Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 11

[ "The width of the bar chart is set by the number of bars in the list. The value 1.0 sets the interval to zero. So as an example, the width 0.8 is used to list for the category variable. I have reproduced the similar horizontal bar chart in the reference using your decorations.\nfig = go.Figure(go.Bar(\n x=top_10_belg['ID'],\n y=top_10_belg['Description'],\n width=[0.8]*len(top_10_belg['Description']), # update\n marker=dict(color='rgba(50, 171, 96, 0.6)',\n line=dict(color='rgba(50, 171, 96, 1.0)',\n width=1)),\n orientation='h'))\n\n\nSince increasing the x-axis is the same as increasing the width of the graph size, the width of the x-axis can be increased by adding the following code.\nfig.update_layout(autosize=True, width=1000)\n\n\n" ]

[ 0 ]

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[]

[ "plotly", "python" ]

[ "your question is bit unclear, according to your question what i understood is that you want to set every product in product with a particular quantity which is in your case will be the minimum quantity?\nif so then you have to inherit the product.product and create a method to search all records and then update the in_hand_qty to your desired minimum value as you have set it already on a field\n" ]

[ 1 ]

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[]

[ "odoo", "odoo_15", "python", "ubuntu" ]

[ "In general newlines (as typically used in Linux) are more portable than carriage return and then newline (as used in Windows). Note also that if you store your code on GitHub or another Git repository, it will convert it properly without you having to do anything.\n", "As John Messenger states, newlines (\\n) as opposed to carriage-return/linefeeds (\\n\\r) are pretty much the canonical line ending everywhere, except for dedicated Windows-only enclaves.\nTo support this, git has the core.autocrlf option. When enabled on Windows systems, files with standard newline endings will be converted to CRLF when checked out, and converted back on the way back in.\nThis seems to be a seamless way to cope with the problem of Windows line-endings when running GUI editors and IDEs on Windows. Your editor is probably expecting CRLF line endings on Windows and will get them when using git with this option, which I believe is set to true by default on Windows installs.\nIf you're not using Git, maybe it's time you did? Even when working alone it has lots of value.\nHowever, some users will be using GitBash / Cygwin / Ming tools on Windows, possibly with GitBash supplied vim, and will not appreciate CRLF line-endings. Those users can turn core.autocrlf off (false) before cloning a repo to avoid inappropriate \"corrections\", and thereby see proper newline-only line endings when editing files. This may also help when using other Linux tools when running on Windows that also expect newline line endings.\nGit has three levels of settings: \"system\" (probably set in C:\\Program Files\\...), \"global\" (set in your home directory Git config file and affecting all your repos) and \"local\" set in each repo's .git/config file. Latter levels override the former levels, which can be helpful if your organisation has locked down your C drive.\nHere are some of the Git commands to query and update your settings (and you can just edit the configuration files themselves as well):\n\ngit config --list --show-origin: Lists all your settings and the file locations where they are set. A setting may be repeated so the lower level will override the higher.\ngit config --get core.autocrlf: Get the effective setting from the combination of settings you currently have in place.\ngit config --system core.autocrlf false: Switch off automatic conversion at the system level, for all repos.\ngit config --global core.autocrlf false: Switch off automatic conversion in all repos from your home directory config file.\n\nRemember, set core.autocrlf to what you need before you clone. Reference here: https://docs.github.com/en/get-started/getting-started-with-git/configuring-git-to-handle-line-endings\n" ]

[ 1, 0 ]

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[]

[ "line_endings", "python" ]

[ "This has nothing to do with Python but with the underlying OS. If you save a text file on Windows, you get CRLF linebreaks, if you save it on Mac/Unix systems, you get LF linebreaks (and on stone-age Macs, CR linebreaks).\nUse an editor that allows you to preserve the line break format of your files. No, Notepad doesn't, but most editors I've seen do. UltraEdit and EditPadPro are the ones I know, and I can recommend both. I'm pretty sure that IDEs like PyDev/Eclipse will handle that too, but I haven't tried.\n", "Make a Python script which replaces '\\r\\n' with '\\n' \nAn run it every time you give the code to your boss.\n:)\n", "I've had the same problem. Yes, doing a simple find-and-replace should fix this problem. However you'll have to do it every time you share code; you might want to think about automating it somehow. I never did do that myself.\n", "This sed one-liner will convert the Python script to have the desired line endings:\nsed 's/\\r\\n/\\n/g' <windows_script >unix_script\n\n", "newlines (\\n), as opposed to carriage-return/linefeeds (\\n\\r), are pretty much the canonical line ending everywhere, except for dedicated Windows-only enclaves.\nYou shouldn't really be repeatedly manually converting line endings. That's not really a \"Good Thing\" Ⓡ.\nTo support this, git has the core.autocrlf option. When enabled on Windows systems, files with standard newline endings will be converted to CRLF when checked out, and converted back on the way back in.\nThis seems to be a seamless way to cope with the problem of Windows line-endings when running GUI editors and IDEs on Windows. Your editor is probably expecting CRLF line endings on Windows and will get them when using git with this option, which I believe is set to true by default on Windows installs.\nIf you're not using Git, maybe it's time you did? Even when working alone it has lots of value. You can version your own code for backing up and providing alternates.\nSome users will be using GitBash / Cygwin / Ming tools on Windows, possibly with GitBash supplied vim, and will not appreciate CRLF line-endings. Those users can turn the Git core.autocrlf setting off (false) before cloning a repo to avoid inappropriate \"corrections\", and thereby see proper newline-only line endings when editing files, even on Windows. This may also help when using other Linux tools when running on Windows that also expect newline line endings.\nGit has three levels of settings: \"system\" (probably set in C:\\Program Files\\...), \"global\" (set in your home directory Git config file and affecting all your repos) and \"local\" set in each repo's .git/config file. Latter levels override the former levels, which can be helpful if your organisation has locked down your C drive.\nHere are some of the Git commands to query and update your settings (and you can just edit the configuration files themselves as well):\n\ngit config --list --show-origin: Lists all your settings and the file locations where they are set. A setting may be repeated so the lower level will override the higher.\ngit config --get core.autocrlf: Get the effective setting from the combination of settings you currently have in place.\ngit config --system core.autocrlf false: Switch off automatic conversion at the system level, for all repos.\ngit config --global core.autocrlf false: Switch off automatic conversion in all repos from your home directory config file.\n\nRemember, set core.autocrlf to what you need before you clone. Reference here: https://docs.github.com/en/get-started/getting-started-with-git/configuring-git-to-handle-line-endings\n" ]

[ 1, 1, 0, 0, 0 ]

[]

[]

[ "line_breaks", "python", "python_2.x" ]

[ "I have the same error, as a result it is not logging dict training logs. I'm training using PyTorch Lightning in DDP. I works on single GPU but gives this warming on multi-gpu system with DDP.\n" ]

[ 0 ]

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[]

[ "python", "pytorch", "state_dict", "warnings" ]

[ "I resolved the issue by adding pytube support. You can see how I did it here.\nhttps://github.com/flyinggoatman/YouTube-Link-Extractor\n" ]

[ 0 ]

[]

[]

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

[ "You need to add break after the line tkMessageBox.askokcancel(\"System\",\"logged\",). Also the else block should be in same indentation of the for line.\nBelow is the modified Chek():\n def Chek():\n for line in open(\"users.txt\", \"r\").readlines():\n loginn_info = line.split()\n if name.get() == loginn_info[1] and passwd.get() == loginn_info[2]:\n tkMessageBox.askokcancel(\"System\", \"logged\")\n break # break out the loop \n else:\n # credential not found\n tkMessageBox.askokcancel(\"System\", \"Error\")\n\n" ]

[ 0 ]

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[]

[ "loops", "messagebox", "python", "tkinter" ]

[ "so i figured it out.\nwhen making that call from Flutter WEB YOU MUST include:\nvar profilepic = await http.MultipartFile.fromBytes(\n\"profilepic\", file, filename: 'hello.png');\nThe filename. this is needed for django restframework and multiformparser to be able to save it as image.\n" ]

[ 1 ]

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[]

[ "django", "django_rest_framework", "flutter", "multipartform_data", "python" ]

[ "from gtts import gTTS\nfrom io import BytesIO\nfrom pygame import mixer\nimport time\n\ndef speak():\n mp3_fp = BytesIO()\n tts = gTTS('KGF is a Great movie to watch', lang='en')\n tts.write_to_fp(mp3_fp)\n tts.save(\"Audio.mp3\")\n return mp3_fp\n\nmixer.init()\nsound = speak()\nsound.seek(0)\nmixer.music.load(sound, \"mp3\")\nmixer.music.play()\n\n" ]

[ 0 ]

[]

[]

[ "google_api", "language_translation", "python" ]

[ "You have to access the span element within h3 to get your desired output\nChange\ntop_post = posts[0].find(\n\"h3\", class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__medium__1kbOh text__heading_3__1kDhc heading__base__2T28j heading__heading_3__3aL54 hero-card__title__33EFM\").text.strip()\n\nto\ntop_post = posts[0].find(\n\"h3\", class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__medium__1kbOh text__heading_3__1kDhc heading__base__2T28j heading__heading_3__3aL54 hero-card__title__33EFM\").find_all(\"span\")[0].text.strip()\n\nFull Code\nimport requests\nfrom bs4 import BeautifulSoup\n\npage = requests.get(\"https://www.reuters.com/business/future-of-money/\")\nsoup = BeautifulSoup(page.content, \"html.parser\")\nhome = soup.find(class_=\"editorial-franchise-layout__main__3cLBl\")\nposts = home.find_all(class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__inherit-font__1Y8w3 text__inherit-size__1DZJi link__underline_on_hover__2zGL4\")\ntop_post = posts[0].find(\n\"h3\", class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__medium__1kbOh text__heading_3__1kDhc heading__base__2T28j heading__heading_3__3aL54 hero-card__title__33EFM\").find_all(\"span\")[0].text.strip()\n\nprint(top_post)\n\nOutput\nFTX ex-CEO Bankman-Fried claims he was unaware of improper use of customer funds -ABC News\n\n" ]

[ 1 ]

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[]

[ "beautifulsoup", "python", "web_scraping" ]

[ "In your example, k is a float, not an integer. The n_neighbors value in KNeighborsClassifier(n_neighbors=k, p=2,metric='euclidean') has to be an integer, not a float.\nYou could convert k into an integer in this example using the math.ceil() function which return the integer that is equal to or greater than the float value. Alternately, you could use the math.floor() function which will return the integer that less than or equal to the input float.\nFor example:\n#Determine k by using sqrt\nimport math\nk = math.sqrt(len(Y_test))\nprint(k)\n#Make k uneven\nk = k-1\nk = math.ceil(k)\nprint(k) # should now be an integer\nprint(type(k)) # <class 'int'>\n\n#KNN Model\nclassifer = KNeighborsClassifier(n_neighbors=k, p=2, metric='euclidean')\nclassifer.fit(X_train, Y_train)\n\n" ]

[ 1 ]

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[]

[ "knn", "pandas", "python", "scikit_learn" ]

[ "There's nothing magical about f-strings. The bad behavior must be caused by something else.\nThere is absolutely no difference in the return values of these two functions:\ndef hello():\n return \"Hello John\"\n\ndef hello_f():\n name = \"John\"\n return f\"Hello {name}\"\n\nAnyone calling these functions would see exactly the same return value: a plain old string. The caller would have absolutely no way of knowing that the string was generated using an f-string template.\nSo there must be some actual difference in the content of the regular string you're using, vs. the output of the build_message() function.\n" ]

[ 3 ]

[]

[]

[ "flask", "python", "twilio", "twilio_api", "whatsapi" ]

[ "The output is correct. \\t adds a variable number of spaces so that the next printed character is at a position which is a multiple of 8 (or whatever is configured in your terminal).\nIn your example the first \\t adds 8 spaces, then you print abhinav (7 characters), the next tab adds 1 space to make it a multiple of 8, then the , in your print statements adds 1 space, then you print gupta:\n a b h i n a v g u p t a \n1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8\n└───── \\t ────┘ \\t, └\\t┘\n\nIf you always want to print 8 spaces, use \" \".\n", "When you print a tab character, Python uses it to align the string to the end of a 4 character boundary so that the next thing that gets printed is aligned to the next boundary. So you will not always get 4 spaces. Rather, you'll get between 1 and 4 spaces. Here's some code to demonstrate this:\nprint('\\ta\\t','bbbb')\nprint('\\taa\\t','bbbb')\nprint('\\taaa\\t','bbbb')\nprint('\\taaaa\\t','bbbb')\nprint('\\taaaaa\\t','bbbb')\nprint('1234567890123456')\n\nResult:\n a bbbb\n aa bbbb\n aaa bbbb\n aaaa bbbb\n aaaaa bbbb\n1234567890123456\n\nThe bbbb values are aligned to the 10th and 14th positions rather than the 9th and 13th because of the extra space character that the Python print() function adds between terms. The second string being aligned to 4 character boundaries is ' bbbb'.\n" ]

[ 0, 0 ]

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[]

[ "printing", "python", "string", "tabs" ]

[ "These is no known vulnerability to decrypt type 9 cisco password, so the answer is that you can not decrypt it.\n" ]

[ 0 ]

[]

[]

[ "cisco", "cracking", "passwords", "python", "scrypt" ]

[ "Maybe something like this, where it iterates over every message sent in the current channel?\[email protected]()\nasync def some_command(ctx):\n mentions = 0\n\n async for message in ctx.channel.history(limit=10000000): # set limit to some big number\n if ctx.author in message.mentions:\n mentions += 1\n\n await ctx.send(mentions)\n\nThe above code counts the number of times the author is mentioned in the current channel.\nOf course, this is highly inefficient, but it gets the job done.\n\nIf you wish to add more parameters (such as the user and the channel):\[email protected]()\nasync def better_command(ctx, user: discord.Member, channel: discord.TextChannel):\n mentions = 0\n\n async for message in channel.history(limit=10000000): # set limit to some big number\n if user in message.mentions:\n mentions += 1\n\n await ctx.send(mentions)\n\n" ]

[ 1 ]

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[]

[ "discord.py", "python" ]

[ "from gtts import gTTS\nfrom io import BytesIO\nfrom pygame import mixer\nimport time\n\ndef speak():\n mp3_fp = BytesIO()\n tts = gTTS('You need to read documetation properly', lang='en')\n tts.write_to_fp(mp3_fp)\n tts.save(\"Audio.mp3\")\n return mp3_fp\n\nmixer.init()\nsound = speak()\nsound.seek(0)\nmixer.music.load(sound, \"mp3\")\nmixer.music.play()\n\n", "It was lang = 'en-US' that caused the error. It's simply: lang = 'en'\n" ]

[ 1, 0 ]

[]

[]

[ "gtts", "operating_system", "python" ]

[ "You can access the fields inside the from block by using square bracket notation instead of dot notation. Here is how your code would look with the changes:\n\ndata=json.dumps(\n{\n \"update_id\": 992108054,\n \"message\": {\n \"delete_chat_photo\": False,\n \"new_chat_members\": [],\n \"date\": 1669931418,\n \"photo\": [],\n \"entities\": [],\n \"message_id\": 110,\n \"group_chat_created\": False,\n \"caption_entities\": [],\n \"new_chat_photo\": [],\n \"supergroup_chat_created\": False,\n \"chat\": {\n \"type\": \"private\",\n \"first_name\": \"test_name\",\n \"id\": 134839552,\n \"last_name\": \"test_l_name\",\n \"username\": \"test_username\"\n },\n \"channel_chat_created\": False,\n \"text\": \"test_text\",\n \"from\": {\n \"last_name\": \"test_l_name\",\n \"is_bot\": False,\n \"username\": \"test_username\",\n \"id\": 134839552,\n \"first_name\": \"test_name\",\n \"language_code\": \"en\"\n }\n }\n})\nx = json.loads(data, object_hook=lambda d: SimpleNamespace(**d))\nprint(x.message.text,\n x.message.chat\n )\nprint(x.message[\"from\"])\n\nIn Python, you use square bracket notation [] to access dictionary keys, and dot notation . to access object attributes. In this case, from is a reserved keyword in Python, so you cannot use dot notation to access it directly. Instead, you must use square bracket notation to access it.\n(ChatGPT AI assisted with this answer)\n", "As from is a keyword, try this x.message.__getattribute__('from'):\n>>> x.message.__getattribute__('from')\nnamespace(last_name='test_l_name',\n is_bot=False,\n username='test_username',\n id=134839552,\n first_name='test_name',\n language_code='en')\n\nAnd to get one of its attribute, say username, you could access like what you have been doing:\n>>> x.message.__getattribute__('from').username\n'test_username'\n\n" ]

[ 0, 0 ]

[]

[]

[ "json", "python", "telegram_bot" ]

[ "Take a look at the docs here\nIf you have an Embed object, obj just use obj.author to get the author. The text I'm assuming you mean the title, which can be accessed by obj.title.\n", "Assuming you have a Message object,\nFirst, to get message author:\nauthor = message.author\n\nTo get the embed from the message:\nembed = discord.Embed.from_data(message.embeds[0]) # gets embed from message\n\nBefore getting the embed text, we first look at the attributes of a discord.Embed object. It has the attributes title and description [source].\nTherefore:\nembedTitle = embed.title # get title\nembedDescription = embed.description # get description\n\n" ]

[ 1, 0 ]

[]

[]

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

[ "Remove Numpy Package and install again. The error will go away.\n" ]

[ 0 ]

[]

[]

[ "importerror", "pandas", "python" ]

[ "You are trying to install onto a mac with an arm processor (see platform : osx-arm64 in the output of conda info). But https://anaconda.org/ml4t/zipline-reloaded shows the channel you are attempting to install from does not have an osx-arm64 build.\n", "Will Holtz was right in pointing out that the problem stems from my being on an M1 Mac and there not yet being a build of zipline-reloaded for M1 Macs. A full answer, however, would also give a resolution. The solution below, copied from here, did the trick for me:\nCONDA_SUBDIR=osx-64 conda create -n [environment] # create a new environment\nconda activate [environment]\nconda env config vars set CONDA_SUBDIR=osx-64 # subsequent commands use intel packages\n\n" ]

[ 1, 0 ]

[]

[]

[ "anaconda", "conda", "mini_forge", "python" ]

[ "If the \"date\" key was actually a date (which currently isn't), this would have worked:\nfor j in range(len(dr)):\n for k in range(j + 1, len(dr)):\n if dr[j][0][\"date\"] < dr[k][0][\"date\"]:\n dr[j], dr[k] = dr[k], dr[j]\n\nYour problem is, because dr is a list of list of dictionaries (i.e. not a list of dictionaries) you have to compare the first date in the list (assuming that all dates within one sublist is equal).\n", "You must be getting an error from attempting to reference a date key from the inner list objects. What you want to do is first grab the first dictionary from each inner list, using [0], and then reference and compare the date key within that dictionary. Also, your use of < will give you a reverse sort. Use > to get the order you show being what you desire. This works:\nfor j in range(len(dr)):\n for k in range(j+1,len(dr)):\n if dr[j][0][\"date\"] > dr[k][0][\"date\"]:\n dr[j],dr[k]=dr[k],dr[j]\n\nNote that this only works after adding quotes around your date strings, and then only works because those strings happen to do the right thing when compared as simple strings. For example, if you had a 2 digit month, I think your sorting wouldn't be right. You should really have datetime objects in your structure and be comparing those.\n" ]

[ 0, 0 ]

[]

[]

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

[ "You need member intents for this to function.\nFor more information on how to enable member intents, read the official documentation, or this answer that explains it quite clearly.\n" ]

[ 0 ]

[]

[]

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

[ "One way to approach it is to convert your instance of the super class into an instance of the subclass using the __class__ attribute.\nclass A:\n def __init__(self):\n self.A_attribute = 'from_A'\n\n\nclass B(A):\n def __init__(self):\n #Create an instance of B from scratch\n self.B_attribute = 'from_B'\n super().__init__()\n\n def init_A(self):\n #Initializes an instance of B from what is already an instance of A\n self.B_attribute = 'from_B'\n\nmyA = A()\nmyA.__class__ = B\nmyA.init_A()\n\n" ]

[ 0 ]

[]

[]

[ "constructor", "inheritance", "python", "super" ]

[ "Example\ndata = {'Time': {0: '2014-09-01 00:00:00', 1: '2014-09-01 01:00:00'},\n 'Humidity': {0: 84, 1: 94},\n 'Condition': {0: 'Cloudy', 1: 'Rainy'}}\ndf = pd.DataFrame(data)\n\ndf\n Time Humidity Condition\n0 2014-09-01 00:00:00 84 Cloudy\n1 2014-09-01 01:00:00 94 Rainy\n\nCode\ni make code for 20T instead 5T, becuz 5T is too short.\n(df.set_axis(pd.to_datetime(df['Time']))\n .reindex(pd.date_range(df['Time'][0], freq='20T', periods=6))\n .assign(Time=lambda x: x.index)\n .reset_index(drop=True).ffill())\n\nresult:\n Time Humidity Condition\n0 2014-09-01 00:00:00 84.0 Cloudy\n1 2014-09-01 00:20:00 84.0 Cloudy\n2 2014-09-01 00:40:00 84.0 Cloudy\n3 2014-09-01 01:00:00 94.0 Rainy\n4 2014-09-01 01:20:00 94.0 Rainy\n5 2014-09-01 01:40:00 94.0 Rainy\n\n" ]

[ 1 ]

[]

[]

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

[ "This is happening because field 'sub_title' has values earlier which are not valid json.\nTable probably already has values for 'sub_title' field and you are changing trying to change earlier fieldtype to jsonfield if this is the case, you should update all values to valid json first.\n" ]

[ 0 ]

[]

[]

[ "django", "python" ]

[ "This should scale well to any depth:\nreduce(lambda obj, attr : getattr(obj, attr, None), (\"id\",\"num\"), myobject)\n\n", "getattr(getattr(myobject, \"id\", None), \"number\", None)\n\nshould work.\n", "my favorites are\nfrom functools import reduce\ntry:\n a = reduce(getattr, (\"id\", \"number\"), myobject)\nexcept AttributeError:\n a = None\n\nor\nfrom operator import attrgetter\ntry:\n a = attrgetter('id.number')(myobject)\nexcept AttributeError:\n a = None\n\n", "Here's a one-liner\na = myobject is not None and myobject.id.number or None\n\nIt doesn't check whether id is None, but that wasn't part of the original question.\n", "A slightly over generic solution keeping in view all members:\nif myobject and myobject.id and myobject.id.number:\n a = myobject.id.number\nelse:\n a = None\n\n", "return myobject.id.number if myobject else None\n\n", "I use the following function which does it for any level.\ndef Resolve(object, attribute:str):\n \"\"\"\n Resolve attribute of an object.\n \"\"\"\n attribute_list = attribute.split(\".\")\n obj = object\n try:\n for att in attribute_list:\n obj = getattr(obj, att)\n except AttributeError:\n obj = None\n \n return obj\n\nTo use it you write:\na = Resolve(myobject, 'id.number')\n\nThe code simply splits the string using the the period character and loops through the attributes. If you had another level say `myobject.id.number.another' you would use:\na = Resolve(myobject, 'id.number.another')\n\n" ]

[ 7, 6, 4, 1, 0, 0, 0 ]

[]

[]

[ "attributes", "object", "python" ]

[ "This will be more complicated that it looks:\n\nTo use session.add_packages('geoip2') in Snowflake you need to accept the Anaconda terms. This is easy if you can ask your account admin.\nBut then you can only get the packages that Anaconda has added to Snowflake in this way. The list is https://repo.anaconda.com/pkgs/snowflake/, and I don't see geoip2 there yet.\nSo you will need to package you own Python code (until Anaconda sees enough requests for geoip2 in the wishlist). I described the process here https://medium.com/snowflake/generating-all-the-holidays-in-sql-with-a-python-udtf-4397f190252b.\nBut wait! GeoIP2 is not pure Python, so you will need to wait until Anaconda packages the C extension libmaxminddb. But this will be harder, as you can see their docs don't offer a straightforward way like other pip installable C libraries.\n\nSo this will be complicated.\nThere are other alternative paths, like a commercial provider of this functionality (like I describe here https://medium.com/snowflake/new-in-snowflake-marketplace-monetization-315aa90b86c).\nThere other approaches to get this done without using a paid dataset, but I haven't written about that yet - but someone else might before I get to do it.\nBtw, years ago I wrote something like this for BigQuery (https://cloud.google.com/blog/products/data-analytics/geolocation-with-bigquery-de-identify-76-million-ip-addresses-in-20-seconds), but today I was notified that Google recently deleted the tables that I had shared with the world (https://twitter.com/matthew_hensley/status/1598386009129058315).\nSo it's time to rebuild in Snowflake. But who (me?) and when is still a question.\n" ]

[ 0 ]

[]

[]

[ "python", "snowflake_cloud_data_platform", "snowpark", "user_defined_functions" ]

[ "try this:\ntable = boto3.resource(\"dynamodb\", \"region\").Table('table_name')\nresponse = table.query(KeyConditionExpression=Key(\"Key\").eq(\"Whatever\"))\n\n" ]

[ 0 ]

[]

[]

[ "boto3", "dynamodb_queries", "python" ]

[ "We are using markers with addoption in conftest.py\ntestcase:\[email protected]_cmd\ndef test_skip_if_no_command_line():\n assert True\n\nconftest.py:\nin function\ndef pytest_addoption(parser):\n parser.addoption(\"--no_cmd\", action=\"store_true\",\n help=\"run the tests only in case of that command line (marked with marker @no_cmd)\")\n\nin function\ndef pytest_runtest_setup(item):\n if 'no_cmd' in item.keywords and not item.config.getoption(\"--no_cmd\"):\n pytest.skip(\"need --no_cmd option to run this test\")\n\npytest call:\n py.test test_the_marker \n -> test will be skipped\n\n py.test test_the_marker --no_cmd\n -> test will run\n\n", "The pytest documentation offers a nice example on how to skip tests marked \"slow\" by default and only run them with a --runslow option:\n# conftest.py\n\nimport pytest\n\n\ndef pytest_addoption(parser):\n parser.addoption(\n \"--runslow\", action=\"store_true\", default=False, help=\"run slow tests\"\n )\n\n\ndef pytest_configure(config):\n config.addinivalue_line(\"markers\", \"slow: mark test as slow to run\")\n\n\ndef pytest_collection_modifyitems(config, items):\n if config.getoption(\"--runslow\"):\n # --runslow given in cli: do not skip slow tests\n return\n skip_slow = pytest.mark.skip(reason=\"need --runslow option to run\")\n for item in items:\n if \"slow\" in item.keywords:\n item.add_marker(skip_slow)\n\nWe can now mark our tests in the following way:\n# test_module.py\nfrom time import sleep\n\nimport pytest\n\n\ndef test_func_fast():\n sleep(0.1)\n\n\[email protected]\ndef test_func_slow():\n sleep(10)\n\nThe test test_func_fast is always executed (calling e.g. pytest). The \"slow\" function test_func_slow, however, will only be executed when calling pytest --runslow.\n", "There are two ways to do that:\nFirst method is to tag the functions with @pytest.mark decorator and run / skip the tagged functions alone using -m option.\[email protected]\ndef test_calc_add():\n assert True\n\[email protected]\ndef test_calc_multiply():\n assert True\n\ndef test_calc_divide():\n assert True\n\nRunning the script as py.test -m anytag test_script.py will run only the first two functions.\nAlternatively run as py.test -m \"not anytag\" test_script.py will run only the third function and skip the first two functions.\nHere 'anytag' is the name of the tag. It can be anything.!\nSecond way is to run the functions with a common substring in their name using -k option.\ndef test_calc_add():\n assert True\n\ndef test_calc_multiply():\n assert True\n\ndef test_divide():\n assert True\n\nRunning the script as py.test -k calc test_script.py will run the functions and skip the last one.\nNote that 'calc' is the common substring present in both the function name and any other function having 'calc' in its name like 'calculate' will also be run.\n", "Following the approach suggested in the pytest docs, thus the answer of @Manu_CJ, is certainly the way to go here.\nI'd simply like to show how this can be adapted to easily define multiple options:\nThe canonical example given by the pytest docs highlights well how to add a single marker through command line options. However, adapting it to add multiple markers might not be straight forward, as the three hooks pytest_addoption, pytest_configure and pytest_collection_modifyitems all need to be evoked to allow adding a single marker through command line option.\nThis is one way you can adapt the canonical example, if you have several markers, like 'flag1', 'flag2', etc., that you want to be able to add via command line option:\n# content of conftest.py\n \nimport pytest\n\n# Create a dict of markers.\n# The key is used as option, so --{key} will run all tests marked with key.\n# The value must be a dict that specifies:\n# 1. 'help': the command line help text\n# 2. 'marker-descr': a description of the marker\n# 3. 'skip-reason': displayed reason whenever a test with this marker is skipped.\noptional_markers = {\n \"flag1\": {\"help\": \"<Command line help text for flag1...>\",\n \"marker-descr\": \"<Description of the marker...>\",\n \"skip-reason\": \"Test only runs with the --{} option.\"},\n \"flag2\": {\"help\": \"<Command line help text for flag2...>\",\n \"marker-descr\": \"<Description of the marker...>\",\n \"skip-reason\": \"Test only runs with the --{} option.\"},\n # add further markers here\n}\n\n\ndef pytest_addoption(parser):\n for marker, info in optional_markers.items():\n parser.addoption(\"--{}\".format(marker), action=\"store_true\",\n default=False, help=info['help'])\n\n\ndef pytest_configure(config):\n for marker, info in optional_markers.items():\n config.addinivalue_line(\"markers\",\n \"{}: {}\".format(marker, info['marker-descr']))\n\n\ndef pytest_collection_modifyitems(config, items):\n for marker, info in optional_markers.items():\n if not config.getoption(\"--{}\".format(marker)):\n skip_test = pytest.mark.skip(\n reason=info['skip-reason'].format(marker)\n )\n for item in items:\n if marker in item.keywords:\n item.add_marker(skip_test)\n\n\nNow you can use the markers defined in optional_markers in your test modules:\n# content of test_module.py\n\nimport pytest\n\n\[email protected]\ndef test_some_func():\n pass\n\n\[email protected]\ndef test_other_func():\n pass\n\n", "If the use-case prohibits modifying either conftest.py and/or pytest.ini, here's how to use environment variables to directly take advantage of the skipif marker.\ntest_reports.py contents:\nimport os\nimport pytest\n\[email protected](\n not os.environ.get(\"MY_SPECIAL_FLAG\"),\n reason=\"MY_SPECIAL_FLAG not set in environment\"\n)\ndef test_skip_if_no_cli_tag():\n assert True\n\ndef test_always_run():\n assert True\n\nIn Windows:\n> pytest -v test_reports.py --no-header\n================== test session starts ===================\ncollected 2 items\n\ntest_reports.py::test_skip_if_no_cli_tag SKIPPED [ 50%]\ntest_reports.py::test_always_run PASSED [100%]\n\n============== 1 passed, 1 skipped in 0.01s ==============\n\n> cmd /c \"set MY_SPECIAL_FLAG=1&pytest -v test_reports.py --no-header\"\n================== test session starts ===================\ncollected 2 items\n\ntest_reports.py::test_skip_if_no_cli_tag PASSED [ 50%]\ntest_reports.py::test_always_run PASSED [100%]\n\n=================== 2 passed in 0.01s ====================\n\nIn Linux or other *NIX-like systems:\n$ pytest -v test_reports.py --no-header\n================= test session starts =================\ncollected 2 items\n\ntest_reports.py::test_skip_if_no_cli_tag SKIPPED [ 50%]\ntest_reports.py::test_always_run PASSED [100%]\n\n============ 1 passed, 1 skipped in 0.00s =============\n\n$ MY_SPECIAL_FLAG=1 pytest -v test_reports.py --no-header\n================= test session starts =================\ncollected 2 items\n\ntest_reports.py::test_skip_if_no_cli_tag PASSED [ 50%]\ntest_reports.py::test_always_run PASSED [100%]\n\n================== 2 passed in 0.00s ==================\n\nMY_SPECIAL_FLAG can be whatever you wish based on your specific use-case and of course --no-header is just being used for this example.\nEnjoy.\n" ]

[ 25, 23, 5, 3, 0 ]

[]

[]

[ "pytest", "python", "unit_testing" ]

[ "\"URL\" is \"https://t.me/username\". if an entity object has username attribute; it's public. else if you're an owner or admin (with needed permissions) you get the invite link by making a seperate request.\nimport telethon.tl.functions as _fn \n\nasync for d in client.iter_dialogs():\n if not d.is_group: continue\n try:\n print(f'Name:{d.name} ID:{d.id} is_user:{d.is_user} is_channel:{d.is_channel} is_group:{d.is_group}')\n\n en = d.entity\n public = hasattr(en, 'username') and en.username\n is_chat = d.is_group and not d.is_channel and not en.deactivated\n admin = en.creator or (en.admin_rights and en.admin_rights.invite_users)\n\n if not d.is_channel or is_chat: continue\n\n if public: print(f'Link: https://t.me/{en.username}')\n\n elif admin:\n if is_chat: r = await client(_fn.messages.GetFullChatRequest(en.id))\n else: r = await client(_fn.channels.GetFullChannelRequest(en))\n\n link = r.full_chat.exported_invite\n print(f'Link: {link.link}')\n except Exception as e:\n print(e)\n error(str(row[1]), d.name, str(e))\n print('____________E_R_R_O_R___________')\n exit()\n\n" ]

[ 0 ]

[]

[]

[ "api", "python", "telegram", "telethon" ]

[ "I'm not familiar with OSMNX graphs but I just ran into a similar issue where the error message\n\nRuntimeError: Could not infer dtype of CLASS\n\ncomes from from_networkx() or more specifically, the convert() subroutine it called.\nI fixed the issue by creating a no-data networkx graph:\nno_data_graph = networkx.DiGraph() # change to Graph() if your graph is undirected\nno_data_graph.add_nodes_from(G.nodes())\nno_data_graph.add_edges_from(G.edges())\n\nI guess in your case you can create a new graph that contains only numeric data fields and remove any data field that is of type Point.\n" ]

[ 0 ]

[]

[]

[ "graph", "osmnx", "python", "pytorch", "pytorch_geometric" ]

[ "When you initialize calculate as list type you can append values with + operator:\nnumberOfcalculations = 3\ncount = 1\ncalculate = []\nwhile count <= numberOfcalculations:\n num = int(input(' number:'))\n num2 = int(input(' other number:'))\n \n calculate += [ num * num2 ]\n print(calculate)\n count = count + 1\n\nAlso you have to change contador somehow or you will end up in an infinite loop maybe. I used count here instead to give the user the ability to input 3 different calculations.\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "First of all, there is a typo in your init. It should be self.value = 1\nTo print a dice value after a roll, you first need to create an object of your class Die.\nnew_dice = Die()\n\nThen, call method roll:\nnew_dice.roll()\n\nNow, the value after the roll is stored in the object. To print the dice value you can do either of the below:\nprint(new_dice.getvalue())\n\nprint(new_dice)\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "The Chromium developers recently added a 2nd headless mode that functions the same way as normal Chrome.\n--headless=chrome\n(the old way was: --headless or options.headless = True)\nThe New Headless Mode Usage:\noptions.add_argument(\"--headless=chrome\")\n\nYou should be able to use that mode and get the same results as headed mode.\nThere's more info on that here: https://bugs.chromium.org/p/chromium/issues/detail?id=706008#c36\n", "To provide extra help to the answer provied by Michael Mintz is very good.\nCreate a headless browser like so:\noptions = webdriver.ChromeOptions() \noptions.add_argument(\"--headless=chrome\")\n\nIt's good to note that when this crashes, the browser will still be open. In this case, trying to open a new browser might result in an error if you are using chrome user profiles:\n (The process started from chrome location C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe is no longer running, so ChromeDriver is assuming that Chrome has crashed.)\n\nIt'll just be invisible and might be difficult to close. It can be closed by the following:\nimport os\n\nos.system(\"taskkill /f /im geckodriver.exe /T\")\nos.system(\"taskkill /f /im chromedriver.exe /T\")\nos.system(\"taskkill /f /im IEDriverServer.exe /T\")\n\n" ]

[ 0, 0 ]

[]

[]

[ "headless", "python", "selenium", "selenium_chromedriver", "selenium_webdriver" ]

[ "Your error comes from for x in n: in your num_to_let function. In Python, we can't use a for loop to traverse through an integer, because Python doesn't know how.\nInstead, you can iterate/traverse through a string. (it is a collection of characters, so Python knows how to traverse through it) Now you can actually cast an integer into a string, which basically means converting its type to a string. You can do so with the str() function. (Read more here) Now seeing as this is actually the goal of the function, perhaps you will use this function instead.\n" ]

[ 1 ]

[]

[]

[ "integer", "iterable", "python" ]

[ "Possible solution:\npaths = {\n 'main_folder': {\n 'file01.txt': 'txt',\n 'file02.txt': 'txt',\n 'folder_sub1': {\n 'file03.txt': 'txt',\n 'file04.txt': 'txt',\n 'file05.txt': 'txt',\n 'folder_sub1-1': {\n 'file06.txt': 'txt',\n 'file07.txt': 'txt',\n 'file08.txt': 'txt'\n }\n },\n 'folder_sub2': {\n 'file09.txt': 'txt',\n 'file10.txt': 'txt',\n 'file11.txt': 'txt'\n }\n }\n}\n\n\n# prefix components:\nspace = ' '\nbranch = '│ '\n# pointers:\ntee = '├── '\nlast = '└── '\n\ndef tree(paths: dict, prefix: str = ''):\n \"\"\"A recursive generator, given a directory Path object\n will yield a visual tree structure line by line\n with each line prefixed by the same characters\n \"\"\"\n # contents each get pointers that are ├── with a final └── :\n pointers = [tee] * (len(paths) - 1) + [last]\n for pointer, path in zip(pointers, paths):\n yield prefix + pointer + path\n if isinstance(paths[path], dict): # extend the prefix and recurse:\n extension = branch if pointer == tee else space\n # i.e. space because last, └── , above so no more |\n yield from tree(paths[path], prefix=prefix+extension)\n\n\nfor line in tree(paths):\n print(line)\n\nRef: List directory tree structure in python?\n", "bigtree is a Python tree implementation that integrates with Python lists, dictionaries, and pandas DataFrame.\nFor this scenario, we can use 3 lines of code,\npath_list = [\n 'main_folder\\\\file01.txt',\n 'main_folder\\\\file02.txt',\n 'main_folder\\\\folder_sub1\\\\file03.txt',\n 'main_folder\\\\folder_sub1\\\\file04.txt',\n 'main_folder\\\\folder_sub1\\\\file05.txt',\n 'main_folder\\\\folder_sub1\\\\folder_sub1-1\\\\file06.txt',\n 'main_folder\\\\folder_sub1\\\\folder_sub1-1\\\\file07.txt',\n 'main_folder\\\\folder_sub1\\\\folder_sub1-1\\\\file08.txt',\n 'main_folder\\\\folder_sub2\\\\file09.txt',\n 'main_folder\\\\folder_sub2\\\\file10.txt',\n 'main_folder\\\\folder_sub2\\\\file11.txt']\n\nfrom bigtree import list_to_tree, print_tree\nroot = list_to_tree(path_list, sep=\"\\\\\")\nprint_tree(root)\n\nThis will result in output,\nmain_folder\n├── file01.txt\n├── file02.txt\n├── folder_sub1\n│ ├── file03.txt\n│ ├── file04.txt\n│ ├── file05.txt\n│ └── folder_sub1-1\n│ ├── file06.txt\n│ ├── file07.txt\n│ └── file08.txt\n└── folder_sub2\n ├── file09.txt\n ├── file10.txt\n └── file11.txt\n\nSource/Disclaimer: I'm the creator of bigtree ;)\n", "A little modification to @rafaeldss's answer\n# prefix components:\nspace = ' '\nbranch = '│ '\n# pointers:\ntee = '├── '\nlast = '└── '\n\ndef tree(paths: dict, prefix: str = '', first: bool = True):\n \"\"\"A recursive generator, given a directory Path object\n will yield a visual tree structure line by line\n with each line prefixed by the same characters\n \"\"\"\n # contents each get pointers that are ├── with a final └── :\n pointers = [tee] * (len(paths) - 1) + [last]\n for pointer, path in zip(pointers, paths):\n if first:\n yield prefix + path\n else:\n yield prefix + pointer + path\n if isinstance(paths[path], dict): # extend the prefix and recurse:\n if first:\n extension = ''\n else:\n extension = branch if pointer == tee else space\n # i.e. space because last, └── , above so no more │\n yield from tree(paths[path], prefix=prefix+extension, first=False)\n\nfor line in tree(paths):\n print(line)\n\ndiffs\n" ]

[ 2, 1, 0 ]

[]

[]

[ "directory_structure", "python", "tree", "treeview" ]

[]

[]

[ "In my case, in my directory with the main python file, there was also a file named signal.py, when i renamed it to signal1.py everything started working. So try to find some other python files in your directory and try rename it.\n" ]

[ -1 ]

[ "python", "pytorch" ]

[ "If you executed the code blocks sequentially, both generated_image_codes and vae should be on the same device, i.e. CPU.\ngenerated_image_codes = torch.cat(generated_image_codes, axis=0).cpu()\n\nand\ntorch.cuda.empty_cache()\nvae.cpu()\n\nTo double check, you can run\nprint(generated_image_codes.device)\nprint(next(vae.parameters()).device)\n\nExpected outputs are\ntorch.device(\"cpu\")\ntorch.device(\"cpu\")\n\n" ]

[ 0 ]

[]

[]

[ "python", "pytorch" ]

[ "There are a few errors in your code\n\nAs the comment mentioned, you are using “or” condition wrongly\nYour “if” and “elif” is not on the same indentation\nTo calculate harmonic mean, it would be better to collect a list of values (instead of a sum), and perform n divide by the sum of reciprocals\nRelating to your logic/flow, it can be less hard coded\n\n" ]

[ 0 ]

[]

[]

[ "add", "formula", "function", "python", "sequence" ]

[ "it was all because of NAT. if you interested in this topic, you can learn about ways to bypass NAT.\nThanks everyone for replies.\n" ]

[ 0 ]

[]

[]

[ "python", "sockets", "udp" ]

[ "mylist.remove(x) removes only the first occurrence of x from the list.\nIf you think there may be more than one, use a while loop:\nwhile 3 in mylist:\n mylist.remove(3)\n\nIf the list is long and/or there might be several 3s in the list, this approach would be more efficient, as it only iterates over the list once:\nmylist = [item for item in mylist if item != 3]\n\n" ]

[ 2 ]

[]

[]

[ "python" ]

[ "ibcontract.includeExpired = True\ndatetime.datetime.today().strftime(\"%Y%m%d-%H:%M:%S\"), # endDateTime,\n" ]

[ 0 ]

[]

[]

[ "dataframe", "historical_db", "interactive_brokers", "python", "tws" ]

[ "Looks like you can replace your whole code with a reindex:\nensure_cols = ['A', 'B', 'C', 'D']\ndf = df.reindex(columns=df.columns.union(ensure_cols))\n\nNB. By default the fill value is NaN, if you really want None use fill_value=None.\nIf you want to fix your code, just use a single loop:\ncol_names = ['A', 'B', 'C', 'D']\nfor c in col_names:\n if c not in df:\n df[c] = None\n\n" ]

[ 1 ]

[]

[]

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

[ "Looking at your code you can use this demo solution in your code as I'm storing objects of Demo class in the Items list. You need to write serialize() and dumper() methods in Items class, and also changes need to be done in reprJSON method for iteration on Items list.\nfrom json import JSONEncoder\n\nclass Demo():\n def __init__(self):\n self.name = ''\n self.demolist = []\n\nclass Item(JSONEncoder):\n\n def __init__(self):\n # super().__init__()\n self.Type = ''\n self.Content = ''\n self.N = None\n self.Parent = None\n self.Items = []\n\n def reprJSON(self):\n d = {}\n for a, v in self.__dict__.items():\n if isinstance(v, list):\n for i in v:\n if d.get(a, []) == []:\n d[a] = []\n d[a].append(self.dumper(i))\n else:\n d[a].append(self.dumper(i))\n else:\n d[a] = v\n return d\n\n def serialize(self):\n return self.__dict__\n\n @staticmethod\n def dumper(obj):\n if \"serialize\" in dir(obj):\n return obj.serialize()\n return obj.__dict__\n\n\n\n\nitemobj = Item()\nd1 = Demo()\nd2 = Demo()\nd1.name = 'akash'\nd1.demolist = [{'good':[4,6,5],'yyy':'why'},{'ho':{'ksks':'333'}}]\nd2.name = 'heheh'\nd2.demolist = [4,6,1111]\nitemobj.Items.extend([d1,d2])\n\nfrom pprint import pprint\npprint(itemobj.reprJSON())\n\nOutput:\n{'Content': '',\n 'Items': [{'demolist': [{'good': [4, 6, 5], 'yyy': 'why'},\n {'ho': {'ksks': '333'}}],\n 'name': 'akash'},\n {'demolist': [4, 6, 1111], 'name': 'heheh'}],\n 'N': None,\n 'Parent': None,\n 'Type': ''}```\n\n", "pip install jsonwhatever\nfrom jsonwhatever import jsonwhatever as jw\n\n\nclass Item():\n def __init__(self):\n self.Type = ''\n self.Content = ''\n self.N = None\n self.Parent = None #Not to reference father class to avoid infinite recursivity\n self.Items = None #You should put None by default to stop recursivity\n\n\n\nobj = Item()\nobj01 = Item()\n\nobj01.Type = '01'\nobj01.Content = 'stuff'\nobj01.N = 9\nobj01.Parent = None\n\nlist_objects = []\n\nlist_objects.append(obj01)\n\nobj.Items = list_objects\n\njson_string = jw.jsonwhatever('list_of_items', obj)\n\nprint(json_string)\n\n\n" ]

[ 1, 0 ]

[]

[]

[ "dictionary", "json", "parsing", "python" ]

[ "use shutil.copytree(src, dest, ...) (https://docs.python.org/3/library/shutil.html#shutil.copytree) for directories (also copies subfiles and -directories. And also don't forget to give it the full path for file and dest (don't know which value your temp3 var holds). If you don't give it a full path, but only a filename, it will search in the current working directory.\n" ]

[ 1 ]

[]

[]

[ "file", "io", "linux", "python" ]

[ "Try this:\ndf['Rule'] = df.State.apply(lambda x: str(\"'\"+str(x)+\"'\"))\ndf['Rule'] = df.groupby(['Description'])['Rule'].transform(lambda x: ', '.join(x))\ndf1 = df.drop_duplicates('Description', keep = 'first')\ndf1['Rule'] = df1['Rule'].apply(lambda x: str(\"(\"+str(x)+\")\"))\n\n" ]

[ 0 ]

[]

[]

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

[ "Here is my solution:\nfrom pyspark.sql import functions as F\n\n# I purposely commented out some part of 2022 so you can see the result\n\ndata = [\n ['2020-03-01', 'x']\n, ['2020-04-01', 'y']\n, ['2020-05-01', 'x']\n, ['2020-06-01', 'x']\n, ['2020-01-01', 'y'] \n, ['2020-01-01', 'y']\n, ['2020-07-01', 'y']\n, ['2020-08-01', 'y']\n, ['2020-09-01', 'y']\n, ['2020-10-01', 'y']\n, ['2020-11-01', 'y']\n, ['2020-12-01', 'y']\n, ['2021-03-01', 'x']\n, ['2021-04-01', 'y']\n, ['2021-05-01', 'x']\n, ['2021-06-01', 'x']\n, ['2021-01-01', 'y'] \n, ['2021-01-01', 'y']\n, ['2021-07-01', 'y']\n, ['2021-08-01', 'y']\n, ['2021-09-01', 'y']\n, ['2021-10-01', 'y']\n, ['2021-11-01', 'y']\n, ['2021-12-01', 'y']\n, ['2022-03-01', 'x']\n, ['2022-04-01', 'y']\n, ['2022-05-01', 'x']\n, ['2022-06-01', 'x']\n, ['2022-01-01', 'y'] \n, ['2022-01-01', 'y']\n, ['2022-07-01', 'y']\n# , ['2022-08-01', 'y']\n# , ['2022-09-01', 'y']\n# , ['2022-10-01', 'y']\n# , ['2022-11-01', 'y']\n# , ['2022-12-01', 'y'] \n \n]\n\ncols = ['mydate', 'id']\n\n# Creating Dataframe\ndf = spark.createDataFrame(data, cols)\n\n# Group by year(mydate)\n# Aggregate by year(mydate) and count distinct the quarter(mydate) where year(mydate) is not 1965\n# Filter for years where the count(quarter(mydate)) != 4\n\nres = df.groupBy(F.year('mydate').alias(\"q_count\")).agg(F.countDistinct(F.quarter('mydate')).alias(\"qrt_count\")). where(F.year('mydate') != 1965).filter(F.col('qrt_count') != 4)\n\nres.display()\n\nHere is the output:\n\n", "I have taken the sample data that you have provided and created a dataframe.\nfrom pyspark.sql.functions import *\n\ndata = [ ['2020-03-01', 'x'] , ['2020-06-01', 'y'] , ['2020-09-01', 'x'] , ['2020-12-01', 'x'] , ['2021-03-01', 'x'] , ['2021-06-01', 'y'] , ['2021-09-01', 'x'] , ['2021-12-01', 'x'] , ['2022-03-01', 'x'] , ['2022-06-01', 'y'] , ['2022-09-01', 'x'] , ['2022-12-01', 'x'] ]\ndf = spark.createDataFrame(data = data, schema=['date', 'id'])\ndisplay(df)\n\n\n\nNow, I have used the following code to get the desired output. Since you don't want to consider the current year and the year 1965.\n\nI have taken the condition where I used datetime library to get the year from today's date. If it is equal to any of the records returned from after grouping and obtaining the years where quarter count is less than 4, then we filter them out.\n\n\nimport datetime\n\ndf.groupBy(year('date').alias(\"quarter_year\"))\\\n .agg(countDistinct(quarter('date')).alias(\"quarter_count\"))\\\n .where((year('date') != 1965)).filter((col('quarter_count') < 4) & (col('quarter_year') != datetime.datetime.today().year)).show()\n\n\n\nLet's say I take out 2020-09-01 and test the code, then it would return the following result.\n\n\n" ]

[ 0, 0 ]

[]

[]

[ "azure_databricks", "pyspark", "python" ]

[ "If you want to convert the strings to dates to later get the values\nimport datetime\n# datetime.datetime(1900, 2, 1, 0, 0)\nd = datetime.datetime.strptime(\"1-Feb\", \"%d-%b\")\nprint(f'{d.month}-{d.day}')\n\nresult:\n2-1\n\n", "You can use pandas.to_datetime :\nnew[\"Room\"]= (\n pd.to_datetime(new[\"Room\"], format=\"%d-%b\", errors=\"coerce\")\n .dt.strftime(\"%#m-%#d\")\n .fillna(new[\"Room\"])\n )\n\nExample :\n col\n0 1-Feb\n1 2-Feb\n2 3-Mar\n3 2-1\n\nGives :\n col\n0 2-1\n1 2-2\n2 3-3\n3 2-1\n\nNB: In case you're facing errors, you need to give a minimal reproducible example to show how look like your original (.csv) opened in a text editor and not in Excel.\n" ]

[ 1, 0 ]

[]

[]

[ "csv", "if_statement", "python" ]

[ "From this answer:\n\nThe keys to be provided to send Emails are not \"SMTP Credentials\" .\nThe keys are instead Global access key which can be retrieved\nhttp://docs.amazonwebservices.com/ses/latest/GettingStartedGuide/GetAccessIDs.html.\n\n" ]

[ 0 ]

[]

[]

[ "amazon_ses", "amazon_web_services", "boto3", "python", "python_3.x" ]

[ "Maybe something like this:\nresultLines = []\nresultSums = {}\nwith open('recent.logs') as f:\n for idx, line in enumerate(f):\n pieces = line.rsplit('.TotalIncomingBytes.Count, value=', 1)\n if len(pieces) != 2: continue\n\n value = pieces[1]\n\n pieces = pieces[0].rsplit(' [metrics-writer-1] [INFO ] metrics - type=GAUGE, name=Topic.', 1)\n if len(pieces) != 2: continue\n\n topic = pieces[1]\n value = int(value)\n\n resultLines.append({\n 'idx': idx,\n 'line': line,\n 'topic': topic,\n 'value': value,\n })\n\n if topic not in resultSums:\n resultSums[topic] = 0\n resultSums[topic] = resultSums[topic] + value\n\nfor topic, value in resultSums.iteritems():\n print(topic, value)\n\n", "Here's the way I would do it. This could also be done with a regular expression.\ndata = \"\"\"\\\n2022-12-02 13:13:10.539 [metrics-writer-1] [INFO ] metrics - type=GAUGE, name=Topic.myTopic1.TotalIncomingBytes.Count, value=20725269\n2022-12-02 13:13:10.539 [metrics-writer-1] [INFO ] metrics - type=GAUGE, name=Topic.myTopic1.TotalIncomingBytes.Count, value=20725269\n2022-12-02 13:13:10.539 [metrics-writer-1] [INFO ] metrics - type=GAUGE, name=Topic.myTopic1.TotalIncomingBytes.Count, value=20725269\n\"\"\"\n\ncounts = {}\n\nfor line in data.splitlines():\n if '[INFO ] metrics' in line:\n parts = line.split(' - ')\n parts = parts[1].split(', ')\n dct = {}\n for part in parts:\n key,val = part.split('=')\n dct[key] = val\n if dct['name'] not in counts:\n counts[dct['name']] = int(dct['value'])\n else:\n counts[dct['name']] += int(dct['value'])\n\nprint(counts)\n\nOutput:\n{'Topic.myTopic1.TotalIncomingBytes.Count': 62175807}\n\nHere's a regex version:\n\npattern = re.compile(r\".* - type=([^,]*), name=([^,]*), value=([^,]*)\")\ncounts = {}\n\nfor line in data.splitlines():\n if '[INFO ] metrics' in line:\n parts = pattern.match(line)\n if parts[2] not in counts:\n counts[parts[2]] = int(parts[3])\n else:\n counts[parts[2]] += int(parts[3])\n\nprint(counts)\n\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[ "Here's a way to make your bot join a voice channel:\nasync def join(ctx):\n channel = ctx.message.author.voice.channel\n if not channel:\n await ctx.send(\"You're not connected to any voice channel !\")\n else:\n voice = get(self.bot.voice_clients, guild=ctx.guild)\n if voice and voice.is_connected():\n await voice.move_to(channel)\n else:\n voice = await channel.connect()\n\nPS : if you add a play command, you'll still have to get the bot's channel and voice with those to lines :\nvoice = get(self.bot.voice_clients, guild=ctx.guild)\nchannel = ctx.message.author.voice.channel\n\n", "I removed your comments and added my own #comments\nvoice_status = ctx.author.voice\nif voice_status: # this check does nothing. This is a discord.VoiceState object\n channel = voice_status.channel\n # channel is None if you are not connected to a voice channel\n # channel is a Channel object if you are connected to a voice channel\n if ctx.voice_client is None:\n # I may be wrong but if I read the docs right this returns the voice client of the guild, something semi-related to the author.\n vc = await channel.connect()\n # You are not connected to a voice channel. So channel is None. Now you are trying to connect to a None channel.\n print(\"test\")\n\n", "ik this is old, but I've spend a long time fixing the problem myself and I haven't found any answers online. My problem was that my bot didn't have the Intents for voice stuff. If your bot is just for a small group of people like your friends, just settings intents to discord.Intents.all() will work. Otherwise you can manually select them.\n", "You have problem discord RTC. Your bot try to connect but rtc is blocked by firewall. You should anable DNS UDP in firewall.\n" ]

[ 1, 0, 0, 0 ]

[]

[]

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

[ "It seems that your curves have a pattern, so you could select the curve of interest using splicing. I had the offset the selection slightly to get the five curves because the first 8 points are not in the same order as the rest of the data. So the initial 8 data points are discarded. But these could be added back in afterwards if required.\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.DataFrame({ 'x': [-6.4165, -4.0227, -7.206, -3.2584, -0.7565, -0.0498, 2.363, -10.7253, -8.0621, -4.6328, -1.4237, 1.8047, 4.8147, -5.3504, -1.7743, 1.1783, 4.3471, 7.4067, -2.6037, 0.8613, 3.8054, 7.023, 9.9776, 0.1733, 3.7137, 6.4672, 9.6489, 12.5674, 3.2124, 6.4983, 9.2606, 12.4003, 15.3578, 6.3128, 9.7676, 12.2103, 15.3182, 18.2495, 9.3947, 12.496, 15.3987, 18.2212, 21.1241, 12.3548, 15.3682, 18.357, 21.0834, 23.9992, 15.3776, 18.3568, 21.1733],\n 'y': [0.3716, 2.63, 3.0652, -0.0392, 2.1039, -0.5159, 1.5329, 3.4654, 5.9083, 5.3028, 4.8455, 4.2297, 3.6074, 8.1889, 7.6165, 6.9698, 6.2411, 5.5988, 10.4623, 9.7628, 9.0202, 8.1962, 7.5563, 12.6547, 11.9097, 10.9363, 10.1246, 9.3369, 14.7492, 13.7562, 12.7241, 11.878, 11.0027, 16.7014, 15.6557, 14.4967, 13.5166, 12.5836, 18.5506, 17.2993, 16.2716, 15.1871, 14.0893, 20.2538, 18.9439, 17.8862, 16.6258, 15.4145, 21.9402, 20.5803, 19.3041]})\n\n# Generate the 5 dataframes\ndf_list = [df.iloc[i+8::5, :] for i in range(5)]\n\n# Generate the plot\nfig = plt.figure()\nfor frame in df_list:\n plt.scatter(frame['x'], frame['y'])\nplt.show()\n\n# Print the data of the innermost curve\nprint(df_list[4])\n\nOUTPUT:\n\nThe 5th dataframe df_list[4] contains the data of the innermost plot.\n x y\n12 4.8147 3.6074\n17 7.4067 5.5988\n22 9.9776 7.5563\n27 12.5674 9.3369\n32 15.3578 11.0027\n37 18.2495 12.5836\n42 21.1241 14.0893\n47 23.9992 15.4145\n\n\nYou can then add the missing data like this:\n# Retrieve the two missing points of the inner curve\ninner_curve = pd.concat([df_list[4], df[5:7]]).sort_index(ascending=True)\nprint(inner_curve)\n\n# Plot the inner curve only\nfig2 = plt.figure()\nplt.scatter(inner_curve['x'], inner_curve['y'], color = '#9467BD')\nplt.show()\n\nOUTPUT: inner curve\n x y\n5 -0.0498 -0.5159\n6 2.3630 1.5329\n12 4.8147 3.6074\n17 7.4067 5.5988\n22 9.9776 7.5563\n27 12.5674 9.3369\n32 15.3578 11.0027\n37 18.2495 12.5836\n42 21.1241 14.0893\n47 23.9992 15.4145\n\nComplete Inner Curve\n\n" ]

[ 1 ]

[]

[]

[ "data_analysis", "python", "scipy", "sorting" ]

[ "The problem here I think is, you change the y_ticks before you pass them to set_major_locator, but you don't want to change the ticks, you actually only want to change the label (as you did for the left y labels).\nChange that part to:\n\"\"\"Change right y-axis tick labels\"\"\"\n# Pull current right y-axis tick labels\nright_yticks = ax.right_ax.get_yticks().tolist()\n# Set new right y-axis tick labels\nax.right_ax.yaxis.set_major_locator(mticker.FixedLocator(right_yticks)) # right_yticks need to be unchanged here\n\n# NOW you change them in a comprehension like you did it for the left y-axis\nax.right_ax.set_yticklabels((f\"{(-1)*tick:.2f}\") for tick in right_yticks)\n\n\n" ]

[ 0 ]

[]

[]

[ "matplotlib", "pandas", "python", "yticks" ]

[ "It's hard to say what you're trying to achieve without showing a sample of your data. But anyway, to fix the error, you need to cast the values as a string with str when calling pandas.Series.apply :\nnew[merged_col] = new[merge_columns].apply(lambda x: '.'.join(str(x)), axis=1)\n\nOr, you can also use pandas.Series.astype:\nnew[merged_col] = new[merge_columns].astype(str).apply(lambda x: '.'.join(x), axis=1)\n\n" ]

[ 0 ]

[]

[]

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

[ "What is the value of cottage and users? What does allUser2[3] value return ? Based on the condition check the value of str(cottage) in str(allUser2[3])\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "You can use apply to index into leader and exchange values with DatasetLabel, although it's not very pretty. \nOne issue is that Pandas won't let us index with NaN. Converting to str provides a workaround. But that creates a second issue, namely, column 9 is of type float (because NaN is float), so 5 becomes 5.0. Once it's a string, that's \"5.0\", which will fail to match the index values in leader. We can remove the .0, and then this solution will work - but it's a bit of a hack.\nWith DatasetLabel as:\n Unnamed:0 0 1 7 8 9 10 11 12\n0 0 A J 1 2 5.0 NaN NaN NaN\n1 1 B K 3 4 NaN NaN NaN NaN\n\nAnd leader as:\n 0 1\n0 0 11\n1 1 8\n2 2 5\n3 3 9\n4 4 8\n5 5 6\n\nThen:\ncols = [\"7\",\"8\",\"9\",\"10\",\"11\",\"12\"]\nupdated = DatasetLabel[cols].apply(\n lambda x: leader.loc[x.astype(str).str.split(\".\").str[0], 1].values, axis=1)\n\nupdated\n 7 8 9 10 11 12\n0 8.0 5.0 6.0 NaN NaN NaN\n1 9.0 8.0 NaN NaN NaN NaN\n\nNow we can concat the unmodified columns (which we'll call original) with updated:\noriginal_cols = DatasetLabel.columns[~DatasetLabel.columns.isin(cols)]\noriginal = DatasetLabel[original_cols]\npd.concat([original, updated], axis=1)\n\nOutput:\n Unnamed:0 0 1 7 8 9 10 11 12\n0 0 A J 8.0 5.0 6.0 NaN NaN NaN\n1 1 B K 9.0 8.0 NaN NaN NaN NaN\n\nNote: It may be clearer to use concat here, but here's another, cleaner way of merging original and updated, using assign:\nDatasetLabel.assign(**updated)\n\n", "The source code shows that this error occurs when you try to broadcast a list-like object (numpy array, list, set, tuple etc.) to multiple columns or rows but didn't specify the index correctly. Of course, list-like objects don't have custom indices like pandas objects, so it usually causes this error.\nSolutions to common cases:\n\nYou want to assign the same values across multiple columns at once. In other words, you want to change the values of certain columns using a list-like object whose (a) length doesn't match the number of columns or rows and (b) dtype doesn't match the dtype of the columns they are being assigned to.1 An illustration may make it clearer. If you try to make the transformation below:\n\nusing a code similar to the one below, this error occurs:\ndf = pd.DataFrame({'A': [1, 5, 9], 'B': [2, 6, 10], 'C': [3, 7, 11], 'D': [4, 8, 12]})\ndf.loc[:2, ['C','D']] = [100, 200.2, 300]\n\nSolution: Duplicate the list/array/tuple, transpose it (either using T or zip()) and assign to the relevant rows/columns.2\ndf.loc[:2, ['C','D']] = np.tile([100, 200.2, 300], (len(['C','D']), 1)).T \n# if you don't fancy numpy, use zip() on a list\n# df.loc[:2, ['C','D']] = list(zip(*[[100, 200.2, 300]]*len(['C','D'])))\n\n\n\n\n\nYou want to assign the same values to multiple rows at once. If you try to make the following transformation\n\nusing a code similar to the following:\ndf = pd.DataFrame({'A': [1, 5, 9], 'B': [2, 6, 10], 'C': [3, 7, 11], 'D': [4, 8, 12]})\ndf.loc[[0, 1], ['A', 'B', 'C']] = [100, 200.2]\n\nSolution: To make it work as expected, we must convert the list/array into a Series with the correct index:\ndf.loc[[0, 1], ['A', 'B', 'C']] = pd.Series([100, 200.2], index=[0, 1])\n\n\nA common sub-case is if the row indices come from using a boolean mask. N.B. This is the case in the OP. In that case, just use the mask to filter df.index:\nmsk = df.index < 2\ndf.loc[msk, ['A', 'B', 'C']] = [100, 200.2] # <--- error\ndf.loc[msk, ['A', 'B', 'C']] = pd.Series([100, 200.2], index=df.index[msk]) # <--- OK\n\n\n\n\n\nYou want to store the same list in some rows of a column. An illustration of this case is:\n\nSolution: Explicitly construct a Series with the correct indices.\n# for the case on the left in the image above\ndf['D'] = pd.Series([[100, 200.2]]*len(df), index=df.index)\n\n# latter case\ndf.loc[[1], 'D'] = pd.Series([[100, 200.2]], index=df.index[[1]])\n\n\n\n\n1: Here, we tried to assign a list containing a float to int dtype columns, which contributed to this error being raised. If we tried to assign a list of ints (so that the dtypes match), we'd get a different error: ValueError: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (2,3) which can also be solved by the same method as above.\n2: An error related to this one ValueError: Must have equal len keys and value when setting with an ndarray occurs if the object being assigned is a numpy array and there's a shape mismatch. That one is often solved either using np.tile or simply transposing the array.\n" ]

[ 10, 0 ]

[]

[]

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

[ "It closes because the program ends.\nYou can:\nWait with time.sleep, for example time.sleep(10) to keep the browser open for 10 seconds after everything is done\nHave the user press enter with input()\nOr detect when the browser is closed. Many ways to do that.\nExample: https://stackoverflow.com/a/52000037/8997916\nYou could also catch the BrowserUnreachable exception in a loop with a small delay\n", "#For Edge Browser\nfrom selenium import webdriver\nfrom selenium.webdriver.edge.service import Service\nfrom webdriver_manager.microsoft import EdgeChromiumDriverManager\n\noptions = webdriver.EdgeOptions()\noptions.add_experimental_option(\"detach\", True)\ndriver = webdriver.Edge(options=options, service=Service(EdgeChromiumDriverManager().install()))\ndriver.maximize_window()\ndriver.get('https://stackoverflow.com/questions/68543285/chrome-browser-closes-immediately-after-loading-from-selenium')\n\n#For Chrome Browser\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\n\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option(\"detach\", True)\ndriver = webdriver.Chrome(options=options, service=Service(ChromeDriverManager().install()))\ndriver.maximize_window()\ndriver.get('https://stackoverflow.com/questions/68543285/chrome-browser-closes-immediately-after-loading-from-selenium')\n\nTry this, this will permanently solve the problem\nThank You\nSubhankar Chakraborty\n" ]

[ 3, 0 ]

[]

[]

[ "chromium", "google_chrome", "python", "selenium", "selenium_chromedriver" ]

[ "FOR DOC File (you can use different thing for download different type of File or USE same code)\ndef save_link(book_link, book_name):\n the_book = requests.get(book_link, stream=True)\n with open(book_name, 'wb') as f:\n for chunk in the_book.iter_content(1024 * 1024 * 2): # 2 MB chunks\n f.write(chunk)\n\nsave_link(\"URL\",\"NAME.docx\")\n\nFOR IMAGE\nimport shutil\n\nurl = 'http://example.com/img.png'\nresponse = requests.get(url, stream=True)\nwith open('img.png', 'wb') as out_file:\n shutil.copyfileobj(response.raw, out_file)\n\n" ]

[ 0 ]

[]

[]

[ "http_headers", "python", "response_headers" ]

[ "According to the official document, we cannot avoid the COLLSCAN on oplog collection.\nSo, in my opinion, in order to reduce the performance impact, watch should be run on the secondary instead of the primary.\n" ]

[ 0 ]

[]

[]

[ "changestream", "mongodb", "pymongo", "python" ]

[ "The twitter api checks if the content is duplicate and if it is duplicate it returns:\n Request returned an error: 403 {\"detail\":\"You are not allowed to create a Tweet with duplicate content.\",\"type\":\"about:blank\",\"title\":\"Forbidden\",\"status\":403}\n\nI added an simple function to check if the previous content is same as the one about to be added\n** Full Code**\nfrom requests_oauthlib import OAuth1Session\nimport os\nimport json\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\n\nuser_id = 000000000000000 # Get userid from https://tweeterid.com/\nbearer_token = \"<BEARER_TOKEN>\"\nconsumer_key = \"<CONSUMER_KEY>\"\nconsumer_secret = \"<CONSUMER_SECRET>\"\n\n\ndef init():\n\n # Get request token\n request_token_url = \"https://api.twitter.com/oauth/request_token?oauth_callback=oob&x_auth_access_type=write\"\n oauth = OAuth1Session(consumer_key, client_secret=consumer_secret)\n\n try:\n fetch_response = oauth.fetch_request_token(request_token_url)\n except ValueError:\n print(\n \"There may have been an issue with the consumer_key or consumer_secret you entered.\"\n )\n\n resource_owner_key = fetch_response.get(\"oauth_token\")\n resource_owner_secret = fetch_response.get(\"oauth_token_secret\")\n print(\"Got OAuth token and secret\")\n\n # Get authorization\n base_authorization_url = \"https://api.twitter.com/oauth/authorize\"\n authorization_url = oauth.authorization_url(base_authorization_url)\n print(\"Please go here and authorize: %s\" % authorization_url)\n verifier = input(\"Paste the PIN here: \")\n\n # Get the access token\n access_token_url = \"https://api.twitter.com/oauth/access_token\"\n oauth = OAuth1Session(\n consumer_key,\n client_secret=consumer_secret,\n resource_owner_key=resource_owner_key,\n resource_owner_secret=resource_owner_secret,\n verifier=verifier,\n )\n oauth_tokens = oauth.fetch_access_token(access_token_url)\n\n access_token = oauth_tokens[\"oauth_token\"]\n access_token_secret = oauth_tokens[\"oauth_token_secret\"]\n\n # Make the request\n oauth = OAuth1Session(\n consumer_key,\n client_secret=consumer_secret,\n resource_owner_key=access_token,\n resource_owner_secret=access_token_secret,\n )\n scraper(oauth, bearer_token)\n\n\ndef bearer_oauth(r):\n \"\"\"\n Method required by bearer token authentication.\n \"\"\"\n\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2UserTweetsPython\"\n return r\n\n\ndef previous_tweet():\n url = \"https://api.twitter.com/2/users/{}/tweets\".format(user_id)\n\n # Tweet fields are adjustable.\n # Options include:\n # attachments, author_id, context_annotations,\n # conversation_id, created_at, entities, geo, id,\n # in_reply_to_user_id, lang, non_public_metrics, organic_metrics,\n # possibly_sensitive, promoted_metrics, public_metrics, referenced_tweets,\n # source, text, and withheld\n params = {\"tweet.fields\": \"text\"}\n response = requests.request(\n \"GET\", url, auth=bearer_oauth, params=params)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n # checking if this is the first post\n if response.json() != {'meta': {'result_count': 0}}:\n # Since twitter changes html to small url I am splitting at \\n to match to new payload\n previous_tweet_text = response.json()[\"data\"][0][\"text\"].split(\"\\n\")[0]\n previous_payload = {\"text\": f\"{previous_tweet_text}\"}\n else:\n previous_payload = {\"text\": f\"\"}\n\n return previous_payload\n\n\ndef scraper(oauth, bearer_token):\n while True:\n page = requests.get(\n \"https://www.reuters.com/business/future-of-money/\")\n soup = BeautifulSoup(page.content, \"html.parser\")\n home = soup.find(class_=\"editorial-franchise-layout__main__3cLBl\")\n posts = home.find_all(\n class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__inherit-font__1Y8w3 text__inherit-size__1DZJi link__underline_on_hover__2zGL4\")\n top_post = posts[0].find(\n \"h3\", class_=\"text__text__1FZLe text__dark-grey__3Ml43 text__medium__1kbOh text__heading_3__1kDhc heading__base__2T28j heading__heading_3__3aL54 hero-card__title__33EFM\").find_all(\"span\")[0].text.strip()\n # Be sure to add replace the text of the with the text you wish to Tweet. You can also add parameters to post polls, quote Tweets, Tweet with reply settings, and Tweet to Super Followers in addition to other features.\n payload = {\n \"text\": f\"{top_post}\\nSource:https://www.reuters.com/business/future-of-money/\"}\n current_checker_payload = {\"text\": payload[\"text\"].split(\"\\n\")[0]}\n previous_payload = previous_tweet()\n if previous_payload != current_checker_payload:\n tweet(payload, oauth)\n else:\n print(\"Content hasn't changed\")\n time.sleep(60)\n\n\ndef tweet(payload, oauth):\n\n # Making the request\n response = oauth.post(\n \"https://api.twitter.com/2/tweets\",\n json=payload,\n )\n\n if response.status_code != 201:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text)\n )\n\n print(\"Response code: {}\".format(response.status_code))\n\n # Showing the response as JSON\n json_response = response.json()\n print(json.dumps(json_response, indent=4, sort_keys=True))\n\n\nif __name__ == \"__main__\":\n init()\n\n** Output**\nResponse code: 201\n{\n\"data\": {\n \"id\": \"1598558336672497664\",\n \"text\": \"FTX ex-CEO Bankman-Fried claims he was unaware of improper use of customer funds -ABC News\\nSource:URL\" #couldn't post short url in stackoverflow\n }\n}\n\nContent hasn't changed\nContent hasn't changed\nContent hasn't changed\n\n\nHope this helps. Happy Coding :)\n" ]

[ 0 ]

[]

[]

[ "beautifulsoup", "python", "tweepy", "twitter", "web_scraping" ]

[ "Generate a dataset\nimport scipy\nimport matplotlib.pyplot as plt\n# generate data with norm(mean = 0,std = 15)\ndata = scipy.stats.norm.rvs(loc = 0,scale = 15,size = 1000,random_state = 0)\n\nPerfrom KS-test\n# perform KS test on your sample versus norm(10,5.5)\nD, p = scipy.stats.kstest(data, 'norm', args= (10, 5.5))\ntest = 'Reject' if p < 0.05 else 'Not reject'\nprint(f'D-statistics: {D:.4f},\\np-value: {p:.4f}, \\ntest-result: {test}')\n\nOut:\n\n'D-statistics:0.5091,p-value:0.0000,test-result:Reject'\n\nAdd a plot with distributions and your data\n# draw a plot to see the distribution and your data hist\n# draw a plot to see the distribution and your data hist\nfig, ax = plt.subplots(1, 1)\nx = np.linspace(data.min(),data.max(),100)\nax.plot(x, scipy.stats.norm.pdf(x,loc = 10, scale = 5.5), 'r-', color='green', lw=1, alpha=0.6, label='norm(10,5.5) pdf')\nax.hist(data, normed=True, histtype='stepfilled', bins=20, alpha=0.2, label='my data distribution')\nax.legend(loc='best', frameon=False)\nplt.title('norm(10,5.5) vs. data')\nplt.show()\n\n\n", "if error occured，‘Polygon’ object has no property ‘normed’\nit was found，normed=True , this property is deprecated，\nchange to use 'density=True'.\nit will work.\n" ]

[ 1, 0 ]

[]

[]

[ "numpy", "pandas", "python", "scipy", "statistics" ]

[ "I believe you wrote your code wrongly, you want the gallon of gas, for 400 miles, 50 miles and 10 miles, and your function in main having the parameter in the wrong place.\nYour function:\ndriving_cost(miles_per_gallon, dollars_per_gallon, miles_driven)\n\nThe function in your main write:\ndriving_cost(miles_per_gallon=400, dollars_per_gallon=miles_per_gallon, miles_driven=dollars_per_gallon)\ndriving_cost(miles_per_gallon=50, dollars_per_gallon=miles_per_gallon, miles_driven=dollars_per_gallon)\ndriving_cost(miles_per_gallon=10, dollars_per_gallon=miles_per_gallon, miles_driven=dollars_per_gallon)\n\nI believe it should be\nprint(driving_cost(miles_per_gallon, dollars_per_gallon, 400))\nprint(driving_cost(miles_per_gallon, dollars_per_gallon, 50))\nprint(driving_cost(miles_per_gallon, dollars_per_gallon, 10))\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "I tried in my environment and got below results:\nInitially I tried with python,\n\nUnfortunately, ShareServiceClient Class which Interacts with A client to interact with the File Share Service at the account level. does not yet support Download operation in the Azure Python SDK.\nShareClient Class which only interacts with specific file Share in the account does not support Download Directory or file share option Python SDK..\nBut there's one class ShareFileClient Class which supports downloading of individual files inside a directory but not entire directory, you can use this class to download the files from directory with Python SDK.\n\nAlso you check Azure Portal > Select your Storage account > File Share and Directory > There's no option in Portal too to download the directory, but there's an option to download specific file.\nAs workaround, if you need to download directory from file-share, you can use Az copy tool command to download the directory in your local machine.\nI tried to download the Directory with Az-copy command and was able to download to it successfully!\nCommand:\nazcopy copy 'https://mystorageaccount.file.core.windows.net/myfileshare/myFileShareDirectory?sv=2018-03-28&ss=bjqt&srs=sco&sp=rjklhjup&se=2019-05-10T04:37:48Z&st=2019-05-09T20:37:48Z&spr=https&sig=/SOVEFfsKDqRry4bk3xxxxxxxx' 'C:\\myDirectory' --recursive --preserve-smb-permissions=true --preserve-smb-info=true\n\nConsole:\n\nLocal environment:\n\n" ]

[ 0 ]

[]

[]

[ "azure", "azure_files", "azure_storage", "python" ]

[ "Hatches in combination with fill_between should to the trick:\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#your code\nfig, ax = plt.subplots()\nplt.title('$f(x)= x^3 + x^2 + 17 $')\nplt.minorticks_on()\nplt.grid()\nplt.xlabel('x')\nplt.ylabel('y')\n\nx = np.linspace(-100, 100)\ny = lambda x: x ** 3 + x ** 2 + 17\nax.axhline(color='green', lw=2, alpha=0.7)\n#end of your code\n\n# use two regions to create two hatches, one for y>0, one for y<0\n# zorder = 2 to keep the hatches in front of the gridline\n# change the amount of '|' to change the hatch density\nfor mask, color in zip([y(x) > 0, y(x) < 0], [\"red\", \"green\"]):\n ax.fill_between(x[mask], y(x[mask]), y2=0, hatch='|||', zorder=2,\n color=\"none\", edgecolor=color, linewidth=0.0)\nax.plot(x, y(x))\nplt.show()\n\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "1st Solution: you can do this using map\ndic1 = {'2012-03-06':['1','4','5'],'2012-03-12':['7','3','10']}\n\nresult_dict = {key: sum(map(int, value)) for key, value in dic1.items()}\nprint(result_dict)\n\nOutput:\n{'2012-03-06': 10, '2012-03-12': 20}\n\n2nd Solution: And convert into expected output easily\ndic1 = {'2012-03-06':['1','3','5'],'2012-03-12':['7','3','10']}\nfor key, value in dic1.items():\n print(f\"{key}, {sum(map(int, value))}\")\n\nOutput:\n2012-03-06, 10\n2012-03-12, 20\n\n", "It can be\ndic1 = dict() \ndic1 = {'2012-03-06':['1','4','5'],'2012-03-12':['7','3','10']} \nfor i in dic1: \n print(i+','+ str(sum(map(int, dic1[i]))))\n\nIn python3, map(func,iter) returns an iterator(list, tuple etc.) of the results after applying the given function to each item of a given iterable\n" ]

[ 2, -2 ]

[]

[]

[ "addition", "dictionary", "integer", "python", "string" ]

[ "Unfortunately Surprise doesn't support partial fit yet.\nIn this thread there are some workarounds and forks with implemented partial fit.\n" ]

[ 0 ]

[]

[]

[ "python", "recommendation_engine" ]

[ "if your target is multid- then you need to choose another loss function ex. MultiRMSE instead of the default function RMSE\n" ]

[ 0 ]

[]

[]

[ "catboost", "catboostregressor", "python" ]

[ "you can use the sequence sql function to create an array of dates using the start and end. this array can be exploded to get new rows.\nsee example below\nspark.sparkContext.parallelize([(start_date, end_date)]). \\\n toDF(['start', 'end']). \\\n withColumn('start', func.to_date('start')). \\\n withColumn('end', func.to_date('end')). \\\n withColumn('date_seq', func.expr('sequence(start, end, interval 1 day)')). \\\n select(func.explode('date_seq').alias('date')). \\\n show(10)\n\n# +----------+\n# | date|\n# +----------+\n# |2022-08-20|\n# |2022-08-21|\n# |2022-08-22|\n# |2022-08-23|\n# |2022-08-24|\n# |2022-08-25|\n# |2022-08-26|\n# |2022-08-27|\n# |2022-08-28|\n# |2022-08-29|\n# +----------+\n# only showing top 10 rows\n\n" ]

[ 0 ]

[]

[]

[ "apache_spark_sql", "dataframe", "date", "pyspark", "python" ]

[ "I am actually working on a code generator, as part of a library that has the objective of allowing a code-first approach when querying GraphQL API servers from python.\nTo give you a preview what will be the outcome:\nclass Book(GQLObject):\n title: str\n year: int\n\nclass Author(GQLObject):\n name: str\n books: list[Book]\n\nI just published the core of the mapper on github:\nhttps://github.com/dapalex/py-graphql-mapper/\nHave a look at the documentation and I hope to publish the code generator soon.\n" ]

[ 0 ]

[]

[]

[ "code_generation", "graphql", "python", "type_hinting" ]

[ "would like to add an answer here posted by John in the udemy QnA section:\nLists and dictionaries are mutable. That means that you can add and remove elements from the list/dictionary and it still remains the same list/dictionary object. It is not necessary to create a new list/dictionary in this case.\nAlmost all other Python objects are immutable. That means that once created, they cannot be altered in any way. For example, if a is an integer, then when you do a += 1 an entirely new integer object is created.\nGlobal variables can be read anywhere in the file, including inside functions.\nThe rule is that functions are not allowed to create new global objects without you giving specific permission using the global keyword.\nTo illustrate the point, let's see what the memory location of the object is:\nmy_list = [1, 2, 3]\nprint(id(my_list), my_list)\nmy_list += [4] # my_list is the same list object\nprint(id(my_list), my_list)\n \nprint()\n \nmy_int = 123\nprint(id(my_int), my_int)\nmy_int += 1 # my_int is a new integer object\nprint(id(my_int), my_int)\n\nSee Mutable vs Immutable Objects in Python\n", "Consider a simpler example\nresources = {\n \"water\": 300,\n \"milk\": 200,\n \"coffee\": 100,\n}\n\ndef test(item, val):\n resources[item] -= val\n\ntest(\"water\", 5)\n\nThis program works. test updates resources even though resources isn't a parameter. Python needs to know which variables used in a function are local and which are not. The rule is simple: variables that are assigned in a function are local to the function. The global keyword breaks that rule and lets you assign variables in the global scope.\nIn this example\ndef test2():\n var1 = \"foo\"\n print(var2)\n global var3\n var3 = \"baz\"\n\nvar1 is assigned in the function, so python makes it local. var2 is referenced in the function but is not assigned, so python looks into the global scope for its value. var3 is assigned, but its also declared global, so the local variable rule is broken and the assignment will go to the global scope.\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[ "Convert values to numpy arrays, for improve solution remove nested ][:\ntest2.loc[test2[\"ID\"]==\"Ke2821Kkkkkk\",[\"Label\", \"Key\"]] = test1.loc[test1[\"ID\"]==\"Ka1129Kkkkkk\",[\"Label\", \"Key\"]].to_numpy()\ntest2.loc[test2[\"ID\"]==\"Ka83acKkkkkk\",[\"Label\", \"Key\"]] = test1.loc[test1[\"ID\"]==\"Ka9045Kkkkkk\",[\"Label\", \"Key\"]].to_numpy()\ntest2.loc[test2[\"ID\"]==\"Ka9a40Kkkkkk\",[\"Label\", \"Key\"]] = test1.loc[test1[\"ID\"]==\"Ka1129Kkkkkk\",[\"Label\", \"Key\"]].to_numpy()\nprint (test2)\n ID Label Key\n9791 Ke53a3Kkkkkk NaN NaN\n9792 Ke1c8dKkkkkk NaN NaN\n9793 Kf69acKkkkkk NaN NaN\n9794 Ke2821Kkkkkk 2.0 2_MIAMI\n9795 Ke0a14Kkkkkk NaN NaN\n9796 Kf6a4cKkkkkk NaN NaN\n9797 Ka83acKkkkkk 50.0 50_ORLANDO\n9798 Kf4698Kkkkkk NaN NaN\n9799 Ke1981Kkkkkk NaN NaN\n9800 Ka9a40Kkkkkk 2.0 2_MIAMI\n\n" ]

[ 1 ]

[]

[]

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

[ "You are returning your function inside your loop. So once if finds a uppercase or lowercase it will directly return from the function. Just remove the return lines.\n", "You should not put return statements inside the loop. The num_case function should look like this:\ndef num_case(str): \n z = 0 \n j = 0 \n for i in str: \n if i.isupper(): \n z = z + 1 \n elif i.islower(): \n j = j + 1 \n \n return (z, j) \n \n \nuppercase, lowercase = num_case('The quick Brow Fox')\n \nprint(\"upper case:\", uppercase) \nprint(\"lowercase:\", lowercase) \n\n", "\nDon't use builtin names like str.\n\ninstead of returning the value inside the loop return or print after the loop.\n\nNo need to add else if you are just going to pass\n\ntry to use understandable variable names while writing the code.\n\n\ndef num_case(str_val):\n upper_count = 0\n lower_count = 0\n for val in str_val:\n if val.isupper():\n upper_count += 1\n elif val.islower():\n lower_count += 1\n\n print('upper case:', upper_count)\n print('lowercase:', lower_count)\n\n\nnum_case('The quick Brow Fox')\n\n" ]

[ 0, 0, 0 ]

[]

[]

[ "lowercase", "python", "string", "uppercase" ]

[ "You can use a trick with pandas.Series.name to replace the column name with 1, then fillna(0).\nFirst make sure to clean up the column names with:\nbook_data.columns= book_data.columns.str.strip()\n\nAnd why not also the values of each row :\nbook_data= book_data.replace(\"\\s+\", \"\", regex=True)\n\nThen try this :\nout= book_data.apply(lambda x: x.replace(x.name, 1), axis=0).fillna(0).astype(int)\n\n# Output :\nprint(out)\n\n bread milk butter jam nutella cheese chips\n0 1 0 1 1 1 0 0\n1 0 0 1 1 1 0 1\n2 0 1 0 0 0 1 0\n3 1 1 1 1 1 1 1\n4 1 1 0 0 1 0 0\n5 1 1 1 1 0 1 1\n6 1 1 0 0 1 0 0\n7 1 0 1 0 0 1 0\n8 1 0 1 1 1 0 0\n9 0 1 1 1 0 1 0\n10 1 0 0 1 1 1 1\n11 1 1 1 1 1 0 0\n12 1 0 1 0 1 1 0\n13 1 0 1 1 1 1 1\n14 1 1 1 1 1 1 1\n15 0 1 1 1 1 1 0\n16 0 1 0 1 1 1 0\n17 1 1 1 1 1 1 1\n18 1 0 1 1 1 1 0\n19 1 1 1 0 1 1 0\n20 0 1 0 0 0 0 1\n\n" ]

[ 0 ]

[]

[]

[ "one_hot_encoding", "python" ]

[ "The equivalent pandas method is melt(). Selecting columns, renaming columns etc. are very similar.\ntotal_RACE_Reshape = total_RACE.melt(\n value_vars=['non_Hispanic_Black_65_percent', 'non_Hispanic_White_65_percent'], \n value_name='Race', \n var_name='Percent'\n)\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python", "r" ]

[ "You don't need to await on a created task.\nIt will run in the background as long as the event loop is active and is not stuck in a CPU bound operation.\nAccording to your comment, you don't have an access to the event loop. In this case you don't have many options other than running in a different thread (which will have its own loop), or changing the loop creation policy in order to get the event loop, which is a very bad idea in most cases.\n", "I found a way to make your test program run. However, it's a hack. It could fail, depending on the internal design of your third party library. From the information you provided, the library has been structured to be a black box. You can't interact with the event loop or schedule a callback. It seems like there might be a very good reason for this.\nIf I were you I would try to contact the library designer and let him know what your problem is. Perhaps there is a better solution. If this is a commercial project, I would make 100% certain that the team understands the issue, before attempting to use my below solution or anything like it.\nThe script below overrides one method (new_event_loop) in the DefaultEventLoopPolicy. When this method is called, I create a task in this loop to execute your periodic function. I don't know how often, or for what purpose, the library will call this function. Also, if the library internally overrides the EventLoopPolicy then this solution will not work. In both of these cases it may lead to unforeseeable consequences.\nOK, enough disclaimers.\nThe only significant change to your test script was to replace the infinite loop in runner with a one that times out. This allowed me to verify that the program shuts down cleanly.\nimport asyncio\n\n# I don't know how the loop argument is used\n# by the third party's run() function,\ndef third_party_run():\n async def runner():\n for _ in range(4):\n print(\"third party code executing\")\n await asyncio.sleep(5)\n\n # but I do know that this third party eventually runs code\n # that looks **exactly** like this.\n try:\n asyncio.run(runner())\n except KeyboardInterrupt:\n return\n\nasync def periodic():\n while True:\n print(\"doing my stuff...\")\n await asyncio.sleep(1)\n\nclass EventLoopPolicyHack(asyncio.DefaultEventLoopPolicy):\n def __init__(self):\n self.__running = None\n super().__init__()\n \n def new_event_loop(self):\n # Override to create our periodic task in the new loop\n # Get a loop from the superclass.\n # This method must return that loop.\n print(\"New event loop\")\n loop = super().new_event_loop()\n if self.__running is not None:\n self.__running.cancel() # I have no way to test this idea\n self.__running = loop.create_task(periodic())\n return loop\n \nasyncio.set_event_loop_policy(EventLoopPolicyHack())\n\nthird_party_run()\n\n" ]

[ 1, 1 ]

[]

[]

[ "python", "python_asyncio" ]

[ "You can use np.floating:\nIn [11]: isinstance(np.float16(1), np.floating)\nOut[11]: True\n\nIn [12]: isinstance(np.float32(1), np.floating)\nOut[12]: True\n\nIn [13]: isinstance(np.float64(1), np.floating)\nOut[13]: True\n\nNote: non-numpy types return False:\nIn [14]: isinstance(1, np.floating)\nOut[14]: False\n\nIn [15]: isinstance(1.0, np.floating)\nOut[15]: False\n\nto include more types, e.g. python floats, you can use a tuple in isinstance:\nIn [16]: isinstance(1.0, (np.floating, float))\nOut[16]: True\n\n", "To check numbers in numpy array, it provides 'character code' for the general kind of data.\nx = np.array([3.6, 0.3])\nif x.dtype.kind == 'f':\n print('x is floating point')\n\nSee other kinds of data here in the manual\nEDITED---------------\nBe careful when using isinstance and is operator to determine type of numbers.\nimport numpy as np\na = np.array([1.2, 1.3], dtype=np.float32)\n\nprint(isinstance(a.dtype, np.float32)) # False\nprint(isinstance(type(a[0]), np.float32)) # False\nprint(a.dtype is np.float32) # False\nprint(type(a[0]) is np.dtype(np.float32)) # False\n\nprint(isinstance(a[0], np.float32)) # True\nprint(type(a[0]) is np.float32) # True\nprint(a.dtype == np.float32) # True\n\n", "The best way to a check if a NumPy array is in floating precision whether 16,32 or 64 is as follows:\nimport numpy as np\na = np.random.rand(3).astype(np.float32)\nprint(issubclass(a.dtype.type,np.floating))\n\na = np.random.rand(3).astype(np.float64)\nprint(issubclass(a.dtype.type,np.floating))\n\na = np.random.rand(3).astype(np.float16)\nprint(issubclass(a.dtype.type,np.floating))\n\nIn this case all will be True.\nThe common solution however can be give wrong outputs as shown below,\nimport numpy as np\na = np.random.rand(3).astype(np.float32)\nprint(isinstance(a,np.floating))\n\na = np.random.rand(3).astype(np.float64)\nprint(isinstance(a,np.floating))\n\na = np.random.rand(3).astype(np.float16)\nprint(isinstance(a,np.floating))\n\nIn this case all will be False\nThe workaround for above though is\nimport numpy as np\na = np.random.rand(3).astype(np.float32)\nprint(isinstance(a[0],np.floating))\n\na = np.random.rand(3).astype(np.float64)\nprint(isinstance(a[0],np.floating))\n\na = np.random.rand(3).astype(np.float16)\nprint(isinstance(a[0],np.floating))\n\nNow all will be True\n" ]

[ 53, 3, 0 ]

[]

[]

[ "numpy", "python" ]

[ "Updated based on comment not to use a separate variable\nYou can use the loop variable i to determine the current decrement value.\n# Loop from 4 to 0, using the current value of i as the decrement value\nfor i in range(4,0,-i):\n Dev.step(2)\n if i == 1 or i == 3:\n Dev.turnLeft()\n Dev.step(i)\n Dev.step(-i)\n Dev.turnRight()\n else:\n Dev.turnRight()\n Dev.step(i+4)\n Dev.step(-i-4)\n Dev.turnLeft()\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "This example produces the requested format based on your before image and includes rows 1 -4 from the original sheet unchanged.\nBasically the code loops through the rows from row 5, the header row for the data in columns B, C, D, and E. It moves each range from A to E across by 1 so that column A is then empty, and sets the value in that row's column A to the value of the latest determined 'ProductItems' using variable 'colA_val'.\n\n'ProductItems' is determined by the leading spaces in the cell text set by the variable 'num_indent'. Judging from the image has 1 leading space. If there is a more definitive way to determine the 'ProductItems' row this part can be changed.\n\nThe following steps are optional \n\nAfter the data is moved 1 column across a loop is used to apply the column widths 1 across so the Headers retain the same width as before. Column A is slighly reduced in width since the larger width text in now in Column B.\nCell B5 in the header row has an outline border added. The cell contains no text as this is not specified in the question.\n\n...\nfrom openpyxl import load_workbook\nfrom openpyxl.utils import get_column_letter\nfrom openpyxl.styles import Border, Side\n\nfilename = \"openpyxl2.xlsx\"\n\n# Open workbook and select sheet\nwb = load_workbook(filename)\nws = wb.active\n\n### Loop the cells\ncolA_val = '' # Hold the text value to enter in to Col A\ncol_width = {} # This dict holds the column dimenions so the col widths can be readjusted\nnum_indent = 1 # How many leading spaces for the 'ProductItems' cell\n\nfor row in ws.iter_rows(min_row=5, max_row=ws.max_row):\n cur_row =row[0].row\n row_colA_value = row[0].value\n ### If this is a row with data\n if row_colA_value is not None:\n ### Determine if 'ProductItems' row from the leading spaces, set by 'num_indent'\n leading_spaces = len(row_colA_value) - len(row_colA_value.lstrip(' '))\n ### Is current row a ProductItems row, if so get details and delete row\n if leading_spaces == num_indent:\n ### One off row 5 header adjustment and column widths collection\n if not col_width:\n ### Adjust header row 5 one column across\n ws.move_range(f'A{cur_row - 1}:E{cur_row - 1}', cols=1)\n ### Get column dimensions \n for x in range(ws.max_column-1):\n col_width[row[x].column] = ws.column_dimensions[row[x].column_letter].width\n ### Get next ProductItem name\n colA_val = row_colA_value.lstrip()\n ### Delete the row the ProductItem was on\n ws.delete_rows(cur_row)\n\n ### move name, units, sales etc cells 1 column across\n ws.move_range(f'A{cur_row}:E{cur_row}', cols=1)\n ### Enter the ProductItem name into cell in column A\n ws.cell(row=cur_row, column=1).value = colA_val\n\n\n### Reset the column widths using the sizes saved in the col_width dictionary\nfor c, d in col_width.items():\n ### Adjust Column A to be a little smaller than before\n if c == 1:\n ws.column_dimensions['A'].width = d - 10\n ### Set the columns to the same width as before\n ws.column_dimensions[get_column_letter(c + 1)].width = d\n\n### Add Text and Border to header section cell B5\nws.cell(row=5, column=2).value = 'Enter text here'\n\nmed_border = Border(left=Side(style='medium'),\n right=Side(style='medium'),\n top=Side(style='medium'),\n bottom=Side(style='medium'))\nws.cell(row=5, column=2).border = med_border\n\nwb.save('out_' + filename)\n\n" ]

[ 0 ]

[]

[]

[ "excel", "openpyxl", "python" ]

[ "\nGenerally speaking, GPUs are 32-bit machines with 64-bit addressing capability. All 64-bit integer operations are emulated. In the simplest case (logical operations, additions, subtractions) each 64-bit integer operation requires the execution of two 32-bit integer instructions. Very roughly, emulation of 64-bit multiplication requires about twenty 32-bit instructions, 64-bit division requires about eighty 32-bit instructions.\n\nhttps://forums.developer.nvidia.com/t/question-about-64-bit-integer-performance/64147\n\nGPUs are optimised for doing 3D rendering calculations. Following the history of OpenGL, these are traditionally done using 32-bit floating point numbers arranged as either vectors of four floats or quaternion matrices of 4x4 floats. So that's the capability GPUs are very good at.\nIf you want to do floating point with more bits, or 64-bit integerarithmetic, you may find it unsupported or slow.\n\nhttps://cs.stackexchange.com/a/121119\n" ]

[ 1 ]

[]

[]

[ "cuda", "numba", "python" ]

[ "Hi, Welcome to StackOverflow!\nBoth of these works:\nwith open('newfile.txt','w') as newfile:\n w=csv.writer(newfile)\n w.writerow(header_a+header_b[1:])\n\n for m in set(dict_a.keys()).union(dict_b.keys()):\n for n in dict_b.get(m, [[]]):\n w.writerow([m]+dict_a.get(m, [])+n)\n\nOR\nwith open('newfile.txt','w') as newfile:\n w=csv.writer(newfile)\n w.writerow(header_a+header_b[1:])\n\n for m in dict_a.keys()+dict_b.keys():\n for n in dict_b.get(m, [[]]):\n w.writerow([m]+dict_a.get(m, [])+n)\n\nComplete Code:\nimport collections\nimport csv\nimport sys\n\ndef c_merge(f1,f2):\n with open(f1,'r') as infile:\n obj=csv.reader(infile)\n header_a=next(obj)\n dict_a={row[0]: row[1:] for row in obj}\n\n with open(f2,'r') as infile:\n obj=csv.reader(infile)\n header_b=next(obj)\n dict_b=collections.defaultdict(list)\n for row in obj:\n dict_b[row[0]].append(row[1:])\n\n with open('newfile.txt','w') as newfile:\n w=csv.writer(newfile)\n w.writerow(header_a+header_b[1:])\n\n for m in set(dict_a.keys()).union(dict_b.keys()):\n for n in dict_b.get(m, [[]]):\n w.writerow([m]+dict_a.get(m, [])+n)\n\n # OR\n\n \"\"\"\n for m in dict_a.keys()+dict_b.keys():\n for n in dict_b.get(m, [[]]):\n w.writerow([m]+dict_a.get(m, [])+n)\n \"\"\"\n\nif __name__ == \"__main__\":\n c_merge(sys.argv[0],sys.argv[1])\n obj=csv.reader(open('newfile.txt','r'))\n for x in obj:\n print(x)\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "The easiest way to solve the problem is to use the sys.path.append method above your import statement to indicate the path. For you, the code should look like this:\nimport sys\nsys.path.append(\"C:/Users/user_1/my_packages\")\n\nfrom packages_i_made import personal_functions as pf\n\nYou set the .env file just to let vscode intellisense (pylance for python) see the package, but in fact the python interpreter still cannot access your package. This effect is the same as adding \"python.analysis.extraPaths\" configuration in settings.json, it will add additional import search resolution path.\nThe difference this brings is that when you write import code you get autocompletion and the yellow warning squiggly line disappears. But when you run the code, you still get errors because the python interpreter doesn't actually have access to your package.\nVS code uses the currently open folder as the workspace. So which folder you open in vscode is also critical.\n", "Open the vscode from your project root folder. It should work then.\n" ]

[ 1, 0 ]

[]

[]

[ "path", "python", "python_import", "visual_studio_code" ]

[ "EC2 launch template is immutable. You must create a new version if you need to modify the current launch template version.\nHere is an example of creating a new version and then making it the default version using AWS SDK v2.\nInstall these two packages:\n\"github.com/aws/aws-sdk-go-v2/service/ec2\"\nec2types \"github.com/aws/aws-sdk-go-v2/service/ec2/types\"\n\nAssuming you created the AWS config:\nfunc createLaunchTemplateVersion(cfg aws.Config) {\n ec2client := ec2.NewFromConfig(cfg)\n template := ec2types.RequestLaunchTemplateData{\n InstanceType: ec2types.InstanceTypeT2Medium}\n createParams := ec2.CreateLaunchTemplateVersionInput{\n LaunchTemplateData: &template,\n LaunchTemplateName: aws.String(\"MyTemplate\"),\n SourceVersion: aws.String(\"1\"),\n }\n outputCreate, err := ec2client.CreateLaunchTemplateVersion(context.Background(), &createParams)\n if err != nil {\n log.Fatal(err)\n }\n if outputCreate.Warning != nil {\n log.Fatalf(\"%v\\n\", outputCreate.Warning.Errors)\n }\n // set the new launch type version as the default version\n modifyParams := ec2.ModifyLaunchTemplateInput{\n DefaultVersion: aws.String(strconv.FormatInt(*outputCreate.LaunchTemplateVersion.VersionNumber, 10)),\n LaunchTemplateName: outputCreate.LaunchTemplateVersion.LaunchTemplateName,\n }\n outputModify, err := ec2client.ModifyLaunchTemplate(context.Background(), &modifyParams)\n if err != nil {\n log.Fatal(err)\n }\n fmt.Printf(\"default version %d\\n\", *outputModify.LaunchTemplate.DefaultVersionNumber)\n}\n\n" ]

[ 2 ]

[]

[]

[ "amazon_ec2", "amazon_web_services", "go", "python" ]

[ "Use Series.dt.ceil by 15 minutes and then Series.dt.floor by 30:\ndf['datetime'] = pd.to_datetime(df['datetime']).dt.ceil('15Min').dt.floor('30Min')\nprint (df)\n datetime\n0 2022-11-15 00:30:00\n1 2022-11-15 00:30:00\n2 2022-11-15 00:30:00\n3 2022-11-15 01:00:00\n4 2022-11-15 01:00:00\n5 2022-11-15 01:00:00\n6 2022-11-15 01:30:00\n7 2022-11-15 01:30:00\n8 2022-11-15 01:30:00\n9 2022-11-15 02:00:00\n10 2022-11-15 02:00:00\n11 2022-11-15 02:00:00\n12 2022-11-15 02:30:00\n13 2022-11-15 02:30:00\n14 2022-11-15 02:30:00\n15 2022-11-15 03:00:00\n16 2022-11-15 03:00:00\n17 2022-11-15 03:00:00\n\n" ]

[ 2 ]

[]

[]

[ "pandas", "python" ]

[ "Avoid repetition, in your loop:\nwhile True:\n\n try:\n reaction, user = await client.wait_for(\"reaction_add\", timeout=60, check=check)\n # waiting for a reaction to be added - times out after x seconds, 60 in this\n # example\n\n if str(reaction.emoji) == \"▶️\":\n cur_page += 1\n \n\n elif str(reaction.emoji) == \"◀️\": \n cur_page -= 1\n\n if cur_page > pages: #check if forward on last page\n cur_page = 1 \n elif cur_page < 1: #check if back on first page\n cur_page = pages \n await message.edit(embed=contents[cur_page-1])\n await message.remove_reaction(reaction, user)\n\n \n except asyncio.TimeoutError:\n await message.delete()\n break\n\n" ]

[ 0 ]

[]

[]

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

[ "I think you need aggregate per 3 columns:\ndf1 = df.groupby(['Type','POS', 'FN'])[['VC','ID','DN']].agg(lambda x: '|'.join(x.unique()))\n\ndf2 = pd.get_dummies(df.set_index(['Type','POS', 'FN'])['Group']).sum(level=[0, 1, 2])\ndf = pd.concat([df1, df2], axis=1)\nprint (df.head(20))\n VC ID DN BPAD FC PC SCZ\nType POS FN \nA1 C1 F1 MI S1 D1 0 1 0 0\nA2 C2 F2 MI S2 D2 0 0 0 1\n C3 F3 IN S3|S4 D3|D4 0 1 0 1\n C4 F4 MI S5 D5 0 0 0 1\nA3 C10 F12 MI S23 D23 0 0 1 0\n C11 F13 MI S24 D24 0 1 0 0\n C12 F16 MI S29 D29 0 1 0 0\n C13 F3 MI S32|S3 D32|D3 0 2 0 0\n C14 F26 MI S41 D41 0 1 0 0\n C15 F39 MI S57 D57 0 0 1 0\n C5 F11 MI S22 D22 0 1 0 0\n F12 MI S23 D23 0 0 1 0\n F13 MI S25 D25 0 1 0 0\n F14 MI S26|S27 D26|D27 0 2 0 0\n F15 MI S28 D28 0 1 0 0\n F16 MI S29 D29 0 1 0 0\n F17 MI S30 D30 0 1 0 0\n F18 MI S31 D31 0 1 0 0\n F19 MI S33 D33 0 1 0 0\n F20 MI S34 D34 0 1 0 0\n\n" ]

[ 1 ]

[]

[]

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

[ "It sounds like you are looking for a way to create a program that can be controlled by the user at runtime. One way to do this is to use a loop that continues until the user specifies that they want to stop the program.\nOne common way to do this is to use a while loop that repeats until the user enters a specific command to exit the program. For example, you could use a while loop that repeats indefinitely, and inside the loop, you could check if the user has entered the command to exit the program. If they have, you can use the break statement to exit the loop and end the program. Here is an example of how this might look:\nwhile True:\n # do some work here\n \n # check if the user wants to exit the program\n user_input = input(\"Enter 'exit' to exit the program: \")\n if user_input == \"exit\":\n break\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "IIUC, use a dense method on pandas.Series.rank with pandas.Series.astype :\ndf['Rank']= df.groupby('ID')['Value'].rank(ascending=False, method='dense').astype(int)\n\n# Output :\nprint(df)\n\n ID Class Value Rank\n0 US A 10 1\n1 US B 10 1\n2 US C 2 2\n3 US D 2 2\n\nInput used :\n ID Class Value\n0 US A 10\n1 US B 10\n2 US C 2\n3 US D 2\n\n" ]

[ 0 ]

[]

[]

[ "duplicates", "python", "rank", "ranking_functions", "unique_values" ]

[ "\nThe range consists of two values, it doesn't have to start with 1.\n\nThe task is to make a list. You create a list1 variable and inside the loop ask for a range for each number\n\nYou don't call the functions you wrote\n import random\n play = True\n while play:\n print(\"Enter number of values needed: \", end = \"\")\n num_values = int(input())\n print(\"Enter start of value range from 1 to: \", end = \"\")\n start = int(input())\n print(\"Enter end of value range from 1 to: \", end = \"\")\n end = int(input())\n list1 = []\n for i in range(0,num_values):\n list1.append(random.randint(start, end))\n print(list1)\n\n def even():\n even_count = 0\n for i in list1:\n if i % 2 == 0:\n even_count += 1\n return even_count\n\n def odd():\n odd_count = 0\n for i in list1:\n if i % 2 != 0:\n odd_count += 1\n return odd_count\n\n even_total = (even() / num_values) * 100\n odd_total = (odd() / num_values) * 100\n print(f\"Odd values:{odd_total}%\")\n print(f\"Even values:{even_total}%\")\n\n\n print(\"Do you want to generate a new set of values? (Y/N)\")\n letter = str(input())\n if letter != 'Y':\n play = False\n\n\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "You can use os and rsync\n--no-whole-file or --no-W parameters use the block-level sync instead of the file level syncing.\n--progress is used for getting the logs of file transfer\nYou can also use file_name.log for adding logs into that file instead of on terminal and that file will be saved at the current location where you are running the program\nfor e.g,\nos.system(\"rsync -r --progress --no-W --stats 'Source path' 'Destination Path' > file_name.log\")\nimport os\nos.system(\"rsync -r --progress --no-W --stats 'Source path' 'Destination Path'\")\n\n", "Your snippet wastes compute/time loading the file into the ram and writing it back to disk. Use the underlying OS to copy the file using pythons pathlib and shutil.\nHave a look at this stackoverflow post. Here's the top answer:\nimport pathlib\nimport shutil\n\nmy_file = pathlib.Path('/etc/hosts')\nto_file = pathlib.Path('/tmp/foo')\n\nshutil.copy(str(my_file), str(to_file)) # For older Python.\nshutil.copy(my_file, to_file) # For newer Python.\n\n" ]

[ 2, 0 ]

[]

[]

[ "azure_blob_storage", "python" ]

[ "It is because the frame fortable1 which holds the table is not part of the content of txtbox since it is just put on top of txtbox using .place(). You need to use txtbox.window_create() to insert the frame into the text box instead.\nBelow is the updated code:\n...\ntxtbox = scrolledtext.ScrolledText(app, width=50, height=10)\ntxtbox.grid(row=0, column=0, sticky=E+W+N+S)\nfortable1 = Frame(txtbox, padx=5, pady=5,width=500,height=200 )\n#fortable1.place(x=0,y=5)\n# insert the frame using .window_create()\ntxtbox.window_create(INSERT, window=fortable1)\ntxtbox.insert(INSERT, \"\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n Physical Properties.\\n.\\n.\\n Physical Properties\")\n...\n\n" ]

[ 1 ]

[]

[]

[ "python", "tkinter", "tkinter_layout", "tkinter_text" ]

[ "After following up on mwaskom's suggestion to use matplotlib.text() (thanks), I was able to get the following to work as expected:\np = sns.pairplot(data=g, hue=\"Label\", palette=\"Set2\", \n diag_kind=\"kde\", size=4, vars=labels)\n#bottom labels\np.fig.text(0.33, -0.01, \"Bin: %s\"%(n), ha ='left', fontsize = 15)\np.fig.text(0.33, -0.04, \"Num Points: %d\"%(len(g)), ha ='left', fontsize = 15)\n\nand other useful functionality:\n# title on top center of subplot\np.fig.suptitle('this is the figure title', verticalalignment='top', fontsize=20)\n\n# title above plot\np.fig.text(0.33, 1.02,'Above the plot', fontsize=20)\n\n# left and right of plot\np.fig.text(0, 1,'Left the plot', fontsize=20, rotation=90)\np.fig.text(1.02, 1,'Right the plot', fontsize=20, rotation=270)\n\n# an example of a multi-line footnote\np.fig.text(0.1, -0.08,\n 'Some multiline\\n'\n 'footnote...',\n fontsize=10)\n\n", "In addition, using the seaborn functionality, you can move the title up.\nAfter your p=sns.pairplot line, add\np.fig.suptitle(\"here is your title\", y=1.05)\nWhich will give you a title that is just above the actual plot area instead of overlapping the plot (depends on what you're plotting and how much you might need the space).\nYou can also use fontweight (bold, etc) in addition to the fontsize.\n" ]

[ 12, 0 ]

[]

[]

[ "matplotlib", "pairplot", "plot_annotations", "python", "seaborn" ]

[ "I solved the problem with librosa package\ndata, sampleRate = await librosa.load(f'{filename}.wav')\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "Did you try to build the latest OpenSSL? I see instructions here: https://www.howtoforge.com/tutorial/how-to-install-openssl-from-source-on-linux/\n(wasn't able to try this because I'm not running Linux). Please let us know if this worked.\n", "check below points:\nTo check which all versions are installed\n\nwhich python\n\n//use python or python3 as per your needs\n\nTo check where all versions are installed\n\nwhere python\n\n//use python or python3 as per your needs\n\nIn mycase i was having two paths as below,\n/usr/bin/python \n/usr/local/bin/python\n\nso, now check VS Code,\n\nCommand + Shipt + P\nSearch/Select Python Interpreter\nselect appropriate version you are using, if not listed Add the respective path and select the same.\n\nThis Solution worked for me:)\n" ]

[ 0, 0 ]

[]

[]

[ "cryptography", "hashlib", "python" ]

[ "inside each app you must put admin file so can django track these files , so in your app1 in admin.py file related to app1 directory app1/admin.py , you need to put this code\nfrom django.contrib import admin\nfrom app1.models import User\n \nadmin.site.register(User)\n\nand in app2 in admin.py related to app2 directoryapp2/admin.py put this :\n\nfrom django.contrib import admin\n\nfrom app2.models import Manager, Employee, Task, Template\n\n\nadmin.site.register(Manager)\nadmin.site.register(Employee)\nadmin.site.register(Task)\nadmin.site.register(Template)\n\n", "Every app should have their own admin.py file by default design. Django will detect it in the respective app folder and reflect it in admin page.\nHowever if what you're trying here is to compile every model in one admin.py. Django wont detect that file by default. Another way to do this, you can try create a new app \"admin\" and register every model in that folder. I never did this because admin.py separated in their own app has its own very significant purpose.\n" ]

[ 0, 0 ]

[]

[]

[ "django", "python" ]

[ "Use DataFrame.loc with filter MTDPerf Series:\nfor dt in feed_tail.index:\n mmtd = feed_tail.loc[dt, 'MTDPerf']\n d = mmtd[feed_tail.loc[dt, 'Close'] > feed_tail.loc[dt, 'SMA13']]\n\n\n print (d)\n\nSeries([], Name: 2022-11-11 00:00:00, dtype: object)\nSeries([], Name: 2022-11-14 00:00:00, dtype: object)\nSymbols\nBALKRISIND.NS 0.02\nKSB.NS -6.11\nName: 2022-11-15 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS -0.33\nKSB.NS -5.36\nName: 2022-11-16 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS 0.79\nKSB.NS -5.85\nName: 2022-11-17 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS 0.51\nKSB.NS -7.85\nName: 2022-11-18 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS -0.86\nName: 2022-11-21 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS -0.63\nName: 2022-11-22 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS -0.82\nKSB.NS -7.23\nName: 2022-11-23 00:00:00, dtype: object\nSymbols\nBALKRISIND.NS 0.64\nKSB.NS -7.47\nName: 2022-11-24 00:00:00, dtype: object()\n\nSolution with DataFrame.where for replace NaNs if no match:\ndf = feed_tail['MTDPerf'].where(feed_tail['Close'] > feed_tail['SMA13'])\nprint (df)\nSymbols BALKRISIND.NS KSB.NS\n2022-11-11 NaN NaN\n2022-11-14 NaN NaN\n2022-11-15 0.02 -6.11\n2022-11-16 -0.33 -5.36\n2022-11-17 0.79 -5.85\n2022-11-18 0.51 -7.85\n2022-11-21 -0.86 NaN\n2022-11-22 -0.63 NaN\n2022-11-23 -0.82 -7.23\n2022-11-24 0.64 -7.47\n\nAnd after reshaping:\ns = feed_tail['MTDPerf'].where(feed_tail['Close'] > feed_tail['SMA13']).stack()\nprint (s)\n Symbols \n2022-11-15 BALKRISIND.NS 0.02\n KSB.NS -6.11\n2022-11-16 BALKRISIND.NS -0.33\n KSB.NS -5.36\n2022-11-17 BALKRISIND.NS 0.79\n KSB.NS -5.85\n2022-11-18 BALKRISIND.NS 0.51\n KSB.NS -7.85\n2022-11-21 BALKRISIND.NS -0.86\n2022-11-22 BALKRISIND.NS -0.63\n2022-11-23 BALKRISIND.NS -0.82\n KSB.NS -7.23\n2022-11-24 BALKRISIND.NS 0.64\n KSB.NS -7.47\ndtype: float64\n\n" ]

[ 1 ]

[]

[]

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

[ "Since Google Colab is a notebook, you can use the ! operator with pip to install the TA-Lib package.\nTry this :\n!pip install TA-Lib\n\nAs suggested by @DarknessPlusPlus, you can also use the magic command % :\n%pip install TA-Lib\n\nThis answer by @jakevdp explains the difference between the two commands.\n# Edit :\nIf you still can't import the modules, run this in a Google Colab cell :\n!curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz -O && tar xzvf ta-lib-0.4.0-src.tar.gz\n!cd ta-lib && ./configure --prefix=/usr && make && make install && cd - && pip install ta-lib\n\n# Result :\n\nBut before that, make sure to uninstall any talib package with :\n%pip uninstall TA-lib talib-binary\n\n" ]

[ 3 ]

[]

[]

[ "python", "ta_lib" ]

[ "I don't think there is one definitive list. You have to collect it from different sources. Start with PEP 425: https://www.python.org/dev/peps/pep-0425/\npython tag: ‘py27’, ‘cp33’\nabi tag: ‘cp32dmu’, ‘none’\nplatform tag: ‘linux_x86_64’, ‘any’ \n--implementation:\ncp: CPython\nip: IronPython\npp: PyPy\njy: Jython\n\n--platform:\nwin32\nlinux_i386\nlinux_x86_64\n\n", "If you have access to the PC (or similar platform) for which you need to download the package, per the documentation, the following function can be called to get the explicit platform name.\ndistutils.util.get_platform()\n\n\nThe platform tag is simply distutils.util.get_platform() with all hyphens - and periods . replaced with underscore _.\n\n\nIn our case, we have offline PCs (which must remain offline); so this approach works perfectly to ensure we download the correct platform for those PCs.\n", "If you are only downloading a single package, you could attempt go to pypi.org and search for what's available.\nE.g. for orjson https://pypi.org/project/orjson/3.8.2/#files, you can see there's things like:\n\nwin_amd64\nmanylinux_2_28_x86_64\nmanylinux_2_28_aarch64\nmanylinux_2_17_x86_64\nmanylinux2014_x86_64\nmanylinux_2_17_armv7l\nmanylinux2014_armv7l\nmanylinux_2_17_aarch64\nmanylinux2014_aarch64\nmacosx_10_9_x86_64\nmacosx_11_0_arm64\nmacosx_10_9_universal2\nmacosx_10_7_x86_64\n\n" ]

[ 13, 4, 0 ]

[]

[]

[ "pip", "python" ]

[ "In addition to the aforementioned (great) editors, you might want to give PyDev a shot as well.\n", "Geany can do this.\n", "Notepad++ has this feature.\n", "Komodo Edit IDE, for Windows, Mac and Linux, for Python, PHP, Ruby, JavaScript, Perl and Web Dev.\n", "I've used Komodo Edit and Notepad++ in the past but my current preference is Sublime Text Edit 2.\nAlthough not free (and actually quite expensive), it can be used in free mode with only an occasional reminder and no other restrictions.\nIt is actually written in Python so you get a Python console built in - you can also get other consoles such as JavaScript. It is VERY flexible & has some very good features. It is also has an excellent community with loads of very useful plugins.\nIt is much lighter on resource usage than Komodo, can use Textmate bundles directly (so gets loads of formatting options for different file types). It is cross-platform and doesn't even need installation on Windows.\n", "Pycharm CE, from Jet Brains, indeed, wonderful. Functions and comments collapse is ready out of the box, as well as edit helpers. Project files and assets organization, integrated python console, powerful debugging tools,... Then, lots of plugins: git integration, tinycode view, extra languages' helpers and highlighters,.... anything you need when coding, but simple and easy to use. There's a Pro (paid) version for those who want even more.\nhttps://www.jetbrains.com/pycharm/download\n(This question is more than 10 years old. I got surprised, nobody answered about Pycharm before...)\n" ]

[ 4, 3, 2, 1, 1, 0 ]

[]

[]

[ "python" ]

[ "You could use a generator, that builds the path when coming back from recursion:\ndef paths(adj, node, parent=None):\n yield [node]\n for child in adj[node]:\n if child != parent:\n for path in paths(adj, child, node):\n yield [*path, node]\n\nHere is how to call it:\n# example tree\nadj = [\n [1],\n [0,2,3,4,5],\n [1],\n [1,10,11],\n [1],[1],[],[],[],[],[3],\n [3,14,15,16,17,18,19],\n [],[],[11],[11],[11],[11],[11],[11]\n]\n\nfor path in paths(adj, 0):\n if len(path) > 1:\n print(\" -> \".join(map(str, path[:-1])) + \" -> root\")\n\nThis will output:\n1 -> root\n2 -> 1 -> root\n3 -> 1 -> root\n10 -> 3 -> 1 -> root\n11 -> 3 -> 1 -> root\n14 -> 11 -> 3 -> 1 -> root\n15 -> 11 -> 3 -> 1 -> root\n16 -> 11 -> 3 -> 1 -> root\n17 -> 11 -> 3 -> 1 -> root\n18 -> 11 -> 3 -> 1 -> root\n19 -> 11 -> 3 -> 1 -> root\n4 -> 1 -> root\n5 -> 1 -> root\n\nIf you want to only output the paths that start at a leaf, then change the generator function to:\ndef paths(adj, node, parent=None):\n children = [child for child in adj[node] if child != parent]\n if not children: # it's a leaf\n yield [node]\n else:\n for child in children:\n for path in paths(adj, child, node):\n yield [*path, node]\n\nCorresponding output:\n2 -> 1 -> root\n10 -> 3 -> 1 -> root\n14 -> 11 -> 3 -> 1 -> root\n15 -> 11 -> 3 -> 1 -> root\n16 -> 11 -> 3 -> 1 -> root\n17 -> 11 -> 3 -> 1 -> root\n18 -> 11 -> 3 -> 1 -> root\n19 -> 11 -> 3 -> 1 -> root\n4 -> 1 -> root\n5 -> 1 -> root\n\n" ]

[ 1 ]

[]

[]

[ "list", "multidimensional_array", "nested", "python", "tree" ]

[ "Decide what event by yourself to send the content of the Input element.\n\nclick a button - Add one button into your layout.\n\nimport PySimpleGUI as sg\n\nlayout = [[sg.Input(key='-IN-'), sg.Button('Submit')]]\nwindow = sg.Window('Title', layout)\nevent, values = window.read()\nif event == 'Submit':\n print(values['-IN-'])\nwindow.close()\n\n\nButton element with Enter key - Wth option bind_return_key=True\n\nimport PySimpleGUI as sg\n\nlayout = [[sg.Input(key='-IN-'), sg.Button('Submit', bind_return_key=True)]]\nwindow = sg.Window('Title', layout)\nevent, values = window.read()\nif event == 'Submit':\n print(values['-IN-'])\nwindow.close()\n\n\nInput element with Enter key - Binding event '<Return>' to your Input element.\n\nimport PySimpleGUI as sg\n\nlayout = [[sg.Input(key='-IN-')]]\nwindow = sg.Window('Title', layout, finalize=True)\nwindow['-IN-'].bind('<Return>', ' ENTER')\nevent, values = window.read()\nif event == '-IN- ENTER':\n print(values['-IN-'])\nwindow.close()\n\n\nclick any keyboard - With option enable_events=True\n\nimport PySimpleGUI as sg\n\nlayout = [[sg.Input(enable_events=True, key='-IN-')]]\nwindow = sg.Window('Title', layout)\nwhile True:\n event, values = window.read()\n if event == sg.WIN_CLOSED:\n break\n elif event == '-IN-':\n print(values['-IN-'])\nwindow.close()\n\n" ]

[ 1 ]

[]

[]

[ "pysimplegui", "python" ]