to your top most package level, i.e:\nsys.path.append(os.path.join(os.path.dirname(__file__), '../..'))\nfrom XPaths.XPaths import XPathsHandler\n...\n\nFrom what I understand scrappy creates its own package - this is why you cannot import files from other directories. This also explains error:\nValueError: Attempted relative import beyond toplevel package\n\n", "I ran into the same problem.\nWhen I used:\nsys.path.append(os.path.join(os.path.dirname(__file__), '../..'))\n\nit appended ../.. to the last file path, which didn't work. I noticed my main file was the last item in the sys.path list. I took that last item and went to the module level to find my main file -- which contains a function called \"extract_notes\". \nimport scrapy\nimport sys\nimport os\n\nmod_path = os.path.dirname(os.path.normpath(sys.path[-1]))\nsys.path.insert(0,mod_path)\n\nfrom pprint import pprint as p\nfrom main import extract_notes\n\nHope that helps.\n", "I have a similar directory structure, with multiple scrapers (say directories scraper1 and scraper2).\nSince I found the sys.path changes as suggested by @ErdraugPl too brittle (see @ethanenglish's problems), especially since Scrapy itself is modifying the sys.path, I chose an OS solution instead of a Python solution: I created a symbolic link to directory /XPaths in both scraper1 and scraper2. That way, I can still maintain a single XPaths module that I can use in both scraper1 and scraper2, and can simply do from XPaths.XPaths import XPathsHandler\n" ]

[ 1, 0, 0 ]

[]

[]

[ "import", "python", "scrapy" ]

[ "Try creating a duplicate row of first occurrence to drive the text of your annotations.\ndf['dupe'] = df.info_version.where(~df.info_version.duplicated(), '')\n\n| | API_paths | info_version | Commit-growth | dupe |\n|---:|------------:|:---------------|----------------:|:----------|\n| 0 | 0 | 0.1-snap | 52 | 0.1-snap |\n| 1 | 20 | 0.1-snap | 104 | |\n| 2 | 35 | 0.1-snap | 156 | |\n| 3 | 50 | 0.1-snap | 208 | |\n| 4 | 105 | 0.1-snap | 260 | |\n| 5 | 119 | 0.1-snap | 312 | |\n| 6 | 133 | 0.1-snap | 364 | |\n| 7 | 0 | 0.1-snap | 408 | |\n| 8 | 1 | 0.9-other | 504 | 0.9-other |\n| 9 | 13 | 0.9-other | 600 | |\n| 10 | 15 | 0.9-other | 644 | |\n| 11 | 161 | 0.9-other | 28 | |\n| 12 | 175 | 0.9-other | 29 | |\n| 13 | 29 | 0.9-other | 700 | |\n| 14 | 42 | 0.9-other | 500 | |\n\n" ]

[ 1 ]

[]

[]

[ "pandas", "plotly", "python" ]

[ "This looks like a good use of pd.melt\nimport pandas as pd\ndf = pd.DataFrame({'August': ['00:00 - 01:00', '01:00 - 02:00', '02:00 - 03:00'], '1/ aug/': ['273,285', '2,708,725', '2,702,913'], '2/ aug/': ['310,135', '2,876,725', '28,409'], '3/ aug/': ['3,077,438', '3,076,075', '307,595'], '4/ aug/': ['2,911,175', '2,876,663', '2,869,738'], '5/ aug/': ['289,075', '2,842,425', '2,839,088']})\n\ndf = df.melt(id_vars='August', var_name='date', value_name='count').rename(columns={'August':'time'})\n\ndf = df[['date','time','count']]\n\nprint(df)\n\nOutput\n date time count\n0 1/ aug/ 00:00 - 01:00 273,285\n1 1/ aug/ 01:00 - 02:00 2,708,725\n2 1/ aug/ 02:00 - 03:00 2,702,913\n3 2/ aug/ 00:00 - 01:00 310,135\n4 2/ aug/ 01:00 - 02:00 2,876,725\n5 2/ aug/ 02:00 - 03:00 28,409\n6 3/ aug/ 00:00 - 01:00 3,077,438\n7 3/ aug/ 01:00 - 02:00 3,076,075\n8 3/ aug/ 02:00 - 03:00 307,595\n9 4/ aug/ 00:00 - 01:00 2,911,175\n10 4/ aug/ 01:00 - 02:00 2,876,663\n11 4/ aug/ 02:00 - 03:00 2,869,738\n12 5/ aug/ 00:00 - 01:00 289,075\n13 5/ aug/ 01:00 - 02:00 2,842,425\n14 5/ aug/ 02:00 - 03:00 2,839,088\n\n", "You can also achieve it with stack():\ndf.set_index('August').stack().reset_index().sort_values('level_1').rename(\n {'August':'time','level_1':'date',0:'count'},axis=1)\n\n time Date count\n0 00:00 - 01:00 1/ aug/ 273,285\n5 01:00 - 02:00 1/ aug/ 2,708,725\n10 02:00 - 03:00 1/ aug/ 2,702,913\n1 00:00 - 01:00 2/ aug/ 310,135\n6 01:00 - 02:00 2/ aug/ 2,876,725\n11 02:00 - 03:00 2/ aug/ 28,409\n2 00:00 - 01:00 3/ aug/ 3,077,438\n7 01:00 - 02:00 3/ aug/ 3,076,075\n12 02:00 - 03:00 3/ aug/ 307,595\n3 00:00 - 01:00 4/ aug/ 2,911,175\n8 01:00 - 02:00 4/ aug/ 2,876,663\n13 02:00 - 03:00 4/ aug/ 2,869,738\n4 00:00 - 01:00 5/ aug/ 289,075\n9 01:00 - 02:00 5/ aug/ 2,842,425\n14 02:00 - 03:00 5/ aug/ 2,839,088\n\n" ]

[ 2, 1 ]

[]

[]

[ "dataframe", "pandas", "python", "python_3.x" ]

[ "Make sure you have python path added to the PATH variable. Then run\npython -m ensurepip\n\n", "Could you try?\npip3 install pyttsx3\n\n", "To me for Ubuntu 20.04 helped the following:\nls -al /usr/bin/python # check before removal that 'python' is link\nsudo rm /usr/bin/python # remove link to old version of python\nsudo ln -s /usr/bin/python3.8 /usr/bin/python # create new link to actual python version\nsudo apt install python3-pip # install missing pip\n\n\"Python: No module named pip\" was because of missing python3-pip.\n", "Start Python Setup again (Download from here) and be sure to tick that Add python to PATH at the bottom of installation.\n", "\nDownload get-pip.py to a folder on your computer.\n\nOpen a command prompt and navigate to the folder containing the\nget-pip.py installer.\n\nRun the following command:\npython get-pip.py\n\n4-) Verify Installation and Check the Pip Version:\npip -V\n\n\n", "This command finally worked for me\npython -m pip install --upgrade pip --trusted-host pypi.org --trusted-host files.pythonhosted.org\n" ]

[ 27, 1, 1, 0, 0, 0 ]

[]

[]

[ "pip", "python" ]

[ "Yes, but you should include the string in a list. So if you use\nsource_objects = ['file_sample_*.json'],\n\nit will ingest all files starting with 'file_sample_' and ending with '.json'.\n", "I had the same problem after updating apache-airflow-providers-google to version 8.5.0. where wildcard stoped to work.\nYou can dowmgrade to version 8.4.0. or if you are using airflow and google operators also to load data to bigquery, filenames are pushed to XCOM from that operators.\nSo as the workaround, I read that values from XCOM and use them as an argument for GCSToBigQueryOperator\n" ]

[ 0, 0 ]

[]

[]

[ "airflow", "python" ]

[ "You can solve it with a loop\ns = [['a', 'b'], ['c', 'd'], ['e', 'f']]\nx = 'a'\nfind = False\nfor i, line in enumerate(s):\n if x in line:\n print(f'find {x} at', i, line.index(x))\n find = True\n break\nif not find:\n print(f'{x} is not in list')\n\n", "More simpler way you can use any()\ns = [['a', 'b'], ['c', 'd'], ['e', 'f']]\n\nfor i, v in enumerate(s):\n if any('a' in x for x in v):\n print(i)\n\nGives #\n0\n\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[ "This is because you insert the value 1 before the #1 position in the list,\nbut because the list updates every iteration, the value in the #1 position\nis always 1.\nto see this clearly, you can print the list in every iteration\n[1,1,2,3]\n[1,1,1,2,3]\n[1,1,1,1,2,3]\n\nthe only thing not updating is the range() that was set to the length\nof the original list (range(0,3)->v=0,v=1,v=2)\n", "The reason isn't exactly that you're modifying the list you're iterating over-- it's because range produces an immutable sequence of a fixed length, created exactly once. Therefore, the number of iterations of the for loop is based on len(my_list) at the beginning, which is 3. The list.insert(i, x) method puts x in index i, and shoves the old elements at indices (i, i+1 ...) to the right.\nThus your code is equivalent to:\nmy_list = [1,2,3]\nfor v in range(3):\n my_list.insert(1, my_list[v])\n\nAnd, if we put parentheses around the new element just inserted in each of the 3 iterations:\nmy_list = [1, 2, 3]\n\nInsert (my_list[0] == 1) at position 1\nmy_list = [1, (1), 2, 3]\n\nInsert (my_list[1] == 1) at position 1\nmy_list = [1, (1), 1, 2, 3]\n\nInsert (my_list[2] == 1) at position 1\nmy_list = [1, (1), 1, 1, 2, 3]\n\nWhile this isn't as dangerous as iterating over the list directly while modifying it, the best way to avoid unexpected behavior is to make a copy of the list beforehand, and index into a different list than the one you're modifying.\n", "You can add a print statement in the loop as follows which will make it easier to understand.\nmy_list = [1,2,3]\nfor v in range (len(my_list)):\n my_list.insert(1,my_list[v])\n print(f\"mylist: {my_list}\")\n print(f\"mylist[v]: {my_list[v]}\")\n print(f\"v: {v}\")\n \n\nprint(my_list)\n\nabove code results:\nmylist: [1, 1, 2, 3]\nmylist[v]: 1\nv: 0\nmylist: [1, 1, 1, 2, 3]\nmylist[v]: 1\nv: 1\nmylist: [1, 1, 1, 1, 2, 3]\nmylist[v]: 1\nv: 2\n[1, 1, 1, 1, 2, 3]\n\n", "The len(my_list) in the range constructor is evaluated once, at the top of the first iteration of the loop, where the length of my_list is 3. This results in the loop variable v being set to 0, 1, and 2 as the loop iterates.\nInside the loop, you insert into the list at the second position (i.e. just after the first element of the list) the value of the existing element v of the list. v changes on each iteration (0, 1, 2) as you expect. But, so does my_list, since you are inserting an element with each iteration. So you get the following transformation of my_list:\nStart: my_list = [ 1 2 3 ]\nv = 0: my_list[0] = 1 -> my_list = [ 1 *1* 2 3 ] (*X* indicates inserted element)\nv = 1: my_list[1] = 1 -> my_list = [ 1 *1* 1 2 3 ]\nv = 2: my_list[2] = 1 -> my_list = [ 1 *1* 1 1 2 3 ]\n\nEssentially, as you prepend elements to the list, the indices of the existing elements are shifting to the right.\n", "Found this website with an explantion on this: Iterate a list using range() and for loop.\nhttps://pynative.com/python-range-function/\n\"When you iterate the list only using a loop, you can access only items. When you iterate the list only using a loop, you can only access its items, but when you use range() along with the loop, you can access the index number of each item.\nThe advantage of using range() to iterate a list is that it allows us to access each item’s index number. Using index numbers, we can access as well as modify list items if required.\"\n" ]

[ 2, 2, 1, 1, 0 ]

[]

[]

[ "python" ]

[ "The reason is because pack by default will cause the containing frame to grow or shrink to fit all of the child widgets, but place does not. If your frame starts out as 1x1 and you use place to add widgets to it, the size will remain 1x1. When you use place, it is your responsibility to make the containing widget large enough to contain its children.\nThis single feature is one of the most compelling reasons to choose grid or pack over place - these other geometry managers do a lot of work for you so that you can think about the layout logically without getting bogged down in the details of the layout.\n" ]

[ 3 ]

[]

[]

[ "customtkinter", "python", "scrollbar", "tkinter" ]

[ "I managed to find an answer in the FastAPI documentation here. The below seemed to work for me.\[email protected](\"/{id}\", response_model=schemas.UserResponse)\ndef get_user(id: int, db: Session = Depends(get_db)):\n \n user = db.query(models.User).filter(models.User.id == id).first()\n print(user) # checking output\n \n if not user:\n return JSONResponse(content = {'status' : '0'}, status_code=400)\n\n return user\n\n" ]

[ 0 ]

[]

[]

[ "fastapi", "get", "json", "postgresql", "python" ]

[ "You are adding teams to eplteams based on some condition:\nif x['HomeTeam'] not in eplteams:\n eplteams.append(x['HomeTeam'])\n teamcount += 1\n\nAnd then using eplteam element in condition:\nif eplteams[i] == match_result:\n points += 3\n\nif eplteams[i] == x['HomeTeam']:\n if match_result == \"Draw\":\n points += 1\n\nif eplteams[i] == x['AwayTeam']:\n if match_result == \"Draw\":\n points += 1\n\nIt doesn't look right.\nElement i in eplteams may not exist at that moment.\n", "You need to populate before your loop through the eplgames or else it might not find the teams that are playing:\ndef populate(eplgames):\n eplteams = []\n for x in eplgames:\n # Populates the eplteams list\n if x['HomeTeam'] not in eplteams:\n eplteams.append(x['HomeTeam'])\n teamcount += 1\n return teamcount, eplteams\n\npopulate before loop:\nteamcount, eplteams = populate(eplgames)\nfor i in range(20):\n points = 0\n # Inner loop going through each match\n for x in eplgames:\n #print(eplteams[i])\n # Works out the match result\n if x['FTHG'] > x['FTAG']:\n match_result = x['HomeTeam']\n\n" ]

[ 0, 0 ]

[]

[]

[ "csv", "for_loop", "list", "python" ]

[ "Put the arguments in a list, then iterate over that.\nparams = [(5, 6), (3, 3)]\nlist_triangles = [Triangle(*p) for p in params]\n\n", "If I understand you correctly, you want to have globaly accessible list of every created object, if so you can declare class variable and then append self to it in constructor to keep track of objects like so\nclass Cat():\n instances = []\n def __init__(self, name):\n self.name = name\n Cat.instances.append(self)\n\na = Cat(\"Bob\")\nb = Cat(\"John\")\n\nprint(Cat.instances)\nprint(Cat.instances[0].name)\n\nWhich results in\n>> [<__main__.Cat object at 0x00000000014BE790>, <__main__.Cat object at 0x00000000014BE610>]\n>> Bob\n\n" ]

[ 3, 1 ]

[]

[]

[ "for_loop", "python" ]

[ "The SIGSEGV signal indicates a \"segmentation violation\" or a \"segfault\". More or less, this equates to a read or write of a memory address that's not mapped in the process.\nThis indicates a bug in your program. In a Python program, this is either a bug in the interpreter or in an extension module being used (and the latter is the most common cause).\nTo fix the problem, you have several options. One option is to produce a minimal, self-contained, complete example which replicates the problem and then submit it as a bug report to the maintainers of the extension module it uses.\nAnother option is to try to track down the cause yourself. gdb is a valuable tool in such an endeavor, as is a debug build of Python and all of the extension modules in use.\nAfter you have gdb installed, you can use it to run your Python program:\ngdb --args python <more args if you want>\n\nAnd then use gdb commands to track down the problem. If you use run then your program will run until it would have crashed and you will have a chance to inspect the state using other gdb commands.\n", "Another possible cause (which I encountered today) is that you're trying to read/write a file which is open. In this case, simply closing the file and rerunning the script solved the issue.\n", "After some times I discovered that I was running a new TensorFlow version that gives error on older computers. I solved the problem downgrading the TensorFlow version to 1.4\n", "When I encounter this problem, I realize there are some memory issues. I rebooted PC and solved it.\n", "This can also be the case if your C-program (e.g. using cpython is trying to access a variable out-of-bound\n\nctypedef struct ReturnRows:\n double[10] your_value\n\ncdef ReturnRows s_ReturnRows # Allocate memory for the struct\ns_ReturnRows.your_value = [0] * 12\n\nwill fail with\nProcess finished with exit code 139 (interrupted by signal 11: SIGSEGV)\n\n", "For me, I was using the OpenCV library to apply SIFT.\nIn my code, I replaced cv2.SIFT() to cv2.SIFT_create() and the problem is gone.\n", "Deleted the python interpreter and the 'venv' folder solve my error.\n", "I got this error in PHP, while running PHPUnit. The reason was a circular dependency.\n", "I received the same error when trying to connect to an Oracle DB using the pyodbc module:\nconnection = pyodbc.connect()\n\nThe error occurred on the following occasions:\n\nThe DB connection has been opened multiple times in the same python\nfile\nWhile in debug mode a breakpoint has been reached\nwhile the connection to the DB being open\n\nThe error message could be avoided with the following approaches:\n\nOpen the DB only once and reuse the connection at all needed places\nProperly close the DB connection after using it\n\nHope, that will help anyone!\n", "11 : SIGSEGV - This signal is arises when an memory segement is illegally accessed.\nThere is a module name signal in python through which you can handle this kind of OS signals.\nIf you want to ignore this SIGSEGV signal, you can do this:\nsignal.signal(signal.SIGSEGV, signal.SIG_IGN)\n\nHowever, ignoring the signal can cause some inappropriate behaviours to your code, so it is better to handle the SIGSEGV signal with your defined handler like this:\ndef SIGSEGV_signal_arises(signalNum, stack):\n print(f\"{signalNum} : SIGSEGV arises\")\n # Your code\n\nsignal.signal(signal.SIGSEGV, SIGSEGV_signal_arises) \n\n", "I encountered this problem when I was trying to run my code on an external GPU which was disconnected. I set os.environ['PYOPENCL_CTX']=2 where GPU 2 was not connected. So I just needed to change the code to os.environ['PYOPENCL_CTX'] = 1.\n", "For me these three lines of code already reproduced the error, no matter how much free memory was available:\nimport numpy as np\nfrom sklearn.cluster import KMeans\n\nX = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])\nkmeans = KMeans(n_clusters=1, random_state=0).fit(X)\n\nI could solve the issue by removing an reinstalling the scikit-learn package. A very similar solution to this.\n", "This can also occur if trying to compound threads using concurrent.futures. For example, calling .map inside another .map call.\nThis can be solved by removing one of the .map calls.\n", "I had the same issue working with kmeans from scikit-learn.\nUpgrading from scikit-learn 1.0 to 1.0.2 solved it for me.\n", "This issue is often caused by incompatible libraries in your environment. In my case, it was the pyspark library.\n", "In my case, reverting my most recent conda installs fixed the situation.\n", "I got this error when importing monai. It was solved after I created a new conda environment. Possible reasons I could imagine were either that there were some conflict between different packages, or maybe that my environment name was the same as the package name I wanted to import (monai).\n", "It can be caused because of numba. For instance, numba does not accept normal python lists instead of numpy arrays.\n" ]

[ 59, 19, 10, 6, 6, 3, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 ]

[ "found on other page.\ninterpreter: python 3.8\ncv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\nthis solved issue for me. \ni was getting SIGSEGV with 2.7, upgraded my python to 3.8 then got different error with OpenCV. and found answer on OpenCV 4.0.0 SystemError: <class 'cv2.CascadeClassifier'> returned a result with an error set.\nbut eventually one line of code fixed it.\n" ]

[ -2 ]

[ "linux_mint", "python", "python_3.5", "segmentation_fault" ]

[ "As the comments suggest, I don't think you need zip() for this to work, instead just try something simpler such as [list(i.values()) for i in mylist]\nYou convert the values into a list with the list() function, and the values are already obtained with the .values() method\n", "Try this [list(i.values()) for i in mylist]\n" ]

[ 2, 1 ]

[]

[]

[ "dictionary", "list", "python" ]

[ "Following this answer and the linked answer in the same post.\n'contains' or 'icontains' looks for the patterns '%string%', which in your case assumes '67' is between characters. But, the number pattern is at the end of your actor name.\nSo, based on the answers I linked, you should probably try endswith or iendswith, in order to look for the pattern '%67'\n", "My data model:\nclass MyCustomModel(models.Model):\n id = models.BigAutoField(primary_key=True)\n actors = models.JSONField(blank=True, null=True)\n\nI ended up with quite hacky lookup operator which replaces '$.\"' into '$[*].\"' in my JSON field queries, which in my case was making the correct query, filtering all the objects whos JSON field with array of objects, contains one of the needed property.\nLookup operator:\nfrom django.db.models.lookups import IContains\nfrom django.db.models import Field\n\n# Custom lookup which acts like the default IContains lookup but also replaces field name to match all JSON array objects ['*'].field_name in the query instead of $.field_name.\n# Maybe this could be done in a better way with better Q field path, but this works for now.\nclass JSONArrayContains(IContains):\n lookup_name = 'jsonarraycontains'\n \n def __init__(self, lhs, rhs):\n self.lookup_name = 'icontains' # we fake the lookup name to get the right operators further\n super().__init__(lhs, rhs)\n \n\n def as_sql(self, compiler, connection):\n lhs_sql, params = self.process_lhs(compiler, connection)\n\n # !! HERE IS THE MAGIC\n # we need to replace params parts which are like '$.\"name\"' into parts like '$[*].\"name\"' if param is string and matches $.\" pattern\n params = [param.replace('$.\"', '$[*].\"') if isinstance(param, str) and param.startswith('$.\"') else param for param in params] \n\n rhs_sql, rhs_params = self.process_rhs(compiler, connection)\n params.extend(rhs_params)\n rhs_sql = self.get_rhs_op(connection, rhs_sql)\n return f'{lhs_sql} {rhs_sql}', params\n\nUsage:\nqueryset.filter(actors__name__jsonarraycontains='67')\n\nWhich is filtering all the records, which\n" ]

[ 0, 0 ]

[]

[]

[ "django", "django_models", "django_rest_framework", "mysql", "python" ]

[ "Shortest Code\n[x for _, x in sorted(zip(Y, X))]\n\nExample:\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1]\n\nZ = [x for _,x in sorted(zip(Y,X))]\nprint(Z) # [\"a\", \"d\", \"h\", \"b\", \"c\", \"e\", \"i\", \"f\", \"g\"]\n\n\nGenerally Speaking\n[x for _, x in sorted(zip(Y, X), key=lambda pair: pair[0])]\n\nExplained:\n\nzip the two lists.\ncreate a new, sorted list based on the zip using sorted().\nusing a list comprehension extract the first elements of each pair from the sorted, zipped list.\n\nFor more information on how to set\\use the key parameter as well as the sorted function in general, take a look at this.\n\n", "Zip the two lists together, sort it, then take the parts you want:\n>>> yx = zip(Y, X)\n>>> yx\n[(0, 'a'), (1, 'b'), (1, 'c'), (0, 'd'), (1, 'e'), (2, 'f'), (2, 'g'), (0, 'h'), (1, 'i')]\n>>> yx.sort()\n>>> yx\n[(0, 'a'), (0, 'd'), (0, 'h'), (1, 'b'), (1, 'c'), (1, 'e'), (1, 'i'), (2, 'f'), (2, 'g')]\n>>> x_sorted = [x for y, x in yx]\n>>> x_sorted\n['a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g']\n\nCombine these together to get:\n[x for y, x in sorted(zip(Y, X))]\n\n", "Also, if you don't mind using numpy arrays (or in fact already are dealing with numpy arrays...), here is another nice solution:\npeople = ['Jim', 'Pam', 'Micheal', 'Dwight']\nages = [27, 25, 4, 9]\n\nimport numpy\npeople = numpy.array(people)\nages = numpy.array(ages)\ninds = ages.argsort()\nsortedPeople = people[inds]\n\nI found it here:\nhttp://scienceoss.com/sort-one-list-by-another-list/\n", "The most obvious solution to me is to use the key keyword arg.\n>>> X = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\n>>> Y = [ 0, 1, 1, 0, 1, 2, 2, 0, 1]\n>>> keydict = dict(zip(X, Y))\n>>> X.sort(key=keydict.get)\n>>> X\n['a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g']\n\nNote that you can shorten this to a one-liner if you care to:\n>>> X.sort(key=dict(zip(X, Y)).get)\n\nAs Wenmin Mu and Jack Peng have pointed out, this assumes that the values in X are all distinct. That's easily managed with an index list:\n>>> Z = [\"A\", \"A\", \"C\", \"C\", \"C\", \"F\", \"G\", \"H\", \"I\"]\n>>> Z_index = list(range(len(Z)))\n>>> Z_index.sort(key=keydict.get)\n>>> Z = [Z[i] for i in Z_index]\n>>> Z\n['A', 'C', 'H', 'A', 'C', 'C', 'I', 'F', 'G']\n\nSince the decorate-sort-undecorate approach described by Whatang is a little simpler and works in all cases, it's probably better most of the time. (This is a very old answer!)\n", "more_itertools has a tool for sorting iterables in parallel:\nGiven\nfrom more_itertools import sort_together\n\n\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1]\n\nDemo\nsort_together([Y, X])[1]\n# ('a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g')\n\n", "I actually came here looking to sort a list by a list where the values matched.\nlist_a = ['foo', 'bar', 'baz']\nlist_b = ['baz', 'bar', 'foo']\nsorted(list_b, key=lambda x: list_a.index(x))\n# ['foo', 'bar', 'baz']\n\n", "Another alternative, combining several of the answers.\nzip(*sorted(zip(Y,X)))[1]\n\nIn order to work for python3:\nlist(zip(*sorted(zip(B,A))))[1]\n\n", "I like having a list of sorted indices. That way, I can sort any list in the same order as the source list. Once you have a list of sorted indices, a simple list comprehension will do the trick:\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1]\n\nsorted_y_idx_list = sorted(range(len(Y)),key=lambda x:Y[x])\nXs = [X[i] for i in sorted_y_idx_list ]\n\nprint( \"Xs:\", Xs )\n# prints: Xs: [\"a\", \"d\", \"h\", \"b\", \"c\", \"e\", \"i\", \"f\", \"g\"]\n\nNote that the sorted index list can also be gotten using numpy.argsort().\n", "zip, sort by the second column, return the first column.\nzip(*sorted(zip(X,Y), key=operator.itemgetter(1)))[0]\n\n", "This is an old question but some of the answers I see posted don't actually work because zip is not scriptable. Other answers didn't bother to import operator and provide more info about this module and its benefits here.\nThere are at least two good idioms for this problem. Starting with the example input you provided:\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1 ]\n\nUsing the \"Decorate-Sort-Undecorate\" idiom\nThis is also known as the Schwartzian_transform after R. Schwartz who popularized this pattern in Perl in the 90s:\n# Zip (decorate), sort and unzip (undecorate).\n# Converting to list to script the output and extract X\nlist(zip(*(sorted(zip(Y,X)))))[1] \n# Results in: ('a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g')\n\nNote that in this case Y and X are sorted and compared lexicographically. That is, the first items (from Y) are compared; and if they are the same then the second items (from X) are compared, and so on. This can create unstable outputs unless you include the original list indices for the lexicographic ordering to keep duplicates in their original order.\nUsing the operator module\nThis gives you more direct control over how to sort the input, so you can get sorting stability by simply stating the specific key to sort by. See more examples here.\nimport operator \n\n# Sort by Y (1) and extract X [0]\nlist(zip(*sorted(zip(X,Y), key=operator.itemgetter(1))))[0] \n# Results in: ('a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g')\n\n", "You can create a pandas Series, using the primary list as data and the other list as index, and then just sort by the index:\nimport pandas as pd\npd.Series(data=X,index=Y).sort_index().tolist()\n\noutput:\n['a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g']\n\n", "A quick one-liner.\nlist_a = [5,4,3,2,1]\nlist_b = [1,1.5,1.75,2,3,3.5,3.75,4,5]\n\nSay you want list a to match list b.\norderedList = sorted(list_a, key=lambda x: list_b.index(x))\n\nThis is helpful when needing to order a smaller list to values in larger. Assuming that the larger list contains all values in the smaller list, it can be done.\n", "Here is Whatangs answer if you want to get both sorted lists (python3).\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1]\n\nZx, Zy = zip(*[(x, y) for x, y in sorted(zip(Y, X))])\n\nprint(list(Zx)) # [0, 0, 0, 1, 1, 1, 1, 2, 2]\nprint(list(Zy)) # ['a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g']\n\nJust remember Zx and Zy are tuples.\nI am also wandering if there is a better way to do that.\nWarning: If you run it with empty lists it crashes.\n", "I have created a more general function, that sorts more than two lists based on another one, inspired by @Whatang's answer.\ndef parallel_sort(*lists):\n \"\"\"\n Sorts the given lists, based on the first one.\n :param lists: lists to be sorted\n\n :return: a tuple containing the sorted lists\n \"\"\"\n\n # Create the initially empty lists to later store the sorted items\n sorted_lists = tuple([] for _ in range(len(lists)))\n\n # Unpack the lists, sort them, zip them and iterate over them\n for t in sorted(zip(*lists)):\n # list items are now sorted based on the first list\n for i, item in enumerate(t): # for each item...\n sorted_lists[i].append(item) # ...store it in the appropriate list\n\n return sorted_lists\n\n", "X = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1 ]\n\nYou can do so in one line:\nX, Y = zip(*sorted(zip(Y, X)))\n\n", "This function should work for arrays.\ndef sortBoth(x,y,reverse=False):\n '''\n Sort both x and y, according to x. \n '''\n xy_sorted=array(sorted(zip(x,y),reverse=reverse)).T\n return xy_sorted[0],xy_sorted[1]\n\n", "list1 = ['a','b','c','d','e','f','g','h','i']\nlist2 = [0,1,1,0,1,2,2,0,1]\n\noutput=[]\ncur_loclist = []\n\nTo get unique values present in list2\nlist_set = set(list2)\n\nTo find the loc of the index in list2 \nlist_str = ''.join(str(s) for s in list2)\n\nLocation of index in list2 is tracked using cur_loclist\n[0, 3, 7, 1, 2, 4, 8, 5, 6]\nfor i in list_set:\ncur_loc = list_str.find(str(i))\n\nwhile cur_loc >= 0:\n cur_loclist.append(cur_loc)\n cur_loc = list_str.find(str(i),cur_loc+1)\n\nprint(cur_loclist)\n\nfor i in range(0,len(cur_loclist)):\noutput.append(list1[cur_loclist[i]])\nprint(output)\n\n", "I think most of the solutions above will not work if the 2 lists are of different sizes or contain different items. The solution below is simple and should fix those issues:\nimport pandas as pd\n\nlist1 = ['B', 'A', 'C'] # Required sort order\nlist2 = ['C', 'A'] # Items to be sorted according to list1\n\nresult = pd.merge(pd.DataFrame(list1), pd.DataFrame(list2))\nprint(list(result[0]))\n\noutput:\n['A', 'C']\n\n\nNote: Any item not in list1 will be ignored since the algorithm will not know what's the sort order to use.\n\n", "Most of the solutions above are complicated and I think they will not work if the lists are of different lengths or do not contain the exact same items. The solution below is simple and does not require any imports.\nlist1 = ['B', 'A', 'C'] # Required sort order\nlist2 = ['C', 'B'] # Items to be sorted according to list1\n\nresult = list1\nfor item in list1:\n if item not in list2: result.remove(item)\n\nprint(result)\n\nOutput:\n['B', 'C']\n\n\nNote: Any item not in list1 will be ignored since the algorithm will not know what's the sort order to use.\n\n", "I think that the title of the original question is not accurate. If you have 2 lists of identical number of items and where every item in list 1 is related to list 2 in the same order (e.g a = 0 , b = 1, etc.) then the question should be 'How to sort a dictionary?', not 'How to sorting list based on values from another list?'. The solution below is the most efficient in this case:\nX = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\"]\nY = [ 0, 1, 1, 0, 1, 2, 2, 0, 1 ]\n\ndict1 = dict(zip(X,Y))\nresult = sorted(dict1, key=dict1.get)\nprint(result)\n\nResult:\n['a', 'd', 'h', 'b', 'c', 'e', 'i', 'f', 'g']\n\n" ]

[ 769, 139, 121, 48, 36, 25, 17, 16, 7, 4, 2, 2, 2, 2, 1, 1, 0, 0, 0, 0 ]

[]

[]

[ "list", "python", "sorting" ]

[ "You need to make sure that the message_content intent is configured correctly on both the discord developer portal and in the code:\nintents = discord.Intents()\nintents.message_content = True\nclient = discord.Bot(intents=intents)\n\n" ]

[ 0 ]

[]

[]

[ "discord.py", "python" ]

[ "A cause of duplicates is that every time you get lnks, you're getting the products you scraped before scrolling as well. You can probably skip duplicate scrapes by initiating scrapedUrls = [] somewhere at the beginning of your code (OUTSIDE of all loops), and then checking urel against it, as well as adding to it\n if urel in scrapedUrls: continue ## add this line\n result = requests.get(urel) ## from your code\n scrapedUrls.append(urel) ## add this line\n\nbut I'm not sure it'll solve your issue.\n\nI don't know why it's happening, but when I try to scrape the links with selenium's find_elements, I get the same url over and over; so I wrote a fuction [getUniqLinks] that you can use to get a unique list of links (prodUrls) by scrolling up to a certain number of times and then parsing page_source to BeautifulSoup. Below are two lines from the printed output of prodUrls = getUniqLinks(fullUrl, rootUrl, max_scrolls=250, tmo=1):\nWITH SELENIUM found 10957 product links [1 unique] \n \nPARSED PAGE_SOURCE ---> found 12583 product links [12576 unique]\n\n(The full function and printed output are at https://pastebin.com/b3gwUAJZ.)\nSome notes:\n\nIf you increase tmo, you can increase max_scrolls too, but it starts getting quite slow after 100 scrolls.\nI used selenium to get links as well, just to print and show the difference, but you can remove all lines that end with # remove to get rid of those unnecessary parts.\nI used selenium's WebDriverWait instead of time.sleep because it stops waiting after the relevant elements have loaded - it raises an error if it doesn't load it the allowed time (tmo), so I found it more convenient and readable to use in a try...except block instead of using driver.implicitly_wait\nI don't know if this is related to whatever is causing your program to hang [since mine is probably just because of the number of elements being too many], but mine also hangs if I try to use selenium to get all the links after scrolling instead of adding to prodLinks in chunks inside the loop.\n\n\nNow, you can loop through prodUrls and get the data you want, but I think it's better to build a list with a separate dictionary for each link [i.e., having a dictionary for each row rather than having a separate list for each column].\nIf you use these two functions, then you just have to prepare a reference dictionary of selectors like\nrefDict = {\n 'title': 'h1.js-lot-title',\n 'price_text': 'div.price-container',\n 'price_amt': 'div.price-container > .price span.price-val',\n 'price_cur': 'div.price-container > .price span.price-cur',\n '.lot-text tr.property': {'k':'td.property-name', 'v':'td.property-value'},\n 'contact_name': '.author > div.name',\n 'contact_phone': '.author > a.phone',\n 'lot_warning': 'div.lot-warning',\n 'div.lot-info': {'sel': 'p', 'sep': ':'},\n 'description': '.lot-text p'\n}\n\nthat can be passed to fillDict_fromTag like in the code below:\n## FIRST PASTE FUNTION DEFINITIONS FROM https://pastebin.com/hKXYetmj\n\nproductDetails = []\npuLen = len(prodUrls)\nfor pi, pUrl in enumerate(prodUrls[:500]):\n print('', end=f'\\rScraping [for {pi+1} of {puLen}] {pUrl}')\n pDets = {'prodId': [w for w in pUrl.split('/') if w][-1]}\n\n resp = requests.get(pUrl)\n if resp.status_code != 200:\n pDets['Error_Message'] = f'{resp.raise_for_status()}'\n pDets['sourceUrl'] = pUrl\n productDetails.append(pDets) \n continue\n \n pSoup = BeautifulSoup(resp.content, 'html.parser')\n pDets = fillDict_fromTag(pSoup, refDict, pDets, rootUrl)\n\n pDets['sourceUrl'] = pUrl\n productDetails.append(pDets)\nprint()\nprodDf = pd.DataFrame(productDetails).set_index('prodId')\nprodDf.to_csv('ProductDetails.csv')\n\n\nI have uploaded both 'prodLinks.csv' and 'ProductDetails.csv' here, although there are only the first 500 scrapes' results since I manually interrupted after around 20 minutes; I'm also pasting the first 3 rows here (printed with print(prodDf.loc[prodDf.index[:3]].to_markdown()))\n| prodId | title | price_text | price_amt | price_cur | Şəhər | Elanın tipi | Elanın tipi [link] | Binanın tipi | Binanın tipi [link] | Sahə, m² | Otaq sayı | Yerləşmə yeri | contact_name | contact_phone | lot_warning | Elanın nömrəsi | Baxışların sayı | Yeniləndi | description | sourceUrl |\n|---------:|:---------------------------------------------------------|:-------------|:------------|:------------|:--------|:---------------|:----------------------------------------------------------------|:---------------|:----------------------------------------------------------------|-----------:|------------:|:----------------|:---------------|:----------------|:-----------------------------------------------------------------------------|-----------------:|------------------:|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|\n| 35828514 | 2-otaqlı yeni tikili kirayə verilir, 20 Yanvar m., 45 m² | 600 AZN | 600 | AZN | Bakı | Kirayə verilir | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B740%5D=3724 | Yeni tikili | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B747%5D=3849 | 45 | 2 | 20 Yanvar m. | Elşad Bəy | (055) 568-12-13 | Diqqət! Beh göndərməmişdən öncə sövdələşmənin təhlükəsiz olduğuna əmin olun! | 35828514 | 105 | 22 Noyabr 2022 | 20 Yanvar metrosuna və Inşatcılar metrosuna 8 - 11 dəiqqə arası olan ərazidə, yeni tikili binada 1 otaq 2 otaq təmirli şəraitiynən mənzil kirayə 600 manata, ailiyə və iş adamına verilir. Qabaqçadan 2 ay ödəniş olsa kamendant pulu daxil, ayı 600 manat olaçaq, mənzili götūrən şəxs 1 ayın 20 % vasitəciyə ödəniş etməlidir. Xahìş olunur, rial olmuyan şəxs zəng etməsin. | https://tap.az/elanlar/dasinmaz-emlak/menziller/35828514 |\n| 35833080 | 1-otaqlı yeni tikili kirayə verilir, Quba r., 60 m² | 40 AZN | 40 | AZN | Quba | Kirayə verilir | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B740%5D=3724 | Yeni tikili | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B747%5D=3849 | 60 | 1 | Quba r. | Orxan | (050) 604-27-60 | Diqqət! Beh göndərməmişdən öncə sövdələşmənin təhlükəsiz olduğuna əmin olun! | 35833080 | 114 | 22 Noyabr 2022 | Quba merkezde her weraiti olan GUNLUK KIRAYE EV.Daimi isti soyuq su hamam metbex wifi.iwciler ve aile ucun elveriwlidir Təmirli | https://tap.az/elanlar/dasinmaz-emlak/menziller/35833080 |\n| 35898353 | 4-otaqlı mənzil, Nizami r., 100 m² | 153 000 AZN | 153 000 | AZN | Bakı | Satılır | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B740%5D=3722 | Köhnə tikili | https://tap.az/elanlar/dasinmaz-emlak/menziller?p%5B747%5D=3850 | 100 | 4 | Nizami r. | Araz M | (070) 723-54-50 | Diqqət! Beh göndərməmişdən öncə sövdələşmənin təhlükəsiz olduğuna əmin olun! | 35898353 | 71 | 27 Noyabr 2022 | X.Dostluğu metrosuna 2 deq mesafede Leninqrad lahiyeli 9 mərtəbəli binanın 5-ci mərtəbəsində 4 otaqlı yaxsi temirli mənzil satılır.Əmlak ofisinə ödəniş alıcı tərəfindən məbləğin 1%-ni təşkil edir. | https://tap.az/elanlar/dasinmaz-emlak/menziller/35898353 |\n\n" ]

[ 0 ]

[]

[]

[ "beautifulsoup", "python", "selenium", "selenium_webdriver", "web_scraping" ]

[ "Honestly, this program doesn't need to be converted recursively, but if so, you would probably write something like this:\ndef rec(n, s = 0): # s = 0 is a default variable, so if we don't specify what s is when we call the function, it's default variable will be 0\n if n == 0: # base case, so if we've run through the entire program n times, it will exit and return the final value\n return s\n \n s = s + n * n # your task, where n replaces i because we change n the same amount of times as i runs\n n -= 1 # we change n so that every time we run the function it eventually reaches 0\n\n return rec(n, s) # recursively run the function again\n\nSince the base case checks if n is 0, if you inputted a negative number, the output would end up as 0.\nTo get around this, I would suggest using something else to check if it's negative, and perhaps running the same function but with the absolute value, and just add the negative sign after. (In theory this should work, have not fully tested it out myself)\nIf you're wondering, I used n to replace i, where n decreases in value throughout the execution. So if n began as 10, n would be 9,8,7,6...0 through each recursive iteration.\n", "A recursive approach to this problem (sum of squares) is not well suited to recursion for two reasons: 1) It's less efficient and 2) It's limited to the depth of recursion which is typically 1000\nHowever, coding is trivial:\ndef rec(n):\n return 0 if n <= 0 else n * n + rec(n-1)\n\nprint(rec(10))\n\nOutput:\n385\n\n" ]

[ 1, 0 ]

[]

[]

[ "function", "iteration", "loops", "python", "recursion" ]

[ "%>% is from the magrittr package. If you have R version 4.1 or later you can use the native |> pipe instead.\n" ]

[ 2 ]

[]

[]

[ "dplyr", "python", "r", "rpy2" ]

[ "In this line\nhighest=scores\n\nYou are assigning a list (scores) to an int var (highest), and this is the reason for the error.\nI think you have to change the line in\nhighest=s\n\n" ]

[ 0 ]

[]

[]

[ "loops", "python" ]

[ "Simple. Split the values then apply zfill on each value and join back\ndf['Q3'].map(lambda x: ' '.join(y.zfill(8) for y in x.split()))\n\n\n0 00000001 00000004\n1 00000001 00000003\n2 00000003 00000004 00000011\n3 00000003 00000004 00000006 00000015 00000016\nName: Q3, dtype: object\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "pandas", "python" ]

[ "Set the size of the canvas explicitly\nself.__image_canvas = ctk.CTkCanvas(self.__image_frame, width=900)\n\n" ]

[ 1 ]

[]

[]

[ "python", "tkinter" ]

[ "Try this\nfrom time import time\n\ndef log(func):\n def wrapper(*args, **kwargs):\n\n start_time = time()\n func(*args, **kwargs)\n end_time = time() - start_time\n \n args_names = func.__code__.co_varnames[:func.__code__.co_argcount]\n args_names ={**dict(zip(args_names, args))}\n with open('log.txt', 'a+') as file:\n file.write(f'{func.__name__}, args={args_names}, kwargs={kwargs}, {end_time}\\n')\n \n return wrapper\n\n@log\ndef some_fun(a, b, c):\n pass\n\n\nsome_fun(5, 10, c=15)\n\n" ]

[ 0 ]

[]

[]

[ "decorator", "logging", "python", "python_3.x" ]

[ "One solution is to use the pattern to subset the target nodes before looking for the shortest paths:\ntarget_nodes = [n for n in G if \"IASW\" in str(n) or \"IUPE\" in str(n)]\n\nWith a list of target nodes, now it's possible to iterate over them and find the shortest path of interest (as you describe).\n" ]

[ 0 ]

[]

[]

[ "networkx", "python", "shortest_path" ]

[ "I assume the function that you want to test is items_bulk_updated.\nSince you are testing ItemsManager.bulk_update() and you want to verify that items_bulk_updated is being called inside that method, the path in your @mock.patch should be the file path where the function is being imported in instead of its origin. This means you need to update\[email protected](\"items.signals.items_bulk_updated\", autospec=True)\n\nto\[email protected](\"<path-to-items-manager-file>.items_bulk_updated\", autospec=True)\n\nwhere <path-to-items-manager-file> as suggested, is the path to your ItemsManager class.\n" ]

[ 1 ]

[]

[]

[ "django", "pytest_django", "python", "python_unittest" ]

[ "Well it seems that, as always, I've found the answer to what's been bugging me for a week twenty minutes after I seriously ask somebody about it. \nSo I've found that I need to make sure two things are done correctly:\n\nThe path can contain only backslashes, no forward slashes.\nIf I want to do something like list a directory, I need to end the path with a backslash, otherwise Python will append /*.* to it, which is a forward slash, which is bad.\n\nHope at least someone will find this useful.\n", "Let me just simplify this for anyone looking for a straight answer:\n\nPath needs to be unicode, prepend string with u like u'C:\\\\path\\\\to\\\\file'\nPath needs to start with \\\\\\\\?\\\\ (which is escaped into \\\\?\\) like u'\\\\\\\\?\\\\C:\\\\path\\\\to\\\\file'\nNo forward slashes only backslashes: / --> \\\\\nIt has to be an absolute path; it does not work for relative paths\n\n", "py 3.8.2\n# Fix long path access:\nimport ntpath\nntpath.realpath = ntpath.abspath\n# Fix long path access.\n\nIn my case, this solved the problem of running a script from a long path.\n(https://developers.google.com/drive/api/v3/quickstart/python)\nBut this is not a universal fix.\nIt looks like the ntpath.realpath implementation has problems. This code replaced it with a dummy.\n", "it works for me\nimport os\nstr1=r\"C:\\Users\\manual\\demodfadsfljdskfjslkdsjfklaj\\inner-2djfklsdfjsdklfj\\inner3fadsfksdfjdklsfjksdgjl\\inner4dfhasdjfhsdjfskfklsjdkjfleioreirueewdsfksdmv\\anotherInnerfolder4aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\\5qbbbbbbbbbbbccccccccccccccccccccccccsssssssssssssssss\\tmp.txt\"\nprint(len(str1)) #346\n\npath = os.path.abspath(str1)\n\nif path.startswith(u\"\\\\\\\\\"):\n path=u\"\\\\\\\\?\\\\UNC\\\\\"+path[2:]\nelse:\n path=u\"\\\\\\\\?\\\\\"+path\n\nwith open(path,\"r+\") as f:\n print(f.readline())\n\nif you get a long path(more then 258 char) issue in windows then try this .\n" ]

[ 17, 11, 1, 0 ]

[]

[]

[ "python", "windows" ]

[ "Just collect lines until you get one which doesn't have the six spaces, and then print what you have accrued so far before starting over. Don't forget to handle the last one when you fall off the end of the loop.\nfn = \"AFilenameHere.txt\"\nlines = []\nwith open(fn, \"r\") as fileObject:\n for line in fileObject:\n if line.startswith(' '):\n lines.append(line[5:])\n else:\n if lines:\n print(\"\".join(lines))\n lines = [line]\nif lines:\n print(\"\".join(lines))\n\nThe if lines: condition checks if there are items in lines from the previous iterations of the for loop, and if so, prints those.\nIt's not clear why your attempt looks for the \"EA\" token; by your description, this simply joins lines which have multiple spaces back with the previous line.\n" ]

[ 0 ]

[]

[]

[ "python", "regex" ]

[ "It seems that the web-Instagram login page has been down for about a week!\nFrom last week until now, most users can't login to Instagram on the web!\nRead chif.j's solution and comments for a temporary fix!\n", "Open your Chrome browser developer tools, and then go to the login page of Instagram. In the network tab, find the request that goes like this: https://www.instagram.com/\nClick on the request and in the response tab, press Ctrl + F and search for csrf_token. Copy the value of csrf, and go to the application tab. In the storage section, click on cookies and insert a cookie with the csrftoken name. Paste the value and make it secure. Now fill the login form and press Enter.\n", "I had the same problem. I used a VPN to connect to the US, and there wasn't any \"CSRF token missing\" message after that.\n", "One liner to run fin JS console in your browser to fix CSRF token issue:\nn=new Date;t=n.getTime();et=t+36E9;n.setTime(et);document.cookie='csrftoken='+document.body.innerHTML.split('csrf_token')[1].split('\\\\\"')[2]+';path=\\;domain=.instagram.com;expires='+n.toUTCString();\n\nOpen JS console, copy paste it, press enter. Reload the page and try to login again.\nThis js snippet will find CSRF token in page source and create cookie for you.\n" ]

[ 14, 12, 2, 1 ]

[]

[]

[ "csrf_token", "instagram", "python", "python_3.x", "selenium" ]

[ "I was facing the same situation a couple of hours ago. What I did to resolve this is creating the files in python with the value of the variable:\ncert = \"\"\"cert\"\"\"\nfile = open(\"cert.txt\",\"w\")\nfile.write(cert)\nfile.close()\n\nAnd then, just pass the path to the psycopg2 connection.\n" ]

[ 0 ]

[]

[]

[ "google_cloud_platform", "postgresql", "psycopg2", "python", "ssl" ]

[ "Regular DOS paths are limited to MAX_PATH (260) characters, including the string's terminating NUL character. You can exceed this limit by using an extended-length path that starts with the \\\\?\\ prefix. This path must be a Unicode string, fully qualified, and only use backslash as the path separator. Per Microsoft's file system functionality comparison, the maximum extended path length is 32760 characters. A individual file or directory name can be up to 255 characters (127 for the UDF filesystem). Extended UNC paths are also supported as \\\\?\\UNC\\server\\share.\nFor example:\nimport os\n\ndef winapi_path(dos_path, encoding=None):\n if (not isinstance(dos_path, unicode) and \n encoding is not None):\n dos_path = dos_path.decode(encoding)\n path = os.path.abspath(dos_path)\n if path.startswith(u\"\\\\\\\\\"):\n return u\"\\\\\\\\?\\\\UNC\\\\\" + path[2:]\n return u\"\\\\\\\\?\\\\\" + path\n\npath = winapi_path(os.path.join(u\"JSONFiles\", \n item[\"category\"],\n item[\"action\"], \n item[\"source\"], \n fileName + \".json\"))\n\n>>> path = winapi_path(\"C:\\\\Temp\\\\test.txt\")\n>>> print path\n\\\\?\\C:\\Temp\\test.txt\n\nSee the following pages on MSDN: \n\nNaming Files, Paths, and Namespaces\nDefining an MS-DOS Device Name\nKernel object namespaces\n\n\nBackground\nWindows calls the NT runtime library function RtlDosPathNameToRelativeNtPathName_U_WithStatus to convert a DOS path to a native NT path. If we open (i.e. CreateFile) the above path with a breakpoint set on the latter function, we can see how it handles a path that starts with the \\\\?\\ prefix.\nBreakpoint 0 hit\nntdll!RtlDosPathNameToRelativeNtPathName_U_WithStatus:\n00007ff9`d1fb5880 4883ec58 sub rsp,58h\n0:000> du @rcx\n000000b4`52fc0f60 \"\\\\?\\C:\\Temp\\test.txt\"\n0:000> r rdx\nrdx=000000b450f9ec18\n0:000> pt\nntdll!RtlDosPathNameToRelativeNtPathName_U_WithStatus+0x66:\n00007ff9`d1fb58e6 c3 ret\n\nThe result replaces \\\\?\\ with the NT DOS devices prefix \\??\\, and copies the string into a native UNICODE_STRING:\n0:000> dS b450f9ec18\n000000b4`536b7de0 \"\\??\\C:\\Temp\\test.txt\"\n\nIf you use //?/ instead of \\\\?\\, then the path is still limited to MAX_PATH characters. If it's too long, then RtlDosPathNameToRelativeNtPathName returns the status code STATUS_NAME_TOO_LONG (0xC0000106). \nIf you use \\\\?\\ for the prefix but use slash in the rest of the path, Windows will not translate the slash to backslash for you:\nBreakpoint 0 hit\nntdll!RtlDosPathNameToRelativeNtPathName_U_WithStatus:\n00007ff9`d1fb5880 4883ec58 sub rsp,58h\n0:000> du @rcx\n0000005b`c2ffbf30 \"\\\\?\\C:/Temp/test.txt\"\n0:000> r rdx\nrdx=0000005bc0b3f068\n0:000> pt\nntdll!RtlDosPathNameToRelativeNtPathName_U_WithStatus+0x66:\n00007ff9`d1fb58e6 c3 ret\n0:000> dS 5bc0b3f068\n0000005b`c3066d30 \"\\??\\C:/Temp/test.txt\"\n\nForward slash is a valid object name character in the NT namespace. It's reserved by Microsoft filesystems, but you can use a forward slash in other named kernel objects, which get stored in \\BaseNamedObjects or \\Sessions\\[session number]\\BaseNamedObjects. Also, I don't think the I/O manager enforces the policy on reserved characters in device and filenames. It's up to the device. Maybe someone out there has a Windows device that implements a namespace that allows forward slash in names. At the very least you can create DOS device names that contain a forward slash. For example:\n>>> kernel32 = ctypes.WinDLL('kernel32')\n>>> kernel32.DefineDosDeviceW(0, u'My/Device', u'C:\\\\Temp')\n>>> os.path.exists(u'\\\\\\\\?\\\\My/Device\\\\test.txt')\nTrue\n\nYou may be wondering what \\?? signifies. This used to be an actual directory for DOS device links in the object namespace, but starting with NT 5 (or NT 4 w/ Terminal Services) this became a virtual prefix. The object manager handles this prefix by first checking the logon session's DOS device links in the directory \\Sessions\\0\\DosDevices\\[LOGON_SESSION_ID] and then checking the system-wide DOS device links in the \\Global?? directory. \nNote that the former is a logon session, not a Windows session. The logon session directories are all under the DosDevices directory of Windows session 0 (i.e. the services session in Vista+). Thus if you have a mapped drive for a non-elevated logon, you'll discover that it's not available in an elevated command prompt, because your elevated token is actually for a different logon session.\nAn example of a DOS device link is \\Global??\\C: => \\Device\\HarddiskVolume2. In this case the DOS C: drive is actually a symbolic link to the HarddiskVolume2 device. \nHere's a brief overview of how the system handles parsing a path to open a file. Given we're calling WinAPI CreateFile, it stores the translated NT UNICODE_STRING in an OBJECT_ATTRIBUTES structure and calls the system function NtCreateFile. \n0:000> g\nBreakpoint 1 hit\nntdll!NtCreateFile:\n00007ff9`d2023d70 4c8bd1 mov r10,rcx\n0:000> !obja @r8\nObja +000000b450f9ec58 at 000000b450f9ec58:\n Name is \\??\\C:\\Temp\\test.txt\n OBJ_CASE_INSENSITIVE\n\nNtCreateFile calls the I/O manager function IoCreateFile, which in turn calls the undocumented object manager API ObOpenObjectByName. This does the work of parsing the path. The object manager starts with \\??\\C:\\Temp\\test.txt. Then it replaces that with \\Global??\\C:Temp\\test.txt. Next it parses up to the C: symbolic link and has to start over (reparse) the final path \\Device\\HarddiskVolume2\\Temp\\test.txt. \nOnce the object manager gets to the HarddiskVolume2 device object, parsing is handed off to the I/O manager, which implements the Device object type. The ParseProcedure of an I/O Device creates the File object and an I/O Request Packet (IRP) with the major function code IRP_MJ_CREATE (an open/create operation) to be processed by the device stack. This is sent to the device driver via IoCallDriver. If the device implements reparse points (e.g. junction mountpoints, symbolic links, etc) and the path contains a reparse point, then the resolved path has to be resubmitted to the object manager to be parsed from the start.\nThe device driver will use the SeChangeNotifyPrivilege (almost always present and enabled) of the process token (or thread if impersonating) to bypass access checks while traversing directories. However, ultimately access to the device and target file has to be allowed by a security descriptor, which is verified via SeAccessCheck. Except simple filesystems such as FAT32 don't support file security.\n", "below is Python 3 version regarding @Eryk Sun's solution.\ndef winapi_path(dos_path, encoding=None):\n if (not isinstance(dos_path, str) and encoding is not None): \n dos_path = dos_path.decode(encoding)\n path = os.path.abspath(dos_path)\n if path.startswith(u\"\\\\\\\\\"):\n return u\"\\\\\\\\?\\\\UNC\\\\\" + path[2:]\n return u\"\\\\\\\\?\\\\\" + path\n\n#Python 3 renamed the unicode type to str, the old str type has been replaced by bytes. NameError: global name 'unicode' is not defined - in Python 3\n", "Adding the solution that helped me fix a similar issue:\npython version = 3.9, windows version = 10 pro.\nI had an issue with the filename itself as it was too long for python's open built-in method. The error I got is that the path simply doesn't exist, although I use the 'w+' mode for open (which is supposed to open a new file regardless whether it exists or not).\nI found this guide which solved the problem with a quick change to window's Registry Editor (specifically the Group Policy). Scroll down to the 'Make Windows 10 Accept Long File Paths' headline.\nDon't forget to update your OS group policy to take effect immediately, a guide can be found here.\nHope this helps future searches as this post is quite old.\n", "There can be multiple reasons for you getting this error. Please make sure of the following:\n\nThe parent directory of the folder (JSONFiles) is the same as the directory of the Python script.\nEven though the folder exists it does not mean the individual file does. Verify the same and make sure the exact file name matches the one that your Python code is trying to access.\n\nIf you still face an issue, share the result of \"dir\" command on the innermost folder you are trying to access.\n", "it works for me\nimport os\nstr1=r\"C:\\Users\\sandeepmkwana\\Desktop\\folder_structure\\models\\manual\\demodfadsfljdskfjslkdsjfklaj\\inner-2djfklsdfjsdklfj\\inner3fadsfksdfjdklsfjksdgjl\\inner4dfhasdjfhsdjfskfklsjdkjfleioreirueewdsfksdmv\\anotherInnerfolder4aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\\5qbbbbbbbbbbbccccccccccccccccccccccccsssssssssssssssss\\tmp.txt\"\nprint(len(str1)) #346\n\npath = os.path.abspath(str1)\n\nif path.startswith(u\"\\\\\\\\\"):\n path=u\"\\\\\\\\?\\\\UNC\\\\\"+path[2:]\nelse:\n path=u\"\\\\\\\\?\\\\\"+path\n\nwith open(path,\"r+\") as f:\n print(f.readline())\n\nIf you get a long path (more than 258 characters) issue in Windows, then try this.\n" ]

[ 42, 13, 1, 0, 0 ]

[]

[]

[ "python", "python_2.7", "windows" ]