Datasets:

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suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "I think you should use response.content instead of the Response object itself to get the response body as bytes.\nMeanwhile, I recommened you to use the Azure Speech SDK for speech synthesis, which has an easy-to-use interface. Refer to the Python Quickstart for TTS for more details.\n" ]

[ 0 ]

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[ "azure", "azure_cognitive_services", "flask", "python" ]

[ "I got it!\nOkay first a bit of maths theory. There are several ways of depicting a line in geometry.\nThe first is the \"slope-intercept\" form: y = mx + c\nThen there's the \"point-slope\" form: y = y1 + m * (x - x1)\nAnd finally there's the \"generalized form\":\n\nNone of these forms are practical for several reasons:\n\nFor the first 2 forms, there's the edge case of vertical lines which means y increases even though x stays the same.\nNear-verticals mean I have to advance x reaaally slowly or else y increases too fast\nEven with the \"generalized form\", to make the segment lengths uniform, I have to handle the iterations differently for horizontally-oriented lines (iterate on x) with vertically-oriented lines (iterate on y)\n\nHowever, just this morning I got reminded that there's yet another form, the \"parametric form\":\n R = P + tD\n\nWhere D is the \"displacement vector\", P is the \"starting point\", and R is the \"resultant vector\". t is a parameter that can be defined any which way you want, depending on D's dimension.\nBy adjusting D and/or t's steps, I can get as precise as I want, and I don't have to concern myself with special cases!\nWith this concept, I can imagine someone walking down the line segment with a marker, and whenever they have traversed a certain distance, replace the marker with another one, and continue.\nBased on this principle, here's the (quick-n-dirty) program:\nimport math\nfrom itertools import pairwise, cycle\nfrom math import sqrt, isclose\nfrom typing import NamedTuple\nfrom PIL import Image, ImageDraw\n\n\nclass Point(NamedTuple):\n x: float\n y: float\n\n def rounded(self) -> tuple[int, int]:\n return round(self.x), round(self.y)\n\n\n# Example data points\npoints: list[Point] = [\n Point(108.0, 272.0),\n Point(150.0, 227.0),\n Point(171.0, 218.0),\n Point(187.0, 221.0),\n Point(192.0, 234.0),\n Point(205, 315),\n Point(216, 402),\n Point(275, 565),\n Point(289, 586),\n Point(312, 603),\n Point(343, 609),\n Point(387, 601),\n Point(420, 577),\n Point(484, 513),\n Point(505, 500),\n Point(526, 500),\n Point(551, 509),\n Point(575, 550),\n Point(575, 594),\n Point(546, 656),\n Point(496, 686),\n Point(409, 712),\n Point(329, 715),\n Point(287, 701),\n]\n\n\nclass ParametricLine:\n def __init__(self, p1: Point, p2: Point):\n self.p1 = p1\n self.x1, self.y1 = p1\n self.p2 = p2\n self.x2, self.y2 = p2\n self._len = -1.0\n\n @property\n def length(self):\n if self._len < 0.0:\n dx, dy = self.displacement\n self._len = sqrt(dx ** 2 + dy ** 2)\n return self._len\n\n @property\n def displacement(self):\n return (self.x2 - self.x1), (self.y2 - self.y1)\n\n def replace_start(self, p: Point):\n self.p1 = p\n self.x1, self.y1 = p\n self._len = -1.0\n\n def get_point(self, t: float) -> Point:\n dx, dy = self.displacement\n xr = self.x1 + (t / self.length) * dx\n xy = self.y1 + (t / self.length) * dy\n return Point(xr, xy)\n\n\nimage = Image.new(\"RGBA\", (1000, 1000))\nidraw = ImageDraw.Draw(image)\n\n\ndef draw(segments: list[tuple[Point, Point]], phase: str):\n drawpoints = []\n prev_p2 = segments[0][0]\n p2 = None\n for p1, p2 in segments:\n assert isclose(p1.x, prev_p2.x)\n assert isclose(p1.y, prev_p2.y)\n drawpoints.append(p1.rounded())\n prev_p2 = p2\n drawpoints.append(p2.rounded())\n if phase == \"dash\" or phase == \"gapp\":\n idraw.line(drawpoints, fill=(255, 255, 0), width=10, joint=\"curve\")\n elif phase == \"pip1\" or phase == \"pip2\":\n idraw.line(drawpoints, fill=(0, 0, 0), width=10, joint=\"curve\")\n\n\ndef main():\n limits: dict[str, float] = {\n \"dash\": 40.0,\n \"pip1\": 8.0,\n \"gapp\": 8.0,\n \"pip2\": 8.0,\n }\n\n pointpairs = pairwise(points)\n climit = cycle(limits.items())\n\n phase, tleft = next(climit)\n segments: list[tuple[Point, Point]] = []\n\n pline: ParametricLine | None = None\n p1 = p2 = Point(math.nan, math.nan)\n while True:\n if pline is None:\n try:\n p1, p2 = next(pointpairs)\n except StopIteration:\n break\n pline = ParametricLine(p1, p2)\n if pline.length > tleft:\n # The line segment is longer than our leftover budget.\n # Find where we should truncate the line and draw the\n # segments until the truncation point.\n p3 = pline.get_point(tleft)\n segments.append((p1, p3))\n draw(segments, phase)\n segments.clear()\n pline.replace_start(p3)\n p1 = p3\n phase, tleft = next(climit)\n else:\n # The segment is shorter than our leftover budget.\n # Record that and reduce the budget.\n segments.append((p1, p2))\n tleft -= pline.length\n pline = None\n if abs(tleft) < 0.01:\n # The leftover is too small, let's just assume that\n # this is insignificant and go to the next phase.\n draw(segments, phase)\n segments.clear()\n phase, tleft = next(climit)\n if segments:\n draw(segments, phase)\n\n image.save(\"results.png\")\n\n\nif __name__ == '__main__':\n main()\n\nAnd here's the result:\n\nA bit rough, but usable for my purposes.\nAnd the beauty of this solution is that by varying what happens in draw() (and the contents of limits), my solution can also handle dashed lines quite easily; just make the limits toggle back and forth between, say, \"dash\" and \"blank\", and in draw() only actually draw a line when phase == \"dash\".\nNote: I am 100% certain that the algorithm can be optimized / tidied up further. As of now I'm happy that it works at all. I'll probably skedaddle over to CodeReview SE for suggestions on optimization.\nEdit: The final version of the code is live and open for review on CodeReview SE. If you arrived here via a search engine because you're looking for a way to draw a patterned line, please use the version on CodeReview SE instead.\n" ]

[ 0 ]

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[ "curve", "python" ]

[ "You can use terms query from elasticsearch to query list of ids:\n{\n \"query\": {\n \"terms\": {\n \"id_list\": [1111,2222,3333,4444,5555]\n }\n }\n}\n\nUpdated Based on comments:\nAs mentioned in documentation maximum of 65,536 terms.\n\nBy default, Elasticsearch limits the terms query to a maximum of\n65,536 terms. This includes terms fetched using terms lookup. You can\nchange this limit using the index.max_terms_count setting.\n\n" ]

[ 0 ]

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[ "boolean_logic", "elasticsearch", "elasticsearch_dsl", "python" ]

[ "Add these two lines in the settings.json to enable type hints\n \"python.analysis.inlayHints.variableTypes\": true,\n \"python.analysis.inlayHints.functionReturnTypes\": true,\n\n\n" ]

[ 0 ]

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[ "python", "visual_studio_code" ]

[ "Try the below locator:\ndriver.find_element(By.XPATH, \".//iframe[@id='body_iframe' and @name='body_iframe']\")\n\n" ]

[ 0 ]

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[ "html", "iframe", "python", "selenium" ]

[ "You did not explain how your deque was updated, and how it should affect the window processing.\nBut here is a proof-of-concept of the algorithm :\nfrom datetime import datetime\nfrom typing import Generator, List, Dict, Union\n\nElement = Dict[str, Union[str, int]]\nSeries = List[Element]\n\ndef sliding_window(series: Series, window_duration: int) -> Generator[Series, None, None]:\n time_format = \"%H:%M:%S\"\n if len(series) > 0:\n for i_ending_item, ending_item in enumerate(series):\n end_window_time = datetime.strptime(ending_item[\"time\"], time_format)\n print(f\"window ends at item n°{i_ending_item} ({end_window_time!r})\")\n window = [ending_item]\n for window_candidate_item in reversed(series[0:max(i_ending_item, 0)]):\n candidate_time = datetime.strptime(window_candidate_item[\"time\"], time_format)\n assert end_window_time > candidate_time\n candidate_delta = end_window_time - candidate_time\n print(f\" {candidate_time=!r} {candidate_delta=!r} {candidate_delta.seconds=!r}\")\n if candidate_delta.seconds < window_duration: # non inclusive\n print(\" added to the window\")\n window.insert(0, window_candidate_item)\n else:\n print(\" stop there\")\n break\n else:\n print(\" reached the beginning of the series\")\n yield window\n\nDATA: Series = [\n {\"time\": \"07:14:40\", \"event\": 24},\n {\"time\": \"07:15:40\", \"event\": 394},\n {\"time\": \"07:16:40\", \"event\": 384},\n {\"time\": \"07:17:40\", \"event\": 394},\n {\"time\": \"07:18:40\", \"event\": 384},\n {\"time\": \"07:19:40\", \"event\": 2},\n {\"time\": \"07:20:40\", \"event\": 24},\n {\"time\": \"07:21:40\", \"event\": 72},\n {\"time\": \"07:22:40\", \"event\": 24},\n {\"time\": \"07:23:40\", \"event\": 72},\n {\"time\": \"07:24:40\", \"event\": 99}\n]\nWINDOW_SIZE = 5*60\n\nfor window in sliding_window(DATA, WINDOW_SIZE):\n print(window, \"sum=\", sum(item[\"event\"] for item in window))\n\nwhich produces\nwindow ends at item n°0 (datetime.datetime(1900, 1, 1, 7, 14, 40))\n reached the beginning of the series\n[{'time': '07:14:40', 'event': 24}] sum= 24\nwindow ends at item n°1 (datetime.datetime(1900, 1, 1, 7, 15, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 14, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n reached the beginning of the series\n[{'time': '07:14:40', 'event': 24}, {'time': '07:15:40', 'event': 394}] sum= 418\nwindow ends at item n°2 (datetime.datetime(1900, 1, 1, 7, 16, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 15, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 14, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n reached the beginning of the series\n[{'time': '07:14:40', 'event': 24}, {'time': '07:15:40', 'event': 394}, {'time': '07:16:40', 'event': 384}] sum= 802\nwindow ends at item n°3 (datetime.datetime(1900, 1, 1, 7, 17, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 16, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 15, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 14, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n reached the beginning of the series\n[{'time': '07:14:40', 'event': 24}, {'time': '07:15:40', 'event': 394}, {'time': '07:16:40', 'event': 384}, {'time': '07:17:40', 'event': 394}] sum= 1196\nwindow ends at item n°4 (datetime.datetime(1900, 1, 1, 7, 18, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 17, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 16, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 15, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 14, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n reached the beginning of the series\n[{'time': '07:14:40', 'event': 24}, {'time': '07:15:40', 'event': 394}, {'time': '07:16:40', 'event': 384}, {'time': '07:17:40', 'event': 394}, {'time': '07:18:40', 'event': 384}] sum= 1580\nwindow ends at item n°5 (datetime.datetime(1900, 1, 1, 7, 19, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 18, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 17, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 16, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 15, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 14, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:15:40', 'event': 394}, {'time': '07:16:40', 'event': 384}, {'time': '07:17:40', 'event': 394}, {'time': '07:18:40', 'event': 384}, {'time': '07:19:40', 'event': 2}] sum= 1558\nwindow ends at item n°6 (datetime.datetime(1900, 1, 1, 7, 20, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 19, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 18, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 17, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 16, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 15, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:16:40', 'event': 384}, {'time': '07:17:40', 'event': 394}, {'time': '07:18:40', 'event': 384}, {'time': '07:19:40', 'event': 2}, {'time': '07:20:40', 'event': 24}] sum= 1188\nwindow ends at item n°7 (datetime.datetime(1900, 1, 1, 7, 21, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 20, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 19, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 18, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 17, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 16, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:17:40', 'event': 394}, {'time': '07:18:40', 'event': 384}, {'time': '07:19:40', 'event': 2}, {'time': '07:20:40', 'event': 24}, {'time': '07:21:40', 'event': 72}] sum= 876\nwindow ends at item n°8 (datetime.datetime(1900, 1, 1, 7, 22, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 21, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 20, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 19, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 18, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 17, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:18:40', 'event': 384}, {'time': '07:19:40', 'event': 2}, {'time': '07:20:40', 'event': 24}, {'time': '07:21:40', 'event': 72}, {'time': '07:22:40', 'event': 24}] sum= 506\nwindow ends at item n°9 (datetime.datetime(1900, 1, 1, 7, 23, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 22, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 21, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 20, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 19, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 18, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:19:40', 'event': 2}, {'time': '07:20:40', 'event': 24}, {'time': '07:21:40', 'event': 72}, {'time': '07:22:40', 'event': 24}, {'time': '07:23:40', 'event': 72}] sum= 194\nwindow ends at item n°10 (datetime.datetime(1900, 1, 1, 7, 24, 40))\n candidate_time=datetime.datetime(1900, 1, 1, 7, 23, 40) candidate_delta=datetime.timedelta(seconds=60) candidate_delta.seconds=60\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 22, 40) candidate_delta=datetime.timedelta(seconds=120) candidate_delta.seconds=120\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 21, 40) candidate_delta=datetime.timedelta(seconds=180) candidate_delta.seconds=180\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 20, 40) candidate_delta=datetime.timedelta(seconds=240) candidate_delta.seconds=240\n added to the window\n candidate_time=datetime.datetime(1900, 1, 1, 7, 19, 40) candidate_delta=datetime.timedelta(seconds=300) candidate_delta.seconds=300\n stop there\n[{'time': '07:20:40', 'event': 24}, {'time': '07:21:40', 'event': 72}, {'time': '07:22:40', 'event': 24}, {'time': '07:23:40', 'event': 72}, {'time': '07:24:40', 'event': 99}] sum= 291\n\nTo me it seems to answer your question : how to have a sliding window based on the time of the events.\nI used a list for the data by simplicity. If you want to share a Minimal Reproducible Example it would be simpler to answer your question.\n" ]

[ 1 ]

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[ "deque", "python", "python_3.x", "queue" ]

[ "Use Series.str.contains with chain list by | for regex OR:\ndf['test1'] = np.where(df['displayed_link'].str.contains('|'.join(promotions)), 'T', 'F')\ndf['test2'] = (df['displayed_link'].str.contains('|'.join(promotions))\n .map({True:'T',False: 'F'}))\n\nIf necessary, use words boundaries \\b\\b:\npat = '|'.join(rf\"\\b{x}\\b\" for x in promotions))\ndf['test3']= np.where(df['displayed_link'].str.contains(pat), 'T', 'F')\ndf['test4']= df['displayed_link'].str.contains(pat).map({True:'T',False: 'F'})\nprint (df)\n displayed_link test1 test2 test3 test4\n0 https://www.coursera.org/articles/what-does-a T T T T\n1 https://www.rasmussen.edu/degrees/technology/ T T T T\n2 https://www.linkedin.com/learning/paths/ F F F F\n3 https://www.snhu1.edu/about-us/newsroom/stem/ T T F F\n\n" ]

[ 1 ]

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[ "pandas", "python", "serpapi" ]

[ "2 Methods:\n# Method 1\ns = \"I ? am ? a ? string\"\nl = ['1', '2', '3']\n\nfor i in l:\n s = s.replace('?', i, 1) \n\nprint(s)\n# Output: I 1 am 2 a 3 string\n\n\n# Method 2\nfrom functools import reduce\ns = \"I ? am ? a ? string\"\nl = ['1', '2', '3']\n\ns_new = reduce(lambda x, y: x.replace('?', y, 1), l, s)\n\nprint(s_new) \n# Output: I 1 am 2 a 3 string\n\n", "If the placeholders (not \"delimiters\") were {} rather than ?, this would be exactly how the built-in .format method handles empty {} (along with a lot more power). So, we can simply replace the placeholders first, and then use that functionality:\n>>> s = \"I ? am ? a ? string\"\n>>> l = ['1', '2', '3']\n>>> s.replace('?', '{}').format(*l)\n'I 1 am 2 a 3 string'\n\nNotice that .format expects each value as a separate argument, so we use * to unpack the list.\nIf the original string contains { or } which must be preserved, we can first escape them by doubling them up:\n>>> s = \"I ? {am} ? a ? string\"\n>>> l = ['1', '2', '3']\n>>> s.replace('?', '{}').format(*l)\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nKeyError: 'am'\n>>> s.replace('{', '{{').replace('}', '}}').replace('?', '{}').format(*l)\n'I 1 {am} 2 a 3 string'\n\n" ]

[ 3, 3 ]

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[]

[ "python" ]

[ "Update 1/12/22 - Async\nFound a much better solution using aiohttp, it also runs the entire list of countries in ~30 seconds instead of 3 hours\nimport json\nimport time\nimport aiohttp\nimport asyncio\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webelement import WebElement\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\n\n\ndef main():\n print(\"Init\")\n driver = init_driver()\n\n print(\"Opening Homepage\")\n url = \"https://www.whed.net/results_institutions.php\"\n driver.get(url)\n time.sleep(1)\n\n print(\"Gathering Countries\")\n countries = get_countries(driver)\n driver.quit()\n\n print(\"Scraping\")\n start = time.time()\n institution_list = asyncio.run(fetch_all(countries))\n\n print(\"Writing out\")\n\n f = open('output.json', 'w')\n f.write(json.dumps(institution_list))\n f.close()\n end = time.time()\n print(f\"Total time: {end - start}s\")\n\n\ndef init_driver():\n chrome_executable = Service(executable_path='chromedriver.exe', log_path='NUL')\n chrome_options = Options()\n chrome_options.add_argument(\"--headless\")\n driver = webdriver.Chrome(service=chrome_executable, options=chrome_options)\n return driver\n\n\ndef get_countries(driver):\n select = Select(driver.find_element(By.ID, \"Chp1\"))\n countries = list(map(lambda c: c.get_attribute('value'), select.options))\n countries.pop(0)\n return countries\n\n\ndef extract_institutions(html, country):\n soup = BeautifulSoup(html, 'html.parser')\n page = soup.find('p', {'class': 'infos'}).text\n print(str(page))\n number_of_institutions = str(page).split()[0]\n if number_of_institutions == 'No':\n print(f\"No results for {country}\")\n return []\n\n results = []\n inst_index = 0\n\n raw = soup.find_all('a', {'class': 'fancybox fancybox.iframe'})\n for i in raw:\n results.append({\n 'name': str(i.text).strip(),\n 'url': 'https://www.whed.net/' + str(i.attrs['href']).strip(),\n 'country': country\n })\n\n inst_index += 1\n\n return {\n 'country': country,\n 'count': number_of_institutions,\n 'records': results\n }\n\n\nasync def get_institutions(country, session):\n try:\n async with session.post(\n url='https://www.whed.net/results_institutions.php',\n data={\"Chp1\": country, \"nbr_ref_pge\": 10000}\n ) as response:\n html = await response.read()\n print(f\"Successfully got {country}\")\n return extract_institutions(html, country)\n except Exception as e:\n print(f\"Unable to get {country} due to {e.__class__}.\")\n\n\nasync def fetch_all(countries):\n async with aiohttp.ClientSession() as session:\n return await asyncio.gather(*[get_institutions(country, session) for country in countries])\n\n\n# Main call\nmain()\n\n\nOld answer using synchronous algorithm\nImproving on @Mithun's answer since it doesn't really work as it'll be stuck on the same page.\nAlso added direct access to the name and url to make it easier in case you want to access those.\nimport time\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\n\nprint(\"Init\")\n\nchrome_executable = Service(executable_path='chromedriver.exe', log_path='NUL')\nchrome_options = Options()\nchrome_options.add_argument(\"--headless\")\ndriver = webdriver.Chrome(service=chrome_executable, options=chrome_options)\n\nprint(\"Opening Homepage\")\nurl = \"https://www.whed.net/results_institutions.php\"\ndriver.get(url)\ntime.sleep(1)\n\nprint(\"Selecting country\")\nselect = Select(driver.find_element(By.ID, \"Chp1\"))\ncountry = \"Albania\"\nselect.select_by_visible_text(country)\ntime.sleep(.5)\n\nprint(\"Searching\")\ndriver.find_element(By.XPATH, \"//input[@value='Go']\").click()\ntime.sleep(1)\n\nprint(\"Parsing\")\nhtml = driver.page_source\nsoup = BeautifulSoup(html, 'html.parser')\n\npage = soup.find('p', {'class': 'infos'}).text\n\nnumber_of_pages = str(page).split()[0]\n\ncounter = 10\nresults = []\nwhile True:\n raw = soup.find_all('a', {'class': 'fancybox fancybox.iframe'})\n for i in raw:\n results.append({\n 'name': str(i.text).strip(),\n 'url': 'https://www.whed.net/' + str(i.attrs['href']).strip(),\n 'country': country\n })\n print(f'{len(results)}/{number_of_pages}')\n\n if counter >= int(number_of_pages):\n break\n counter += 10\n\n driver.find_element(By.LINK_TEXT, \"Next page\").click()\n time.sleep(0.5)\n soup = BeautifulSoup(driver.page_source, 'html.parser')\ndriver.quit()\nprint(results)\n\n" ]

[ 0 ]

[ "You can use Selenium to scrape data. The following code will help you scrape the university names for \"United States of America (all)\". Similarly, you can scrape for other countries as well using Loop or entering the name manually. If you need the field of study for every university, you can scrape its href using bs4 and its field of study.\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.select import Select\ndriver = webdriver.Chrome(r\"chromedriver.exe\")\nurl = \"https://www.whed.net/results_institutions.php\"\ndriver.get(url)\ntime.sleep(1)\nselect = Select(driver.find_element(By.ID, \"Chp1\"))\nselect.select_by_visible_text(\"United States of America (all)\")\ntime.sleep(1)\ndriver.find_element(By.XPATH, \"//input[@value='Go']\").click()\ntime.sleep(1)\nhtml = driver.page_source\nsoup = BeautifulSoup(html, 'html.parser')\npage = soup.find('p', {'class': 'infos'}).text\nnumber_of_pages = str(page).split()[0]\ncounter = 10\nwhile counter < int(number_of_pages):\n raw = soup.find_all('div', {'class': 'details'})\n for i in raw:\n i = (str(i.text).lstrip())\n i = i.replace(\"\\n\",\"\")\n i = i.replace(\"\\r\", \"\")\n i = i.replace(\"\\t\", \"\")\n print(i)\n next_page = driver.find_element(By.LINK_TEXT, \"Next page\").click()\n counter += 10\ndriver.quit()\n\n" ]

[ -1 ]

[ "python" ]

[ "Use:\ndf = df.loc[df.index.repeat(df['end'].sub(df['start']))]\n\ndf['number'] = df['start'].add(df.groupby(level=0).cumcount())\n\ndf = df.reset_index(drop=True)\nprint (df)\n item id qty start end number\n0 1 3 1000 1003 1000\n1 1 3 1000 1003 1001\n2 1 3 1000 1003 1002\n\n" ]

[ 1 ]

[]

[]

[ "pandas", "python" ]

[ "You need to add the parameter as_dict=True like so:\n cursor = conn.cursor(as_dict=True)\n\nYou will then be able to access row['id'] if it is a field name in the result set.\nAs per documentation below:\nhttps://pythonhosted.org/pymssql/pymssql_examples.html#rows-as-dictionaries\n", "Look at the version of pymssql that you are using. Only since 1.0.2 does it return a dict, earlier versions seem to return tuple.\n", "It is possible to set as_dict=True while creating the connection itself.\npymssql.connect(server='.', user='', password='', database='', as_dict=True,)\n\nhttps://pythonhosted.org/pymssql/ref/pymssql.html?highlight=connect#pymssql.connect\n", "Specifying results as dictionaries can be done on a cursor by cursor basis:\nimport pymysql\nfrom pymysql.cursors import DictCursor\n\n# create database connection\n# connect to database\nmydb = pymysql.connect( ... )\n\nmycursor = mydb.cursor(cursor=DictCursor)\n\n\n" ]

[ 5, 1, 0, 0 ]

[]

[]

[ "dictionary", "pymssql", "python" ]

[ "You can iterate through all the files/dirs in a directory using os.walk().\nWe are just iterating over all files in a dir and then moving them to a destination directory using os.rename()\nimport os\ndirectory = '/tmp/dataset'\n\ndef move_all_files_to_destination_dir(original_dir, destination_dir):\n # moves all NESTED files in original_dir to destination_dir\n files = []\n for root, dirs, files in os.walk(original_dir):\n for name in files:\n os.rename(root + os.sep + name, destination_dir + os.sep + name)\n \nsubdirs = [f.path for f in os.scandir(directory) if f.is_dir()] # [Folder1, Folder2]\nfor d in subdirs:\n move_all_files_to_destination_dir(d, d)\n # remove the subdirectories of d after we have moved all files under it\n for sub in os.scandir(d):\n if sub.is_dir():\n shutil.rmtree(sub)\n\n" ]

[ 1 ]

[]

[]

[ "directory", "directory_structure", "merge", "python" ]

[ "Try this:\nconst pp = JSON.parse(\"{{fas_json|safe}}\");\n\n" ]

[ 0 ]

[]

[]

[ "django", "javascript", "json", "python" ]

[ "Incorrect syntax for python. To concatenate strings, stick them together like such:\na = 'a' + 'b' + 'c'\n\n", "string formatting is invalid, and also need use & in between different url params\n# python3\nurl = f\"https://rest.coinapi.io/v1/orderbooks/{symbol_id}/history?time_start={time_start}&limit={limit}&limit_levels={limit_levels}\"\n\n# python 2\nurl = \"https://rest.coinapi.io/v1/orderbooks/{symbol_id}/history?time_start={time_start}&limit={limit}&limit_levels={limit_levels}\".format(symbol_id=symbol_id, time_start=time_start, limit=limit, limit_levels=limit_levels)\n\nhttps://docs.python.org/3/tutorial/inputoutput.html\nhttps://docs.python.org/2/tutorial/inputoutput.html\n" ]

[ 0, 0 ]

[]

[]

[ "python", "time", "url" ]

[ "it happens to work on mine though.\nmain.py\nfrom flask import Flask, redirect, url_for, render_template \n\napp = Flask(__name__)\n\[email protected](\"/<name>\")\ndef home(name):\n return render_template(\"index.html\", content=name)\n\n\nif __name__ == \"__main__\":\n app.run()\n\nindex.html:\n<head>\n <title>ETF World!</title>\n </head>\n <body>\n <h1>Explore the world of Exchange Traded Funds</h1>\n <p>{{content}}</p>\n </body>\n</html>\n\nalso make sure to put index.html to the template folder because flask look into that folder.\n\n" ]

[ 1 ]

[]

[]

[ "flask", "html", "python" ]

[ "Use DataFrame.explode():\nvideo_df = video_df.explode('tags', ignore_index=True)\n\nprint(video_df)\n# channelTitle video_id tags\n#0 Channel1 ojPuGJaiVjE tag3\n#1 Channel1 ojPuGJaiVjE tag4\n#2 Channel1 ojPuGJaiVjE tag5\n#3 Channel1 NdWI3sov93I tag1\n#4 Channel1 NdWI3sov93I tag4\n#5 Channel1 67PYna-rScE tag1\n#6 Channel1 67PYna-rScE tag2\n#7 Channel1 67PYna-rScE tag3\n#8 Channel1 67PYna-rScE tag5\n#9 Channel2 lNoDeZzn_4o NaN\n#10 Channel3 QJSOGP-nJto tag3\n\nBy default, DataFrame.explode() keeps the index value for exploding rows, so you need to specify ignore_index=True to reindex.\n" ]

[ 1 ]

[]

[]

[ "data_wrangling", "python", "split" ]

[ "First we need some tweaking in the edge control.\nBecause \"if sides[i] == sides[i]:\" statement returns always True.\nInstead, let's write a function that checks that all edges are the same.\ndef is_same_edges(edges) -> bool:\n sum_edges = sum(edges)\n return (sum_edges / len(edges)) == edges[0]\n\nThis function calculates the sum of all the elements in the list it takes as a parameter. Then it divides the length of the list it takes as a parameter and checks it with any of the lists element. If it is the same, all the elements are the same.\nNow that we've done the edge checks, we can move on to the angle check.\ndef is_opposite_angles_same(angles) -> bool:\n return angles[0] == angles[2] and angles[1] == angles[3]\n\nThis function returns whether the opposite angles are equal.\nOkay, we have the functions we need. Now let's edit your function.\ndef rhombus(sList, aList):\n if is_same_edges(sList) and is_opposite_angles_same(aList):\n rhombus = sList\n print(rhombus)\n\n", "Here's an approach that should work.\nFirst, define a function that evaluates your conditions to check whether something is a rhombus.\ndef is_rhombus(sList, aList):\n if len(set(sList)) == 1 and aList[0] == aList[2] and aList[1] == aList[3]:\n return True\n else:\n return False\n\nis_rhombus checks whether the set obtained by converting sList into a set has a length of 1 - if yes, that means all members of sList were equal to each other. It also checks whether opposite angles in aList are equal to each other. if both conditions are satisfied, it returns True, else returns False.\nNext, because we know all rhombuses are squares, but not necessarily the other way round, we create another function called is_square:\ndef is_square(sList, aList):\n if is_rhombus(sList, aList) and len(set(aList)) == 1:\n return True\n else:\n return False\n\nis_square also takes sList and aList, and it calls the previously defined function is_rhombus. If the shape IS a rhombus, is_square checks whether in addition to being a rhombus, all the angles are equal - using the same approach employed by is_rhombus above.\nFinally, with both these functions defined, we enter your while True loop, and after we get the sides and angles from the user, we go into a little if-elif-else block where we first check whether the shape is a square - because that's the narrowest option available to us. If the shape is a square, we print a message to the screen accordingly. If it's not a square, it could still be a rhombus, so we check for that. If it is a rhombus, we print that message to screen. If it's not even a rhombus, we say it's not a rhombus (no point also saying it's not a square - if it's not a rhombus, it can't possibly be a square).\nwhile True:\n\n# Input, Validation, Repetition\n\n print(\"=== Please enter Sides ===\\n\")\n sList = [] # list for sides\n for i in range(0,4,1):\n sides = float(input(\"Please enter side %i : \" %(i + 1 ) ) )\n if sides < 0:\n sides = float(input(\"Value must be positive! Please enter side %i : \" %(i + 1 )))\n sList.append(sides)\n print()\n\n print(\"=== Please enter angles ===\\n\")\n aList = [] # list for angles\n for i in range(0,4,1):\n angles = float(input(\"Please enter angle %i : \" %(i + 1 ) ) )\n if angles < 0:\n angles = float(input(\"Value must be positive! Please enter angle %i : \" %(i + 1 )))\n aList.append(angles)\n print(\"=======================\\n\")\n\n# Rhombus\n if is_square(sList, aList):\n print ('This is a square!')\n elif is_rhombus(sList, aList):\n print ('This is a rhombus!')\n else:\n print ('This is not a rhombus.')\n \n keep = input(\"Would you like to repeat? (1-Yes, 2-No): \")\n if keep != '1':\n break\n\nTrialling this on a square:\n=== Please enter Sides ===\n\n\nPlease enter side 1 : 40\n\nPlease enter side 2 : 40\n\nPlease enter side 3 : 40\n\nPlease enter side 4 : 40\n\n=== Please enter angles ===\n\n\nPlease enter angle 1 : 90\n\nPlease enter angle 2 : 90\n\nPlease enter angle 3 : 90\n\nPlease enter angle 4 : 90\n=======================\n\nThis is a square!\n\nWould you like to repeat? (1-Yes, 2-No): n\n\nTrialling this on a rhombus:\n=== Please enter Sides ===\n\n\nPlease enter side 1 : 40\n\nPlease enter side 2 : 40\n\nPlease enter side 3 : 40\n\nPlease enter side 4 : 40\n\n=== Please enter angles ===\n\n\nPlease enter angle 1 : 60\n\nPlease enter angle 2 : 120\n\nPlease enter angle 3 : 60\n\nPlease enter angle 4 : 120\n=======================\n\nThis is a rhombus!\n\nWould you like to repeat? (1-Yes, 2-No): n\n\nLastly, trialling it on a non-rhombus:\n=== Please enter Sides ===\n\n\nPlease enter side 1 : 5\n\nPlease enter side 2 : 6\n\nPlease enter side 3 : 4\n\nPlease enter side 4 : 3\n\n=== Please enter angles ===\n\n\nPlease enter angle 1 : 10\n\nPlease enter angle 2 : 20\n\nPlease enter angle 3 : 40\n\nPlease enter angle 4 : 30\n=======================\n\nThis is not a rhombus.\n\nWould you like to repeat? (1-Yes, 2-No): n\n\nOf course it bears remembering that any shape with four equal sides is a rhombus. That opposite angles will be equal to each other is an attribute of rhombuses, not a criterion for something to be a rhombus. Recognising this, you could redefine your is_rhombus function differently to ignore aList altogether, and rely only on sList to tell us whether the shape is a rhombus.\ndef is_rhombus(sList):\n if sum(sList)/4 == sList[0]:\n return True\n else:\n return False\n\nThen, we change the way we call these functions - if the shape isn't a rhombus, why make the user enter a bunch of angles? You could rearrange your while True block like this:\nwhile True:\n\n# Input, Validation, Repetition\n\n print(\"=== Please enter Sides ===\\n\")\n sList = [] # list for sides\n for i in range(0,4,1):\n sides = float(input(\"Please enter side %i : \" %(i + 1 ) ) )\n if sides < 0:\n sides = float(input(\"Value must be positive! Please enter side %i : \" %(i + 1 )))\n sList.append(sides)\n print()\n \n if is_rhombus(sList):\n print(\"=== Please enter angles ===\\n\")\n aList = [] # list for angles\n for i in range(0,4,1):\n angles = float(input(\"Please enter angle %i : \" %(i + 1 ) ) )\n if angles < 0:\n angles = float(input(\"Value must be positive! Please enter angle %i : \" %(i + 1 )))\n aList.append(angles)\n print(\"=======================\\n\")\n \n if is_square(sList, aList):\n print ('This is a square!')\n else:\n print ('This is a rhombus!')\n else:\n print ('This is not a rhombus.')\n\n\n keep = input(\"Would you like to repeat? (1-Yes, 2-No): \")\n if keep != '1':\n break\n\nThis behaviour is identical to before - except if four unequal sides are provided, we don't bother asking for a bunch of angles.\n" ]

[ 0, 0 ]

[]

[]

[ "list", "python", "shapes" ]

[ "Change\n\"keras.engine as KE\"\nto\n\"keras.layers as KE\"\n" ]

[ 1 ]

[]

[]

[ "keras", "mask_rcnn", "python" ]

[ "You have to use 'for' loop to iterate through all the elements in 'events' element:\nevents = WebDriverWait(driver, 30).until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, \"section#leftColumn\")))\nprint(\"Required elements found\")\nfor event in events:\n event.find_element(By.XPATH,\"//a[text()='1 Min']\").click()\n\n", "Maybe change the last line to;\ndriver.find_element\nAlso you can check for that XPATH with WebDriverWait until located.\n" ]

[ 1, 0 ]

[]

[]

[ "python", "selenium", "web_crawler", "web_scraping" ]

[ "One way to speed up the process is to use a feature descriptor that is more efficient than ORB. For example, you can use SIFT or SURF which are more efficient than ORB. You can also use a combination of feature descriptors such as ORB + SIFT or ORB + SURF.\nAnother way to speed up the process is to use a faster feature matching algorithm such as FLANN or KNN. FLANN is usually faster than KNN, but it is also more memory intensive.\nYou can also use a pre-trained model such as a convolutional neural network (CNN) to identify the images. This will be much faster than using feature descriptors and matching algorithms. However, it will require more training data and more computing power.\n", "another option, you can use pretrained model called SuperGlue: https://github.com/magicleap/SuperGluePretrainedNetwork\n" ]

[ 3, 1 ]

[]

[]

[ "cbir", "computer_vision", "machine_learning", "opencv", "python" ]

[ "In your settings.py add this variable:\nimport os\n\nTEMPLATE_DIR = os.path.join(BASE_DIR, \"templates\")\n\nAdd TEMPLATE_DIR in TEMPLATE_DIRS.\nThen, at the root of your project create a folder named templates. Or create a folder named templates inside your app's directory. Inside the templates folder place your dashboard folder.\n", "How to solve template does not exist?\nStep 1: Try to find the template setting in settings.py file and check if it is correct. If it is not correct, then add path in DIRS in TEMPLATES EX: DIRS: [os.path.join(BASE_DIR, 'templates')].\nStep 2: After step1, check the correct urls path if there is some spelling mistakes while navigating in browser then also you will get template does not exists error.\nStep 3: Check the spelling of template files and also check spelling of templates while rendering in views.\n" ]

[ 0, 0 ]

[]

[]

[ "django", "django_templates", "django_views", "python" ]

[ "While saving try to add the child field in the serializer:\nclass ScheduleSerializer(serializers.ModelSerializer):\n day_of_the_week = serializers.SerializerMethodField()\n def get_day_of_the_week(self, instance):\n\n return instance.day_of_the_week[1:-1].split(',')\n\n\n class Meta():\n model = Schedule\n fields = \"__all__\"\n\n" ]

[ 1 ]

[]

[]

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

[ "There are these issues:\n\nThe recursive call is made with the same values for both arguments, which means the recursion -- if it starts -- will never end: the conditions are always the same. The aim of recursion is to solve a smaller problem, so you should pass a shorter string -- without the prefix that was just matched. So pass s[end:].\n\nThe recursive call is made as if it returns a boolean, but you'll want to know how many partitions were made, so making that call in an if condition is not going to help with that. You need to make that call inside the if block and do something with the result you got from it.\n\nWith return 1 you're actually saying the recursive call only managed to chop the string up into one partition, but this is not true. Another way to put it: your function is only able to return 0 or 1, nothing else. Instead, the returned value should depend on the number you get back from recursion.\n\nIt is wrong to have a return inside the loop. There might be cases where you have success with a word, but actually need an alternative, longer word first in order to benefit from shorter words in the rest of the string, and so you cannot assume that the first success corresponds to the optimal solution. The loop must continue to make iterations, and only when the loop has completed, you can know which of the valid options is the optimal one.\n\n\nHere is the corrected code:\ndef wordBreak(s, wordDict):\n if len(s) == 0:\n return 0\n result = float('-inf')\n for end in range(1, len(s) + 1):\n if s[:end] in wordDict:\n result = max(result, wordBreak(s[end:], wordDict))\n return 1 + result\n\n" ]

[ 0 ]

[]

[]

[ "algorithm", "dynamic_programming", "python", "recursion" ]

[ "You are implicitly checking that the input value is of type int by converting the input string. If you insist on doing that then you might find this easier:\nfrom time import sleep\n\ndef print_slow(*args):\n for arg in args:\n for c in str(arg):\n print(c, flush=True, end='')\n sleep(0.1)\n print()\n\nv = int(input('Choose any number: '))\n\nprint_slow('Did you say ', v, '?')\n\n", "Replace print_slow(\"Did you say\", num1) by print_slow(f\"Did you say {num1}\") and you should be good to go. This is (in my opinion) a slightly simpler solution than Cobra's. Here we are using Formatted strings(example) which you will encounter often going forward and that you should take a couple of minutes to understand.\nAlso, as mentioned in the comments of your post, DO NOT NAME A VARIABLE str (or more generally, do not name a variable with a type name) for it will mess things up later.\n" ]

[ 0, 0 ]

[]

[]

[ "input", "printing", "python", "python_2.7" ]

[ "Comments\n\nDespite having def getRegisteredEnvironment(self): it wasn't indented, so it's not recognized as a class method.\nself is a keyword used in conjunction with classes (class methods or attributes) - not functions. self is implied to be the instantiated object (eg a = Environment(...) -> self would refer to a) or the module's (I can't think of the proper term) class.\nYou didn't have your addTopographyToEnvironment class method defined.\nIn terms of your Environment class, you aren't using the variables you are passing to the class, so I made that change as well - I don't know if that was intentional or not.\nAs per your comment from the other answer, if you had def my_class_method(self) and you try to invoke it through an object with additional parameters, like so a = my_object(); a.my_class_method(\"Mountains\"), you should get an error of the sorts, \"2 positional arguments passed, expected 1.\".\nYour main problem is that you are doing Environment.class_method() and not creating an object from the class. Do a = Environment(whatever arguments here) to create an object from the class, then do a.addTopographyToEnvironment(\"Mountains\") to do what you were going to do with \"Mountains\" and that object. What you have currently may be right, its just is missing the proper implementation, but the below article does a great job explaining the differences between all of them (Class Methods vs Static Methods vs Instance Methods), and is definitely worth the read.\n\nclass EnvironmentInfo:\n def __init__(self, perceivableFood, perceivableCreatures, regionTopography, lightVisibility):\n self.perceivableFood = perceivableFood\n self.perceivableCreatures = perceivableCreatures\n self.regionTopography = regionTopography\n self.lightVisibility = lightVisibility\n\nclass Environment:\n def __init__(self, creatureRegistry, foodRegistry, topographyRegistery, lightVisibility):\n logging.info(\"Creating new environment\")\n self.creatureRegistry = creatureRegistry\n self.foodRegistry = foodRegistry\n self.topographyRegistery = topographyRegistery\n self.lightVisibility = lightVisibility\n\n def displayEnvironment(self):\n creatureRegistry = []\n foodRegistry = []\n topographyRegistery = ['Grasslands']\n lightVisibility = True\n print (f\"Creatures: {creatureRegistry} Food Available: {foodRegistry} Topography: {topographyRegistery} Contains Light: {lightVisibility}\")\n\n def addTopographyToEnvironment(self, environment):\n return \"Whatever this is supposed to return.\" + environment\n\n def getRegisteredEnvironment(self):\n return self.topographyRegistry\n\nif __name__ == \"__main__\":\n print (Environment.displayEnvironment()) #Display hardcoded attributes\n print (Environment.addTopographyToEnvironment(\"Mountains\"))#NameError\n print (Environment.getRegisteredEnvironment()) #NameError\n\n\nObject Instantiation In Python\nWith all that out of the way, I will answer the question as is posed, \"Is there a way to grab list attributes that have been initialized using self and append data to them in Python?\". I am assuming you mean the contents of the list and not the attributes of it, the attributes would be \"got\" or at least printed with dir()\nAs a simple example:\nclass MyClass:\n def __init__(self, my_list):\n self.my_list = my_list\n\n\nif __name__ == \"__main__\":\n a = MyClass([1, 2, 3, 4, 5])\n print(a.my_list)\n # will print [1, 2, 3, 4, 5]\n a.my_list.append(6)\n print(a.my_list)\n # will print [1, 2, 3, 4, 5, 6]\n print(dir(a.my_list))\n # will print all object methods and object attributes for the list associated with object \"a\".\n\n\nSub Classing In Python\nGiven what you have above, it looks like you should be using method sub classing - this is done with the keyword super. From what I can guess, it would look like you'd implement that kind of like this:\nclass EnvironmentInfo:\n def __init__(self, perceivableFood, perceivableCreatures, regionTopography, lightVisibility):\n self.perceivableFood = perceivableFood\n self.perceivableCreatures = perceivableCreatures\n self.regionTopography = regionTopography\n self.lightVisibility = lightVisibility\n\nclass Environment(EnvironmentInfo):\n def __init__(self, creatureRegistry, foodRegistry, topographyRegistery, lightVisibility, someOtherThingAvailableToEnvironmentButNotEnvironmentInfo):\n logging.info(\"Creating new environment\")\n super.__init__(foodRegistry, creatureRegistry, topographyRegistery, lightVisibility)\n self.my_var1 = someOtherThingAvailableToEnvironmentButNotEnvironmentInfo\n\n def displayEnvironment(self):\n creatureRegistry = []\n foodRegistry = []\n topographyRegistery = ['Grasslands']\n lightVisibility = True\n print (f\"Creatures: {creatureRegistry} Food Available: {foodRegistry} Topography: {topographyRegistery} Contains Light: {lightVisibility}\")\n\n def addTopographyToEnvironment(self, environment):\n return \"Whatever this is supposed to return.\" + environment\n\n def getRegisteredEnvironment(self):\n return self.topographyRegistry\n\n def methodAvailableToSubClassButNotSuper(self)\n return self.my_var1\n\nif __name__ == \"__main__\":\n a = Environment([], [], [], True, \"Only accessible to the sub class\")\n print(a.methodAvailableToSubClassButNotSuper())\n\nas the article describes when talking about super(), methods and attributes from the super class are available to the sub class.\n\nExtra Resources\nClass Methods vs Static Methods vs Instance Methods - \"Difference #2: Method Defination\" gives an example that would be helpful I think.\nWhat is sub classing in Python? - Just glanced at it; probably an okay read.\n", "Self represents the instance of the class and you don't have access to it outside of the class, by the way when you are calling object methods of a class you don't need to pass self cause it automatically be passed to the method you just need to pass the parameters after self so if you want to call an object method like addTopographyToEnvironment(self, newVal) you should do it like:\nEnvironment.addTopographyToEnvironment(\"Mountains\")\n\nand it should work fine\n" ]

[ 1, 0 ]

[]

[]

[ "object", "python", "self" ]

[ "Option 1\nYou can use the same approach you did on your other test class, but set the fixture scope to class:\nclass TestClass:\n\n @pytest.fixture(scope='class', autouse=True)\n def _inventory_cleanup(self, registration_setup):\n log('Cleanup Done!')\n \n def test_1():\n ...\n\n def test_2():\n ...\n \n def test_3():\n ...\n\nBut you will then need to change the scope of the fixture registration_setup to class to avoid a ScopeMismatch error.\nOption 2\nTo keep using it with a function scope, I suggest having two fixtures with the same behavior, but with different scopes, like this:\[email protected]()\ndef registration_setup_for_function(\n test_data, # fixture 1\n credentials, # fixture 2\n deployment # fixture 3\n deployment_object # fixture 4\n):\n # pre-test cleanup\n do_cleanup()\n yield\n # post-test cleanup\n do_cleanup()\n\n\[email protected](scope='class')\ndef registration_setup_for_class(\n test_data, # fixture 1\n credentials, # fixture 2\n deployment # fixture 3\n deployment_object # fixture 4\n):\n # pre-test cleanup\n do_cleanup()\n yield\n # post-test cleanup\n do_cleanup()\n\nIf your other fixtures 1, 2, 3 and 4 have function scope, you will have to change them also.\nOption 3\nIf you don't want to have two identical fixtures with different scopes, you can do something like this:\nIn a conftest.py file in the project root:\ndef pytest_configure(config):\n config.first_test_executed = False\n\nThen, wherever you have your fixture:\[email protected]()\ndef registration_setup(\n test_data, # fixture 1\n credentials, # fixture 2\n deployment, # fixture 3\n deployment_object, # fixture 4\n request # Note the request fixture here\n):\n if 'TestClassWhereFixtureShouldRunOnlyOnce' in request.node.nodeid:\n if not request.config.first_test_executed:\n # pre-test cleanup\n do_cleanup()\n yield\n # post-test cleanup\n do_cleanup()\n request.config.first_test_executed = True\n else:\n # pre-test cleanup\n do_cleanup()\n yield\n # post-test cleanup\n do_cleanup()\n\nI know it is still a bit repeated, but this way your tests inside the class will call the registration_setup fixture only once for the whole class, while other tests will call it always. Maybe you will find a better way knowing this now.\nMore info on the documentation:\nFixtures can introspect the requesting test context\nA session-fixture which can look at all collected tests\npytest_configure(config)\n", "Huge thanks to @Marco.S for his answer. Just a small additional check to get it to work\ndef reg_setup(request):\n if 'TestClassWhereFixtureShouldRunOnlyOnce' in request.node.nodeid:\n if not request.config.first_test_executed:\n # pre-test cleanup\n do_cleanup()\n request.config.first_test_executed = True\n yield\n if 'test_last' in request.node.nodeid: # Check for the last test case.\n # post-test cleanup\n do_cleanup()\n else:\n # pre-test cleanup\n do_cleanup()\n yield\n # post-test cleanup\n do_cleanup()\n\n" ]

[ 0, 0 ]

[]

[]

[ "fixtures", "pytest", "pytest_fixtures", "python", "selenium" ]

[ "It is better to standardize the data even though being on same scale. Standardizing would reduce the distance (specifically euclidean) that would help weights to not vary much from the point intial to them. Having huge seperated distance would rather have more calculation involved. Also distance calculation done in KNN requires feature values to scaling is always prefered.\n" ]

[ 0 ]

[]

[]

[ "knn", "machine_learning", "python", "standardization" ]

[ "You can install specific version of python through the anaconda prompt, using:\nconda install python = 2.7.8 or conda install python = 3.5.0 (for example).\nYou can even create a dedicated python environnement for a specific version:\nconda create --name py36 python=3.6\n", "Anton B answer is correct, but if you just want to download the correct installer, you can use the following:\n\nFor Anaconda you can use this: https://docs.anaconda.com/anaconda/packages/oldpkglists/\nFor miniconda you can use the tables on the following page: https://docs.conda.io/en/latest/miniconda.html\n\nA similar question was previously asked here:\nHow to get anaconda/miniconda vs python versions mapping table?\n" ]

[ 1, 1 ]

[]

[]

[ "anaconda", "python" ]

[ "python app/main.py will make app/ the first entry in sys.path, so app imports within won't work.\nDo python -m app.main to run app/main.py as a module without having Python touch sys.path.\n" ]

[ 1 ]

[]

[]

[ "fastapi", "python" ]

[ "You have to specify a mode to the open() function. In this case I think you're trying to read the file, so your mode would be \"r\". Your code should be:\nwith open(r'path/to/read/','r') as file: \n data = json.load(file)\n\nYour code should run now.\n", "Your path should not contain spaces. Please modify the file path.\nGenerally speaking, the file path is best to be in full English with no spaces and no special characters.\n\n", "import sys\nimport os\nimport json\n\ndef JsonRead(str):\n with open(str, encoding='utf-8') as f:\n data = json.load(f)\n return data\n\nnew_Data = JsonRead(filePath)\n\nThen import JsonRead in project\n" ]

[ 1, 0, 0 ]

[]

[]

[ "json", "python", "visual_studio_code" ]

[ "try to replace layer_names[i[0] - 1] with layer_name[i-1]\ndef getOutputsNames(net):\n # Get the names of all the layers in the network\n layersNames = net.getLayerNames()\n # Get the names of the output layers, i.e. the layers with unconnected outputs\n return [layersNames[i - 1] for i in net.getUnconnectedOutLayers()]\n\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x", "visual_c++", "visual_studio", "yolo" ]

[ "pyodbc is available by default in aws glue python shell jobs. Please keep track of the official aws docs to know the latest running version. You may also find a list of other supported and managed libraries in below link\nhttps://docs.aws.amazon.com/glue/latest/dg/add-job-python.html\nYou could go ahead and import pyodbc.\n" ]

[ 0 ]

[]

[]

[ "aws_glue", "pyodbc", "python" ]

[ "You should preserve the original tokenization. To do this, manually create the Doc in order to skip the tokenizer in the pipeline:\nimport spacy\nfrom spacy.tokens import Doc\n\nnlp = spacy.load(model)\nwords = [\"here\", \"are\", \"the\", \"original\", \"tokens\"]\ndoc = Doc(nlp.vocab, words=words)\n\n# apply the model to the doc (it skips the tokenizer for an input `Doc`)\ndoc = nlp(doc)\n\n" ]

[ 1 ]

[]

[]

[ "conll", "nlp", "pandas", "python", "spacy" ]

[ "try this\nvar_python_books = {\n 'name': 'Python Notebooks',\n 'sub-menu': [{\n 'name': 'Python Research Notebook',\n 'snippet': [\n 'import os, sys, json, time',\n '',\n 'import numpy as np',\n 'import pandas as pd',\n 'import matplotlib.pyplot as plt',\n 'from scipy import stats',\n '',\n '%matplotlib inline',\n 'plt.style.use(\"ggplot\")',\n '%config InlineBackend.figure_format = \"retina\"',\n ]\n },\n {'name': 'Getting Data', 'sub-menu': []},\n {'name': 'Visualizations', 'sub-menu': []}\n ]\n};\n\nfor i in var_python_books['sub-menu']:\n if i.get('snippet'):\n print(i['snippet'])\n\n>>> ['import os, sys, json, time', '', 'import numpy as np', 'import pandas as pd', 'import matplotlib.pyplot as plt', 'from scipy import stats', '', '%matplotlib inline', 'plt.style.use(\"ggplot\")', '%config InlineBackend.figure_format = \"retina\"']\n\n" ]

[ 1 ]

[]

[]

[ "python", "regex" ]

[ "I am not sure that I get the question right, but if you need a special field value from foreign key you can use smth like:\nPrograma_periodo.objects.values(\"id\", \"periodo__periodo\", \"programa__programa\")\n\nWith double underscore. Try it in the shell first. Check the docs here https://docs.djangoproject.com/en/4.0/ref/models/querysets/#values\n" ]

[ 1 ]

[]

[]

[ "django", "django_queryset", "python" ]

[ "At MainWindow on the Qt Designer\nMouse Right Click --> Change styleSheet --> Add Resources\nadd your image\n" ]

[ 0 ]

[]

[]

[ "pyqt", "pyqt5", "python", "qt", "qt_designer" ]

[ "It seems that adding numeric_only flag would solve your problem:\n# Panda 1.5.2\nimport pandas as pd\n\nd = {'prod': ['p1', 'p2', 'p3'], 'p1': [10, 20.0, 30], 'p2': [7 ,6 ,4], 'p3': [12,50,5], 'MAX': ['','','']}\ndf = pd.DataFrame(data=d)\nprint(type(df)) # <class 'pandas.core.frame.DataFrame'>\ndf['MAX'] = df.idxmax(1, numeric_only=True)\nprint(df)\n\nCheck your pandas version. I have 1.5.2\npip show pandas\n\nCheck type of your data as @Niko suggests\n" ]

[ 0 ]

[]

[]

[ "dataframe", "python" ]

[ "Sequential deleting is a bad idea since deletion in a list is O(n). Instead do something like this:\ndef delete_rand_items(items,n):\n to_delete = set(random.sample(range(len(items)),n))\n return [x for i,x in enumerate(items) if not i in to_delete]\n\n", "You can use random.sample like this:\nimport random\n\na = [1,2,3,4,5,6,7,8,9,10]\n\nno_elements_to_delete = len(a) // 4\nno_elements_to_keep = len(a) - no_elements_to_delete\nb = set(random.sample(a, no_elements_to_keep)) # the `if i in b` on the next line would benefit from b being a set for large lists\nb = [i for i in a if i in b] # you need this to restore the order\nprint(len(a)) # 10\nprint(b) # [1, 2, 3, 4, 5, 8, 9, 10]\nprint(len(b)) # 8\n\nTwo notes on the above.\n\nYou are not modifying the original list in place but you could.\nYou are not actually deleting elements but rather keeping elements but it is the same thing (you just have to adjust the ratios)\nThe drawback is the list-comprehension that restores the order of the elements\n\n\nAs @koalo says in the comments the above will not work properly if the elements in the original list are not unique. I could easily fix that but then my answer would be identical to the one posted by@JohnColeman. So if that might be the case just use his instead.\n", "Is the order meaningful? \nif not you can do something like: \nshuffle(data)\ndata=data[:len(data)-n]\n\n", "I suggest using numpy indexing as in\nimport numpy as np\ndata = np.array([1,2,3,4,5,6,7,8,9,10])\nn = len(data)/4\nindices = sorted(np.random.choice(len(data),len(data)-n,replace=False))\nresult = data[indices]\n\n", "I think it will be more convenient this way:\nimport random\nn = round(len(data) *0.3)\nfor i in range(n):\n data.pop(random.randrange(len(data)))\n\n" ]

[ 11, 5, 1, 0, 0 ]

[]

[]

[ "list", "python", "random" ]

[ "For each part of your question, you can use a trick.\nname: you can use regular-expression with rlike() function.\ndate: you can cast date string to date format and check if it is valid.\nname: you can cast to integer and check if it is valid.\nnote that if a cast is not valid pyspark returns Null.\nschema = ['age', 'name', 'date']\ndata = [\n (\"1\", \"A1\", '30-12-2022'),\n (\"2\", \"Aa\", '36-11-2022'),\n (\"3\", \"Aa\", '2022-10-12'),\n (\"4a\", \"Aa\", '30-11-2022'),\n (\"5\", \"Aa\", '30-11-2022'),\n]\ndf = spark.createDataFrame(data = data, schema = schema)\n(\n df\n .filter(F.col('name').rlike(\"^[a-zA-Z]+$\"))\n .filter(F.to_date(F.col('date'), 'dd-MM-yyyy').isNotNull())\n .filter(F.col('age').cast('int').isNotNull())\n).show()\n\n+---+----+----------+\n|age|name| date|\n+---+----+----------+\n| 5| Aa|30-11-2022|\n+---+----+----------+\n\n" ]

[ 0 ]

[]

[]

[ "pyspark", "python" ]

[ "From a high-level view, it seems to me that a recursive approach to this should work as follows:\n\nCheck if the grid is valid:\n\nIf the grid is invalid, return immediately\nElse, check if the grid is complete:\n\nIf the the grid is complete, add (a copy of) it to the list of\nsolutions\nElse, the grid is valid and incomplete, so find the first empty cell and run the function recursively on the grid with all possible values filled in for that box (and make sure to clear any modified cells at the end, after the loop)\n\n\n\n\n\nThen, the list of solutions is generated, and the length of that list is the number of possible solutions. (If you find there are a lot of solutions and generating the list takes a very long time, you may just want to make a counter for the number of solutions found.)\nImplementing this shouldn't be too difficult using what you have, since you already have functions for finding the first empty cell and verifying whether the grid is valid, etc.\nHow does that sound?\n", "As asked:\n\nHow would you modify the code that it finds all possible solutions and gives out the number of solutions?\n\nIf you don't want to return (\"give out\") the solutions themselves, but the number of solutions, then you need to maintain a counter, and use the count you get back from the recursive call to update the owned counter:\ndef solve(bo):\n find = find_empty(bo)\n if not find:\n return 1\n\n count = 0\n row, col = find\n for num in range(1, 10):\n if valid(bo, num, (row, col)):\n bo[row][col] = num \n count += solve(bo)\n bo[row][col] = 0 \n\n return count\n\nIn the main program, you would no longer print the board, as you don't expect the filled board now, but a number:\n print(solve(board)) # Will output 1 for your example board.\n\nGetting all solutions\nIf you don't just want to know the count, but every individual solution itself, then I would go for a generator function, that yields each solution:\ndef solve(bo):\n find = find_empty(bo)\n if not find:\n yield [row[:] for row in bo] # Make a copy\n return\n \n row, col = find\n for num in range(1, 10):\n if valid(bo, num, (row, col)):\n bo[row][col] = num \n yield from solve(bo)\n bo[row][col] = 0 \n\nThen the main program can do:\n count = 0\n for solution in solve(board):\n print(\"SOLUTION:\")\n print_board(solution)\n count += 1\n print(\"NUMBER of SOLUTIONS:\", count)\n\n" ]

[ 0, 0 ]

[]

[]

[ "backtracking", "python", "recursion", "sudoku" ]

[ "You can wrap it in square brackets [] and add a comma between the dictionaries for valid json.\n[{\n \"category\": \"CRIME\",\n \"headline\": \"There Were 2 Mass Shootings In Texas Last Week, But Only 1 On TV\",\n \"authors\": \"Melissa Jeltsen\",\n \"link\": \"https://www.huffingtonpost.com/entry/texas-amanda-painter-mass-shooting_us_5b081ab4e4b0802d69caad89\", \"short_description\": \"She left her husband. He killed their children. Just another day in America.\",\n \"date\": \"2018-05-26\"\n},\n{\n \"category\": \"ENTERTAINMENT\",\n \"headline\": \"Will Smith Joins Diplo And Nicky Jam For The 2018 World Cup's Official Song\",\n \"authors\": \"Andy McDonald\",\n \"link\": \"https://www.huffingtonpost.com/entry/will-smith-joins-diplo-and-nicky-jam-for-the-official-2018-world-cup-song_us_5b09726fe4b0fdb2aa541201\",\n \"short_description\": \"Of course, it has a song.\",\n \"date\": \"2018-05-26\"\n}]\n\nRead file::\nimport pandas as pd\n\n\ndf = pd.read_json(\"/path/to/file/db.json\")\nprint(df)\n\nOutput:\n category headline authors link short_description date\n0 CRIME There Were 2 Mass Shootings In Texas Last Week, But Only 1 On TV Melissa Jeltsen https://www.huffingtonpost.com/entry/texas-amanda-painter-mass-shooting_us_5b081ab4e4b0802d69caad89 She left her husband. He killed their children. Just another day in America. 2018-05-26\n1 ENTERTAINMENT Will Smith Joins Diplo And Nicky Jam For The 2018 World Cup's Official Song Andy McDonald https://www.huffingtonpost.com/entry/will-smith-joins-diplo-and-nicky-jam-for-the-official-2018-... Of course, it has a song. 2018-05-26\n\n" ]

[ 1 ]

[]

[]

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

[ "Try this:\nclass Schedule(models.Model):\n name = models.CharField(max_length=100)\n start_time = models.DateTimeField(auto_now_add=True)\n end_time = models.DateTimeField(null=True, blank=True)\n day_of_the_week = models.JSONField(default=list)\n\nclass ScheduleSerializer(serializers.ModelSerializer):\n day_of_the_week = serializers.ListField(\n child=serializers.IntegerField(),\n )\n\n class Meta():\n model = Schedule\n fields = \"__all__\"\n\n", "Your solution should work with one small modification:\nmodels.CharField(validators=[int_list_validator], max_length=100)\n\n" ]

[ 1, 0 ]

[]

[]

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

[ "Try this:\nWebDriverWait(driver, 10).until(EC.presence_of_element_located((By.ID, \"ModalFocusTrapZone30\")))\ndriver.find_element(By.ID, \"Dropdown31-option\").click()\ntime.sleep(1)\ndriver.find_element(By.XPATH, \".//*[contains(text(), 'Šilutės 28B, Klaipėda')]\").click()\ntime.sleep(1)\ndriver.find_element(By.XPATH, \".//*[@data-automationid='splitbuttonprimary']//span[text()='OK']\").click()\n\n" ]

[ 1 ]

[]

[]

[ "python", "selenium", "web_scraping" ]

[ "Since the first element could be a varying number of letters, the simplest solution would be to use the regex approach for getting the first section.\nFor example:\nimport re\n\ncompounds = [\"Ag(AuS)2\", \"HTiF\", \"ZrTaN3\"]\n\nfor compound in compounds:\n match = re.match(r\"[A-Z][a-z]*\", compound)\n if match:\n fist_element = match.group(0)\n print(fist_element)\n\nthis will print out the first element of each compound.\nNote: If there are some more complex compounds and you need to adjust your regex, I recommend using https://regex101.com/ as a playground.\nOnce you have that information it just needs to be connected with the element in the second column which would be easiest if you mapped that column to a dictionary resembling:\n{ H: 0, He: 1, Li: 2 ...}\n\nwhich would allow you to simply get the element index by calling dict_with_elements.get(first_element).\nFrom there on, the rest is just looping and writing data. I hope this helps.\n" ]

[ 2 ]

[]

[]

[ "categorical_data", "floating_point", "grouping", "python", "string" ]

[ "round before fillna:\ndf['skor_change_w_ts'] = (pd.to_numeric(df['skor_change_w_ts'], errors='coerce')\n .round(2).fillna(\"null\", downcast='infer')\n )\n\nExample input:\ndf = pd.DataFrame({'skor_change_w_ts': [1, 2.6666, 'null']})\n\nOutput:\n skor_change_w_ts\n0 1.0\n1 2.67\n2 null\n\n", "You don't need to call .fillna() at all, coerce will do that for you.\ndf['skor_change_w_ts'] = (pd.to_numeric(df['skor_change_w_ts'], errors='coerce').round(2) \n\nShould do the trick.\n" ]

[ 1, 1 ]

[]

[]

[ "dataframe", "numeric", "pandas", "python", "rounding" ]

[ "or you can try pandas\nfrom pandas import *\nxls = ExcelFile('path_to_file.xls')\ndf = xls.parse(xls.sheet_names[0])\nprint df.to_dict()\n\n", "d = {}\nwb = xlrd.open_workbook('foo.xls')\nsh = wb.sheet_by_index(2) \nfor i in range(138):\n cell_value_class = sh.cell(i,2).value\n cell_value_id = sh.cell(i,0).value\n d[cell_value_class] = cell_value_id\n\n", "This script allows you to transform an excel data table to a list of dictionaries:\nimport xlrd\n\nworkbook = xlrd.open_workbook('foo.xls')\nworkbook = xlrd.open_workbook('foo.xls', on_demand = True)\nworksheet = workbook.sheet_by_index(0)\nfirst_row = [] # The row where we stock the name of the column\nfor col in range(worksheet.ncols):\n first_row.append( worksheet.cell_value(0,col) )\n# transform the workbook to a list of dictionaries\ndata =[]\nfor row in range(1, worksheet.nrows):\n elm = {}\n for col in range(worksheet.ncols):\n elm[first_row[col]]=worksheet.cell_value(row,col)\n data.append(elm)\nprint data\n\n", "You can use Pandas to do this. Import pandas and Read the excel as a pandas dataframe.\nimport pandas as pd\nfile_path = 'path_for_your_input_excel_sheet'\ndf = pd.read_excel(file_path, encoding='utf-16')\n\nYou can use pandas.DataFrame.to_dict to convert a pandas dataframe to a dictionary. Find the documentation for the same here\ndf.to_dict()\n\nThis would give you a dictionary of the excel sheet you read.\nGeneric Example :\ndf = pd.DataFrame({'col1': [1, 2],'col2': [0.5, 0.75]},index=['a', 'b'])\n\n>>> df\ncol1 col2\na 1 0.50\nb 2 0.75\n>>> df.to_dict()\n{'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}\n", "If you want to convert your Excel data into a list of dictionaries in python using pandas,\nBest way to do that:\nexcel_file_path = 'Path to your Excel file'\nexcel_records = pd.read_excel(excel_file_path)\nexcel_records_df = excel_records.loc[:, ~excel_records.columns.str.contains('^Unnamed')]\nrecords_list_of_dict=excel_records_df.to_dict(orient='record')\nPrint(records_list_of_dict)\n\n", "I'd go for:\nwb = xlrd.open_workbook('foo.xls')\nsh = wb.sheet_by_index(2) \nlookup = dict(zip(sh.col_values(2, 0, 138), sh.col_values(0, 0, 138)))\n\n", "There is also a PyPI package for that: https://pypi.org/project/sheet2dict/\nIt is parsing excel and csv files and returning it as an array of dictionaries.\nEach row is represented as a dictionary in the array.\nLike this way :\nPython 3.9.0 (default, Dec 6 2020, 18:02:34)\n[Clang 12.0.0 (clang-1200.0.32.27)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n# Import the library\n>>> from sheet2dict import Worksheet\n\n# Create an object\n>>> ws = Worksheet()\n\n# return converted rows as dictionaries in the array \n>>> ws.xlsx_to_dict(path='Book1.xlsx')\n[\n {'#': '1', 'question': 'Notifications Enabled', 'answer': 'True'}, \n {'#': '2', 'question': 'Updated', 'answer': 'False'}\n]\n\n", "if you can convert it to csv this is very suitable.\nimport dataconverters.commas as commas\nfilename = 'test.csv'\nwith open(filename) as f:\n records, metadata = commas.parse(f)\n for row in records:\n print 'this is row in dictionary:'+row\n\n", "If you use, openpyxl below code might help:\nimport openpyxl\nworkbook = openpyxl.load_workbook(\"ExcelDemo.xlsx\")\nsheet = workbook.active\nfirst_row = [] # The row where we stock the name of the column\nfor col in range(1, sheet.max_column+1):\n first_row.append(sheet.cell(row=1, column=col).value)\ndata =[]\nfor row in range(2, sheet.max_row+1):\n elm = {}\n for col in range(1, sheet.max_column+1):\n elm[first_row[col-1]]=sheet.cell(row=row,column=col).value\n data.append(elm)\nprint (data)\n\ncredit to: Python Creating Dictionary from excel data\n", "I Tried a lot of ways but this is the most effective way i found\nimport pyexcel as p\ndef read_records():\n records = p.get_records(file_name=\"File\")\n products = [row for row in records]\n return products\n\n" ]

[ 49, 20, 17, 5, 2, 1, 1, 0, 0, 0 ]

[]

[]

[ "python", "xlrd" ]

[ "This will be a nice solution for you.\nAll i'm doing here is every time you iterate inside your for loop you are creating a new dictionary of the details you want to put in for a contact and adding that dictionary to the original dictionary, therefor adding a new contact as a dictionary. There should be some extra error handling of course but this for now may be a good solution.\nmy_contacts = {1: {\"Name\": \"Tom Jones\",\n \"Number\": \"911\",\n \"Birthday\": \"22.10.1995\",\n \"Address\": \"212 street\"},\n 2: {\"Name\": \"Bob Marley\",\n \"Number\": \"0800838383\",\n \"Birthday\": \"22.10.1991\",\n \"Address\": \"31 street\"}\n }\n\n\ndef add_contact():\n user_input = int(input(\"please enter how many contacts you wanna add: \"))\n index = len(my_contacts) + 1\n for _ in range(user_input):\n details = {}\n name = input(\"Enter the name: \")\n number = input(\"Enter the number: \")\n birthday = input(\"Enter the birthday\")\n address = input(\"Enter the address\")\n\n details[\"Name\"] = name\n details[\"Number\"] = number\n details[\"Birthday\"] = birthday\n details[\"Address\"] = address\n\n my_contacts[index] = details\n index += 1\n\n", "So basically what you have here:\nmy_contacts = {\"1\": { \"Tom Jones\", \"911\", \"22.10.1995\"},\n \"2\": { \"Bob Marley\", \"0800838383\", \"22.10.1991\"}\n }\n\nIs a dict of sets (mapping a string to a set), not a dict of dicts.\n1.Pass the current contacts to your function (so you can add new contact, not create them from scratch)\n2.Create a new dict for the new contact\n3.Add it to the passed contacts dict\ndef add_contact(contacts: dict)->None:\n user_input = int(input(\"please enter how many contacts you wanna add: \"))\n for i in range(user_input):\n name = input(\"Enter the name: \")\n number = input(\"Enter the number: \")\n birthday = input(\"Enter the birthday\")\n address = input(\"Enter the address\")\n new_contact = {\"name\":name, \"number\":number, \"birthday\":birthday, \"address\":address}\n next_key = str(max([int(k) for k in contacts.keys()])+1)\n contacts[new_key] = new_contact\n\n" ]

[ 0, 0 ]

[]

[]

[ "contacts", "dictionary", "list", "nested", "python" ]

[ "Use a pivot and rename:\ndf['pairs'] = df.groupby('Status').cumcount()\n\n(df.pivot(index=['pairs', 'Transaction group', 'Application number'],\n columns='Status', values='Created time')\n .rename(columns={'application': 'start_time', 'calculation': 'end_time'})\n .reset_index()\n)\n\nOutput:\nStatus pairs Transaction group Application number start_time end_time\n0 A 221110000363 HB202211100902000100 2022-11-10 09:05:00 2022-11-10 13:42:00\n1 B 221110000363 HB202211100902000200 2022-11-10 14:02:00 2022-11-10 14:07:00\n2 C 221110000363 HB202211100902000200 2022-11-10 14:43:00 2022-11-10 15:03:00\n\n" ]

[ 0 ]

[]

[]

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

[ "Running each line with pip install may be a workaround.\ncat requirements.txt | xargs -n 1 pip install\n\nNote: -a parameter is not available under MacOS, so old cat is more portable.\n", "This solution handles empty lines, whitespace lines, # comment lines, whitespace-then-# comment lines in your requirements.txt.\ncat requirements.txt | sed -e '/^\\s*#.*$/d' -e '/^\\s*$/d' | xargs -n 1 pip install\n\nHat tip to this answer for the sed magic.\n", "For windows users, you can use this:\nFOR /F %k in (requirements.txt) DO ( if NOT # == %k ( pip install %k ) )\n\nLogic: for every dependency in file(requirements.txt), install them and ignore those start with \"#\".\n", "For Windows:\npip version >=18\nimport sys\nfrom pip._internal import main as pip_main\n\ndef install(package):\n pip_main(['install', package])\n\nif __name__ == '__main__':\n with open(sys.argv[1]) as f:\n for line in f:\n install(line)\n\npip version <18\nimport sys\nimport pip\n\ndef install(package):\n pip.main(['install', package])\n\nif __name__ == '__main__':\n with open(sys.argv[1]) as f:\n for line in f:\n install(line)\n\n", "The xargs solution works but can have portability issues (BSD/GNU) and/or be cumbersome if you have comments or blank lines in your requirements file.\nAs for the usecase where such a behavior would be required, I use for instance two separate requirement files, one which is only listing core dependencies that need to be always installed and another file with non-core dependencies that are in 90% of the cases not needed for most usecases. That would be an equivalent of the Recommends section of a debian package.\nI use the following shell script (requires sed) to install optional dependencies:\n#!/bin/sh\n\nwhile read dependency; do\n dependency_stripped=\"$(echo \"${dependency}\" | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')\"\n # Skip comments\n if [[ $dependency_stripped == \\#* ]]; then\n continue\n # Skip blank lines\n elif [ -z \"$dependency_stripped\" ]; then\n continue\n else\n if pip install \"$dependency_stripped\"; then\n echo \"$dependency_stripped is installed\"\n else\n echo \"Could not install $dependency_stripped, skipping\"\n fi\n fi\ndone < recommends.txt\n\n", "Building on the answer by @MZD, here's a solution to filter out all text starting with a comment sign #\ncat requirements.txt | grep -Eo '(^[^#]+)' | xargs -n 1 pip install\n\n", "For Windows using PowerShell:\nforeach($line in Get-Content requirements.txt) {\n if(!($line.StartsWith('#'))){\n pip install $line\n }\n}\n\n", "One line PowerShell:\nGet-Content .\\requirements.txt | ForEach-Object {pip install $_}\nIf you need to ignore certain lines then:\nGet-Content .\\requirements.txt | ForEach-Object {if (!$_.startswith(\"#\")){pip install $_}}\nOR\nGet-Content .\\requirements.txt | ForEach-Object {if ($_ -notmatch \"#\"){pip install $_}}\n", "Thanks, Etienne Prothon for windows cases. \nBut, after upgrading to pip 18, pip package don't expose main to public. So you may need to change code like this. \n # This code install line by line a list of pip package \n import sys\n from pip._internal import main as pip_main\n\n def install(package):\n pip_main(['install', package])\n\n if __name__ == '__main__':\n with open(sys.argv[1]) as f:\n for line in f:\n install(line)\n\n", "Another option is to use pip install --dry-run to get a list of packages that you need to install and then keep trying it and remove the ones that don't work.\n", "A very general solution\nThe following code installs all requirements for:\n\nmultiple requirement files (requirements1.txt, requirements2.txt)\nignores lines with comments #\nskips packages, which are not instalable\nruns pip install each line (not each word as in some other answers)\n\n$ (cat requirements1.txt; echo \"\"; cat requirements2.txt) | grep \"^[^#]\" | xargs -L 1 pip install\n\n" ]

[ 395, 27, 12, 9, 4, 4, 3, 2, 0, 0, 0 ]

[ "For Windows:\nimport os\nfrom pip.__main__ import _main as main\n\nerror_log = open('error_log.txt', 'w')\n\ndef install(package):\n try:\n main(['install'] + [str(package)])\n except Exception as e:\n error_log.write(str(e))\n\nif __name__ == '__main__':\n f = open('requirements1.txt', 'r')\n for line in f:\n install(line)\n f.close()\n error_log.close()\n\n\nCreate a local directory, and put your requirements.txt file in it.\nCopy the code above and save it as a python file in the same directory. Remember to use .py extension, for instance, install_packages.py\nRun this file using a cmd: python install_packages.py\nAll the packages mentioned will be installed in one go without stopping at all. :)\n\nYou can add other parameters in install function. Like:\n main(['install'] + [str(package)] + ['--update'])\n" ]

[ -2 ]

[ "pip", "python" ]

[ "You can compare most common value per columns by DataFrame.mode by DataFrame.eq, set missing values by DataFrame.where if no match, reshape by DataFrame.stack, last convert to DataFrame:\ndf1 = df.set_index('A').drop('E', axis=1)\n\nprint (df1)\n B C D\nA \n5 4 2 3\n5 d 2 g <- added new not matched value\n5 4 2 h\n4 4 2 3\n4 4 g 3\n\ns = df1.mode().iloc[0]\n\n\ndf2 = (df1.where(df1.ne(s)).stack()\n .rename_axis(['A','cols'])\n .reset_index(name='val'))\nprint (df2)\n A cols val\n0 5 B d\n1 5 D g\n2 5 D h\n3 4 C g\n\nEDIT:\ndf1 = df.set_index(['A','E'])\nprint (df1)\n B C D\nA E \n5 2022-11-29 4 2 3\n 2022-11-30 d 2 3\n 2022-11-29 4 2 h\n4 2022-11-28 4 2 3\n 2022-11-29 4 g 3\n\ns = df1.mode().iloc[0]\n\n\ndf2 = (df1.where(df1.ne(s)).stack()\n .rename_axis(['A','E','cols'])\n .reset_index(name='val')\n )\nprint (df2)\n A E cols val\n0 5 2022-11-30 B d\n1 5 2022-11-29 D h\n2 4 2022-11-29 C g\n\n" ]

[ 2 ]

[]

[]

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

[ "Check out what you can find under Python complexity metrics.\nA tool called Wily may be of use. However, what counts as good practices will be very different for Java and Python.\n", "The closest that I know of is https://github.com/seddonym/import-linter\nIt's capable only of linting - as the name suggests - imports between modules so it has far fewer capabilities than ArchUnit, but I used it with great success in several clean/hexagonal architecture projects.\nTo use it to the full extent you need to wisely split your projects into modules so that the import statements \"follow\" (or rather \"match\") your designated architecture.\nSome example of the rules that I often use:\n[importlinter:contract:8]\nname = Modules .core cannot depend on shared infrastructure\ntype = forbidden\nsource_modules =\n src.services.service1.modules.module1.core\n src.services.service2.modules.module1.core\n src.services.service2.modules.module2.core\n\nforbidden_modules =\n src.shared.infrastructure\n\n\n[importlinter:contract:3]\nname = modules inside services shall be independent\ntype = independence\nmodules = \n src.services.service1.modules.module1\n src.services.service2.modules.module1\n src.services.service2.modules.module2\n\n", "There is also pytest-archon. Its syntax is simpler than import-linter's and uses pytest instead of separate config files.\n" ]

[ 2, 0, 0 ]

[]

[]

[ "architecture", "archunit", "django", "python", "testing" ]

[ "You could consider using a while-loop:\ndef spiralOrder(self, matrix: list[list[int]]) -> list[int]:\n result = []\n left, right = 0, len(matrix[0]) - 1\n up, down = 0, len(matrix) - 1\n step = 0\n while left <= right and up <= down:\n match step % 4:\n case 0:\n for i in range(down, up - 1, -1):\n result.append(matrix[i][right])\n right -= 1\n case 1:\n for i in range(right, left - 1, -1):\n result.append(matrix[up][i])\n up += 1\n case 2:\n for i in range(up, down + 1):\n result.append(matrix[i][left])\n left += 1\n case 3:\n for i in range(left, right + 1):\n result.append(matrix[down][i])\n down -= 1\n step += 1\n return result\n\nExample Usage 1:\nprint(spiralOrder([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n[9, 6, 3, 2, 1, 4, 7, 8, 5]\n\nExample Usage 2:\nprint(spiralOrder([[1, 2], [3, 4], [5, 6]])\n[6, 4, 2, 1, 3, 5]\n\nExample Usage 3:\nprint(spiralOrder([[3]])\n[3]\n\nNote: [3] is a not of the type list[list[int]] hence I am assuming it is a typo in your question.\n" ]

[ 1 ]

[]

[]

[ "matrix", "python" ]

[ "https://mlflow.org/docs/latest/python_api/mlflow.client.html#mlflow.client.MlflowClient.log_param\nlog_param(run_id: str, key: str, value: Any)\n", "The solution is to use the mlflow.client module instead of the mlflow module as stated in the documentation of the mlflow client:\n\nThe mlflow.client module provides a Python CRUD interface to MLflow\nExperiments, Runs, Model Versions, and Registered Models. This is a\nlower level API that directly translates to MLflow REST API calls. For\na higher level API for managing an “active run”, use the mlflow\nmodule.\n\n\nmlflow client documentation\n\n@Andre: Thanks for pointing me in the right direction.\n" ]

[ 0, 0 ]

[]

[]

[ "contextmanager", "functional_programming", "machine_learning", "mlflow", "python" ]

[ "try this code..\ndef stem(text):\n y=[]\n for i in text.split():\n y.append(ps.stem(i))\n return \" \".join(y)\n\nnew_df['tags']=new_df['tags'].apply(stem)\n\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "I think read_namespaced_pod has the information (spec.nodeName).\nhttps://github.com/kubernetes-client/python/blob/master/kubernetes/docs/CoreV1Api.md#read_namespaced_pod\n" ]

[ 0 ]

[]

[]

[ "api", "client", "kubernetes", "python" ]

[ "Will something simple like this work?\nscore = len([x for x in real_value if x in extracted_value])/len(extracted_value)\nprint(score)\n>>> 0.5\n\n", "The metric you're looking for is recall.\n@sfat's solution works well for a single document, you can then get the average over multiple documents by summing the scores and then dividing by the len of documents.\nFor more advanced scoring for your retrieval, check the F-Score section of the linked article.\n", "To check how many values are shared between extracted_value and real_value. I believe you're looking for the recall of your model, you can use set operations, specifically & (and) divided by your ground truth (real_values):\nrecall = len(set(real_value) & set(extracted_value))/len(real_values)\n\nor if you want exactly which specific values are shared, which you could always take the len of:\nshared_vals = set(real_value) & set(extracted_value)\n\nIf you want to then calculate recall with shared_vals:\nrecall = len(shared_vals)/len(real_value)\n\n" ]

[ 1, 0, 0 ]

[]

[]

[ "performance", "python" ]

[ "You have to have the version that your Selenium driver was downloaded for.\nIf you want to maintain your 107.0.5304.121 version on Chrome, download the selenium 107.x.x.x driver\nhttps://chromedriver.chromium.org/downloads\nYou can check here all the latest versions\n" ]

[ 0 ]

[]

[]

[ "linux", "python", "selenium", "selenium_chromedriver" ]

[ "Each model class must be read by the interpreter before create_all is called, otherwise they will not be in the metadata and so will not be created. So what you need to do is:\n\nDefine Base in a module.\nAll the files that declare model classes must import the module that defines Base, and use that Base as their superclass.\nIn (say) main.py, import Base and all the model classes (so that they are read by the interpreter) and then do Base.metadata.create_all().\n\nYou may be able to adjust the above steps a little, but understand that generally you won't be able to declare Base and do create_all() in the same file without creating circular dependencies\n" ]

[ 0 ]

[]

[]

[ "orm", "python", "python_3.x", "sqlalchemy" ]

[ "You have a network problem.\nBoth the server and the client complain that the other side unexpectedly hung up on them. So it was some misconfigured network component in the middle that cut the line. You have two options:\n\nfix the network\n\nlower tcp_keepalives_idle on the PostgreSQL client or server, so that the operating system sends \"keepalive packets\" frequently and the network doesn't consider the connection idle\n\n\nYou might want to read this for more details.\n", "Finally, I found a solution. I still don't know what the problem was. It isn't a connection issue, cause some parsers with the same IP and same network connections work normally. And I'm was still able to add data with the same script, but in a separate project file.\nMy solution is to add 'keepalives' settings in connection:\nconn = psycopg2.connect(\nhost=hostname,\ndbname=database,\nuser=username,\npassword=password,\nport=port_id,\nkeepalives=1,\nkeepalives_idle=30,\nkeepalives_interval=10,\nkeepalives_count=5)\n\n" ]

[ 0, 0 ]

[]

[]

[ "postgresql", "psycopg2", "python" ]

[ "There is a solution for this, from Github pull request #110. Basically, all you need to do is change one character in one line of the fake_useragent/utils.py source code.\nTo do this on your system, open /usr/local/lib/python3.9/dist-packages/fake_useragent/utils.py† in your favorite text editor using admin privileges. Go to line 99, and change the w3\n html = html.split('<table class=\"w3-table-all notranslate\">')[1]\n# ^^ change this\n\nto ws:\n html = html.split('<table class=\"ws-table-all notranslate\">')[1]\n# ^^ to this\n\nSave the file (with admin permissions), restart your Python session, and your code should work just fine.\n\n† To find the fake_useragent directory in which utils.py resides, run the following code:\nimport fake_useragent\nprint(fake_useragent.__file__)\n\nFor example, on my Windows laptop, this printed\n'C:\\\\Users\\\\mattdmo\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\fake_useragent\\\\__init__.py'\n\nso the folder to open is C:\\Users\\mattdmo\\AppData\\Roaming\\Python\\Python310\\site-packages\\fake_useragent.\n", "I tried UserAgent(use_cache_server=False, verify_ssl=False) but didn't work.\nUpgrading the version to 0.1.13 worked for me.\npip3 install fake-useragent==0.1.13\n\nI tried pip install fake-useragent -U but it somehow didn't upgrade the package correctly.\nFrom the package owner: https://github.com/fake-useragent/fake-useragent/pull/136#issuecomment-1302431518\n" ]

[ 15, 0 ]

[]

[]

[ "python", "user_agent" ]

[ "fake_useragent is only to mimic as a user agent but not official. Hence it throws error when detected. For the most recent call lost I did the following:\n\npip3 install fake_useragent --upgrade\nua = UserAgent(use_cache_server = False, verify_ssl=False) #Very important step, after this step you can write your code\nua = UserAgent() #creating object of UserAgent in my case\nheader = {'user-agent': ua.chrome} #accessing\nprint(ua.chrome)\n\n", "This is not the official API so google is probably detecting that you are scraping their content \"FakeUserAgentError\". Try using the official API(you get 100 free searches a day). Then try something like this here\n", "I tried UserAgent(use_cache_server = False, verify_ssl=False) but didn't work.\nUpgrading the version to 0.1.13 worked for me.\npip3 install fake-useragent==0.1.13\n\nI tried pip install fake-useragent -U but it somehow didn't upgrade the package correctly.\nFrom the package owner: https://github.com/fake-useragent/fake-useragent/pull/136#issuecomment-1302431518\n" ]

[ 1, 0, 0 ]

[]

[]

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

[ "You may have several installations of pandas library. So uninstall pandas using\n\"pip uninstall pandas\".\n(If on linux use:\n\" sudo apt-get purge python3-pandas\")\nand install again.\nThis helped solve my problem.\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "One option using numpy broadcasting:\nstep = 0.01\nup = np.arange(0, 0.3+step, step)\n\nout = pd.Series((df['col2'].to_numpy()[:,None] <= up).sum(axis=0), index=up)\n\nOutput:\n0.00 0\n0.01 0\n0.02 0\n0.03 0\n0.04 0\n0.05 0\n0.06 0\n0.07 0\n0.08 0\n0.09 0\n0.10 0\n0.11 1\n0.12 1\n0.13 1\n0.14 1\n0.15 1\n0.16 1\n0.17 1\n0.18 1\n0.19 1\n0.20 2\n0.21 2\n0.22 2\n0.23 2\n0.24 2\n0.25 3\n0.26 3\n0.27 3\n0.28 3\n0.29 4\n0.30 5\ndtype: int64\n\nWith pandas.cut and cumsum:\nstep = 0.01\n\nup = np.arange(0, 0.3+step, step)\n(pd.cut(df['col2'], up, labels=up[1:].round(2))\n .value_counts(sort=False).cumsum()\n)\n\nOutput:\n0.01 0\n0.02 0\n0.03 0\n0.04 0\n0.05 0\n0.06 0\n0.07 0\n0.08 0\n0.09 0\n0.1 0\n0.11 1\n0.12 1\n0.13 1\n0.14 1\n0.15 1\n0.16 1\n0.17 1\n0.18 1\n0.19 1\n0.2 2\n0.21 2\n0.22 2\n0.23 2\n0.24 2\n0.25 3\n0.26 3\n0.27 3\n0.28 3\n0.29 4\n0.3 5\nName: col2, dtype: int64\n\n" ]

[ 2 ]

[]

[]

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

[ "This is more complex but significantly faster than any kind of string manipulation as it's essentially just integer arithmetic.\nfrom timeit import timeit\n\nlob = [[0, 1, 1, 0, 0, 1, 0, 1], [0, 1, 1, 1, 0, 1, 1, 1]]\n\ndef v1():\n result = []\n for e in lob:\n r = 0\n for _e in e:\n r = r * 2 + _e\n result.append(r)\n return result\n\ndef v2():\n return [int(''.join([str(y) for y in x]), 2) for x in lob]\n\nassert v1() == v2()\n\nfor func in v1, v2:\n print(func.__name__, timeit(func))\n\nOutput:\nv1 0.6906622060014342\nv2 2.173182999999881\n\n" ]

[ 2 ]

[]

[]

[ "arrays", "list", "python" ]

[ "Your df_unknown_places_filtered has no rows.\n" ]

[ 0 ]

[]

[]

[ "dataframe", "for_loop", "python" ]

[ "The integral corresponds to computing the area under the curve.\nThe most easy way to compute (or approximate) the integral \"numerically\" is using the rectangle rule which is basically approximating the area under the curve by summing area of rectangles (see https://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions).\nPractically in your case, it quite straightforward since it is a step function.\nFirst, I recomment to use numpy arrays instead of list (more handy for numerical computing):\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.array([0,1,3,4,6,7,8,11,13,15])\ny = np.array([8,5,2,2,2,5,6,5,9,9])\n\nplt.plot(x,y)\n\nThen, we compute the width of rectangles using np.diff():\nw = np.diff(x)\n\nThen, the height of the same rectangles (multiple possibilities exist):\nh = y[:-1]\n\nHere I chose the 2nd value of each two successive y values. the top right angle of rectangle is on the curve. You can choose the mean value of each two successive y values h = (y[1:]+y[:-1])/2 in which the middle of the top of the rectangle coincide with the curve.\nThen , you will need to multiply and sum:\narea = (w*h).sum()\n\n" ]

[ 1 ]

[]

[]

[ "graph", "integral", "list", "matplotlib", "python" ]

[ "import click\n\[email protected]()\[email protected]('-v', '--verbose', count=True)\ndef log(verbose):\n click.echo(f\"Verbosity: {verbose}\")\n\nif __name__ == '__main__':\n log()\n\nThen calling it like\n$ python simple.py \nVerbosity: 0\n\n$ python simple.py -v\nVerbosity: 1\n\nThe way you try to run it, suggest you think about command group, i.e. nesting commands\nimport click\n\[email protected]()\ndef cli():\n pass\n\[email protected]('log')\[email protected]('-v', '--verbose', count=True)\ndef log(verbose):\n click.echo(f\"Verbosity: {verbose}\")\n\[email protected]('greet')\ndef greet():\n click.echo(\"Hello\")\n\nif __name__ == '__main__':\n cli()\n\nThen\n$ python simple.py greet\nHello\n\n$ python simple.py log -v -v # or just -vv\nVerbosity: 2\n\nNext step would be setuptools integration, etc.\n" ]

[ 0 ]

[]

[]

[ "command", "python", "python_click" ]

[ "Only this worked for me\nimport pymysql\nimport paramiko\nimport pandas as pd\nfrom paramiko import SSHClient\nfrom sshtunnel import SSHTunnelForwarder\nfrom os.path import expanduser\n\nhome = expanduser('~')\nmypkey = paramiko.RSAKey.from_private_key_file(home + pkeyfilepath)\n# if you want to use ssh password use - ssh_password='your ssh password', bellow\n\nsql_hostname = 'sql_hostname'\nsql_username = 'sql_username'\nsql_password = 'sql_password'\nsql_main_database = 'db_name'\nsql_port = 3306\nssh_host = 'ssh_hostname'\nssh_user = 'ssh_username'\nssh_port = 22\nsql_ip = '1.1.1.1.1'\n\nwith SSHTunnelForwarder(\n (ssh_host, ssh_port),\n ssh_username=ssh_user,\n ssh_pkey=mypkey,\n remote_bind_address=(sql_hostname, sql_port)) as tunnel:\n conn = pymysql.connect(host='127.0.0.1', user=sql_username,\n passwd=sql_password, db=sql_main_database,\n port=tunnel.local_bind_port)\n query = '''SELECT VERSION();'''\n data = pd.read_sql_query(query, conn)\n conn.close()\n\n", "I'm guessing you'll need port forwarding. I recommend sshtunnel.SSHTunnelForwarder \nimport mysql.connector\nimport sshtunnel\n\nwith sshtunnel.SSHTunnelForwarder(\n (_host, _ssh_port),\n ssh_username=_username,\n ssh_password=_password,\n remote_bind_address=(_remote_bind_address, _remote_mysql_port),\n local_bind_address=(_local_bind_address, _local_mysql_port)\n) as tunnel:\n connection = mysql.connector.connect(\n user=_db_user,\n password=_db_password,\n host=_local_bind_address,\n database=_db_name,\n port=_local_mysql_port)\n ...\n\n", "from sshtunnel import SSHTunnelForwarder\nimport pymysql\nimport pandas as pd\n\ntunnel = SSHTunnelForwarder(('SSH_HOST', 22), ssh_password=SSH_PASS, ssh_username=SSH_UNAME,\n remote_bind_address=(DB_HOST, 3306)) \ntunnel.start()\nconn = pymysql.connect(host='127.0.0.1', user=DB_UNAME, passwd=DB_PASS, port=tunnel.local_bind_port)\ndata = pd.read_sql_query(\"SHOW DATABASES;\", conn)\n\ncredits to https://www.reddit.com/r/learnpython/comments/53wph1/connecting_to_a_mysql_database_in_a_python_script/\n", "If your private key file is encrypted, this is what worked for me:\n mypkey = paramiko.RSAKey.from_private_key_file(<<file location>>, password='password')\n sql_hostname = 'sql_hostname'\n sql_username = 'sql_username'\n sql_password = 'sql_password'\n sql_main_database = 'sql_main_database'\n sql_port = 3306\n ssh_host = 'ssh_host'\n ssh_user = 'ssh_user'\n ssh_port = 22\n\n\n with SSHTunnelForwarder(\n (ssh_host, ssh_port),\n ssh_username=ssh_user,\n ssh_pkey=mypkey,\n ssh_password='ssh_password',\n remote_bind_address=(sql_hostname, sql_port)) as tunnel:\n conn = pymysql.connect(host='localhost', user=sql_username,\n passwd=sql_password, db=sql_main_database,\n port=tunnel.local_bind_port)\n query = '''SELECT VERSION();'''\n data = pd.read_sql_query(query, conn)\n print(data)\n conn.close()\n\n", "You may only write the path to the private key file: ssh_pkey='/home/userName/.ssh/id_ed25519' (documentation is here: https://sshtunnel.readthedocs.io/en/latest/).\nIf you use mysql.connector from Oracle you must use a construction\ncnx = mysql.connector.MySQLConnection(...\nImportant: a construction \ncnx = mysql.connector.connect(...\ndoes not work via an SSh! It is a bug.\n(The documentation is here: https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html).\nAlso, your SQL statement must be ideal. In case of an error on SQL server side, you do not receive an error message from SQL-server.\nimport sshtunnel\nimport numpy as np\n\nwith sshtunnel.SSHTunnelForwarder(ssh_address_or_host='ssh_host',\n ssh_username=\"ssh_username\",\n ssh_pkey='/home/userName/.ssh/id_ed25519',\n remote_bind_address=('localhost', 3306),\n ) as tunnel:\n cnx = mysql.connector.MySQLConnection(user='sql_username',\n password='sql_password',\n host='127.0.0.1',\n database='db_name',\n port=tunnel.local_bind_port)\n cursor = cnx.cursor()\n cursor.execute('SELECT * FROM db_name.tableName;')\n arr = np.array(cursor.fetchall())\n cursor.close()\n cnx.close()\n\n", "This works for me:\nimport mysql.connector\nimport sshtunnel\n\nwith sshtunnel.SSHTunnelForwarder(\n ('ip-of-ssh-server', 'port-in-number-format'),\n ssh_username = 'ssh-username',\n ssh_password = 'ssh-password',\n remote_bind_address = ('127.0.0.1', 3306)\n) as tunnel:\n connection = mysql.connector.connect(\n user = 'database-username',\n password = 'database-password',\n host = '127.0.0.1',\n port = tunnel.local_bind_port,\n database = 'databasename',\n )\n mycursor = connection.cursor()\n query = \"SELECT * FROM datos\"\n mycursor.execute(query)\n\n", "Paramiko is the best python module to do ssh tunneling. Check out the code here:\nhttps://github.com/paramiko/paramiko/blob/master/demos/forward.py\nAs said in comments this one works perfect.\nSSH Tunnel for Python MySQLdb connection\n", "Best practice is to parameterize the connection variables.\nHere is how I have implemented. Works like charm!\nimport mysql.connector\nimport sshtunnel\nimport pandas as pd\nimport configparser\n\nconfig = configparser.ConfigParser()\nconfig.read('c:/work/tmf/data_model/tools/config.ini')\n\nssh_host = config['db_qa01']['SSH_HOST']\nssh_port = int(config['db_qa01']['SSH_PORT'])\nssh_username = config['db_qa01']['SSH_USER']\nssh_pkey = config['db_qa01']['SSH_PKEY']\nsql_host = config['db_qa01']['HOST']\nsql_port = int(config['db_qa01']['PORT'])\nsql_username = config['db_qa01']['USER']\nsql_password = config['db_qa01']['PASSWORD']\n\nwith sshtunnel.SSHTunnelForwarder(\n (ssh_host,ssh_port),\n ssh_username=ssh_username,\n ssh_pkey=ssh_pkey,\n remote_bind_address=(sql_host, sql_port)) as tunnel:\n connection = mysql.connector.connect(\n host='127.0.0.1',\n port=tunnel.local_bind_port,\n user=sql_username,\n password=sql_password)\n query = 'select version();'\n data = pd.read_sql_query(query, connection)\n print(data)\n connection.close()\n\n", "Someone said this in another comment. If you use the python mysql.connector from Oracle then you must use a construction cnx = mysql.connector.MySQLConnection(....\nImportant: a construction cnx = mysql.connector.connect(... does not work via an SSH! This bug cost me a whole day trying to understand why connections were being dropped by the remote server:\nwith sshtunnel.SSHTunnelForwarder(\n (ssh_host,ssh_port),\n ssh_username=ssh_username,\n ssh_pkey=ssh_pkey,\n remote_bind_address=(sql_host, sql_port)) as tunnel:\n connection = mysql.connector.MySQLConnection(\n host='127.0.0.1',\n port=tunnel.local_bind_port,\n user=sql_username,\n password=sql_password)\n query = 'select version();'\n data = pd.read_sql_query(query, connection)\n print(data)\n connection.close()\n\n" ]

[ 42, 30, 9, 5, 4, 1, 0, 0, 0 ]

[]

[]

[ "mysql", "python", "python_2.7", "ssh" ]

[ "I've been digging a bit deeper and it turns out this issue is caused by several devices using the same engine ID.\nSee https://github.com/easysnmp/easysnmp/issues/156 for more information.\n" ]

[ 0 ]

[]

[]

[ "easysnmp", "python" ]

[ "You could do a pd.melt:\ndf.reset_index().rename(columns={'index':'x'}).melt(var_name='y', value_name='z', id_vars='x')\n\nIt essentially transform the dataframe to the long format, making each row to have three entries: x, y and z.\n", "If you don't want to rewite the function, then using loop for to apply the function seems a easy way. you can do this\nidx = df.index\ncols = df.columns\nvals = df.to_numpy()\nr = [ \n [f(x,y,z) for y, z in zip(cols, vals[i])]\n for i, x in enumerate(idx)\n]\n# if you want to recreate a dataframe\ndf_root = pd.DataFrame(data=r, index=idx, columns=cols)\n\nthere is a list comprehension on the index that includes a list comprehension on both the columns and the values of the row at the same time. vals[i] access the values from the row at position i. The result r is a list of length number of rows (N) and each item is a list of length number of columns (M). you don't need this structure especially but it is a easy way to build a dataframe with same index-columns as the original data.\nNote that it will still be long, you have about 25 million operations to do, even if f is optimized.\n", "I finally did this :\nmatrix = df_prices.values\nx_matrix = np.tile(x, (y.size, 1)).transpose()\ny_matrix = np.tile(y, (x.size, 1)) \nf_vect = np.frompyfunc(f, 3, 1)\nres = f_vect (matrix , x_matrix , y_matrix )\n\nPerformance-wise it was optimal without having to meddle with vectorizing myself the ultra-optimized but not vectorized root solver f -- which by the way is a C++ function that I expose to python with pybind11.\n" ]

[ 0, 0, 0 ]

[]

[]

[ "dataframe", "numpy", "pandas", "pybind11", "python" ]

[ "When designing your data pipelines, you may encounter use cases that require more complex task flows than \"Task A > Task B > Task C.\" For example, you may have a use case where you need to decide between multiple tasks to execute based on the results of an upstream task. Or you may have a case where part of your pipeline should only run under certain external conditions. Fortunately, Airflow has multiple options for building conditional logic and/or branching into your DAGs.\n", "I found a dirty way around it.\nWhat I did is creating 1 additional task using DummyOperator called task_pass.\n task_pass = DummyOperator(\n task_id = 'pass_to_task_group'\n )\n\nSo the DAG flow now looks like this:\ntask_start >> task_branch_table >> task_end\ntask_start >> task_branch_table >> task_pass >> task_pg_to_gcs_init >> task_end\n\nAlso there is 1 mistake that I made from the code above, notice that the params I set was worker. This is wrong because worker is the constant while the thing that I need to iterate is the i variable. So I change it from:\nparams: worker\n\nto:\nparams: i\n\n" ]

[ 0, 0 ]

[]

[]

[ "airflow", "airflow_2.x", "python" ]

[ "You could use something like this:\nimport itertools\nimport statistics\n\nnumber_of_classes = int(input(\"Enter the number of Classes: \"))\n\ndef ask_numbers(name, amount_to_ask):\n print('Please enter the numbers for Column', name)\n for how_many_asked_yet in range(amount_to_ask):\n yield (int(input(f'{how_many_asked_yet + 1}) ')))\n print()\n\ncolumns_names = ('A', 'B')\n\ncolumns = {name: tuple(ask_numbers(name, number_of_classes)) for name in columns_names}\n\ndef compute_c_values(source_column):\n values_accumulator = zip(source_column, itertools.accumulate(source_column))\n current_value, previous_value = next(values_accumulator)\n yield (f'{current_value}', current_value)\n \n for current_value, sum_value in values_accumulator:\n yield (f'{previous_value}+{current_value}={sum_value}', sum_value)\n previous_value = sum_value\n\ncolumns['C'] = tuple(compute_c_values(columns['A']))\n\ncolumns['D'] = tuple(c_value - b_value for (b_value, (_, c_value)) in zip(columns['B'], columns['C']))\n\ncolumns['E'] = tuple(0 if d_value < 0 else 1 for d_value in columns['D'])\n\noutput_header_footer_format = \"{:6} | {:10} | {:10} | {:15} | {:10} | {:10}\"\nprint(output_header_footer_format.format('Class', 'Column A', 'Column B', 'Column C', 'Column D', 'Column E'))\nprint('-------|------------|------------|-----------------|------------|-----------')\n\noutput_line_format = \"{0:6} | {1:10} | {2:10} | {3[0]:^15} | {4:10} | {5:^10}\"\n\nfor class_nb, output_values in enumerate(zip(*columns.values())):\n print(output_line_format.format(class_nb, *output_values))\n\nprint(output_header_footer_format.format('', '', '', '', '', ''))\n\naverages = {\n 'C' : statistics.mean(c_value for (_, c_value) in columns['C']),\n 'D' : statistics.mean(columns['D'])\n}\nprint(output_header_footer_format.format('' , '', 'Averages =', averages['C'], averages['D'], ''))\n\nTo fully understand this code you will need to read about:\n\nGenerators\nGenerator expressions\nDictionary comprehensions\n.format() method of string objects\nTuples unpacking\nUnpacking Argument List\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "I can not recommend any resources. But from your tags I quess you want to use Python for your client and server. So for this task you can use asyncua.\nA good starting point are the various examples. Also you can find some docs.\nFor debuging the server and understanding I would recomend to use UAExpert where you can browse your server and read/write values.\n" ]

[ 0 ]

[]

[]

[ "communication_protocol", "opc_ua", "python", "raspberry_pi" ]

[ "You have not gotten a few different errors, you get the same one each time. Item.Score is ambiguous.\nCan you share a snippet of your CSV file, that will help us determine the issue.\n" ]

[ 0 ]

[]

[]

[ "amazon_dynamodb", "amazon_s3", "python" ]

[ "wx.media will play direct from a URL, although I have no idea if it can access a YouTube url directly (I'm not a fan).\nHere's a very bare bones player accessing a url rather than a file.\nI would suggest that you test the media link to see if it is a url or a file, then use LoadURI or Load depending on the outcome.\nimport wx\nimport wx.media\n\nclass TestPanel(wx.Frame):\n def __init__(self):\n wx.Frame.__init__(self, None, title='Media Player')\n panel = wx.Panel(self, -1, size=(500, 700))\n self.player = wx.media.MediaCtrl(panel, style=wx.SIMPLE_BORDER)\n self.stop = wx.Button(panel, -1, \"Stop\")\n sizer = wx.BoxSizer(wx.VERTICAL)\n sizer.Add(self.player, 1, flag=wx.ALL, border=5)\n sizer.Add(self.stop, 0, wx.ALL, 5)\n panel.SetSizer(sizer)\n\n self.media = 'http://clips.vorwaerts-gmbh.de/VfE_html5.mp4'\n self.player.Bind(wx.media.EVT_MEDIA_LOADED, self.play)\n self.player.Bind(wx.media.EVT_MEDIA_FINISHED, self.quit)\n self.Bind(wx.media.EVT_MEDIA_STOP, self.quit)\n self.Bind(wx.EVT_BUTTON, self.quit, self.stop)\n self.Bind(wx.EVT_CLOSE, self.quit)\n self.timer = wx.Timer(self)\n self.Bind(wx.EVT_TIMER, self.OnTimer, self.timer)\n self.timer.Start(250)\n if self.player.LoadURI(self.media):\n pass\n else:\n print(\"Media not found\")\n self.quit(None)\n self.Show()\n\n def OnTimer(self, event):\n if self.player.GetState() == wx.media.MEDIASTATE_PLAYING:\n print(\"Progress:\",self.player.Tell())\n\n def play(self, event):\n self.player.Play()\n\n def quit(self, event):\n self.timer.Stop()\n if self.player.GetState() == wx.media.MEDIASTATE_PLAYING:\n self.player.Stop()\n self.Destroy()\n\nif __name__ == '__main__':\n app = wx.App()\n Frame = TestPanel()\n app.MainLoop()\n\nWorthy of note, you may have to choose your backend player via the szBackend parameter.\nAvailable options are:\n\nMEDIABACKEND_DIRECTSHOW\nMEDIABACKEND_QUICKTIME\nMEDIABACKEND_GSTREAMER\nMEDIABACKEND_WMP10\n\nIf you want something more complicated than wx.media offers, you will have to roll your own.\nGstreamer and Vlc both work well on Linux, on other operating systems, I have no idea.\nEdit:\nIf you must stream direct from youtube, use something like pafy to isolate the url.\nFor the above code:\nadd import pafy\ninstead of specifying the url to self.media replace\nself.media = 'http://clips.vorwaerts-gmbh.de/VfE_html5.mp4'\nwith the following:\n# youtube url via pafy\n url = \"https://www.youtube.com/watch?v=iL-jC7XyLeo\"\n video = pafy.new(url)\n best = video.getbest()\n playurl = best.url\n self.media = playurl\n\nThen continue as above\n" ]

[ 0 ]

[]

[]

[ "accessibility", "python", "wxpython", "youtube" ]

[ "Search in the docs: I did it for you\nThe docs is the most useful thing readily available to you.\n.\n.\nmessage = piece + \" turn\\n\" +L1 + \"\\n\" + L2 + \"\\n\" + L3 + \"\\n\" + L4 + \"\\n\" + L5 + \"\\n\" + L6\nm = await ctx.send(message, view=view1) #btw this still sends the message\n.\n.\nm.edit(content=message, view=...)\n\n" ]

[ 0 ]

[]

[]

[ "button", "discord", "discord.py", "python" ]

[ "This is the pip documentation for pyproject.toml\n\n...This file contains build system requirements and information, which are used by pip to build the package.\n\nSo this is not the correct place. Looking at the side bar we can see there is an entry for Requirements File Format which is the \"old\" requirements.txt file\n", "Quoting myself from here\n\nMy current assumption is: [...] you put your (mostly unpinned) dependencies to pyproject.toml instead of setup.py, so you library can be installed as a dependency of something else without causing much troubles because of issues resolving version constraints.\n\n\nOn top of that, for \"deployable applications\" (for lack of a better term), you still want to maintain a separate requirements.txt with exact version pinning.\n\nWhich has been confirmed by a Python Packaging Authority (PyPA) member and clarification of PyPA's recommendations should be updated accordingly at some point.\n" ]

[ 1, 0 ]

[ "I suggest switching to poetry, it's way better than a standard pip for dependency management. And because it uses pyproject.toml your dependencies and configs are in one place so it's easier to manage everything \n" ]

[ -1 ]

[ "python", "python_packaging", "requirements.txt" ]

[ "You should also use PIPE for reading the output;\nfrom subprocess import Popen, PIPE, STDOUT\n\ncommand = f\"shell command with arguments\"\nprocess = Popen(command, shell=True, stdout=PIPE, stderr=STDOUT)\n\nwith process.stdout:\n for line in iter(process.stdout.readline, b''):\n print(line.decode(\"utf-8\").strip())\n\nfor more details: Run subprocess and print output to logging\n" ]

[ 0 ]

[]

[]

[ "keyword", "loops", "python", "robotframework", "time" ]

[ "This should be doable using findContours(). A wire is always a (roughly) straigt line, right ?\n\nPaint the classified boxes white, as you said\nthreshold() to get a binary image with the wires (and other symbols and letters) in white, everything else black.\nrun findContours() on that to extract objects.\nGet the bounding boxes (minAreaRect) for all contours\ndiscard all contours with a too wide side ratio, those are letter or symbols, keep only those slim enough to be a wire\n\nNow you got all wires as objects, similiar to the junction list. Now, for how to merge those two... Some options that come to mind:\n\nGrow the boxes by a certain amount, and check if they overlap.\nInterpolate a line from the wire boxes and check if they cross any intersection box close by.\nOr the other way around: draw a line between intersections and check how much of it goes through a certain wire box.\n\nThis is a pure math problem, and i don't know what you performance requirements are. So i'll leave it at that.\n" ]

[ 0 ]

[]

[]

[ "deep_learning", "image_processing", "object_detection", "opencv", "python" ]

[ "There is a django.contrib.auth.models.User object attached to the request. You can access it in a view via request.user. You must have the auth middleware installed, though.\ndef view(request):\n if request.user.is_authenticated:\n user = request.user\n print(user)\n # do something with user\n\n" ]

[ 0 ]

[]

[]

[ "auth0", "django_forms", "django_models", "django_views", "python" ]

[ "Both pivot_table and groupby are used to aggregate your dataframe. The difference is only with regard to the shape of the result.\nUsing pd.pivot_table(df, index=[\"a\"], columns=[\"b\"], values=[\"c\"], aggfunc=np.sum) a table is created where a is on the row axis, b is on the column axis, and the values are the sum of c. \nExample:\ndf = pd.DataFrame({\"a\": [1,2,3,1,2,3], \"b\":[1,1,1,2,2,2], \"c\":np.random.rand(6)})\npd.pivot_table(df, index=[\"a\"], columns=[\"b\"], values=[\"c\"], aggfunc=np.sum)\n\nb 1 2\na \n1 0.528470 0.484766\n2 0.187277 0.144326\n3 0.866832 0.650100\n\nUsing groupby, the dimensions given are placed into columns, and rows are created for each combination of those dimensions.\nIn this example, we create a series of the sum of values c, grouped by all unique combinations of a and b.\ndf.groupby(['a','b'])['c'].sum()\n\na b\n1 1 0.528470\n 2 0.484766\n2 1 0.187277\n 2 0.144326\n3 1 0.866832\n 2 0.650100\nName: c, dtype: float64\n\nA similar usage of groupby is if we omit the ['c']. In this case, it creates a dataframe (not a series) of the sums of all remaining columns grouped by unique values of a and b.\nprint df.groupby([\"a\",\"b\"]).sum()\n c\na b \n1 1 0.528470\n 2 0.484766\n2 1 0.187277\n 2 0.144326\n3 1 0.866832\n 2 0.650100\n\n", "pivot_table = groupby + unstack and groupby = pivot_table + stack hold True.\nIn particular, if columns parameter of pivot_table() is not used, then groupby() and pivot_table() both produce the same result (if the same aggregator function is used).\n# sample\ndf = pd.DataFrame({\"a\": [1,1,1,2,2,2], \"b\": [1,1,2,2,3,3], \"c\": [0,0.5,1,1,2,2]})\n\n# example\ngb = df.groupby(['a','b'])[['c']].sum()\npt = df.pivot_table(index=['a','b'], values=['c'], aggfunc='sum')\n\n# equality test\ngb.equals(pt) #True\n\n\nIn general, if we check the source code, pivot_table() internally calls __internal_pivot_table(). This function creates a single flat list out of index and columns and calls groupby() with this list as the grouper. Then after aggregation, calls unstack() on the list of columns.\nIf columns are never passed, there is nothing to unstack on, so groupby and pivot_table trivially produce the same output.\nA demonstration of this function is:\ngb = (\n df\n .groupby(['a','b'])[['c']].sum()\n .unstack(['b'])\n)\npt = df.pivot_table(index=['a'], columns=['b'], values=['c'], aggfunc='sum')\n\ngb.equals(pt) # True\n\nAs stack() is the inverse operation of unstack(), the following holds True as well:\n(\n df\n .pivot_table(index=['a'], columns=['b'], values=['c'], aggfunc='sum')\n .stack(['b'])\n .equals(\n df.groupby(['a','b'])[['c']].sum()\n )\n) # True\n\nIn conclusion, depending on the use case, one is more convenient than the other but they can both be used instead of the other and after correctly applying stack()/unstack(), both will result in the same output.\nHowever, there's a performance difference between the two methods. In short, pivot_table() is slower than groupby().agg().unstack(). You can read more about it from this answer.\n", "It's more appropriate to use .pivot_table() instead of .groupby() when you need to show aggregates with both rows and column labels.\n.pivot_table() makes it easy to create row and column labels at the same time and is preferable, even though you can get similar results using .groupby() with few extra steps.\n", "Difference between pivot_table and groupby\npivot_table\n\ngropby\n\n" ]

[ 127, 15, 12, 0 ]

[]

[]

[ "dataframe", "pandas", "pandas_groupby", "pivot_table", "python" ]

[ "I think you can use \"inspect\" library for this purpose;\nfrom inspect import currentframe\n\ndef get_line();\n return currentframe().f_back_f_lineno\n\nprint(\"this is sample:\", get_line())\n\n" ]

[ 0 ]

[]

[]

[ "debugging", "icecream", "python" ]

[ "The matrix1 is a list of dim 1 you try to index it as it already is a 2-d list. Try to use the b list to append the elements of the transposed matrix and then append it to matrix1 like below:\nn = int(input(\"Enter the size of square matrix\"))\nmatrix = []\nfor i in range(n): \n a =[]\n for j in range(n): \n a.append(int(input(\"Enter the entries rowwise:\")))\n matrix.append(a)\nmatrix1 = []\nfor i in range(0,n):\n b = []\n for j in range(0,n):\n b.append( matrix[j][i])\n matrix1.append(b)\nfor i in range(n):\n for j in range(n):\n print(matrix1[i][j], end = \" \")\nprint()\n\n", "You can use zip function also for transpose.\nExample\nCode:-\nmatrix=[[1,2,3],[4,5,6],[7,8,9]]\n#print(matrix)\nres=[]\nfor i in zip(*matrix):\n res.append(i)\nprint(res)\n\nOutput:-\n[(1, 4, 7), (2, 5, 8), (3, 6, 9)]\n\n", "If the name of the matrix is \"matrix\" just go with:\ntransposed = list(zip(*matrix))\n\n" ]

[ 2, 1, 1 ]

[]

[]

[ "python" ]

[]

[]

[ "You can simplify your inn function with the built in set operations in python:\ndef inn(nums1,nums2):\n return set(nums1) & set(nums2)\n\n", "In the first listing, j is an element of the list num1.\nIn the second listing, j is used as the index of an element in the list num1.\nYou understood the difference when you replaced\n if j in b:\n print(j)\n\nin the first listing with\n if nums1[j] in b:\n print(nums1[j])\n\nin the second listing.\nBut you forgot the difference in the next line, which is the same in both listings. It should be\n a.add(nums1[j])\n\nin the second listing.\nThen both outputs would be the same.\n" ]

[ -2, -2 ]

[ "list", "python", "range", "set" ]

[ "Here is a very basic example using pandas, datetime index and matplotlib altogether. It is important to make sure the index is of type DatetimeIndex.\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.DataFrame({'a':[3,4,5]}, index=pd.date_range('2020-01-03', '2020-01-05', freq='D'))\ndf_2 = pd.DataFrame({'b':[1,2,3,4,5], 'c':[1,2,2,2,2]}, index=pd.date_range('2020-01-01', '2020-01-05', freq='D'))\n\n# plot daily vaccinated\nfig, ax1= plt.subplots(1,figsize=(10,5))\n# set up\nax1.plot(df.index, df['a'], label= 'Total Vaccinated', c='Red')\n# axis and legend settings\nax1.set_ylabel('population (millions)', size= 14)\nax1.set_title('Monthly Vaccinated Numbers', size= 20)\nplt.xticks(rotation=45)\nplt.grid()\n\nax1.legend(loc=\"upper left\")\n\n##########################################################\n# plot daily covid cases\nax2 = ax1.twinx()\n\n# np.arrange looks at the number of rows\nax2.xaxis.tick_top()\nax2.plot(df_2.index, df_2['b'], color='Orange')\n\nplt.xlabel('date', fontsize=14)\nplt.ylabel('population (thousands)', fontsize=14)\n\nplt.grid(False)\n\n\nThis means in your case, you have to make sure to set parse_dates=True and set the index, when your read your data. Using pd.read_csv() this could look like\ndf = pd.read_csv('covid.csv', sep=',', parse_dates=True, index_col=0)\n\nBecause you have to DataFrames, you have so make yure both have a DatetimeIndex. Afterwards just replace the columns in the two calls with ac.plot().\nComment:\nIf you want to plot all columns of a Dataframe, ax2.plot(df_2.index, df_2) works. If your want to select a subset of columns ax2.plot(df_2.index, df_2[['b', 'c']]) is doing the job.\n" ]

[ 0 ]

[]

[]

[ "matplotlib", "pandas", "python", "x_axis" ]

[ "I'm not sure what you mean, is this the result you want to achieve?\nmyData = pd.DataFrame({'dataset': ['cat', 'cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'dog', 'bird', 'bird', 'bird', 'bird'], \n 'category_1': ['orange', 'orange', 'white', 'white', 'black', 'brown', 'brown', 'black', 'red', 'green', 'red', 'green'], \n 'category_2': ['this_cat', 'that_cat', 'this_cat', 'that_cat', 'this_dog', 'that_dog', 'this_dog', 'that_dog', 'this_bird', 'that_bird', 'this_bird', 'that_bird'],\n 'values': ['1', '8', '9', '2', '5', '4', '3', '10', '0', '2', '7', '9']\n })\n\nresult = {}\nfor i, animals in myData.groupby('dataset'):\n tuples = animals.groupby(['category_1', 'category_2'])['values'].mean().reset_index()\n tuples = pd.DataFrame(tuples)\n matrix = tuples.pivot(index='category_2', columns='category_1', values='values').reset_index()\n result[i] = matrix\ndisplay(result)\n\n" ]

[ 0 ]

[]

[]

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

[ "You should convert the TextIO to string and you should use \"loads\" function;\nfrom json import loads\n\nwith open(SECRET) as jf:\n data = loads(jf.read())\n\n\n" ]

[ 0 ]

[]

[]

[ "jupyter_notebook", "python" ]

[ "You forgot the padding=same in inception_block_4. That would change the output size of the block to such that it does not fit the other blocks:\nself.inception_block_4 = nn.Conv2d(256,64,(1,1),stride=(1,2), padding='same')\n\nedit:\nThis is not possible for strided convolutions, so instead a pointwise convolution should be used as mentioned in the paper:\n'A branch with a pointwise convolution with a kernel size of 1, 1 with a stride of (1, 2)'.\nor depth-wise separable convolution, more on how that achieve that here.\n" ]

[ 0 ]

[]

[]

[ "python", "pytorch" ]

[ "You are right, DataFrameWriter.copy_into_location does not have the storage integration parameter.\nYou can create an external stage object pointing to your S3 location using your storage integration.\n create stage my_stage_s3\n storage_integration = my_storage_int\n url = 's3://mybucket/encrypted_files/'\n file_format = my_format;\n\nThen, in your copy_into_location call, you specify the location as \"@my_stage_s3/\"\n" ]

[ 1 ]

[]

[]

[ "amazon_s3", "python", "snowflake_cloud_data_platform", "snowpark" ]

[ "The get_paginator function parameter must be the name of the operation. It looks like you're trying to paginate on the get_tables function so\n paginator = client.get_paginator(['TableList'])\n\nshould be:\n paginator = client.get_paginator('get_tables')\n\nOnce you have the paginator object, you need to call paginator.paginate to retrieve the iterator. You can send your database parameters like so:\n page_iterator = paginator.paginate(\n DatabaseName=databaseDict['Name'],\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n\nSee the documention for this function here.\nNow that you have the iterator, you can call a for loop by enumerating on it:\nfor page_index, page in enumerate(page_iterator):\n\n", "Here's a full working example on how to do it using paginator.\nRemember to provide region_name and database_name.\nimport boto3\n\nregion_name = '<PROVIDE_AWS_REGION_NAME>'\ndatabase_name = '<PROVIDE_YOUR_DATABASE_NAME>'\ncatalog_name = 'AwsDataCatalog'\n\nathena = boto3.client('athena', region_name=region_name)\n\npaginator = athena.get_paginator('list_table_metadata')\nresponse_iterator = paginator.paginate(\n CatalogName=catalog_name,\n DatabaseName=database_name\n)\n\ntable_names = []\nfor page in response_iterator:\n table_names.extend(\n (i['Name'] for i in page['TableMetadataList'])\n )\n\nprint(table_names)\n\n" ]

[ 2, 0 ]

[]

[]

[ "amazon_athena", "aws_glue", "boto3", "paginator", "python" ]

[ "Give this a try\nfrom panel import panel as pn\n\n" ]

[ 0 ]

[]

[]

[ "panel", "python" ]

[ "I'm using Selenium with Chrome, not Firefox, but the following worked for me:\n\ncapture page height before scrolling down;\nscroll down using key down;\ncapture page height after scrolling down;\nif page height was same before and after scrolling, stop scrolling\n\nMy code looks like this:\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver import Chrome\nfrom selenium.webdriver.common.keys import Keys\n\ndriver = webdriver.Chrome()\ndriver.get(\"www.yourTargetURL.com\")\n\nreached_page_end = False\nlast_height = driver.execute_script(\"return document.body.scrollHeight\")\n\nwhile not reached_page_end:\n driver.find_element_by_xpath('//body').send_keys(Keys.END) \n time.sleep(2)\n new_height = driver.execute_script(\"return document.body.scrollHeight\")\n if last_height == new_height:\n reached_page_end = True\n else:\n last_height = new_height\n\ndriver.quit()\n\n", "Just in case, if someone is using playwright. This code snippet is very similar to the ATJ answer.\nimport time\nfrom playwright.sync_api import sync_playwright\n\n\ndef run(playwright):\n page = playwright.chromium.launch(headless=False).new_page()\n page.goto(\"URL\")\n\n reached_end = False\n last_height = page.evaluate(\"() => document.body.scrollHeight\") # scrollHeight: 5879\n\n while not reached_end:\n page.keyboard.press(\"End\")\n time.sleep(2)\n\n new_height = page.evaluate(\"() => document.body.scrollHeight\")\n if new_height == last_height:\n reached_end = True\n else:\n last_height = new_height\n\n page.close()\n\n\nwith sync_playwright() as playwright:\n run(playwright)\n\n", "We can use a hard count while scrolling and as soon we reach that max count we get out of the loop.\n b=0;\n boolean x = true;\n while (x){\n WebElement button = null;\n try {\n button = driver.findElement(By.xpath(\"//*[@id='vjs_video_3']/div[7]/div[1]/button[1]\"));\n x= false;\n } catch (Exception ex){\n JavascriptExecutor js = (JavascriptExecutor) driver;\n js.executeScript(\"javascript:window.scrollBy(50, 80)\");\n \n try {\n Thread.sleep(500);\n } catch (InterruptedException e) {\n e.printStackTrace();\n } \n js.executeScript(\"javascript:window.scrollBy(50, 50)\"); \n b++;\n System.out.println(\"\\n\"+ b);\n if(b>50) {\n System.out.println(\"out!\");\n break;\n }\n\n// js.executeScript(\"javascript:window.scrollBy(50, 180)\");\n// Thread.sleep(1000);\n// js.executeScript(\"javascript:window.scrollBy(50, 150)\"); // button is missing\n }\n \n\n }\n}\n\n" ]

[ 2, 0, 0 ]

[ "You can check document.body.scrollTop by before and after each scroll attempt if there is no data to fetch then this value will stay the same \ndistanceToTop = driver.execute_script(\"return document.body.scrollTop);\")\n\n" ]

[ -1 ]

[ "python", "selenium" ]

[ "You can also do this:\nticket = Ticket.objects.filter(customer=customer).order_by(\"-id\").first() or None\nif ticket is not None: \n now = datetime.now().date()\n set_date = ticket.date_posted\n check_time = now - set_date <= timedelta(hours=24)\n if check_time:\n print('working')\n else:\n print('not working')\n context = {\"check_time\": check_time}\n\ninstead of:\nticket = Ticket.objects.filter(customer=customer).order_by(\"-id\")[0]\n\n", "Instead of this:\nticket = Ticket.objects.filter(customer=customer).order_by(\"-id\")[0]\n\nUse this using exists() which is a very efficient way if there is any object exist in DB:\ntickets = Ticket.objects.filter(customer=customer).order_by(\"-id\")\nif tickets.exists():\n ticket = tickets.first()\nelse:\n ticket = None\n\nUpdate\nYou can do the query inside the filter function.\ntickets = Ticket.objects.filter(customer=customer, date_posted__lte=timezone.now().date() - timedelta(hours=24))\n\ncontext = {\"check_time\": tickets.exists()}\n\n" ]

[ 2, 1 ]

[]

[]

[ "django", "python" ]

[ "You need specify column after groupby and call GroupBy.head without agg:\ndf_e_new = df.groupby(pd.Grouper(key=\"Date\",freq=\"D\"))[df.columns[33]].head(3)\n \n\nOr use SeriesGroupBy.nlargest for top3 sorted values:\ndf_e_new = df.groupby(pd.Grouper(key=\"Date\",freq=\"D\"))[df.columns[33]].nlargest(3)\n\nFor sum top3 values use lambda function:\ndf_e_new = (df.groupby(pd.Grouper(key=\"Date\",freq=\"D\"))[df.columns[33]]\n .agg(lambda x: x.nlargest(3).sum())\n .reset_index(name='top3sum'))\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "You can change the background color by specifying a Hex Color Code in the following argument under layout:\nbackground_color='#DAE0E6'\n\nYou can use a Color Picker like this one https://htmlcolorcodes.com/color-picker/ to get your color\nYou can also use:\nwindow = sg.Window('Virus Simulation', layout, background_color='hex_color_code')\n\nTo change the color of a window object\n", "I'm a tad confused in 2 ways.\n1 - I don't see a call to sg.theme() in your code, so I don't know where it was in the code. WHERE it is placed matters. Always place the theme as early as possible, definitely before making your layout.\n2 - I don't know what \"it isn't working this time means\". Again, it's more of needing to see a complete example to get it.\nThe sample code in the question that you said normally works was weirdly formatted so something must have been scrambled.\nThe question shows: sg.theme=\"color\nBut as Jason has pointed out, the value passed to sg.theme() is more than a color. The \"Theme Name Formula\" is described in the main PySimpleGUI documentation here - https://pysimplegui.readthedocs.io/en/latest/#theme-name-formula. In case there's a problem getting to that section, here's what it says:\nTheme Name Formula\nThemes names that you specify can be \"fuzzy\". The text does not have to match exactly what you see printed. For example \"Dark Blue 3\" and \"DarkBlue3\" and \"dark blue 3\" all work.\n\nOne way to quickly determine the best setting for your window is to simply display your window using a lot of different themes. Add the line of code to set the theme - theme('Dark Green 1'), run your code, see if you like it, if not, change the theme string to 'Dark Green 2' and try again. Repeat until you find something you like.\n\nThe \"Formula\" for the string is:\n\nDark Color #\n\nor\n\nLight Color #\n\nColor can be Blue, Green, Black, Gray, Purple, Brown, Teal, Red. The # is optional or can be from 1 to XX. Some colors have a lot of choices. There are 13 \"Light Brown\" choices for example.\n\nIf you want to only change the background color of your theme, then you can use individual color names or hex values. sg.theme_background_color('#FF0000') or sg.theme_background_color('red') will set the background color to red.\nHope that helps with themes.\n\nNice work on using the PSG coding conventions. Looking at your code was effortless as a result. Zero guesswork as to what I was seeing. Great to see and it helps in numerous ways when you use them.\n", "I tinkered to change the background color and found this solution.\n\nCreate a theme as a dictionary\nAdd the theme with theme_add_new function\nSpecify this theme\n\nThe question code edited:\nimport PySimpleGUI as sg\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n\nnew_theme = {\"BACKGROUND\": '#DAE0E6', \"TEXT\": sg.COLOR_SYSTEM_DEFAULT, \"INPUT\": sg.COLOR_SYSTEM_DEFAULT,\n \"TEXT_INPUT\": sg.COLOR_SYSTEM_DEFAULT, \"SCROLL\": sg.COLOR_SYSTEM_DEFAULT,\n \"BUTTON\": sg.OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, \"PROGRESS\": sg.COLOR_SYSTEM_DEFAULT, \"BORDER\": 1,\n \"SLIDER_DEPTH\": 1, \"PROGRESS_DEPTH\": 0\n }\n\nsg.theme_add_new('MyTheme', new_theme)\nsg.theme('MyTheme')\n\n\ndef draw_figure(canvas, fig):\n if canvas.children:\n for child in canvas.winfo_children():\n child.destroy()\n figure_canvas_agg = FigureCanvasTkAgg(fig, master=canvas)\n figure_canvas_agg.draw()\n figure_canvas_agg.get_tk_widget().pack(side='right', fill='both', expand=1)\n\n\n# ------------------------------- PySimpleGUI CODE\n\nlayout = [\n [sg.Text(\"Population size: \"), sg.Input(key=\"-POPULATIONSIZE-\")],\n [sg.Text(\"Duration: \"), sg.Input(key=\"-DURATION-\")],\n [sg.Text(\"R Number: \"), sg.Input(key=\"-RNUMBER-\")],\n [sg.Text(\"Starting Infections: \"), sg.Input(key=\"-STARTINGINFECTIONS-\")],\n [sg.B('OK'), sg.B('Exit')],\n [sg.Canvas(key='controls_cv')],\n [sg.T('Figure:')],\n [sg.Column(\n layout=[\n [sg.Canvas(key='fig_cv',\n size=(400 * 2, 400)\n )]\n ],\n # background_color='#DAE0E6',\n pad=(0, 0)\n )],\n]\n\nwindow = sg.Window('Virus Simulation', layout,)\n\nwhile True:\n event, values = window.read()\n print(event, values)\n if event in (sg.WIN_CLOSED, 'Exit'):\n break\n elif event == 'OK':\n # ------------------------------- PASTE YOUR MATPLOTLIB CODE HERE\n plt.figure(1)\n fig = plt.gcf()\n DPI = fig.get_dpi()\n # ------------------------------- you have to play with this size to reduce the movement error when the mouse hovers over the figure, it's close to canvas size\n fig.set_size_inches(404 * 2 / float(DPI), 404 / float(DPI))\n # -------------------------------\n x = list(range(1, 100))\n y = list(range(1, 100))\n plt.plot(x, y, color=\"r\")\n plt.title('Virus Infections Data')\n plt.xlabel('Time in Days')\n plt.ylabel('Infections')\n plt.grid()\n\n # ------------------------------- Instead of plt.show()\n draw_figure(window['fig_cv'].TKCanvas, fig,)\n\n\nwindow.close()\n\n" ]

[ 3, 0, 0 ]

[]

[]

[ "pysimplegui", "python", "tkinter", "user_interface" ]

[ "I figured out the solution:\nheart_x += 0 - heart_speed_x\n\nand\nheart_y += 0 - heart_speed_y\n\nThe problem was that the either or both of the x and y position constantly changed after hitting the surface. This was because the speed of the variables stayed the same. So in the solution, I made it so that the x any y positions only change once once it conditionally checks for collision.\n" ]

[ 0 ]

[]

[]

[ "pygame", "python" ]

[ "After a few days of researching I cloned the repository and found in the login.js file this piece of code:\n// redirect to next URL if it was provided\nlet new_path = this.parseGETParam('next') || '/admin';\nwindow.location = origin + new_path;\n\nThe only thing to do is to change /admin to /home or whatever path you want to put. No need to change anything in the callback function.\nI hope it will help someone.\n" ]

[ 0 ]

[]

[]

[ "django", "django_authentication", "python" ]

[ "First off 500 errors are something is wrong on Googles end. There is nothing you can do to fix it. Its often caused by the server being over loaded, and your script timing out.\nA tip would be to never run on the hour, everyone that has a cron job set up has it set to run on the hour and your going to be completing for server processing with everyone else.\nI cant see anything wrong with the code you have.\nPRO tip. the google apis python client library has back off built in. So if your seeing a 500 error the library has already retried the call about ten times before you are even seeing the error.\n" ]

[ 1 ]

[]

[]

[ "google_analytics_api", "google_api", "google_api_python_client", "python" ]

[ "change\nfrom rest_framework import serializers\nfrom forum.models import Thread\nfrom forum.models import Post\n\nclass ThreadSerializer(serializers.ModelSerializer):\n class Meta: \n Thread_Model = Thread\n Thread_Fields = ['id', 'thread_id', 'title', 'desc', 'created_at']\n\n\nclass PostSerializer(serializers.ModelSerializer):\n class Meta:\n Post_Model = Post\n Post_Fields = ['id', 'post_id', 'post_content', 'post_time', 'thread_id']\n\nto:\nfrom rest_framework import serializers\nfrom forum.models import Thread\nfrom forum.models import Post\n\nclass ThreadSerializer(serializers.ModelSerializer):\n class Meta: \n model = Thread\n fields = ['id', 'thread_id', 'title', 'desc', 'created_at']\n\n\nclass PostSerializer(serializers.ModelSerializer):\n class Meta:\n model = Post\n fields = ['id', 'post_id', 'post_content', 'post_time', 'thread_id']\n\n" ]

[ 0 ]

[]

[]

[ "django", "postman", "python", "request" ]

[ "fuzzyset indexes strings by their bigrams and trigrams so it finds approximate matches in O(log(N)) vs O(N) for difflib. For my fuzzyset of 1M+ words and word-pairs it can compute the index in about 20 seconds and find the closest match in less than a 100 ms.\n", "Perhaps you can build an index of the trigrams (three consecutive letters) that appear in each list. Only check strings in a against strings in b that share a trigram.\nYou might want to look at the BLAST bioinformatics tool; it does approximate sequence alignments against a sequence database.\n", "RapidFuzz\nis the super-fast lib for fuzzy string matching. It has the same API as famous fuzzywuzzy, but times faster and MIT licensed.\n", "Try this\nhttps://code.google.com/p/pylevenshtein/\nThe Levenshtein Python C extension module contains functions for fast computation of - Levenshtein (edit) distance, and edit operations - string similarity - approximate median strings, and generally string averaging - string sequence and set similarity It supports both normal and Unicode strings.\n", "I had tried few methods for fuzzy match. the best one was cosine similarity, with threshold as per your need (i kept 80% fuzzy match).\n", "Benchmarks in 2022\ntl;dr: RapidFuzz was fastest.\nTest: Pick the best string match from 1.000.000 elements.\nTested on my old i7 notebook with 32gb RAM.\nBest to worst:\n\nRapidFuzz (drop-in replacement for TheFuzz): ~20ms\nfuzzyset2: ~320ms\nTheFuzz (ex fuzzywuzzy): ~7s\n\n" ]

[ 7, 3, 3, 1, 0, 0 ]

[]

[]

[ "difflib", "fuzzy_comparison", "levenshtein_distance", "performance", "python" ]