Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "Try the below code, its working:\nfrom selenium import webdriver\nimport undetected_chromedriver as uc\n\ndriver = uc.Chrome(use_subprocess=True)\ndriver.maximize_window()\n\ndriver.get(\"https://tempail.com/\")\n\n" ]

[ 0 ]

[]

[]

[ "python", "selenium", "undetected_chromedriver" ]

[ "To save tsv file on google colab, .to_csv function can be used as follows:\ndf.to_csv('path_in_drive/filename.tsv', sep='\\t', index=False, header=False)\nstk = pd.read_csv('path_in_drive/filename.tsv') #to read the file\n", "Don't know if this is a solution to your problem as it doesn't upload the files, but with this solution you can import files that are also on your google drive.\nfrom google.colab import drive\ndrive.mount('/gdrive')\n%cd /gdrive/My\\ Drive/{'.//'}\nAfter mounting you should be able to load files into your script like on your desktop\n", "import pandas as pd\nfrom google.colab import files\nimport io\n\n#firstly, upload file to colab\nuploaded = files.upload()\n#secondly, get path to file in colab\nfile_path = io.BytesIO(uploaded['file_name.tsv'])\n#the last step is familiar to us\ndf = pd.read_csv(file_path, sep='\\t', header=0)\n\n" ]

[ 1, 0, 0 ]

[]

[]

[ "google_colaboratory", "pandas", "python", "svm" ]

[]

[]

[ "you can change np.sqrt(9) to numexpr.evaluate('9**0.5')\n" ]

[ -1 ]

[ "numexpr", "numpy", "python", "sympy" ]

[ "Can you try the following?\nimport sys\nfrom awsglue.transforms import *\nfrom awsglue.utils import getResolvedOptions\nfrom pyspark.context import SparkContext\nfrom awsglue.context import GlueContext\nfrom awsglue.job import Job\n\n## @params: [JOB_NAME]\nargs = getResolvedOptions(sys.argv, ['JOB_NAME'])\n\nsc = SparkContext()\nglueContext = GlueContext(sc)\nspark = glueContext.spark_session\njob = Job(glueContext)\njob.init(args['JOB_NAME'], args)\n\ndatasource0 = glueContext.create_dynamic_frame.from_catalog(database = \"gluedatabse\", table_name = \"postgresgluetest_public_account\", transformation_ctx = \"datasource0\")\n\napplymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [(\"user_id\", \"string\", \"user_id\", \"string\"), (\"username\", \"string\", \"username\", \"string\"),(\"password\", \"string\", \"password\", \"string\")], transformation_ctx = \"applymapping1\")\n\n## Force one partition, so it can save only 1 file instead of 19\nrepartition = applymapping1.repartition(1)\n\ndatasink2 = glueContext.write_dynamic_frame.from_options(frame = repartition, connection_type = \"s3\", connection_options = {\"path\": \"s3://BUCKETNAMENOTSHOWN\"}, format = \"csv\", transformation_ctx = \"datasink2\")\njob.commit()\n\nAlso, if you want to check how many partitions you have currently, you can try the following code. I'm guessing there are 19, that's why is saving 19 files back to s3:\n ## Change to Pyspark Dataframe\n dataframe = DynamicFrame.toDF(applymapping1)\n ## Print number of partitions \n print(dataframe.rdd.getNumPartitions())\n ## Change back to DynamicFrame\n datasink2 = DynamicFrame.fromDF(dataframe, glueContext, \"datasink2\")\n\n", "If these are not big datasets, then you could easily consider converting glue dynamicframe (glue_dyf) to spark df (spark_df) and then spark df to pandas df (pandas_df) as below:\nspark_df = DynamicFrame.toDF(glue_dyf)\npandas_df = spark_df.toPandas()\npandas_df.to_csv(\"s3://BUCKETNAME/subfolder/FileName.csv\",index=False)\n\nIn this method, you need not worry about repartition for small volumes of data. Large datasets are advised to be treated like the previous answers by leveraging glue workers, spark partitions..\n" ]

[ 7, 0 ]

[]

[]

[ "amazon_s3", "amazon_web_services", "aws_glue", "jobs", "python" ]

[ "Ok so I will share how I handled the issue which is not perfect but it will maybe help.\nThe \"solution\" was to remove every data from the first figure by disabling them with hover_data as follow in the choropleth map variables:\nhover_data={\"alpha-3\":False,\"sub-region\":False}\n\nThis solution isn't perfect as a pointer is still here on each countries but at least nothing is displayed aside from the bubbles...\n" ]

[ 0 ]

[]

[]

[ "choropleth", "plotly", "python", "python_3.x" ]

[ "Internet searches on this topic came up with surprisingly few results. I figured I'd post the question and answer here for others interested in this topic.\nThe following client and server applications demonstrate how to accomplish this on Linux with the standard python interpreter. No extensions are required but, due to the use of embedded constants, the code is Linux-specific.\nServer:\n#!/usr/bin/env python\n\nimport struct\nfrom socket import socket, AF_UNIX, SOCK_STREAM, SOL_SOCKET\n\nSO_PEERCRED = 17 # Pulled from /usr/include/asm-generic/socket.h\n\ns = socket(AF_UNIX, SOCK_STREAM)\n\ns.bind('/tmp/pass_cred')\ns.listen(1)\n\nconn, addr = s.accept()\n\ncreds = conn.getsockopt(SOL_SOCKET, SO_PEERCRED, struct.calcsize('3i'))\n\npid, uid, gid = struct.unpack('3i',creds)\n\nprint 'pid: %d, uid: %d, gid %d' % (pid, uid, gid)\n\nClient:\n#!/usr/bin/env python\n\nfrom socket import socket, AF_UNIX, SOCK_STREAM, SOL_SOCKET\n\nSO_PASSCRED = 16 # Pulled from /usr/include/asm-generic/socket.h\n\ns = socket(AF_UNIX, SOCK_STREAM)\n\ns.setsockopt(SOL_SOCKET, SO_PASSCRED, 1)\n\ns.connect('/tmp/pass_cred')\n\ns.close()\n\nUnfortunately, the SO_PEERCRED and SO_PASSCRED constants are not exported by python's socket module so they must be entered by hand. Although these value are unlikely to change it is possible. This should be considered by any applications using this approach.\n", "Here is a Python 3 version of Rakis' server.py that does not use hardcoded IDs and also displays user and group names of the client:\n#!/usr/bin/env python\nimport os\nimport atexit\nimport grp\nimport pwd\nimport struct\nfrom socket import socket, AF_UNIX, SOCK_STREAM, SOL_SOCKET, SO_PEERCRED\n\ndef remove_sock():\n try:\n os.unlink('/tmp/pass_cred')\n except FileNotFoundError:\n pass\n\nremove_sock()\natexit.register(remove_sock)\n\ns = socket(AF_UNIX, SOCK_STREAM)\ns.bind('/tmp/pass_cred')\ns.listen(1)\n\nconn, addr = s.accept()\n\ncreds = conn.getsockopt(SOL_SOCKET, SO_PEERCRED, struct.calcsize('3i'))\n\npid, uid, gid = struct.unpack('3i',creds)\n\nuser_name = pwd.getpwuid(uid)[0]\ngroup_name = grp.getgrgid(gid)[0]\n\nprint(f'pid={pid}, uid={uid}({user_name}), gid={gid}({group_name})')\n\nAlso updated to make reusable (you cannot bind the socket to a filename that already exists, so we clean the socket both before bind and after normal process exit)\n" ]

[ 23, 1 ]

[]

[]

[ "credentials", "linux", "python", "sockets" ]

[ "This is related by local and global scope, you can update using change the name of function parameter names;\na = 0\n\n\ndef func_a(local_a):\n global a\n a += 1\n\nfunc_a(a)\nprint('a = ', a)\n# output: a = 1\n\n\"global a\" meaning this function will use the global scope of a.\nIf you try to use with this way;\na = 0\nb = []\nc = []\nd = 'a'\n\n\ndef func_a(a):\n global a\n a += 1\n\nfunc_a(a)\nprint('a = ', a)\n\nYou will get an error like this;\n File \"glob_and_local.py\", line 8\n global a\n ^^^^^^^^\nSyntaxError: name 'a' is parameter and global\n\nBecause same names are conflicting.\n", "Here, a, b, c, d are global variables.\nFor a:\na is an integer. when you are calling func_a(a) you are only making changes in the local scope but not returning anything. That's why there is no change in the global variable a.\nSame thing happening with global variables c and d.\nFor b:\nYou are passing an array and appending an array into it.\nappend method makes changes in global variables too.\nNOTE:\nhere, b += [1] is equivalent to b.append(1)\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "Adding python-dev as per this other answer works, but throws an alarming number of compiler warnings (errors?), so I went with a variation of @SergeyKoralev's answer, which needed some tweaking before it worked.\nHere's the changes I needed to make this work:\n\nChange to python3 and pip3 everywhere.\nAdd a statement to upgrade pip itself.\nSeparate the awscli install in a separate RUN command.\n\nHere's a full repro that does seem to work:\nFROM library/node:6.11.2\n\nRUN apt-get update && \\\n apt-get install -y \\\n python3 \\\n python3-pip \\\n python3-setuptools \\\n groff \\\n less \\\n && pip3 install --upgrade pip \\\n && apt-get clean\n\nRUN pip3 --no-cache-dir install --upgrade awscli\n\nCMD [\"/bin/bash\"]\n\nYou can probably also keep the aws install in the same RUN layer if you add a shell command before the install that refreshes things after upgrading pip. Not sure how though.\n", "All the answers are about aws-cli version 1, If you want version 2 try the below\nFROM node:lts-stretch-slim\n\nRUN apt-get update && \\\n apt-get install -y \\\n unzip \\\n curl \\\n && apt-get clean \\\n && curl \"https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip\" -o \"awscliv2.zip\" \\\n && unzip awscliv2.zip \\\n && ./aws/install \\\n && rm -rf \\\n awscliv2.zip \\\n && apt-get -y purge curl \\\n && apt-get -y purge unzip \n\nCMD [\"/bin/bash\"]\n\n", "As you have correctly stated, pip installing on the docker image you are using is an older one not supporting --no-cache-dir. You can try updating that or you can also fix the second problem which is about missing python source headers. This can be fixed by installing python-dev package. Just add that to the list of packages installed in the Dockerfile:\nFROM library/node:6.11.2\n\nRUN apt-get update && \\\n apt-get install -y \\\n python \\\n python-dev \\\n python-pip \\\n python-setuptools \\\n groff \\\n less \\\n && pip install --upgrade awscli \\\n && apt-get clean\n\nCMD [\"/bin/bash\"]\n\nYou can then run aws which should be on your path.\n", "Your image is based on Debian Jessie, so you are installing Python 2.7. Try using Python 3.x:\napt-get install -y python3-pip\npip3 install awscli\n\n", "Install AWS CLI in docker container using below command:\napt upgrade -y;apt update;apt install python3 python3-pip python3-setuptools -y; python3 -m pip --no-cache-dir install --upgrade awscli\n\nTo check the assumed role or AWS identity run below command:\naws sts get-caller-identity\n\n" ]

[ 28, 7, 5, 5, 0 ]

[]

[]

[ "amazon_web_services", "docker", "pip", "python" ]

[ "From the PyKeePass documentation, the syntax is:\n\nfind_entries_by_notes (notes, regex=False, flags=None, tree=None, history=False, first=False)\nwhere title, username, password, url, notes and path are strings. These functions have optional regex boolean and flags string arguments, which means to interpret the string as an XSLT style regular expression with flags.\n\nThus, you need to use the i-flag like this:\nfind_entries_by_notes(\".*chocolate.*\", regex=True, \"i\")\n\n", "It seems that now the documentation has been changed and instead of find_entries_by_notes function, you should use find_entries function instead.\nSee: https://github.com/libkeepass/pykeepass#finding-entries\nfind_entries function has optional regex boolean and flags string arguments, which means to interpret search strings as XSLT style regular expressions with flags.\nfind_entries(\".*chocolate.*\", regex=True, flags='i')\n\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "You can use:\nimport json\nd = json.loads(result.decode('utf-8'))['Results']['WebServiceOutput0'][0]\n\nd['Scored Labels']\n# 1.7306872933250431e-07\n\nmax_p = max((k for k in d if k.startswith('Scored Probabilities_')), key=d.get)\n# 'Scored Probabilities_7'\n\nd[max_p]\n# 0.9999483485221952\n\n" ]

[ 1 ]

[]

[]

[ "max", "python" ]

[ "Are you sure this endpoint is not needed for authorization?\nI think it needs authorization, you can use it this code;\nimport requests\n\nurl = \"your URL\"\n\npayload={}\nfiles=[\n ('upload_file',('20220212235319_1509.jpg',open('/20220212235319_1509.jpg','rb'),'image/jpeg'))\n]\nheaders = {\n 'Accept-Language': 'en-US',\n 'Authorization': 'Bearer yourToken'\n}\n\nresponse = requests.request(\"POST\", url, headers=headers, data=payload, files=files)\n\nprint(response.text)\n\nor basic auth;\nimport requests\n\nurl = \"https://example.com\"\npayload={}\nfiles=[\n ('file',('myfile.jpg',open('/path/to/myfile.jpg','rb'),'image/jpeg'))\n]\n\nresponse = requests.request(\"POST\", url, auth=(\"my_username\",\"my_password\"), data=payload, files=files)\nprint(response.text)\n\nfor more details; Upload Image using POST form data in Python-requests\n" ]

[ 0 ]

[]

[]

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

[ "You can use:\ndf['date'] = pd.to_datetime(df['date'])\n\ngroup = df['stage'].ne(df['stage'].shift()).cumsum()\n\nout = (df\n .groupby(['project', 'stage', group], sort=False)\n .agg(**{'begin_stage': ('date', 'min'), 'end_stage': ('date', 'max')})\n .droplevel(-1)\n .apply(lambda s: s.dt.strftime('%-d-%b-%Y').str.lower())\n .reset_index()\n)\n\nOutput:\n project stage begin_stage end_stage\n0 33 New 3-sep-2022 10-sep-2022\n1 33 Preparation 11-sep-2022 23-sep-2022\n2 33 New 24-sep-2022 28-sep-2022\n\n" ]

[ 0 ]

[]

[]

[ "group_by", "pandas", "python", "time_series" ]

[ "I received an error when trying to run\nfrom nltk.corpus import stopwords\nThe documentation from NLTK says to use the following for stop words\n>>> import nltk\n>>> nltk.download()\n\nnltk.org/data.html\nOnce I used nltk.download() for the stopwords I slightly modified your function to incorporate list comprehension.\ndef removestopwords(tokens):\n \"\"\"This function removes the stop words from the tokens\"\"\"\n stop_words = set(stopwords.words(\"english\")) #get the stop words\n return [words.lower() for words in tokens.split(' ') if words not in stop_words]\n\n" ]

[ 0 ]

[]

[]

[ "for_loop", "python", "stop_words" ]

[ "Defining attributes of a class in the class namespace directly is totally acceptable and is not special per se for the packages you mentioned. Since the class namespace is (among other things) essentially a blueprint for instances of that class, defining attributes there can actually be useful, when you want to e.g. provide all public attributes with type annotations in a single place in a consistent manner.\nConsider also that a public attribute does not necessarily need to be reflected by a parameter in the constructor of the class. For example, this is entirely reasonable:\nclass Foo:\n a: list[int]\n b: str\n\n def __init__(self, b: str) -> None:\n self.a = []\n self.b = b\n\nIn other words, just because something is a public attribute, that does not mean it should have to be provided by the user upon initialization. To say nothing of protected/private attributes.\nWhat is special about Pydantic (to take your example), is that the metaclass of BaseModel as well as the class itself does a whole lot of magic with the attributes defined in the class namespace. Pydantic refers to a model's typical attributes as \"fields\" and one bit of magic allows special checks to be done during initialization based on those fields you defined in the class namespace. For example, the constructor must receive keyword arguments that correspond to the non-optional fields you defined.\nfrom pydantic import BaseModel\n\n\nclass MyModel(BaseModel):\n field_a: str\n field_b: int = 1\n\n\nobj = MyModel(\n field_a=\"spam\", # required\n field_b=2, # optional\n field_c=3.14, # unexpected/ignored\n)\n\nIf I were to omit field_a during construction of a MyModel instance, an error would be raised. Likewise, if I had tried to pass field_b=\"eggs\", an error would be raised.\nSo the fact that you don't write your own __init__ method is a feature Pydantic provides you. You only define the fields and an appropriate constructor is \"magically\" there for you already.\nAs for the drawback you mentioned, where you don't get any auto-suggestions, that is true by default for all IDEs. Static type checkers cannot understand that dynamic constructor and simply infer what arguments are expected. Currently this is solved via extensions, such as the mypy plugin and the PyCharm plugin. Maybe soon the @dataclass_transform decorator from PEP 681\nwill standardize this for similar packages and thus improve support by static type checkers.\nIt is also worth noting that even the standard library's dataclasses only work via special extensions in type checkers.\nTo your other question, there is obviously some impact on methods of such classes (by design), though the specifics are not always obvious. You should of course not simply write your own __init__ method without being careful to call the superclass' __init__ properly inside it. Also, @property-setters currently don't work as you would expect it (though it is debatable if it even makes sense to use properties on Pydantic models).\nTo wrap up, this approach is not only not bad practice, it is a great idea to reduce boilerplate code and it is extremely common these days, as evidenced by the fact that hugely popular and established packages (like the aforementioned Pydantic, as well as e.g. SQLAlchemy, Django and others) use this pattern to a certain extent.\n", "Pydantic has its own (rewriting) magic, but SQLalchemy is a bit easier to explain.\nA SA model looks like this :\n>>> from sqlalchemy import Column, Integer, String\n>>> class User(Base):\n...\n... id = Column(Integer, primary_key=True)\n... name = Column(String)\n\nColumn, Integer and String are descriptors. A descriptor is a class that overrides the get and set methods. In practice, this means the class can control how data is accessed and stored.\nFor example this assignment would now use the __set__ method from Column:\nclass User(Base):\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nuser = User()\nuser.name = 'John' \n\nThis is the same as user.name.__set__('John') , however, because of the MRO, it finds a set method in Column, so uses that instead. In a simplified version the Column looks something like this:\nclass Column:\n def __init__(self, field=\"\"):\n self.field= field\n def __get__(self, obj, type):\n return obj.__dict__.get(self.field)\n def __set__(self, obj, val):\n if validate_field(val)\n obj.__dict__[self.field] = val\n else:\n print('not a valid value')\n\n(This is similar to using @property. A Descriptor is a re-usable @property)\n" ]

[ 1, 0 ]

[]

[]

[ "attributes", "class", "pydantic", "python", "sqlalchemy" ]

[ "keep same table for singer/writer ans use type field. You can also filter easily.\nmodels.py be like:-\nartistTypes = (\n ('Singer', 'Singer'),\n ('Writer', 'Writer'),\n )\n\nclass artistName(models.Model):\n name = models.CharField()\n nickName = models.CharField()\n artistType = models.CharField(max_length=10, choices=artistTypes)\n numReleases = models.IntegerField()\n\nclass timeSpent(models.Model):\n artistID = models.ForeignKey(artistName, on_delete = models.CASCADE)\n dayWeek = models.IntegerField() \n startHour = models.TimeField()\n stopHour = models.TimeField()\n\n" ]

[ 0 ]

[]

[]

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

[ "You can directly install or upload wheel file as shown in the below image.\nGo to cluster -> install library\n\nFor more information refer this link to installing wheel file.\n" ]

[ 0 ]

[]

[]

[ "azure_databricks", "bigdata", "databricks", "python", "python_wheel" ]

[ "As far as I can tell, the Autogluon library does not currently support using IPUs. The Poplar SDK supports PyTorch and PyTorch Lightning, which Autogluon is based on, so the library could in principle be supported. I'd be really interested to hear more about what you want to use Autogluon for!\nIn the meantime, there are many IPU resources available, including:\n\nPublic examples, including a wide range of applications written for the IPU: https://github.com/graphcore/examples\nTutorials: https://github.com/graphcore/tutorials\nThe Graphcore Optimum library, which has optimised implementations of HuggingFace transformer models: https://github.com/huggingface/optimum-graphcore\nThe model garden, with links to the public examples and Paperspace notebooks: https://www.graphcore.ai/resources/model-garden\n\nI'm sorry that what you want isn't supported, but I hope that the above is at least helpful.\nFor the TPU error, it looks like the environment in your instance comes with TensorFlow pre-installed, but it doesn't look like Autogluon uses TensorFlow. You might need to look into how to use PyTorch on TPUs with PyTorch/XLA: https://github.com/pytorch/xla\nWith thanks,\nCallum\n" ]

[ 0 ]

[]

[]

[ "automl", "ipu", "python", "tpu" ]

[ "Assuming you put all the values ​​that will be used as headers into an array using this code:\nimport pandas as pd \ndata = pd.read_csv(\"yourtable.csv\")\ni = 0\nheaders = []\nwhile i < len(data. index):\n headers.append(data.loc[i, \"header of headers\"])\n i = i + 1\n\n\n\nYou can create your table using\ndf = pd.DataFrame(columns=headers)\n\nEDIT:\nWith the new info just use:\ndf = pd.DataFrame(columns=list)\n\n" ]

[ 0 ]

[]

[]

[ "bigdata", "pandas", "python" ]

[ "I found a solution. At first I reduced the item pairs by applying a threshold for diff (keep pairs having diff < 10000000).\nThen I run this code to create the clusters (groups):\ndef get_groups(self, pairs):\n\n def contains(list, filter):\n for x in list:\n if filter(x):\n return True\n return False\n\n def in_group(a, b, group):\n has_a = contains(group, lambda x: x[\"path\"] == a[\"path\"])\n has_b = contains(group, lambda x: x[\"path\"] == b[\"path\"])\n if has_a:\n if not has_b:\n group.append(b)\n return True\n elif has_b:\n if not has_a:\n group.append(b)\n return True\n return False\n\n def in_groups(a, b, groups):\n found = False\n for group in groups:\n if in_group(a, b, group):\n found = True\n break\n\n if not found:\n groups.append([a, b])\n\n groups = []\n for pair in pairs:\n in_groups(pair[\"a\"], pair[\"b\"], groups)\n \n return groups\n\nThis creates groups having one element for each group of similar items.\n" ]

[ 0 ]

[]

[]

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

[ "You can use mask and cumsum:\ndf['Forecast'] = df['Forecast'].mask(df['Forecast'].eq(0), df['Appt']).cumsum()\n\n# or\ndf['Forecast'] = np.where(df['Forecast'].eq(0), df['Appt'], df['Forecast']).cumsum()\n\nOutput:\n Date Appt Forecast\n0 2022-12-01 12 37\n1 2022-12-01 10 47\n2 2022-12-01 5 52\n3 2022-12-01 4 56\n4 2022-12-01 13 69\n\n", "You have to make sure that your column has datetime/date type, then you may filter df like this:\n# previous code&imports\nyesterday = datetime.now().date() - timedelta(days=1)\ndf[df[\"date\"] == yesterday][\"your_column\"].sum()\n\n" ]

[ 0, 0 ]

[]

[]

[ "pandas", "python", "time_series" ]

[ "I just had this same issue (see my comment above). What worked for me was to go into \"Edit Configurations\", delete the configuration that was copied over from the original PC, and create my own configuration (basically with the same inputs as before).\n", "This might happen if a run configuration was imported from another computer.\nRun configuration can contain a path to a Python interpreter from that computer.\nBy default, run configurations are stored in .idea/runConfigurations/*.xml.\nIf you pull up .idea/runConfigurations/<your-configuration>.xml you'll probably notice a fixed path to the Python interpreter somewhere in there, e.g.:\n<option name=\"SDK_HOME\" value=\"$USER_HOME$/.local/share/virtualenvs/your-project-name-XXXXXXXX/bin/python\" />\n\nalongside with\n<option name=\"IS_MODULE_SDK\" value=\"false\" />\n\nIt that's true try changing those lines to\n<option name=\"SDK_HOME\" value=\"\" />\n<option name=\"IS_MODULE_SDK\" value=\"true\" />\n\nThis will make your IntelliJ IDE look for Python in your local project's virtual environment folder.\n\nBasically, like folks have already mentioned before, simple recreation of your run configuration by hand will help because it will disregard other machine's settings and apply yours, but sometimes it may cumbersome (especially, if your run configuration contains some project-specific environment variables and other stuff which can be tedious to copy over).\nSo, just changing those two lines in the run configuration XML file should help without recreating it manually again from scratch.\nThe nastiest thing about this situation with Python path is that when you import a run configuration for a Python project from another machine with Python path being hardcoded PyCharm will still keep showing you yours in the Edit Configurations window making it really hard to track down and fix, so in this very specific case don't believe what you see in the run configuration UI -- just go and check the run configuration XML config behind.\n", "I faced the same problem,and I have solved it.\nYou need to open Run/Debug Configurations of Pycharm.\nThen find Parameters of your py file, Copy and delete the parameters, and then fill in the parameters as they are\n", "The IDE itself proposed to make recently created venv as interpreter, I agreed and after that this mistake occured. The decision: I came to the run->edit configurations and I saw that interpreter path wasn't setted. Than I've added the path by myself and everything worked\n" ]

[ 2, 1, 0, 0 ]

[]

[]

[ "pycharm", "python" ]

[ "There are 2 things to be aware of:\n\nNot every encoding (in your case latin-1) can represent every possible character. An encoding maps bit-patterns to characters. An encoding that uses 7 bits (and an 8th check-bit) is only able to represent 2^7 characters. The designers of the encoding thus have to make decisions about which characters are in the encoding.\nNot every font can represent every encoding. Font files take up room, so there is a drive to limit the set of characters you want to represent. Each character takes up some rendering instructions (and thus bytes). Not to mention you need to hire an artist to actually design the characters, which takes money.\n\nYou are currently running into an error because of (1). You could simply read the file with another encoding.\nKeep in mind though that when you are creating a PDF, you are (implictly perhaps) using a font.\nPDF (ISO 32000) defines 14 standard type 1 fonts. These fonts are special. A conforming reader (e.g. Adobe Reader) is required to have them on hand. That means when you are creating a PDF (or writing a software library to create a PDF) it is a lot easier to use one of the standard 14 than any other font. You don't have to do any tricky \"insert the font bytes in the PDF\". Loads of PDF-creation libraries will default to one of the 14 if no font is specified.\nThese fonts all have an encoding. And those encodings may not support the characters that are currently in your text file. This can also be solved by using a non-default font.\n" ]

[ 0 ]

[]

[]

[ "pdf", "python", "replit", "txt" ]

[ "On Mac Os 12.6.1 and Python 3.10.8 System Interpreter I was able to get libraqm to work with Pillow. Configuration follows:\n\nPython Interpreter version 10.8 installed with Homebrew.\nLibraqm version 0.9.0 installed with Homebrew.\nPillow version 9.3.0 installed with pip.\n\nFirst, I tried using a python venv, and I got UserWarning: Raqm layout was requested, but Raqm is not available. Falling back to basic layout.\nNext, I added the following to the code you posted: print(features.check(\"raqm\")) and it printed False as expected.\nI used my Python System Interpreter (3.10.8), installed Pillow and I was able to get libraqm to load with Pillow.\nImage without libraqm. Image with libraqm\nI added a fonts directory with the font specified on your snippet above. I loaded the font with the following code:\nfeatures.check_feature(feature='raqm')\nprint(features.check(\"raqm\"))\ncustom_font = os.path.abspath(\n os.path.join(\n os.path.dirname(__file__), 'fonts/KawkabMono-Bold.ttf'\n )\n)\n\nIf the librqam library is not loaded by the python interpreter, then your ouput will be like you described it ( individual characters).\n", "I think you additionally need fribidi and harfbuzz installed before PIL/Pillow:\nbrew install fribidi harfbuzz\n\nNote that you can get PIL/Pillow's configuration, build settings, features and supported formats with:\npython3 -m PIL\n\n" ]

[ 0, 0 ]

[]

[]

[ "image_processing", "python", "python_3.x", "python_imaging_library" ]

[ "I guess right syntax is this?\nimport numpy as np\nA = np.array ([[2,1,4,1], [3,4,-1,-1] , [1,-4,1,5] , [2,-2,1,3]], dtype = float)\nprint(A)\n\nGives #\n[[ 2. 1. 4. 1.]\n [ 3. 4. -1. -1.]\n [ 1. -4. 1. 5.]\n [ 2. -2. 1. 3.]]\n\n" ]

[ 1 ]

[]

[]

[ "matrix", "numpy", "python", "vector" ]

[ "You have to run manage.py <tab><tab> not python manage.py <tab><tab>\nAs the Official Documentation says: https://docs.djangoproject.com/en/dev/ref/django-admin/#bash-completion\n", "I've been using this old original bash-completion for Django. still works :) you don't need to ./manage.py ... try python manage.py [tab]\nhttps://gist.github.com/vigo/5c25936a682845932bdaf17126c4167d\ntested on macOS Ventura, python 3.11.0 + Django 4.1.3\n" ]

[ 4, 0 ]

[]

[]

[ "bash_completion", "django", "django_manage.py", "python" ]

[ "I believe your goal is as follows.\n\nYou want to retrieve the values from only one column instead of all cells.\nYou want to achieve this using pygsheets.\n\nIn this case, how about using get_col instead of get_all_values? When this is reflected in your script, how about the following modification?\nFrom:\ncells = cyber_worksheet.get_all_values(returnas='cell',include_tailing_empty=False, include_tailing_empty_rows=False)\ncolor_code = []\nfor r in cells:\n for c in r:\n color_code.append(c.color)\n\nTo:\ncells = cyber_worksheet.get_col(1, returnas=\"cell\", include_tailing_empty=False)\ncolor_code = []\nfor r in cells:\n color_code.append(r.color)\n\n\nIn this case, the values are retrieved from column \"A\". When you want to retrieve the values from the column \"B\", please modify cyber_worksheet.get_col(1, returnas=\"cell\", include_tailing_empty=False) to cyber_worksheet.get_col(2, returnas=\"cell\", include_tailing_empty=False).\nIn this case, it seems that the value of cells is a one-dimensional array, and each element is each row.\n\nNote:\n\nIf you use get_all_values, how about the following modification?\n col = 1 # Column \"A\"\n cells = cyber_worksheet.get_all_values(returnas=\"cell\", include_tailing_empty=False, include_tailing_empty_rows=False)\n color_code = []\n for r in cells:\n color_code.append(r[col - 1].color)\n\n\n\nReference:\n\nget_col(col, returnas='matrix', include_tailing_empty=True, **kwargs)\n\n" ]

[ 0 ]

[]

[]

[ "google_sheets", "google_sheets_api", "pygsheets", "python" ]

[ "It seems like you should follow the pattern of using Sorted Set as a secondary index.\nSee: https://redis.io/topics/indexes\n", "You should use ZRANGE (https://redis.io/commands/zrange/).\nUsing your dataset, you could use this approach:\nZADD ratingindex 198 u_345\nZADD ratingindex 180 u_144\nZADD ratingindex 179 u_267\n\nand then, to retrieve 2 (or N) keys with highest values you should use:\nZRANGE ratingindex 0 1 withscores rev\nThis command will retrieve two keys (from 0 to 1) with values (withscores) from ratingindex ordered from highest to lowest (rev).\nFinally you just need to adapt this direct commands to redis-py library.\nHope it helps.\n" ]

[ 2, 1 ]

[]

[]

[ "python", "redis" ]

[ "You need to have win32com installed on your machine. Here is my code:\nimport win32com.client as win32\nfname = \"full+path+to+xls_file\"\nexcel = win32.gencache.EnsureDispatch('Excel.Application')\nwb = excel.Workbooks.Open(fname)\n\nwb.SaveAs(fname+\"x\", FileFormat = 51) #FileFormat = 51 is for .xlsx extension\nwb.Close() #FileFormat = 56 is for .xls extension\nexcel.Application.Quit()\n\n", "Here is my solution, without considering fonts, charts and images:\n$ pip install pyexcel pyexcel-xls pyexcel-xlsx\n\nThen do this::\nimport pyexcel as p\n\np.save_book_as(file_name='your-file-in.xls',\n dest_file_name='your-new-file-out.xlsx')\n\nIf you do not need a program, you could install one additinal package pyexcel-cli::\n$ pip install pyexcel-cli\n$ pyexcel transcode your-file-in.xls your-new-file-out.xlsx\n\nThe transcoding procedure above uses xlrd and openpyxl.\n", "I've had to do this before. The main idea is to use the xlrd module to open and parse a xls file and write the\ncontent to a xlsx file using the openpyxl module.\nHere's my code. Attention! It cannot handle complex xls files, you should add you own parsing logic if you are going to use it.\nimport xlrd\nfrom openpyxl.workbook import Workbook\nfrom openpyxl.reader.excel import load_workbook, InvalidFileException\n\ndef open_xls_as_xlsx(filename):\n # first open using xlrd\n book = xlrd.open_workbook(filename)\n index = 0\n nrows, ncols = 0, 0\n while nrows * ncols == 0:\n sheet = book.sheet_by_index(index)\n nrows = sheet.nrows\n ncols = sheet.ncols\n index += 1\n\n # prepare a xlsx sheet\n book1 = Workbook()\n sheet1 = book1.get_active_sheet()\n\n for row in xrange(0, nrows):\n for col in xrange(0, ncols):\n sheet1.cell(row=row, column=col).value = sheet.cell_value(row, col)\n\n return book1\n\n", "I found none of answers here 100% right. So I post my codes here:\nimport xlrd\nfrom openpyxl.workbook import Workbook\n\ndef cvt_xls_to_xlsx(src_file_path, dst_file_path):\n book_xls = xlrd.open_workbook(src_file_path)\n book_xlsx = Workbook()\n\nsheet_names = book_xls.sheet_names()\nfor sheet_index, sheet_name in enumerate(sheet_names):\n sheet_xls = book_xls.sheet_by_name(sheet_name)\n if sheet_index == 0:\n sheet_xlsx = book_xlsx.active\n sheet_xlsx.title = sheet_name\n else:\n sheet_xlsx = book_xlsx.create_sheet(title=sheet_name)\n\n for row in range(0, sheet_xls.nrows):\n for col in range(0, sheet_xls.ncols):\n sheet_xlsx.cell(row = row+1 , column = col+1).value = sheet_xls.cell_value(row, col)\n\nbook_xlsx.save(dst_file_path)\n\n", "The answer by Ray helped me a lot, but for those who search a simple way to convert all the sheets from a xls to a xlsx, I made this Gist:\nimport xlrd\nfrom openpyxl.workbook import Workbook as openpyxlWorkbook\n\n# content is a string containing the file. For example the result of an http.request(url).\n# You can also use a filepath by calling \"xlrd.open_workbook(filepath)\".\n\nxlsBook = xlrd.open_workbook(file_contents=content)\nworkbook = openpyxlWorkbook()\n\nfor i in xrange(0, xlsBook.nsheets):\n xlsSheet = xlsBook.sheet_by_index(i)\n sheet = workbook.active if i == 0 else workbook.create_sheet()\n sheet.title = xlsSheet.name\n\n for row in xrange(0, xlsSheet.nrows):\n for col in xrange(0, xlsSheet.ncols):\n sheet.cell(row=row, column=col).value = xlsSheet.cell_value(row, col)\n\n# The new xlsx file is in \"workbook\", without iterators (iter_rows).\n# For iteration, use \"for row in worksheet.rows:\".\n# For range iteration, use \"for row in worksheet.range(\"{}:{}\".format(startCell, endCell)):\".\n\nYou can find the xlrd lib here and the openpyxl here (you must download xlrd in your project for Google App Engine for example).\n", "I'm improve performance for @Jackypengyu method.\n\nXLSX: working per row, not per cell (http://openpyxl.readthedocs.io/en/default/api/openpyxl.worksheet.worksheet.html#openpyxl.worksheet.worksheet.Worksheet.append)\nXLS: read whole row excluding empty tail, see ragged_rows=True (http://xlrd.readthedocs.io/en/latest/api.html#xlrd.sheet.Sheet.row_slice)\n\nMerged cells will be converted too.\nResults\nConvert same 12 files in same order:\nOriginal:\n0:00:01.958159\n0:00:02.115891\n0:00:02.018643\n0:00:02.057803\n0:00:01.267079\n0:00:01.308073\n0:00:01.245989\n0:00:01.289295\n0:00:01.273805\n0:00:01.276003\n0:00:01.293834\n0:00:01.261401\n\nImproved:\n0:00:00.774101\n0:00:00.734749\n0:00:00.741434\n0:00:00.744491\n0:00:00.320796\n0:00:00.279045\n0:00:00.315829\n0:00:00.280769\n0:00:00.316380\n0:00:00.289196\n0:00:00.347819\n0:00:00.284242\n\nSolution\ndef cvt_xls_to_xlsx(*args, **kw):\n \"\"\"Open and convert XLS file to openpyxl.workbook.Workbook object\n\n @param args: args for xlrd.open_workbook\n @param kw: kwargs for xlrd.open_workbook\n @return: openpyxl.workbook.Workbook\n\n\n You need -> from openpyxl.utils.cell import get_column_letter\n \"\"\"\n\n book_xls = xlrd.open_workbook(*args, formatting_info=True, ragged_rows=True, **kw)\n book_xlsx = Workbook()\n\n sheet_names = book_xls.sheet_names()\n for sheet_index in range(len(sheet_names)):\n sheet_xls = book_xls.sheet_by_name(sheet_names[sheet_index])\n\n if sheet_index == 0:\n sheet_xlsx = book_xlsx.active\n sheet_xlsx.title = sheet_names[sheet_index]\n else:\n sheet_xlsx = book_xlsx.create_sheet(title=sheet_names[sheet_index])\n\n for crange in sheet_xls.merged_cells:\n rlo, rhi, clo, chi = crange\n\n sheet_xlsx.merge_cells(\n start_row=rlo + 1, end_row=rhi,\n start_column=clo + 1, end_column=chi,\n )\n\n def _get_xlrd_cell_value(cell):\n value = cell.value\n if cell.ctype == xlrd.XL_CELL_DATE:\n value = datetime.datetime(*xlrd.xldate_as_tuple(value, 0))\n\n return value\n\n for row in range(sheet_xls.nrows):\n sheet_xlsx.append((\n _get_xlrd_cell_value(cell)\n for cell in sheet_xls.row_slice(row, end_colx=sheet_xls.row_len(row))\n ))\n\n for rowx in range(sheet_xls.nrows):\n if sheet_xls.rowinfo_map[rowx].hidden != 0:\n print sheet_names[sheet_index], rowx\n sheet_xlsx.row_dimensions[rowx+1].hidden = True\n for coly in range(sheet_xls.ncols):\n if sheet_xls.colinfo_map[coly].hidden != 0:\n print sheet_names[sheet_index], coly\n coly_letter = get_column_letter(coly+1)\n sheet_xlsx.column_dimensions[coly_letter].hidden = True\n\n return book_xlsx\n\n", "You can use pandas IO functions:\nimport pandas as pd\n\ndf = pd.read_excel('file_2003.xls', header=None)\ndf.to_excel('file_2003.xlsx', index=False, header=False)\n\n", "Simple solution\nI required a simple solution to convert couple of xls to xlsx format. There are plenty of answers here, but they are doing some \"magic\" that I do not completely understand.\nA simple solution was given by chfw, but not quite complete.\nInstall dependencies\nUse pip to install\npip install pyexcel-cli pyexcel-xls pyexcel-xlsx\n\nExecute\nAll the styling and macros will be gone, but the information is intact.\nFor single file\npyexcel transcode your-file-in.xls your-new-file-out.xlsx\n\nFor all files in the folder, one liner\nfor file in *.xls; do; echo \"Transcoding $file\"; pyexcel transcode \"$file\" \"${file}x\"; done;\n\n", "I tried @Jhon Anderson's solution, works well but got an \"year is out of range\" error when there are cells of time format like HH:mm:ss without date. There for I improved the algorithm again:\ndef xls_to_xlsx(*args, **kw):\n\"\"\"\n open and convert an XLS file to openpyxl.workbook.Workbook\n ----------\n @param args: args for xlrd.open_workbook\n @param kw: kwargs for xlrd.open_workbook\n @return: openpyxl.workbook.Workbook对象\n \"\"\"\n book_xls = xlrd.open_workbook(*args, formatting_info=True, ragged_rows=True, **kw)\n book_xlsx = openpyxl.workbook.Workbook()\n\n sheet_names = book_xls.sheet_names()\n for sheet_index in range(len(sheet_names)):\n sheet_xls = book_xls.sheet_by_name(sheet_names[sheet_index])\n if sheet_index == 0:\n sheet_xlsx = book_xlsx.active\n sheet_xlsx.title = sheet_names[sheet_index]\n else:\n sheet_xlsx = book_xlsx.create_sheet(title=sheet_names[sheet_index])\n for crange in sheet_xls.merged_cells:\n rlo, rhi, clo, chi = crange\n sheet_xlsx.merge_cells(start_row=rlo + 1, end_row=rhi,\n start_column=clo + 1, end_column=chi,)\n\n def _get_xlrd_cell_value(cell):\n value = cell.value\n if cell.ctype == xlrd.XL_CELL_DATE:\n datetime_tup = xlrd.xldate_as_tuple(value,0) \n if datetime_tup[0:3] == (0, 0, 0): # time format without date\n value = datetime.time(*datetime_tup[3:])\n else:\n value = datetime.datetime(*datetime_tup)\n return value\n\n for row in range(sheet_xls.nrows):\n sheet_xlsx.append((\n _get_xlrd_cell_value(cell)\n for cell in sheet_xls.row_slice(row, end_colx=sheet_xls.row_len(row))\n ))\n return book_xlsx\n\nThen work perfect!\n", "CONVERT XLS FILE TO XLSX\nUsing python3.6\nI have just come accross the same issue and after hours of struggle I solved it by doing the ff, you probably wont need all of the packages: (I will be as clear as posslbe)\nmake sure to install the following packages before proceeding\npip install pyexcel,\npip install pyexcel-xls,\npip install pyexcel-xlsx,\npip install pyexcel-cli\nstep 1:\nimport pyexcel\n\nstep 2: \"example.xls\",\"example.xlsx\",\"example.xlsm\"\nsheet0 = pyexcel.get_sheet(file_name=\"your_file_path.xls\", name_columns_by_row=0)\n\nstep3: create array from contents\nxlsarray = sheet.to_array() \n\nstep4: check variable contents to verify\nxlsarray\n\nstep5: pass the array held in variable called (xlsarray) to a new workbook variable called(sheet1)\nsheet1 = pyexcel.Sheet(xlsarray)\n\nstep6: save the new sheet ending with .xlsx (in my case i want xlsx)\nsheet1.save_as(\"test.xlsx\")\n\n", "Try using win32com application. Install it in your machine.\nimport sys, os\nimport win32com.client\ndirectory = 'C:\\\\Users\\\\folder\\\\'\nfor file in os.listdir(directory):\n dot = file.find('.')\n end = file[dot:]\n OutFile =file[0:dot] + \".xlsx\"\n App = win32com.client.Dispatch(\"Excel.Application\")\n App.Visible = True\n workbook= App.Workbooks.Open(file)\n workbook.ActiveSheet.SaveAs(OutFile, 51) #51 is for xlsx \n workbook.Close(SaveChanges=True)\n App.Quit()\n\nThank you.\n", "Well I kept it simple and tried with Pandas:\nimport pandas as pd\n\ndf = pd.read_excel (r'Path_of_your_file\\\\name_of_your_file.xls')\n\ndf.to_excel(r'Output_path\\\\new_file_name.xlsx', index = False)\n\n", "The Answer from Ray was clipping the first row and last column of the data. Here is my modified solution (for python3):\ndef open_xls_as_xlsx(filename):\n# first open using xlrd\nbook = xlrd.open_workbook(filename)\nindex = 0\nnrows, ncols = 0, 0\nwhile nrows * ncols == 0:\n sheet = book.sheet_by_index(index)\n nrows = sheet.nrows+1 #bm added +1\n ncols = sheet.ncols+1 #bm added +1\n index += 1\n\n# prepare a xlsx sheet\nbook1 = Workbook()\nsheet1 = book1.get_active_sheet()\n\nfor row in range(1, nrows):\n for col in range(1, ncols):\n sheet1.cell(row=row, column=col).value = sheet.cell_value(row-1, col-1) #bm added -1's\n\nreturn book1\n\n", "Tried @Jhon's solution 1st, then I turned into pyexcel as a solution\npyexcel.save_as(file_name=oldfilename, dest_file_name=newfilename)\n\nIt works properly until I tried to package my project to a single exe file by PyInstaller, I tried all hidden imports option, following error still there:\n File \"utils.py\", line 27, in __enter__\n pyexcel.save_as(file_name=self.filename, dest_file_name=newfilename)\n File \"site-packages\\pyexcel\\core.py\", line 77, in save_as\n File \"site-packages\\pyexcel\\internal\\core.py\", line 22, in get_sheet_stream\n File \"site-packages\\pyexcel\\plugins\\sources\\file_input.py\", line 39, in get_da\nta\n File \"site-packages\\pyexcel\\plugins\\parsers\\excel.py\", line 19, in parse_file\n File \"site-packages\\pyexcel\\plugins\\parsers\\excel.py\", line 40, in _parse_any\n File \"site-packages\\pyexcel_io\\io.py\", line 73, in get_data\n File \"site-packages\\pyexcel_io\\io.py\", line 91, in _get_data\n File \"site-packages\\pyexcel_io\\io.py\", line 188, in load_data\n File \"site-packages\\pyexcel_io\\plugins.py\", line 90, in get_a_plugin\n File \"site-packages\\lml\\plugin.py\", line 290, in load_me_now\n File \"site-packages\\pyexcel_io\\plugins.py\", line 107, in raise_exception\npyexcel_io.exceptions.SupportingPluginAvailableButNotInstalled: Please install p\nyexcel-xls\n[3192] Failed to execute script\n\nThen, I jumped to pandas:\npd.read_excel(oldfilename).to_excel(newfilename, sheet_name=self.sheetname,index=False)\n\nUpdate @ 21-Feb 2020\nopenpyxl provides the function: append\nenable the ability to insert rows to a xlxs file which means user could read the data from a xls file and insert them into a xlsx file.\n\nappend([‘This is A1’, ‘This is B1’, ‘This is C1’])\nor append({‘A’ : ‘This is A1’, ‘C’ : ‘This is C1’})\nor append({1 : ‘This is A1’, 3 : ‘This is C1’})\n\nAppends a group of values at the bottom of the current sheet:\n\nIf it’s a list: all values are added in order, starting from the first column\nIf it’s a dict: values are assigned to the columns indicated by the keys (numbers or letters)\n\n", "@CaKel and @Jhon Anderson solution:\ndef _get_xlrd_cell_value(cell):\n value = cell.value\n if cell.ctype == xlrd.XL_CELL_DATE:\n # Start: if time is 00:00 this fix is necessary\n if value == 1.0:\n datetime_tup = (0, 0, 0)\n else:\n # end\n datetime_tup = xlrd.xldate_as_tuple(value, 0)\n\n if datetime_tup[0:3] == (0, 0, 0):\n value = datetime.time(*datetime_tup[3:])\n else:\n value = datetime.datetime(*datetime_tup)\n return value\n\nAnd now this code runs perfect for me !\n", "This is a solution for MacOS with old xls files (e.g. Excel 97 2004).\nThe best way I found to deal with this format, if excel is not an option, is to open the file in openoffice and save it to another format as csv files.\n", "The use of win32com (pywin32), as the answer of @kvdogan is primarily the perfect approach.Some requisites:\n\nUsing Windows (obviously);\nHave MSExcel installed;\nEnsure the path for file is the FULL path;\n\nAlso, the Pywin32 project is not up-to-date on SourceForge. Instead, use github: https://github.com/mhammond/pywin32\nThere's a .chm document, which you can read with SumatraPDF, for example, in the folder of project once installed.\n#My answer contains no code at all.\n\nEDIT: I don't have enough reputation to make comments. I'm sorry for the practical flood.\n", "This code works for me:\nimport pandas as pd\n\ndf = pd.read_html(\"test.xls\") \ndfs= []\nfor i in range(len(df)):\n dfs.append(df[i])\n\n \ndef multiple_dfs(df_list, sheets, file_name, spaces):\n writer = pd.ExcelWriter(file_name,engine='xlsxwriter') \n row = 0\n for dataframe in df_list:\n dataframe.to_excel(writer,sheet_name=sheets,startrow=row , startcol=0) \n row = row + len(dataframe.index) + spaces + 1\n writer.save()\n\nmultiple_dfs(dfs, 'xlsx', 'test.xlsx', 1)\n\n" ]

[ 44, 27, 23, 12, 7, 6, 6, 3, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0 ]

[]

[]

[ "python", "uno" ]

[ "generic case: filling all groups\nI would use fillna with a groupby.shift using a custom group extracting the substring from CLASS with str.extract:\ngroup = df['CLASS'].str.extract('([^_]+)', expand=False)\ndf['TOP'] = df['TOP'].fillna(df.groupby(group)['BOT'].shift())\n\nOutput:\n CLASS TOP BOT\n0 AG_1 200.0 230.0\n1 AG_2 230.0 250.0\n2 AG_3 250.0 380.0\n3 MAR 600.0 NaN\n4 GOM NaN 640.0\n\nIntermediate group:\n0 AG\n1 AG\n2 AG\n3 MAR\n4 GOM\nName: CLASS, dtype: object\n\nspecial case: only AG group\nm = df['CLASS'].str.startswith('AG')\n\ndf.loc[m, 'TOP'] = df.loc[m, 'TOP'].fillna(df.loc[m, 'BOT'].shift())\n\nExample:\n CLASS TOP BOT\n0 AG_1 200.0 230.0\n1 AG_2 230.0 250.0\n2 AG_3 250.0 380.0\n3 MAR_1 600.0 601.0\n4 MAR_2 NaN NaN # this is not filled\n5 GOM NaN 640.0\n\n" ]

[ 1 ]

[]

[]

[ "pandas", "python" ]

[ "To me it looks like you need an intermediate table. You have a db.Model \"Player\" with basic information as on a player card. Then a \"User\" and a \"Team\" model. The team has a relation to the user, now you create a \"UserCollection\" db.Model, which contains references such as PlayerId, UserId and TeamId if set.\n" ]

[ 0 ]

[ "You should use \"ManytoManyField\" like this;\nfrom django.db import models\n\nclass Publication(models.Model):\n title = models.CharField(max_length=30)\n\n class Meta:\n ordering = ['title']\n\n def __str__(self):\n return self.title\n\nclass Article(models.Model):\n headline = models.CharField(max_length=100)\n publications = models.ManyToManyField(Publication)\n\n class Meta:\n ordering = ['headline']\n\n def __str__(self):\n return self.headline\n\nit gives to you add more then one relation without new line or table;\na1 = Article(headline='Django lets you build web apps easily')\np1 = Publication(title='The Python Journal')\np2 = Publication(title='Science News')\np3 = Publication(title='Science Weekly')\np1.save()\np2.save()\np3.save()\na1.save()\n\nand you can start the add new relations;\na1.publications.add(p1, p2)\na1.publications.add(p3)\n\nfor more details:\nhttps://docs.djangoproject.com/en/4.1/topics/db/examples/many_to_many/\n" ]

[ -1 ]

[ "database_design", "python", "sqlalchemy" ]

[ "Your code was almost there. This is one way you can make it work,\ncoords = ((1, 2), (3, 4), (5, 6), (7, 8))\n\ncoords = tuple(x + y for x, y in zip(coords[0::2], coords[1::2]))\n\nLike in your code, it loops through two slices of coords using zip. But now it takes each element of the two slices (x and y) and adds them to form a 4 element inner tuple.\n", "This code would solve the problem:\ncoords = ((1, 2), (3, 4), (5, 6), (7, 8))\ncoords = tuple([(tuple([i for i in coords[x]])+tuple([i for i in coords[x+1]])) for x in range(0,len(coords)-1,2)])\nprint(coords)\n\nOutput:\n((1, 2, 3, 4), (5, 6, 7, 8))\n\n" ]

[ 8, 2 ]

[]

[]

[ "python", "tuples" ]

[ "You can pass on the win you want blanked out as an argument to the blank_screen function:\nimport functions \nwin = visual.Window([1440,900], color=[-1,-1,-1], fullscr=True)\ndur = 4\nfunctions.blank_screen(duration=dur, win=win)\n\nand\ndef blank_screen(duration, win):\n blank = TextStim(win, text='')\n blank.draw()\n win.flip()\n\n" ]

[ 4 ]

[]

[]

[ "function", "python" ]

[ "An easy way is to use the .to_directed method on the existing graph:\nfrom networkx import barabasi_albert_graph\nG = barabasi_albert_graph(200, 2)\nprint(G.is_directed())\n# False\nG_directed = G.to_directed()\nprint(G_directed.is_directed())\n# True\n\n" ]

[ 0 ]

[]

[]

[ "complex_networks", "graph", "graph_theory", "networkx", "python" ]

[ "The problem is in the following line:\nif __name__ == \"__space_shooter__\":\n\nThe __name__ variable will not contain the file name of the current script. If the script is ran directly, it will contain \"__main__\". If the script is imported by another script, it will contain the file name of that other script.\nIn order to check the script's file name, which you according to the comment wanted to do, you have to use the __file__ variable. The only problem is that the __file__ variable does not only containt the file name, but also the file path and the file extension. That's why I would use the str.endswith function, which checks whether a certain string ends with a given string. There are also some more complicate and reliable ways, but I will leave it to this:\nif __file__.endswith(\"space_shooter.py\"):\n\nHowever, it is more common to check whether the current file is being ran directly instead of being imported from another file. This allows other files to import and use the functions and classes present in the file, without that everything in the file is ran too.\nFor this, you have to use the __name__ variable:\nif __name__ == \"__main__\":\n\nAs said above, the __name__ variable will contain \"__main__\" when the file is ran directly. Thus we can compare __name__ with it to know whether the file is ran directly or not.\nFor more information on the __name__ variable, there is more explanation and a useful example.\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "The cleanest way to do this, or to do other modifications to a class definition, is to define a metaclass.\nAlternatively, just apply your decorator at the end of the class definition using inspect:\nimport inspect\n\nclass Something:\n def foo(self): \n pass\n\nfor name, fn in inspect.getmembers(Something, inspect.isfunction):\n setattr(Something, name, decorator(fn))\n\nIn practice of course you'll want to apply your decorator more selectively. As soon as you want to decorate all but one method you'll discover that it is easier and more flexible just to use the decorator syntax in the traditional way.\n", "Everytime you think of changing class definition, you can either use the class decorator or metaclass. e.g. using metaclass\nimport types\n\nclass DecoMeta(type):\n def __new__(cls, name, bases, attrs):\n\n for attr_name, attr_value in attrs.iteritems():\n if isinstance(attr_value, types.FunctionType):\n attrs[attr_name] = cls.deco(attr_value)\n\n return super(DecoMeta, cls).__new__(cls, name, bases, attrs)\n\n @classmethod\n def deco(cls, func):\n def wrapper(*args, **kwargs):\n print \"before\",func.func_name\n result = func(*args, **kwargs)\n print \"after\",func.func_name\n return result\n return wrapper\n\nclass MyKlass(object):\n __metaclass__ = DecoMeta\n\n def func1(self): \n pass\n\nMyKlass().func1()\n\nOutput:\nbefore func1\nafter func1\n\nNote: it will not decorate staticmethod and classmethod\n", "Following code works for python2.x and 3.x\nimport inspect\n\ndef decorator_for_func(orig_func):\n def decorator(*args, **kwargs):\n print(\"Decorating wrapper called for method %s\" % orig_func.__name__)\n result = orig_func(*args, **kwargs)\n return result\n return decorator\n\ndef decorator_for_class(cls):\n for name, method in inspect.getmembers(cls):\n if (not inspect.ismethod(method) and not inspect.isfunction(method)) or inspect.isbuiltin(method):\n continue\n print(\"Decorating function %s\" % name)\n setattr(cls, name, decorator_for_func(method))\n return cls\n\n@decorator_for_class\nclass decorated_class:\n def method1(self, arg, **kwargs):\n print(\"Method 1 called with arg %s\" % arg)\n def method2(self, arg):\n print(\"Method 2 called with arg %s\" % arg)\n\n\nd=decorated_class()\nd.method1(1, a=10)\nd.method2(2)\n\n", "Update for Python 3:\nimport types\n\n\nclass DecoMeta(type):\n def __new__(cls, name, bases, attrs):\n\n for attr_name, attr_value in attrs.items():\n if isinstance(attr_value, types.FunctionType):\n attrs[attr_name] = cls.deco(attr_value)\n\n return super().__new__(cls, name, bases, attrs)\n\n @classmethod\n def deco(cls, func):\n def wrapper(*args, **kwargs):\n print(\"before\",func.__name__)\n result = func(*args, **kwargs)\n print(\"after\",func.__name__)\n return result\n return wrapper\n\n(and thanks to Duncan for this)\n", "Of course that the metaclasses are the most pythonic way to go when you want to modify the way that python creates the objects. Which can be done by overriding the __new__ method of your class. But there are some points around this problem (specially for python 3.X) that I'd like to mention:\n\ntypes.FunctionType doesn't protect the special methods from being decorated, as they are function types. As a more general way you can just decorate the objects which their names are not started with double underscore (__). One other benefit of this method is that it also covers those objects that exist in namespace and starts with __ but are not function like __qualname__, __module__ , etc.\nThe namespace argument in __new__'s header doesn't contain class attributes within the __init__. The reason is that the __new__ executes before the __init__ (initializing).\nIt's not necessary to use a classmethod as the decorator, as in most of the times you import your decorator from another module.\nIf your class is contain a global item (out side of the __init__) for refusing of being decorated alongside checking if the name is not started with __ you can check the type with types.FunctionType to be sure that you're not decorating a non-function object.\n\n\nHere is a sample metacalss that you can use:\nclass TheMeta(type):\n def __new__(cls, name, bases, namespace, **kwds):\n # if your decorator is a class method of the metaclass use\n # `my_decorator = cls.my_decorator` in order to invoke the decorator.\n namespace = {k: v if k.startswith('__') else my_decorator(v) for k, v in namespace.items()}\n return type.__new__(cls, name, bases, namespace)\n\nDemo:\ndef my_decorator(func):\n def wrapper(self, arg):\n # You can also use *args instead of (self, arg) and pass the *args\n # to the function in following call.\n return \"the value {} gets modified!!\".format(func(self, arg))\n return wrapper\n\n\nclass TheMeta(type):\n def __new__(cls, name, bases, namespace, **kwds):\n # my_decorator = cls.my_decorator (if the decorator is a classmethod)\n namespace = {k: v if k.startswith('__') else my_decorator(v) for k, v in namespace.items()}\n return type.__new__(cls, name, bases, namespace)\n\n\nclass MyClass(metaclass=TheMeta):\n # a = 10\n def __init__(self, *args, **kwargs):\n self.item = args[0]\n self.value = kwargs['value']\n\n def __getattr__(self, attr):\n return \"This class hasn't provide the attribute {}.\".format(attr)\n\n def myfunction_1(self, arg):\n return arg ** 2\n\n def myfunction_2(self, arg):\n return arg ** 3\n\nmyinstance = MyClass(1, 2, value=100)\nprint(myinstance.myfunction_1(5))\nprint(myinstance.myfunction_2(2))\nprint(myinstance.item)\nprint(myinstance.p)\n\nOutput:\nthe value 25 gets modified!!\nthe value 8 gets modified!!\n1\nThis class hasn't provide the attribute p. # special method is not decorated.\n\nFor checking the 3rd item from the aforementioned notes you can uncomment the line a = 10 and do print(myinstance.a) and see the result then change the dictionary comprehension in __new__ as follows then see the result again:\nnamespace = {k: v if k.startswith('__') and not isinstance(v, types.FunctionType)\\\n else my_decorator(v) for k, v in namespace.items()}\n\n", "I will repeat my answer here, for a similar issue\nIt can be done many different ways. I will show how to make it through meta-class, class decorator and inheritance.\nby changing meta class\nimport functools\n\n\nclass Logger(type):\n @staticmethod\n def _decorator(fun):\n @functools.wraps(fun)\n def wrapper(*args, **kwargs):\n print(fun.__name__, args, kwargs)\n return fun(*args, **kwargs)\n return wrapper\n\n def __new__(mcs, name, bases, attrs):\n for key in attrs.keys():\n if callable(attrs[key]):\n # if attrs[key] is callable, then we can easily wrap it with decorator\n # and substitute in the future attrs\n # only for extra clarity (though it is wider type than function)\n fun = attrs[key]\n attrs[key] = Logger._decorator(fun)\n # and then invoke __new__ in type metaclass\n return super().__new__(mcs, name, bases, attrs)\n\n\nclass A(metaclass=Logger):\n def __init__(self):\n self.some_val = \"some_val\"\n\n def method_first(self, a, b):\n print(a, self.some_val)\n\n def another_method(self, c):\n print(c)\n\n @staticmethod\n def static_method(d):\n print(d)\n\n\nb = A()\n# __init__ (<__main__.A object at 0x7f852a52a2b0>,) {}\n\nb.method_first(5, b=\"Here should be 5\")\n# method_first (<__main__.A object at 0x7f852a52a2b0>, 5) {'b': 'Here should be 5'}\n# 5 some_val\nb.method_first(6, b=\"Here should be 6\")\n# method_first (<__main__.A object at 0x7f852a52a2b0>, 6) {'b': 'Here should be 6'}\n# 6 some_val\nb.another_method(7)\n# another_method (<__main__.A object at 0x7f852a52a2b0>, 7) {}\n# 7\nb.static_method(7)\n# 7\n\nAlso, will show two approaches how to make it without changing meta information of class (through class decorator and class inheritance). The first approach through class decorator put_decorator_on_all_methods accepts decorator to wrap all member callable objects of class.\ndef logger(f):\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n print(f.__name__, args, kwargs)\n return f(*args, **kwargs)\n\n return wrapper\n\n\ndef put_decorator_on_all_methods(decorator, cls=None):\n if cls is None:\n return lambda cls: put_decorator_on_all_methods(decorator, cls)\n\n class Decoratable(cls):\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n\n def __getattribute__(self, item):\n value = object.__getattribute__(self, item)\n if callable(value):\n return decorator(value)\n return value\n\n return Decoratable\n\n\n@put_decorator_on_all_methods(logger)\nclass A:\n def method(self, a, b):\n print(a)\n\n def another_method(self, c):\n print(c)\n\n @staticmethod\n def static_method(d):\n print(d)\n\n\nb = A()\nb.method(5, b=\"Here should be 5\")\n# >>> method (5,) {'b': 'Here should be 5'}\n# >>> 5\nb.method(6, b=\"Here should be 6\")\n# >>> method (6,) {'b': 'Here should be 6'}\n# >>> 6\nb.another_method(7)\n# >>> another_method (7,) {}\n# >>> 7\nb.static_method(8)\n# >>> static_method (8,) {}\n# >>> 8\n\nAnd, recently, I've come across on the same problem, but I couldn't put decorator on class or change it in any other way, except I was allowed to add such behavior through inheritance only (I am not sure that this is the best choice if you can change codebase as you wish though).\nHere class Logger forces all callable members of subclasses to write information about their invocations, see code below.\nclass Logger:\n\n def _decorator(self, f):\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n print(f.__name__, args, kwargs)\n return f(*args, **kwargs)\n\n return wrapper\n\n def __getattribute__(self, item):\n value = object.__getattribute__(self, item)\n if callable(value):\n decorator = object.__getattribute__(self, '_decorator')\n return decorator(value)\n return value\n\n\nclass A(Logger):\n def method(self, a, b):\n print(a)\n\n def another_method(self, c):\n print(c)\n\n @staticmethod\n def static_method(d):\n print(d)\n\nb = A()\nb.method(5, b=\"Here should be 5\")\n# >>> method (5,) {'b': 'Here should be 5'}\n# >>> 5\nb.method(6, b=\"Here should be 6\")\n# >>> method (6,) {'b': 'Here should be 6'}\n# >>> 6\nb.another_method(7)\n# >>> another_method (7,) {}\n# >>> 7\nb.static_method(7)\n# >>> static_method (7,) {}\n# >>> 7\n\nOr more abstractly, you can instantiate base class based on some decorator. \ndef decorator(f):\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n print(f.__name__, args, kwargs)\n return f(*args, **kwargs)\n return wrapper\n\n\nclass Decoratable:\n def __init__(self, dec):\n self._decorator = dec\n\n def __getattribute__(self, item):\n value = object.__getattribute__(self, item)\n if callable(value):\n decorator = object.__getattribute__(self, '_decorator')\n return decorator(value)\n return value\n\n\nclass A(Decoratable):\n def __init__(self, dec):\n super().__init__(dec)\n\n def method(self, a, b):\n print(a)\n\n def another_method(self, c):\n print(c)\n\n @staticmethod\n def static_method(d):\n print(d)\n\nb = A(decorator)\nb.method(5, b=\"Here should be 5\")\n# >>> method (5,) {'b': 'Here should be 5'}\n# >>> 5\nb.method(6, b=\"Here should be 6\")\n# >>> method (6,) {'b': 'Here should be 6'}\n# >>> 6\nb.another_method(7)\n# >>> another_method (7,) {}\n# >>> 7\nb.static_method(7)\n# >>> static_method (7,) {}\n# >>> 7\n\n", "There's another slightly similar thing you might want to do in some cases. Sometimes you want to trigger the attachment for something like debugging and not on all the classes but for every method of an object you might want a record of what it's doing.\ndef start_debugging():\n import functools\n import datetime\n filename = \"debug-{date:%Y-%m-%d_%H_%M_%S}.txt\".format(date=datetime.datetime.now())\n debug_file = open(filename, \"a\")\n debug_file.write(\"\\nDebug.\\n\")\n\n def debug(func):\n @functools.wraps(func)\n def wrapper_debug(*args, **kwargs):\n args_repr = [repr(a) for a in args] # 1\n kwargs_repr = [f\"{k}={v!r}\" for k, v in kwargs.items()] # 2\n signature = \", \".join(args_repr + kwargs_repr) # 3\n debug_file.write(f\"Calling {func.__name__}({signature})\\n\")\n value = func(*args, **kwargs)\n debug_file.write(f\"{func.__name__!r} returned {value!r}\\n\") # 4\n debug_file.flush()\n return value\n return wrapper_debug\n\n for obj in (self):\n for attr in dir(obj):\n if attr.startswith('_'):\n continue\n fn = getattr(obj, attr)\n if not isinstance(fn, types.FunctionType) and \\\n not isinstance(fn, types.MethodType):\n continue\n setattr(obj, attr, debug(fn))\n\nThis function will go through some objects (only self currently) and replace all functions and methods that do not start with _ with a debugging decorator.\nThe method used for this of just iterating the dir(self) is not addressed above but totally works. And can be called externally from the object and much more arbitrarily.\n", "In Python 3 you could also write a simple function that overwrites/applies a decorator to certain methods like so:\nfrom functools import wraps\nfrom types import MethodType\n\ndef logged(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n res = func(*args, **kwargs)\n print(\"logging:\", func.__name__, res)\n return res\n return wrapper\n\nclass Test:\n def foo(self):\n return 42\n ...\n\ndef aspectize(cls, decorator):\n for name, func in cls.__dict__.items():\n if not name.startswith(\"__\"):\n setattr(cls, name, MethodType(decorator(func), cls)) # MethodType is key\n\naspectize(Test, logged)\nt = Test()\nt.foo() # printing \"logging: foo 42\"; returning 42\n\n", "I came to this question from:\nHow to decorate all functions of a class without typing it over and over for each method?\nAnd I want add a one note:\nAnswers with class decorators or repalcing class methods like this one:\nhttps://stackoverflow.com/a/6307868/11277611\nWill not work with staticmethod.\nYou will get TypeError, unexpected argument because your method will get self/cls as first argument.\nProbably:\nDecorated class doesn't know about decorators of self methods and can't be distincted even with inspect.ismethod.\nI come to such quickfix:\nI'm not checked it closely but it passes my (no so comprehensive) tests.\nUsing dynamically decorators is already a bad approach, so, it must be okay as temporary solution.\nTLD:TD Add try/exception to use with staticmethod\ndef log_sent_data(function):\n @functools_wraps(function)\n def decorator(*args, **kwargs):\n # Quickfix\n self, *args = args\n try: # If method has self/cls/descriptor\n result = function(self, *args, **kwargs)\n except TypeError:\n if args: # If method is static but has positional args\n result = function(*args, **kwargs)\n else: # If method is static and doesn't has positional args\n result = function(**kwargs)\n # End of quickfix\n return result\n return decorator\n\n" ]

[ 42, 37, 12, 7, 2, 2, 0, 0, 0 ]

[ "You could override the __getattr__ method. It's not actually attaching a decorator, but it lets you return a decorated method. You'd probably want to do something like this:\nclass Eggs(object):\n def __getattr__(self, attr):\n return decorate(getattr(self, `_` + attr))\n\nThere's some ugly recursion hiding in there that you'll want to protect against, but that's a start.\n" ]

[ -1 ]

[ "class", "class_method", "decorator", "oop", "python" ]

[ "I believe what you have is calling the proto method, you should also call the fit_predict method on it, something like below:\nkproto = KPrototypes(n_clusters=3, verbose=2, max_iter=20).\n\nkproto.fit_predict(df, categorical=[3, 4, 5]) # categorical column indices\n\n" ]

[ 0 ]

[]

[]

[ "arrays", "cluster_analysis", "prototype", "python" ]

[ "You have public key stored in your key vault. Not a private key.\nYou cannot authenticate using public key. You have to store the private key to the vault.\n" ]

[ 0 ]

[]

[]

[ "azure_synapse", "python", "sftp" ]

[ "You can use collect_set on status field to get the distinct statuses on your desired partition. use the result to flag the records. collect_set returns an array field which can be used to check the length (using size) and its contents (using array_contains).\nsee example below\ndata_sdf. \\\n withColumn('vehicle_prod', func.coalesce('vehicle', 'production')). \\\n withColumn('vehicle_prod_eu', \n func.collect_set('status').over(wd.partitionBy('vehicle_prod', 'eu').orderBy('id').rowsBetween(-sys.maxsize, sys.maxsize))\n ). \\\n withColumn('flag', \n ((func.size('vehicle_prod_eu') == 1) & \n (func.array_contains('vehicle_prod_eu', 'NOT_OK'))).cast('int')\n ). \\\n withColumn('part', \n func.collect_set('asis').over(wd.partitionBy('vehicle_prod', 'eu').orderBy('id').rowsBetween(-sys.maxsize, sys.maxsize))\n ). \\\n withColumn('part', func.concat_ws(',', 'part')). \\\n orderBy('id'). \\\n show()\n\n# +---+-------+----------+------+----+------------+------+------------+---------------+----+-------------+\n# | id|vehicle|production| asis| eu| eu_variant|status|vehicle_prod|vehicle_prod_eu|flag| part|\n# +---+-------+----------+------+----+------------+------+------------+---------------+----+-------------+\n# | 1| A3345| null|PQ1298| FV1| FV1_variant| OK| A3345| [OK]| 0| PQ1298|\n# | 2| A3346| A3346|PQ1287| FV2| FV2_variant|NOT_OK| A3346| [NOT_OK]| 1|PQ1287,PQ1207|\n# | 3| A3346| A3346|PQ1207| FV2| FV2_variant|NOT_OK| A3346| [NOT_OK]| 1|PQ1287,PQ1207|\n# | 4| null| A3347| null| QP9| QP9_variant| OK| A3347| [NOT_OK, OK]| 0| |\n# | 5| null| A3347| null| QP9| QP9_variant|NOT_OK| A3347| [NOT_OK, OK]| 0| |\n# | 6| null| A3347| null| QP3| QP3_variant| OK| A3347| [OK]| 0| |\n# | 7| null| A3348|MP6553|YR34|YR34_variant|NOT_OK| A3348| [NOT_OK]| 1| MP6553|\n# | 8| null| A3348|MP6554|YR35|YR35_variant|NOT_OK| A3348| [NOT_OK]| 1| MP6554|\n# | 9| null| A3348|MP6554|YR35|YR35_variant|NOT_OK| A3348| [NOT_OK]| 1| MP6554|\n# +---+-------+----------+------+----+------------+------+------------+---------------+----+-------------+\n\n" ]

[ 1 ]

[]

[]

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

[ "The first trick to do is to exclude y argument (it is a fixed value\nfor all rows from x).\nThe second trick is to pass the signature: Both arguments are arrays and\nthe result is a scalar.\nSo, to vectorize your function, run:\nvfunc = np.vectorize(func, excluded=['y'], signature='(n),(n)->()')\n\nThen, when you call vfunc(X,y), you will get:\narray([0, 3, 6])\n\n" ]

[ 1 ]

[]

[]

[ "arrays", "function", "numpy", "python", "vectorization" ]

[ "Did you add custom user model in settings.py file\nadd following line in settings.py\nreplace users with your app name in which your custom User model is present\nAUTH_USER_MODEL = 'users.User'\n\n" ]

[ 0 ]

[]

[]

[ "django", "postgresql", "python" ]

[ "Error message: ValueError: invalid literal for int() with base 10: '??? ??? ?????'\nAs per exception int() with base 10: '??? ??? ?????' doesn't qualify as int.\nCheck in blog.0001_initial migrations for '??? ??? ?????' and modify that value with a valid int.\nYou might have accidentally provided garbage default value while rerunning makemigrations command which isn't an int()\n", "I had the same error.\nMy problem:\nI had:\nmodels.y\nexpiration_date = models.DateField(null=True, max_length=20)\n\nAnd a validation in my:\nforms.py\n \ndef clean_expiration_date(self):\n data = self.cleaned_data['expiration_date']\n if data < datetime.date.today():\nreturn data\n\nAnd it gave me the here mentioned error. But only on a certain page.\nMy solution:\nI changed the DateField to DateTimeField\nAnd it fixed my problem in my case.\n" ]

[ 5, 0 ]

[]

[]

[ "django", "python" ]

[ "Assuming the values are only 1 or NaN, you can groupby.ffill and groupby.bfill and only keep the values that are identical:\ng = df.groupby('group')['var1']\n\ns1 = g.ffill()\ns2 = g.bfill()\n\ndf['var2'] = s1.where(s1.eq(s2))\n\nOutput:\n var1 group var2\n0 NaN 1 NaN\n1 NaN 1 NaN\n2 1.0 1 1.0\n3 NaN 1 1.0\n4 NaN 1 1.0\n5 1.0 1 1.0\n6 NaN 1 NaN\n7 NaN 1 NaN\n8 1.0 2 1.0\n9 NaN 2 1.0\n10 1.0 2 1.0\n11 NaN 2 NaN\n\nIntermediates:\n var1 group var2 ffill bfill\n0 NaN 1 NaN NaN 1.0\n1 NaN 1 NaN NaN 1.0\n2 1.0 1 1.0 1.0 1.0\n3 NaN 1 1.0 1.0 1.0\n4 NaN 1 1.0 1.0 1.0\n5 1.0 1 1.0 1.0 1.0\n6 NaN 1 NaN 1.0 NaN\n7 NaN 1 NaN 1.0 NaN\n8 1.0 2 1.0 1.0 1.0\n9 NaN 2 1.0 1.0 1.0\n10 1.0 2 1.0 1.0 1.0\n11 NaN 2 NaN 1.0 NaN\n\n" ]

[ 2 ]

[]

[]

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

[ "I tried to reproduce the same in my environment and got the below results:\nI created an Azure Virtual Machine using the below code:\ncredential = AzureCliCredential()\nsubscription_id = os.environ[\"AZURE_SUBSCRIPTION_ID\"] = \"XXXXXXXX\"\nresource_client = ResourceManagementClient(credential, subscription_id)\nRESOURCE_GROUP_NAME = \"Imran\"\nLOCATION = \"eastus\"\nrg_result = resource_client.resource_groups.create_or_update(RESOURCE_GROUP_NAME,\n{\n\"location\": LOCATION\n}\n)\n\nVNET_NAME = \"testvnet\"\nSUBNET_NAME = \"subnet1\"\nIP_NAME = \"IP\"\nIP_CONFIG_NAME = \"ipconfig\"\nNIC_NAME = \"testnic\"\nnetwork_client = NetworkManagementClient(credential, subscription_id)\npoller = network_client.virtual_networks.begin_create_or_update(RESOURCE_GROUP_NAME,\nVNET_NAME,\n{\n\"location\": LOCATION,\n\"address_space\": {\n\"address_prefixes\": [\"10.0.0.0/16\"]\n}\n}\n)\nvnet_result = poller.result() \npoller = network_client.subnets.begin_create_or_update(RESOURCE_GROUP_NAME,\nVNET_NAME, SUBNET_NAME,\n{ \"address_prefix\": \"10.0.0.0/24\" }\n)\nsubnet_result = poller.result(\nprint(f\"Provisioned virtual subnet {subnet_result.name} with address prefix {subnet_result.address_prefix}\")\npoller = network_client.public_ip_addresses.begin_create_or_update(RESOURCE_GROUP_NAME,\nIP_NAME,\n{\n\"location\": LOCATION,\n\"sku\": { \"name\": \"Standard\" },\n\"public_ip_allocation_method\": \"Static\",\n\"public_ip_address_version\" : \"IPV4\"\n}\n) \nip_address_result = poller.result() \npoller = network_client.network_interfaces.begin_create_or_update(RESOURCE_GROUP_NAME,\nNIC_NAME,\n{\n\"location\": LOCATION,\n\"ip_configurations\": [ {\n\"name\": testconfig,\n\"subnet\": { \"id\": subnet_result.id },\n\"public_ip_address\": {\"id\": ip_address_result.id }\n}]\n}\n)\nnic_result = poller.result()\ncompute_client = ComputeManagementClient(credential, subscription_id)\nVM_NAME = \"linuxvm\"\nUSERNAME = \"****\"\nPASSWORD = \"****\" \npoller = compute_client.virtual_machines.begin_create_or_update(RESOURCE_GROUP_NAME, VM_NAME,\n{\n\"location\": LOCATION,\n\"storage_profile\": {\n\"image_reference\": {\n\"publisher\": 'Canonical',\n\"offer\": \"UbuntuServer\",\n\"sku\": \"16.04.0-LTS\",\n\"version\": \"latest\"\n}\n},\n\"hardware_profile\": {\n\"vm_size\": \"Standard_DS1_v2\"\n},\n\"os_profile\": {\n\"computer_name\": VM_NAME,\n\"admin_username\": USERNAME,\n\"admin_password\": PASSWORD\n},\n\"network_profile\": {\n\"network_interfaces\": [{\n\"id\": nic_result.id,\n}]\n}\n}\n) \nvm_result = poller.result() \nprint(f\"Provisioned virtual machine {vm_result.name}\")\n\nAzure Virtual Machine got created successfully like below:\n\nTo add the Extension while creating Azure Virtual Machine, make use of VirtualMachineExtensionsOperations Class like below:\nVirtualMachineExtensionsOperations(*args, **kwargs) \n\nbegin_create_or_update\n(resource_group_name: str, \nvm_name: str, vm_extension_name: str, extension_parameters: _models.VirtualMachineExtension, *, \ncontent_type: str = \"'application/json'\", **kwargs: Any) -> \nLROPoller[_models.VirtualMachineExtension]\n\n" ]

[ 1 ]

[]

[]

[ "azure", "python", "sdk", "virtual_machine" ]

[ "I got the solution by\nvar_dict = final_var.__class__.__dict__\nall_fields = var_dict['__fields__']\n\ndefault_val = next((v.__getattribute__('default') for i, v in all_fields.items()), None)\n\n" ]

[ 0 ]

[]

[]

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

[ "Your idea of using IPython.core.display with HTML is imho the best approach for that kind of task. matplotlib is super inefficient when it comes to plotting such a huge number of images (especially if you have them as URLs).\nThere's a small package I built based on that concept - it's called ipyplot\nimport ipyplot\n\nimages = data1['url'].values\nlabels = data1['id'].values\n\nipyplot.plot_images(images, labels, img_width=150)\n\nYou would get a plot similar to this:\n\n", "The best way to show a grid of images in the Jupyter notebook is probably using matplotlib to create the grid, since you can also plot images on matplotlib axes using imshow.\nI'm using a 3x165 grid, since that is 495 exactly. Feel free to mess around with that to change the dimensions of the grid.\nimport urllib\nf, axarr = plt.subplots(3, 165)\ncurr_row = 0\nfor index, row in data1.iterrows():\n # fetch the url as a file type object, then read the image\n f = urllib.request.urlopen(row[\"url\"])\n a = plt.imread(f)\n\n # find the column by taking the current index modulo 3\n col = index % 3\n # plot on relevant subplot\n axarr[col,curr_row].imshow(a)\n if col == 2:\n # we have finished the current row, so increment row counter\n curr_row += 1\n\n", "Just add this code below to your cell:\ndisplay(IPython.display.HTML('''\n <flexthis></flexthis>\n <style> div.jp-Cell-outputArea:has(flexthis) {\n display:flex; flex-wrap: wrap;\n }</style>\n'''))\n\nand then, on the same cell, use calls to IPython.display.Image or anything else you want to show.\n\nThis code above allows you to add custom CSS to that specific output cell. I'm using a flex box (display:flex), but you can change it to your liking.\n" ]

[ 15, 7, 0 ]

[ "I can only do it by \"brute force\":\nHowever, I only manage to do it manually:\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n%matplotlib inline\n\nimg1=mpimg.imread('Variable_8.png')\nimg2=mpimg.imread('Variable_17.png')\nimg3=mpimg.imread('Variable_18.png')\n ...\n\nfig, ((ax1, ax2, ax3), (ax4,ax5,ax6)) = plt.subplots(2, 3, sharex=True, sharey=True) \n\nax1.imshow(img1)\nax1.axis('off')\nax2.imshow(img2)\nax2.axis('off')\n ....\n\nDo not know if ot helps\n" ]

[ -2 ]

[ "html", "jupyter_notebook", "matplotlib", "python" ]

[ "The problem is not the Arabic characters, but the underscore. You can include it with:\nre_path(r'detail/(?P<slug>[\\w_-]+)/$', detail_course, name='detail_courses')\nThat being said, normally a slug does not contain an underscore, so your slugging algorithm does not seem to work properly.\nYou can use Django's slugify(…) function [Django-doc] for this:\nprint(slugify(u'تحلیل_بیت_کوین', allow_unicode=True))\nتحلیل_بیت_کوین\n\n" ]

[ 0 ]

[]

[]

[ "django", "python" ]

[ "Fixed. \nThe answer appears to be \n\nUninstall python via the windows apps and features\nRemove the c:\\program files\\python38 directory\nRemove the ..\\AppData\\Roaming\\Python directory\nReinstall\n\nNot sure what the \"root\" problem was, but a total wipe of python fixed it. Note that the python windows installer does not remove enough python stuff.\nThank you @nneonneo for getting me thinking in the right direction.\n", "You can just run pytest from priviledged (admin) cmd for the first time. For me the issue was fixed and pytest runs from ordinary cmd now without hanging.\n", "I have the similar problem\nafter i install pytest-cov or allure-pytest, pytest will hang\nif i uninstall them , everything is back to normal\nI have tried to install different pytest version, It didn't help\nbut, I tried the method you provide(uninstall python, remove the diretories), it works~, great！\nbut, when I install allure-pytest again, it fails again.\nfinally, I found that, I can't install package in privilege cmd window\nIf I install them in privilege cmd windows, it will make pytest hang!\nso, if anyone encounter the problem I have, you can try my way.\n", "In my case, the package anyio, a dependency of JupyterLab, was causing this problem. It was installed at the administrator level. I uninstalled it and reinstalled at the user level and pytest started working again.\n", "Using Ubuntu 18.04/20.04 + Python 3.8.2\nMy tests were hanging in a similar matter after showing results.\nRemoving pytest-cov didn't help.\nWhat helped was moving from Python 3.8.2 to a newer version (like 3.8.12)\n", "I ran into the same issue. In my case my IDE didn't load the correct conda environment. So make sure you're in the right environment for your application and use conda activate myEnv to change it if necessary.\n" ]

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

[]

[]

[ "pytest", "python" ]

[ "In igraph you can use the VertexCover to draw polygons around clusters (as also suggested by Szabolcs in his comment). You have to supply the option mark_groups when plotting the cover, possibly with some additional palette if you want. See some more detail in the documentation here.\nIn order to construct the VertexCover, you first have to make sure you get integer indices for each node in the graph you created. You can do that using g_test.vs.find.\nclusters = [[g_test.vs.find(name=v).index for v in cl] for cl in list_community]\ncover = ig.VertexCover(g_test, clusters)\n\nAfter that, you can simply draw the cover like\nig.plot(cover,\n mark_groups=True,\n palette=ig.RainbowPalette(3))\n\nresulting in the following picture\n\n", "Here is a script that somewhat achieves what you're looking for. I had to handle the cases of single-, and two-nodes communities separately, but for greater than two nodes this draws a polygon within the nodes.\nI had some trouble with matplotlib not accounting for overlapping edges and faces of polygons which meant the choice was between (1) not having the polygon surround the nodes or (2) having an extra outline just inside the edge of the polygon due to matplotlib overlapping the widened edge with the fill of the polygon. I left a comment on how to change the code from option (2) to option (1).\nI also blatantly borrowed a convenience function from this post to handle correctly sorting the nodes in the polygon for appropriate filling by matplotlib's plt.fill().\nOption 1:\n\nOption 2:\n\nFull code:\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import cm\n\ndef sort_xy(x, y):\n\n x0 = np.mean(x)\n y0 = np.mean(y)\n\n r = np.sqrt((x-x0)**2 + (y-y0)**2)\n\n angles = np.where((y-y0) > 0, np.arccos((x-x0)/r), 2*np.pi-np.arccos((x-x0)/r))\n\n mask = np.argsort(angles)\n\n x_sorted = x[mask]\n y_sorted = y[mask]\n\n return x_sorted, y_sorted\n\nG = nx.karate_club_graph()\n\npos = nx.spring_layout(G, seed=42)\nfig, ax = plt.subplots(figsize=(8, 10))\nnx.draw(G, pos=pos, with_labels=True)\n\ncommunities = nx.community.louvain_communities(G)\n\nalpha = 0.5\nedge_padding = 10\ncolors = cm.get_cmap('viridis', len(communities))\n\nfor i, comm in enumerate(communities):\n\n if len(comm) == 1:\n cir = plt.Circle((pos[comm.pop()]), edge_padding / 100, alpha=alpha, color=colors(i))\n ax.add_patch(cir)\n\n elif len(comm) == 2:\n comm_pos = {k: pos[k] for k in comm}\n coords = [a for a in zip(*comm_pos.values())]\n x, y = coords[0], coords[1]\n plt.plot(x, y, linewidth=edge_padding, linestyle=\"-\", alpha=alpha, color=colors(i))\n\n else:\n comm_pos = {k: pos[k] for k in comm}\n coords = [a for a in zip(*comm_pos.values())]\n x, y = sort_xy(np.array(coords[0]), np.array(coords[1]))\n plt.fill(x, y, alpha=alpha, facecolor=colors(i), \n edgecolor=colors(i), # set to None to remove edge padding\n linewidth=edge_padding)\n\n" ]

[ 2, 1 ]

[]

[]

[ "graph", "igraph", "networking", "python", "visualization" ]

[ "Modifying lists that you are currently itterating over is not a good approch. Instead take a new list and append values in else.\nx = '-1,0;1,0;0,-1;0,+1'\n\nx = x.split(';')\n\nnew = []\n\nfor i in x:\n if ('+' in i) or ('-' in i):\n pass\n else:\n new.append(i)\n \nx = ';'.join(new) \nprint(x)\n\nGives #\n1,0\n\n", "In your example there is a problem.\nx = '-1,0;1,0;0,-1;0,+1'.split(';')\n\ncounter = 0\nfor i in x:\n counter +=1\n if '+' in i or '-' in i:\n x.remove(i)\n\nprint(x, counter)\n\nLook at this. U modify a list over which u iterate.\nIf u remove n'th element, the (n+1) has now index n, (n+2) has n+1 so ...\nU skips some elements\nIn my opinion better solution is to use list comprehension:\nx = '-1,0;1,0;0,-1;0,+1'.split(';')\n\nexpected_result = [el for el in x\n if \"-\" not in el and\n \"+\" not in el]\n\nOr for loop but creates new list\nexpected_2 = []\nfor i in x:\n if '+' not in i and '-' not in i:\n expected_2.append(i)\n\n" ]

[ 2, 1 ]

[]

[]

[ "conditional_statements", "if_statement", "python" ]

[ "def get_event(id): #call your event api here\n return str(id) + \"_event\"\n\ndef get_points(): #call your points api here\n return 100\n\n\nd = {1443956: 'Sean McBride', 1281609: 'Maghnus Og Dunne', 4841686: 'Pearse Bowes', 406739: 'Adam Mcconville', 196345: 'Niall McCurdy', 808057: 'John McDonald', 6365597: 'Tony Cassidy', 1322001: 'Tiarnan Mccaffrey', 350275: 'Eoghan McCurdy', 4820159: 'Ciaran McKeown', 7185401: 'Ryan Russell', 5203794: 'Michael Devenny', 3145058: 'Declan Lees'} \n\nnew_dict = {} \n#this loop gives your expected structure \nfor i in d: \n new_dict[i] ={}\n new_dict[i][d[i]]={}\n event = get_event(i)\n new_dict[i][d[i]][event]=get_points()\n\nOutput as per above code\n{1443956: {'Sean McBride': {'1443956_event': 100}}, 1281609: {'Maghnus Og Dunne': {'1281609_event': 100}}, 4841686: {'Pearse Bowes': {'4841686_event': 100}}, 406739: {'Adam Mcconville': {'406739_event': 100}}, 196345: {'Niall McCurdy': {'196345_event': 100}}, 808057: {'John McDonald': {'808057_event': 100}}, 6365597: {'Tony Cassidy': {'6365597_event': 100}}, 1322001: {'Tiarnan Mccaffrey': {'1322001_event': 100}}, 350275: {'Eoghan McCurdy': {'350275_event': 100}}, 4820159: {'Ciaran McKeown': {'4820159_event': 100}}, 7185401: {'Ryan Russell': {'7185401_event': 100}}, 5203794: {'Michael Devenny': {'5203794_event': 100}}, 3145058: {'Declan Lees': {'3145058_event': 100}}}\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "I had the same issue and managed to fix it the following way:\nI saw that the upgrade script tried to copy files from\n/private/tmp/d20221130-21318-e53mkn/ca-certificates/./2022-10-11 to /usr/local/Cellar/ca-certificates/./2022-10-11 and I got a Permission denied meaning - I (the current mac user) could not edit/create files there.\nSo i went to /usr/local/Cellar/ and saw that my user was not the owner of the folder ca-certificates and several others. So i just changed the onwer like this:\ncd /usr/local/Cellar/\n\nsudo chown -R REPLACE_WITH_YOUR_USERNAME:admin *\n\nAnd this fix the issue for me. A simple brew upgrde upgraded all my packages after that.\nHope this helps !\n" ]

[ 0 ]

[]

[]

[ "homebrew", "python" ]

[ "modifying your code, look the part which are different, mostly the mistake was in the part which you are not calculating based on n 0-10 :\nn = np.arange(0,10)\nt = np.linspace(-2 * np.pi, 2 *np.pi, 10000)\ndef a(n):\n return ((-1)**(n))*4/(np.pi*(2*n+1))\nf = 0\nfor i in n:\n f += a(i)*np.cos((2*i +1) * t)\n\n\nhowever you could write you could in matrix form, and avoid looping, using the vector and broadcasting:\nn = np.arange(10)[:,None]\nt = np.linspace(-2 * np.pi, 2 *np.pi, 10000)[:,None]\na = ((-1) ** n) * 4 / (np.pi*(2*n + 1))\nf = (a * np.cos((2 * n + 1) * t.T )).sum(axis=0)\n\n" ]

[ 0 ]

[]

[]

[ "fft", "python", "wolfram_mathematica" ]

[ "Here's an approach which does not need to fetch the entire file (full version available here).\nIt does require boto (or boto3), though (unless you can mimic the ranged GETs via AWS CLI; which I guess is quite possible as well).\nimport sys\nimport zlib\nimport zipfile\nimport io\n\nimport boto\nfrom boto.s3.connection import OrdinaryCallingFormat\n\n\n# range-fetches a S3 key\ndef fetch(key, start, len):\n end = start + len - 1\n return key.get_contents_as_string(headers={\"Range\": \"bytes=%d-%d\" % (start, end)})\n\n\n# parses 2 or 4 little-endian bits into their corresponding integer value\ndef parse_int(bytes):\n val = ord(bytes[0]) + (ord(bytes[1]) << 8)\n if len(bytes) > 3:\n val += (ord(bytes[2]) << 16) + (ord(bytes[3]) << 24)\n return val\n\n\n\"\"\"\nbucket: name of the bucket\nkey: path to zipfile inside bucket\nentry: pathname of zip entry to be retrieved (path/to/subdir/file.name) \n\"\"\"\n\n# OrdinaryCallingFormat prevents certificate errors on bucket names with dots\n# https://stackoverflow.com/questions/51604689/read-zip-files-from-amazon-s3-using-boto3-and-python#51605244\n_bucket = boto.connect_s3(calling_format=OrdinaryCallingFormat()).get_bucket(bucket)\n_key = _bucket.get_key(key)\n\n# fetch the last 22 bytes (end-of-central-directory record; assuming the comment field is empty)\nsize = _key.size\neocd = fetch(_key, size - 22, 22)\n\n# start offset and size of the central directory\ncd_start = parse_int(eocd[16:20])\ncd_size = parse_int(eocd[12:16])\n\n# fetch central directory, append EOCD, and open as zipfile!\ncd = fetch(_key, cd_start, cd_size)\nzip = zipfile.ZipFile(io.BytesIO(cd + eocd))\n\n\nfor zi in zip.filelist:\n if zi.filename == entry:\n # local file header starting at file name length + file content\n # (so we can reliably skip file name and extra fields)\n\n # in our \"mock\" zipfile, `header_offset`s are negative (probably because the leading content is missing)\n # so we have to add to it the CD start offset (`cd_start`) to get the actual offset\n\n file_head = fetch(_key, cd_start + zi.header_offset + 26, 4)\n name_len = parse_int(file_head[0:2])\n extra_len = parse_int(file_head[2:4])\n\n content = fetch(_key, cd_start + zi.header_offset + 30 + name_len + extra_len, zi.compress_size)\n\n # now `content` has the file entry you were looking for!\n # you should probably decompress it in context before passing it to some other program\n\n if zi.compress_type == zipfile.ZIP_DEFLATED:\n print zlib.decompressobj(-15).decompress(content)\n else:\n print content\n break\n\nIn your case you might need to write the fetched content to a local file (due to large size), unless memory usage is not a concern.\n", "So here is the code that allows you to open a file on Amazon S3 as if it were a normal file. Notice I use the aws command, rather than the boto3 Python module. (I don't have access to boto3.) You can open the file and seek on it. The file is cached locally. If you open the file with the Python ZipFile API and it's a ZipFile, you can then read individual parts. You can't write, though, because S3 doesn't support partial writes.\nSeparately, I implement s3open(), which can open a file for reading or writing, but it doesn't implement the seek interface, which is required by ZipFile.\nfrom urllib.parse import urlparse\nfrom subprocess import run,Popen,PIPE\nimport copy\nimport json\nimport os\nimport tempfile\n\n# Tools for reading and write files from Amazon S3 without boto or boto3\n# http://boto.cloudhackers.com/en/latest/s3_tut.html\n# but it is easier to use the aws cli, since it's configured to work.\n\ndef s3open(path, mode=\"r\", encoding=None):\n \"\"\"\n Open an s3 file for reading or writing. Can handle any size, but cannot seek.\n We could use boto.\n http://boto.cloudhackers.com/en/latest/s3_tut.html\n but it is easier to use the aws cli, since it is present and more likely to work.\n \"\"\"\n from subprocess import run,PIPE,Popen\n if \"b\" in mode:\n assert encoding == None\n else:\n if encoding==None:\n encoding=\"utf-8\"\n assert 'a' not in mode\n assert '+' not in mode\n\n if \"r\" in mode:\n p = Popen(['aws','s3','cp',path,'-'],stdout=PIPE,encoding=encoding)\n return p.stdout\n\n elif \"w\" in mode:\n p = Popen(['aws','s3','cp','-',path],stdin=PIPE,encoding=encoding)\n return p.stdin\n else:\n raise RuntimeError(\"invalid mode:{}\".format(mode))\n\n\n\n\nCACHE_SIZE=4096 # big enough for front and back caches\nMAX_READ=65536*16\ndebug=False\nclass S3File:\n \"\"\"Open an S3 file that can be seeked. This is done by caching to the local file system.\"\"\"\n def __init__(self,name,mode='rb'):\n self.name = name\n self.url = urlparse(name)\n if self.url.scheme != 's3':\n raise RuntimeError(\"url scheme is {}; expecting s3\".format(self.url.scheme))\n self.bucket = self.url.netloc\n self.key = self.url.path[1:]\n self.fpos = 0\n self.tf = tempfile.NamedTemporaryFile()\n cmd = ['aws','s3api','list-objects','--bucket',self.bucket,'--prefix',self.key,'--output','json']\n data = json.loads(Popen(cmd,encoding='utf8',stdout=PIPE).communicate()[0])\n file_info = data['Contents'][0]\n self.length = file_info['Size']\n self.ETag = file_info['ETag']\n\n # Load the caches\n\n self.frontcache = self._readrange(0,CACHE_SIZE) # read the first 1024 bytes and get length of the file\n if self.length > CACHE_SIZE:\n self.backcache_start = self.length-CACHE_SIZE\n if debug: print(\"backcache starts at {}\".format(self.backcache_start))\n self.backcache = self._readrange(self.backcache_start,CACHE_SIZE)\n else:\n self.backcache = None\n\n def _readrange(self,start,length):\n # This is gross; we copy everything to the named temporary file, rather than a pipe\n # because the pipes weren't showing up in /dev/fd/?\n # We probably want to cache also... That's coming\n cmd = ['aws','s3api','get-object','--bucket',self.bucket,'--key',self.key,'--output','json',\n '--range','bytes={}-{}'.format(start,start+length-1),self.tf.name]\n if debug:print(cmd)\n data = json.loads(Popen(cmd,encoding='utf8',stdout=PIPE).communicate()[0])\n if debug:print(data)\n self.tf.seek(0) # go to the beginning of the data just read\n return self.tf.read(length) # and read that much\n\n def __repr__(self):\n return \"FakeFile<name:{} url:{}>\".format(self.name,self.url)\n\n def read(self,length=-1):\n # If length==-1, figure out the max we can read to the end of the file\n if length==-1:\n length = min(MAX_READ, self.length - self.fpos + 1)\n\n if debug:\n print(\"read: fpos={} length={}\".format(self.fpos,length))\n # Can we satisfy from the front cache?\n if self.fpos < CACHE_SIZE and self.fpos+length < CACHE_SIZE:\n if debug:print(\"front cache\")\n buf = self.frontcache[self.fpos:self.fpos+length]\n self.fpos += len(buf)\n if debug:print(\"return 1: buf=\",buf)\n return buf\n\n # Can we satisfy from the back cache?\n if self.backcache and (self.length - CACHE_SIZE < self.fpos):\n if debug:print(\"back cache\")\n buf = self.backcache[self.fpos - self.backcache_start:self.fpos - self.backcache_start + length]\n self.fpos += len(buf)\n if debug:print(\"return 2: buf=\",buf)\n return buf\n\n buf = self._readrange(self.fpos, length)\n self.fpos += len(buf)\n if debug:print(\"return 3: buf=\",buf)\n return buf\n\n def seek(self,offset,whence=0):\n if debug:print(\"seek({},{})\".format(offset,whence))\n if whence==0:\n self.fpos = offset\n elif whence==1:\n self.fpos += offset\n elif whence==2:\n self.fpos = self.length + offset\n else:\n raise RuntimeError(\"whence={}\".format(whence))\n if debug:print(\" ={} (self.length={})\".format(self.fpos,self.length))\n\n def tell(self):\n return self.fpos\n\n def write(self):\n raise RuntimeError(\"Write not supported\")\n\n def flush(self):\n raise RuntimeError(\"Flush not supported\")\n\n def close(self):\n return\n\n", "Here's an improved version of the already given solution - now it uses boto3 and handles files which are larger than 4GiB:\nimport boto3\nimport io\nimport struct\nimport zipfile\n\ns3 = boto3.client('s3')\n\nEOCD_RECORD_SIZE = 22\nZIP64_EOCD_RECORD_SIZE = 56\nZIP64_EOCD_LOCATOR_SIZE = 20\n\nMAX_STANDARD_ZIP_SIZE = 4_294_967_295\n\ndef lambda_handler(event):\n bucket = event['bucket']\n key = event['key']\n zip_file = get_zip_file(bucket, key)\n print_zip_content(zip_file)\n\ndef get_zip_file(bucket, key):\n file_size = get_file_size(bucket, key)\n eocd_record = fetch(bucket, key, file_size - EOCD_RECORD_SIZE, EOCD_RECORD_SIZE)\n if file_size <= MAX_STANDARD_ZIP_SIZE:\n cd_start, cd_size = get_central_directory_metadata_from_eocd(eocd_record)\n central_directory = fetch(bucket, key, cd_start, cd_size)\n return zipfile.ZipFile(io.BytesIO(central_directory + eocd_record))\n else:\n zip64_eocd_record = fetch(bucket, key,\n file_size - (EOCD_RECORD_SIZE + ZIP64_EOCD_LOCATOR_SIZE + ZIP64_EOCD_RECORD_SIZE),\n ZIP64_EOCD_RECORD_SIZE)\n zip64_eocd_locator = fetch(bucket, key,\n file_size - (EOCD_RECORD_SIZE + ZIP64_EOCD_LOCATOR_SIZE),\n ZIP64_EOCD_LOCATOR_SIZE)\n cd_start, cd_size = get_central_directory_metadata_from_eocd64(zip64_eocd_record)\n central_directory = fetch(bucket, key, cd_start, cd_size)\n return zipfile.ZipFile(io.BytesIO(central_directory + zip64_eocd_record + zip64_eocd_locator + eocd_record))\n\n\ndef get_file_size(bucket, key):\n head_response = s3.head_object(Bucket=bucket, Key=key)\n return head_response['ContentLength']\n\ndef fetch(bucket, key, start, length):\n end = start + length - 1\n response = s3.get_object(Bucket=bucket, Key=key, Range=\"bytes=%d-%d\" % (start, end))\n return response['Body'].read()\n\ndef get_central_directory_metadata_from_eocd(eocd):\n cd_size = parse_little_endian_to_int(eocd[12:16])\n cd_start = parse_little_endian_to_int(eocd[16:20])\n return cd_start, cd_size\n\ndef get_central_directory_metadata_from_eocd64(eocd64):\n cd_size = parse_little_endian_to_int(eocd64[40:48])\n cd_start = parse_little_endian_to_int(eocd64[48:56])\n return cd_start, cd_size\n\ndef parse_little_endian_to_int(little_endian_bytes):\n format_character = \"i\" if len(little_endian_bytes) == 4 else \"q\"\n return struct.unpack(\"<\" + format_character, little_endian_bytes)[0]\n\ndef print_zip_content(zip_file):\n files = [zi.filename for zi in zip_file.filelist]\n print(f\"Files: {files}\")\n\n", "import io\n\n\nclass S3File(io.RawIOBase):\n def __init__(self, s3_object):\n self.s3_object = s3_object\n self.position = 0\n\n def __repr__(self):\n return \"<%s s3_object=%r>\" % (type(self).__name__, self.s3_object)\n\n @property\n def size(self):\n return self.s3_object.content_length\n\n def tell(self):\n return self.position\n\n def seek(self, offset, whence=io.SEEK_SET):\n if whence == io.SEEK_SET:\n self.position = offset\n elif whence == io.SEEK_CUR:\n self.position += offset\n elif whence == io.SEEK_END:\n self.position = self.size + offset\n else:\n raise ValueError(\"invalid whence (%r, should be %d, %d, %d)\" % (\n whence, io.SEEK_SET, io.SEEK_CUR, io.SEEK_END\n ))\n\n return self.position\n\n def seekable(self):\n return True\n\n def read(self, size=-1):\n if size == -1:\n # Read to the end of the file\n range_header = \"bytes=%d-\" % self.position\n self.seek(offset=0, whence=io.SEEK_END)\n else:\n new_position = self.position + size\n\n # If we're going to read beyond the end of the object, return\n # the entire object.\n if new_position >= self.size:\n return self.read()\n\n range_header = \"bytes=%d-%d\" % (self.position, new_position - 1)\n self.seek(offset=size, whence=io.SEEK_CUR)\n\n return self.s3_object.get(Range=range_header)[\"Body\"].read()\n\n def readable(self):\n return True\n\n\nif __name__ == \"__main__\":\n import zipfile\n\n import boto3\n\n s3 = boto3.resource(\"s3\")\n s3_object = s3.Object(bucket_name=\"bukkit\", key=\"bagit.zip\")\n\n s3_file = S3File(s3_object)\n\n with zipfile.ZipFile(s3_file) as zf:\n print(zf.namelist())\n\nReference:\n\nhttps://alexwlchan.net/2019/02/working-with-large-s3-objects/\n\n" ]

[ 6, 3, 2, 0 ]

[]

[]

[ "amazon_s3", "boto", "boto3", "python", "zip" ]

[ "OK, well, that was simple, no need to mess around with from_rgb, because of what styles support.\nAnd, after reading Will's answer, I've modified my original solution to use saved themes. Thanks again for an awesome library, Will!\n\nAlternatively you can use a CSS-like syntax to specify a color with a “#” followed by three pairs of hex characters, or in RGB form with three decimal integers. The following two lines both print “Hello” in the same color (purple):\nconsole.print(\"Hello\", style=\"#af00ff\")\n\nFound a 20-element list here (last 2 are black and white which I am skipping).\nhivis.theme.ini\n[styles]\n\n; High constrast palette from \n; https://sashamaps.net/docs/resources/20-colors/\n\nhivis0 = #e6194b\nhivis1 = #3cb44b\nhivis2 = #ffe119\nhivis3 = #4363d8\nhivis4 = #f58231\nhivis5 = #911eb4\nhivis6 = #46f0f0\nhivis7 = #f032e6\nhivis8 = #bcf60c\nhivis9 = #fabebe\nhivis10 = #008080\nhivis11 = #e6beff\nhivis12 = #9a6324\nhivis13 = #fffac8\nhivis14 = #800000\nhivis15 = #aaffc3\nhivis16 = #808000\nhivis17 = #ffd8b1\nhivis18 = #000075\nhivis19 = #808080\n;hivis20 = #ffffff\n;hivis21 = #000000\n\nscript.py\nfrom rich.console import Console\nfrom rich.theme import Theme\n\nwith open(\"hivis.theme.ini\") as fi:\n theme = Theme.from_file(fi)\n\npalette = [name for name in theme.styles.keys() if name.startswith(\"hivis\")]\n\nconsole = Console(theme=theme)\nconsole.print(\"[hivis1] First Hivis. [hivis2] Second Hivis.\")\nfor ix, rgb in enumerate(palette):\n console.print(f\"[{palette[ix]}] {rgb}\")\n\n\n\n", "Consider using Rich's themes to give names to your colors. That way you won't have to reference your list. You can do print(\"Hello [color3]World![/]\")\n" ]

[ 1, 1 ]

[]

[]

[ "python", "rich", "tui" ]

[ "I think you are looking for sys.exit(), but since you edited your question, I am not sure anymore. Try this:\nimport sys\n\nword = \"test\"\n\ndef check():\n with open(\"tex.txt\", \"r\") as file:\n for line_number, line in enumerate(file, start=1): \n if word in line:\n return 0\n \n return 1\n\nis_word_found = check() # store the return value of check() in variable `is_word_found`\nprint(is_word_found) # prints value of `is_word_found` to standard output\nsys.exit(is_word_found) # exit the program with status code `is_word_found`\n\nBTW: as @gimix and @VPfB mentioned, your check() function did return a value immediately after it checked the first line of your file. I included a fix for that, now 1 is returned only if the word is not found is any line of your file.\n" ]

[ 1 ]

[]

[]

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

[ "The error is in the parts you aren't showing us, because your code works fine. I'm guessing you don't have newlines separating the lines.\nC:\\tmp>type x.py\n\ntextData=\"\"\"\\\nR$M21,2021-06-08,1.3236,1.3238,1.3226,1.3237,290,343\nR$M21,2021-06-09,1.3232,1.3243,1.3231,1.3233,48,343\nR$M21,2021-06-10,1.3239,1.3253,1.3238,1.3244,406,292\nR$M21,2021-06-11,1.3249,1.3261,1.3217,1.324,107,0\nR$M21,2021-06-14,1.3252,1.3271,1.3252,1.3261,107,0\"\"\"\n\nimport pandas as pd\nfrom io import StringIO\n\ndf = pd.read_csv(StringIO(textData), sep=\",\")\nprint(df)\n\nC:\\tmp>python x.py\n R$M21 2021-06-08 1.3236 1.3238 1.3226 1.3237 290 343\n0 R$M21 2021-06-09 1.3232 1.3243 1.3231 1.3233 48 343\n1 R$M21 2021-06-10 1.3239 1.3253 1.3238 1.3244 406 292\n2 R$M21 2021-06-11 1.3249 1.3261 1.3217 1.3240 107 0\n3 R$M21 2021-06-14 1.3252 1.3271 1.3252 1.3261 107 0\n\nC:\\tmp>\n\n", "First, make sure to add newline after each line, best through os.linesep.\nThen set the StringIO buffer \"head position\" to start, aka 0, before passing it to pandas:\nimport os\nimport pandas as pd\nfrom io import StringIO\n\nbuffer = StringIO()\nbuffer.write('hello,23,2022,bye' + os.linesep)\nbuffer.write('world,43,2025,then' + os.linesep)\nbuffer.seek(0)\ndf = pd.read_csv(buffer, sep=',', header=None)\n\nprint(df)\n\nThis will yield:\n 0 1 2 3\n0 hello 23 2022 bye\n1 world 43 2025 then\n\n[Python-3.9]\n" ]

[ 2, 0 ]

[]

[]

[ "csv", "pandas", "python", "python_3.x", "stringio" ]

[ "you are missing the mandatory parameters symbol and interval, the query should be like this:\nhttps://api.binance.com/api/v3/klines?symbol=BTCUSDT&interval=1h\nyou need to import only requests:\nimport requests\n\nmarket = 'BTCUSDT'\ntick_interval = '1h'\n\nurl = 'https://api.binance.com/api/v3/klines?symbol='+market+'&interval='+tick_interval\ndata = requests.get(url).json()\n\nprint(data)\n\nPlease check the official Binance REST API documentation here: https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md\n", "The requests python package has a params, json argument, so you don't need to import any of those packages you are importing.\nimport requests\n\nurl = 'https://api.binance.com/api/v3/klines'\nparams = {\n 'symbol': 'BTCUSDT',\n 'interval': '1h'\n}\nresponse = requests.get(url, params=params)\nprint(response.json())\n\n", "collect data into dataframe\nimport requests\nimport datetime\nimport pandas as pd\nimport numpy as np\n\ndef get_binance_data_request_(ticker, interval='4h', limit=500, start='2018-01-01 00:00:00'):\n \"\"\"\n interval: str tick interval - 4h/1h/1d ...\n \"\"\"\n columns = ['open_time','open', 'high', 'low', 'close', 'volume','close_time', 'qav','num_trades','taker_base_vol','taker_quote_vol', 'ignore']\n start = int(datetime.datetime.timestamp(pd.to_datetime(start))*1000)\n url = f'https://www.binance.com/api/v3/klines?symbol={ticker}&interval={interval}&limit={limit}&startTime={start}'\n data = pd.DataFrame(requests.get(url).json(), columns=columns, dtype=np.float)\n data.index = [pd.to_datetime(x, unit='ms').strftime('%Y-%m-%d %H:%M:%S') for x in data.open_time]\n usecols=['open', 'high', 'low', 'close', 'volume', 'qav','num_trades','taker_base_vol','taker_quote_vol']\n data = data[usecols]\n return data\n\nget_binance_data_request_('ETHUSDT', '1h')\n\nresult:\n\n" ]

[ 29, 4, 0 ]

[]

[]

[ "api", "binance", "candlestick_chart", "json", "python" ]

[ "Even though pipenv had so many problems. I suggest you use it when you are new to virtual env.\nJust\npip install pipenv\ncd $your-work-directory\npipenv shell\n\nThen you created your project env.\nYou can active it by:\ncd $your-work-directory\npipenv shell\n\nYou can install packages by:\ncd $your-work-directory\npipenv install $yourpackage --skip-lock\n\n", "\nOpen the command prompt\nGo to the project directory, where you created virtual environment\nAnd type the same as error shows, as in my case it was\n\nFile D:\\Coding files\\Python*recommendation-system\\venv\\Scripts\\activate.ps1* cannot be loaded because running scripts is disabled on this system.\n\nSo I typed recommendation-system\\venv\\Scripts\\activate.ps1\n\nAnd it resolved my problem.\n", "Use this and it will work:\ncd $working directory u have virtual env on\npipenv shell\n", "you can use this command it worked for me for reusing your existing venv\n $ workon then the name of your existing venv\n\n" ]

[ 1, 0, 0, 0 ]

[]

[]

[ "cmd", "django", "python", "windows" ]

[ "You can set the ticks manually:\nax.set_xticks(df.index, [i / 200 for i in df.index])\n\n" ]

[ 1 ]

[]

[]

[ "pandas", "python" ]

[ "Issue was related to the pytest.\nThis post helped. Just need to add -p no:logging to the run command or to the pytest.ini file\n" ]

[ 0 ]

[]

[]

[ "logging", "loguru", "python", "webdriver" ]

[ "Solved\nI solved my neural network. A brief description follows in case it helps anyone else. Thanks to all those that helped with suggestions. \nBasically, I had implemented it with a fully matrix approach ie. the backpropagation uses all examples each time. I later tried implementing it as a vector approach ie. backpropagation with each example. This was when I realised that the matrix approach doesn't update the parameters each example, so one run through this way is NOT the same as one run through each example in turn, effectively the whole training set is backpropagated as one example. Hence, my matrix implementation does work, but after many iterations, which then ends up taking longer than the vector approach anyway! Have opened a new question to learn more about this specific part but there we go, it just needed a lot of iterations with the matrix approach or a more gradual example by example approach.\n", "You can \"debug\" your neural network using Tensorleap. It's a neural network debugging platform which uses some explainability algorithms. It allows you to upload your model and dataset and get a lot of information about your trained model.\nMNIST is already in as a demo project.\nThey have a free trial for 14 days as I know.\n" ]

[ 1, 0 ]

[]

[]

[ "backpropagation", "machine_learning", "mnist", "neural_network", "python" ]

[ "In order to avoid the ns0 prefix the default namespace should be set before reading the XML data.\nET.register_namespace('', \"http://www.topografix.com/GPX/1/1\")\nET.register_namespace('', \"http://www.topografix.com/GPX/1/0\")\n\n", "You need to register all your namespaces before you parse xml file.\nFor example: If you have your input xml like this\nand Capabilities is the root of your Element tree.\n<Capabilities xmlns=\"http://www.opengis.net/wmts/1.0\"\n xmlns:ows=\"http://www.opengis.net/ows/1.1\"\n xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n xmlns:gml=\"http://www.opengis.net/gml\"\n xsi:schemaLocation=\"http://www.opengis.net/wmts/1.0 http://schemas.opengis.net/wmts/1.0/wmtsGetCapabilities_response.xsd\"\n version=\"1.0.0\">\n\nThen you have to register all the namespaces i.e attributes present with xmlns like this:\nET.register_namespace('', \"http://www.opengis.net/wmts/1.0\")\nET.register_namespace('ows', \"http://www.opengis.net/ows/1.1\")\nET.register_namespace('xlink', \"http://www.w3.org/1999/xlink\")\nET.register_namespace('xsi', \"http://www.w3.org/2001/XMLSchema-instance\")\nET.register_namespace('gml', \"http://www.opengis.net/gml\")\n\n", "It seems that you have to declare your namespace, meaning that you need to change the first line of your xml from:\n<ns0:trk>\n\nto something like:\n<ns0:trk xmlns:ns0=\"uri:\">\n\nOnce did that you will no longer get ParseError: for unbound prefix: ..., and:\nelem.tag = elem.tag[(len('{uri:}'):]\n\nwill remove the namespace.\n", "If you try to print the root, you will see something like this:\nhttp://www.host.domain/path/to/your/xml/namespace}RootTag' at 0x0000000000558DB8>\nSo, to avoid the ns0 prefix, you have to change the default namespace before parsing the XML data as below:\nET.register_namespace('', \"http://www.host.domain/path/to/your/xml/namespace\")\n\n", "Or you could regex it away:\ndef remove_xml_namespace(xml_str: str) -> str:\n xml_str = re.sub(r\"<([^:]+):(\\w+).+(?=xmlns)[^>]+>([\\s\\S]*)</(\\1):(\\2)>\", r\"\\3\", xml_str)\n # replace namespace elements from end tag\n xml_str = re.sub(r\"</[^:]*:\", r\"</\", xml_str)\n # replace namespace from start tags\n xml_str = re.sub(r\"<[^/][^:]*:([^/>]*)(/?)>\", r\"<\\1\\2>\", xml_str)\n return xml_str\n\n" ]

[ 89, 46, 1, 1, 0 ]

[]

[]

[ "elementtree", "python" ]

[ "You can use a merge before fillna:\nout = df1.fillna(df1[['Subid']].merge(df2, how='left'))\n\nOutput:\n Id Subid feature1 feature2 feature3 feature4 feature5\n0 A1 A 2.600000 55.000000 0.266667 22.000000 17.333333\n1 A2 A 6.300000 18.333333 0.500000 22.666667 17.333333\n2 A3 A 2.966667 18.333333 0.300000 46.000000 52.000000\n3 B1 B 1.650000 44.000000 0.000000 16.500000 26.500000\n4 B2 B 3.300000 69.000000 0.000000 33.000000 53.000000\n\n", "You can use fillna to fill up the np.nans values and merge the second dataframe with matching Subid's\nresult = df1.fillna(df1[['Subid']].merge(df2, on='Subid', how='left'))\n\n", "for f in [f for f in df1.columns if f.startswith('feature')]:\n df1[f]=df1[f].mask(pd.isnull, df1[['Subid']].merge(df2[['Subid', f]])[f])\n\nreturns:\n Id Subid feature1 feature2 feature3 feature4 feature5\n0 A1 A 2.600000 55.000000 0.266667 22.000000 17.333333\n1 A2 A 6.300000 18.333333 0.500000 22.666667 17.333333\n2 A3 A 2.966667 18.333333 0.300000 46.000000 52.000000\n3 B1 B 1.650000 44.000000 0.000000 16.500000 26.500000\n4 B2 B 3.300000 69.000000 0.000000 33.000000 53.000000\n\n" ]

[ 2, 1, 0 ]

[]

[]

[ "fillna", "group_by", "lambda", "pandas", "python" ]

[ "you can use something like this:\nstart = '2020-01-01'\nend = '2021-03-31'\nfirst_day = pd.date_range(start, end, freq='MS').astype(str).to_list() #get first day of month given range\nend_day = pd.date_range(start, end, freq='M').strftime(\"%Y-%m-%d 23:59:59\").astype(str).to_list() #get last day of month of given date\ndates = dict(zip(first_day, end_day)) #convert lists to dictionary\n#dates={'2020-01-01':'2020-01-31'}....\n\ntrain_purchases['date']=pd.to_datetime(train_purchases['date'])\nfor k,v in dates.items():\n mask = train_purchases.loc[(train_purchases['date'] > k) & (train_purchases['date'] <= v)].sort_values(by=[\"item_id\"]).item_id.value_counts()[:1]\n print(mask.index[0],\" id sold\", mask.iloc[0], \"times. \")\n'''\n8060 id sold 564 times. \n8060 id sold 421 times. \n8060 id sold 375 times. \n8060 id sold 610 times. \n8060 id sold 277 times. \n8060 id sold 280 times. \n8622 id sold 290 times. \n8060 id sold 374 times. \n8060 id sold 638 times. \n8060 id sold 563 times. \n8060 id sold 1580 times. \n8060 id sold 765 times. \n19882 id sold 717 times. \n19882 id sold 570 times. \n19882 id sold 690 times. \n\n'''\n\nNote\ntrain_purchases['date'] > '2020-01-01'\n\nIf you use it as above, the first day of the month is not included in the calculation. If you want the first day of the month, you should use it as below\ntrain_purchases['date'] >= '2020-01-01'\n\n" ]

[ 0 ]

[]

[]

[ "csv", "frequency", "numpy", "pandas", "python" ]

[ "n = int(input(\"Enter a integer: \"))\nres = \"\"\ns = 0\nx,y = [n,0] if n < 0 else [1, n+1]\nfor i in range(x, y, 1):\n res += f\"({i}) +\"\n s += i\nres = res[:-2] + \"=\" + str(s)\nprint()\nprint(res)\n\n", "This code will handle both positive and negative num.\nwhile True:\n num = int(input(\"enter an integer: \"))\n if num == 0:\n break\n # Create the correct range based on num being positive or negative\n nums = range(num, 0) if num < 0 else range(1, num+1)\n # Create the string \"a + b + c...\" or \"(-a) + (-b) + (-c)...\"\n eq = ' + '.join(f\"({n})\" if n < 0 else str(n) for n in nums)\n # Print results\n print(f\"{eq} = {sum(nums)}\")\n\nExample run:\nenter an integer: 5\n1 + 2 + 3 + 4 + 5 = 15\nenter an integer: -5\n(-5) + (-4) + (-3) + (-2) + (-1) = -15\nenter an integer: 0\n\n" ]

[ 1, 1 ]

[]

[]

[ "python" ]

[ "It is fairly straight forward. Use email library (documentation). AFAIK it is a standard built in library, so no additional installation required. Your could would look like this:\nimport smtplib, ssl\nfrom email.mime.text import MIMEText\n\nemail = \"fromemailhere\"\npassword = \"passwordhere\"\nreceiver = \"toemailhere\"\n\nmessage = \"\"\"\nHello World\n\"\"\"\nmessage = MIMEText(message, \"plain\")\nmessage[\"Subject\"] = \"Hello World\"\nmessage[\"From\"] = email\n\nport = 465\nsslcontext = ssl.create_default_context()\nconnection = smtplib.SMTP_SSL(\n \"smtp.gmail.com\",\n port,\n context=sslcontext\n)\n\nconnection.login(email, password)\nconnection.sendmail(email, receiver, message.as_string())\n\nprint(\"sent\")\n\n" ]

[ 1 ]

[]

[]

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

[ "df['colname'] = df['colname'].astype(int) works when changing from float values to int atleast.\n", "I have tried following:\ndf['column']=df.column.astype('int64')\n\nand it worked for me.\n", "You can use reindex by sorted column by sort_values, cast to int by astype:\ndf = pd.DataFrame({'A':[1,2,3],\n 'B':[4,5,6],\n 'colname':['7','3','9'],\n 'D':[1,3,5],\n 'E':[5,3,6],\n 'F':[7,4,3]})\n\nprint (df)\n A B D E F colname\n0 1 4 1 5 7 7\n1 2 5 3 3 4 3\n2 3 6 5 6 3 9\n\nprint (df.colname.astype(int).sort_values())\n1 3\n0 7\n2 9\nName: colname, dtype: int32\n\nprint (df.reindex(df.colname.astype(int).sort_values().index))\n A B D E F colname\n1 2 5 3 3 4 3\n0 1 4 1 5 7 7\n2 3 6 5 6 3 9\n\nprint (df.reindex(df.colname.astype(int).sort_values().index).reset_index(drop=True))\n A B D E F colname\n0 2 5 3 3 4 3\n1 1 4 1 5 7 7\n2 3 6 5 6 3 9\n\nIf first solution does not works because None or bad data use to_numeric:\ndf = pd.DataFrame({'A':[1,2,3],\n 'B':[4,5,6],\n 'colname':['7','3','None'],\n 'D':[1,3,5],\n 'E':[5,3,6],\n 'F':[7,4,3]})\n\nprint (df)\n A B D E F colname\n0 1 4 1 5 7 7\n1 2 5 3 3 4 3\n2 3 6 5 6 3 None\n\nprint (pd.to_numeric(df.colname, errors='coerce').sort_values())\n1 3.0\n0 7.0\n2 NaN\nName: colname, dtype: float64\n\n", "To simply change one column, here is what you can do:\ndf.column_name.apply(int)\nyou can replace int with the desired datatype you want e.g (np.int64), str, category.\nFor multiple datatype changes, I would recommend the following:\ndf = pd.read_csv(data, dtype={'Col_A': str,'Col_B':int64})\n", "The documentation provides all the information needed. Let's take the toy dataframe from the docs:\nd = {'col1': [1, 2], 'col2': [3, 4]}\n\nIf we want to cast col1 to int32 we can use, for instance, a dictionary:\ndf.astype({'col1': 'int32'})\n\nIn addition, the approach above allows to avoid the SettingWithCopyWarning.\n" ]

[ 31, 10, 6, 2, 0 ]

[]

[]

[ "pandas", "python" ]

[ "One can use this in Python:\nopus_path = 'something.opus'\nwav_path = 'something.wav'\nos.system(f'ffmpeg -i \"{opus_path}\" -vn \"{wav_path}\"')\n\nThis can obviously be applied in a loop if you want:\nfor opus_path,wav_path in zip(opus_paths,wav_paths):\n os.system(f'ffmpeg -i \"{opus_path}\" -vn \"{wav_path}\"')\n\n" ]

[ 1 ]

[]

[]

[ "audio", "opus", "python", "tensorflow", "wav" ]

[ "I found a solution by trial and error. I had to add in setup.py in options\n\"replace_paths\": [(\"*\", \"\")]\n\nThe solution was shown in enter link description here\nnow I have the path to error starting by the folder of the compiled source code\nBefore: user/Desktop/projectFolder/..../something.py\nNow: projectFolder/..../something.py\nWhich what I wanted exactly.\n" ]

[ 0 ]

[]

[]

[ "cx_freeze", "exe", "python" ]

[ "If there is no specific reason to define a method not inside of the class definition it would be preferred to keep everything together. So it is definitely better to define your method in your class to provide better readability of your code. Especially if other persons will have to work with your code it could be harder to find the definition of it (even if it's just to understand what is happening).\nAnother thing I noticed in you code is the line:\nif True: # for scoping\nYou should keep in mind, that an if-statement does not come with its own scope, these belong to methods, classes and modules. So your if-statement does not provide any functionality.\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x" ]

[ "\nFirstly i dont know if this causes any problems but avoid importing\ninside your code, to my best of knowledge importing always goes in\nthe beginning\nSecondly i think your error is caused on the if statement. There you\ntry to search for the 'tax_code' as it is a Dataframe attribute. Try\nusing it inside [''] as you would normally do\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "I guess you didn't define the screen size for the driver while in headless mode the default screen size is 800,600.\nSo, to make your Selenium code working try setting the screen size to maximal or 1920,1080. As following:\nfrom selenium.webdriver.chrome.options import Options\n\noptions = Options()\noptions.add_argument(\"--headless\")\noptions.add_argument(\"window-size=1920,1080\")\n\nOr\noptions = Options()\noptions.add_argument(\"start-maximized\")\n\n" ]

[ 0 ]

[]

[]

[ "heroku", "python", "screen_size", "selenium", "webdriverwait" ]

[ "Python has a time library you can use. Something like this maybe.\nimport time\n\ncoins = 10\n\n\ndef user_task():\n start_time = time.time()\n # user completes task here\n end_time = time.time()\n time_taken = end_time - start_time\n\n return time_taken\n \n\ntotal_time_taken = user_task()\n\nif total_time_taken >= 50:\n coins = coins - 1\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "There are some closing brackets missing.\nwhile walls:\n rand_wall = walls[int(random.random()*len(walls))-1]\n if rand_wall[1] != 0 and rand_wall[1] != mazeheight-1:\n if maze[rand_wall[0][rand_wall[1]-1] == \"u\" and maze[rand_wall[0][rand_wall[1]+1]]] == \"c\": # added two closing brackets before the == sign.\n print(\"no\")\n elif maze[rand_wall[0][rand_wall[1]-1] == \"c\" and maze[rand_wall[0][rand_wall[1]+1]]] == \"u\": # same as before\n print(\"no\")\n\nIt should run now, but I don't know the expected behavior. Next time try to post the full stack trace.\n", "I would clean this up to avoid the chance of syntax error. I'm assuming, for example, that\nmaze[rand_wall[0][rand_wall[1]-1]\n\nshould be\nmaze[rand_wall[0]][rand_wall[1]-1] \n ^\n add missing ]\n\n\nwhile walls:\n x, y = random.choice(walls)\n if y != 0 and y != mazeheight-1:\n row = maze[x]\n if row[y-1] == \"u\" and row[y+1] == \"c\":\n print(\"no\")\n elif row[y-1] == \"c\" and row[y+1] == \"u\":\n print(\"no\")\n\n" ]

[ 0, 0 ]

[]

[]

[ "if_statement", "python" ]

[ "\ntlsv1 alert unknown ca\n\nThe server is sending a TLS alert back since it cannot validate your client certificate - the certificate authority (ca) which signed the certificate is unknown to the server. You either need to disable client certificate validation in your server or (better) make the server trust your client certificate.\n\nIt is a self signed certificate so I am not sure what CA is it expecting.\n\nA self-signed certificate is signed by itself, i.e. the CA is the certificate itself.\n", "It looks like the server isn't able to validate your client certificate. If you're just using a pair of self-signed certificates for the client and server, then the server needs to also use the client's certificate as its CA, since it will attempt to validate it was signed by the CA - which in this case is the client.\nI recently wrote a blog on deploying mTLS with self-signed certificates which might help you as it contains more details, specifically with how to configure the client and server. Check it out here: https://otterize.com/blog/so-you-want-to-deploy-mtls\n" ]

[ 0, 0 ]

[]

[]

[ "mtls", "python", "python_3.9", "python_requests", "ssl" ]

[ "Use the fit_columns_on_grid_load param, this is False by default.\nAgGrid(df, fit_columns_on_grid_load=True)\n\n", "Combined with @ferdy's answer. If I use:\ngb = GridOptionsBuilder.from_dataframe(df, min_column_width=30)\nAgGrid(df, gridOptions=gb.build(), fit_columns_on_grid_load=True)\n\nThen it works securely.\n", "Thanks for this combination! I had to add the fit_columns_on_grid in a separate configuration line though, so:\ngb = GridOptionsBuilder.from_dataframe(df)\ngb.configure_default_column(min_column_width=150)\ninteractive_table = AgGrid(df, gridOptions=gb.build(), fit_columns_on_grid_load=True)\n\n" ]

[ 2, 1, 1 ]

[]

[]

[ "ag_grid", "python", "streamlit" ]

[ "X[:, (X==0).any(axis=0)]=0\n\nExplanation\n(X==0) is matrix of booleans of the same shape than X, saying if each element of X is 0\n(X==0).any() says is True iff at least of those booleans is True\n(X==0).any(axis=0) does that only along axis 0, so it gives an array of n booleans, one per columns, each of them True iff one boolean of X==0 is True in that column.\nX[:, (X==0).any(axis=0)] are all rows of columns that are True in the previous array, that is that contain a 0.\nSo X[:, (X==0).any(axis=0)]=0 puts everything to 0 in those columns\n" ]

[ 1 ]

[]

[]

[ "numpy", "python" ]

[ "because of the nature of the equation, it will give the value of the total pixels from the top of the chart as your min max and start end also start from the top. If you want the remaining pixels, You will have to subtract the current answer from the highest pixel possible which is end_y. Your equation will become\ny = end_y - ((percentage_within_range * (end_y - start_y)) + start_y)\n\nas far as my understanding of the question, I came upon this answer.\n" ]

[ 2 ]

[]

[]

[ "charts", "math", "python" ]

[ "Apart from one hot encoding (which might create way too many columns in this case), mean target encoding does exactly what you need (encodes the category with its mean target value). You should be vary about the target leakage in case of rare categories though. sklearn-compatible category_encoders library provides several robust implementations, such as LeaveOneOutEncoder()\n" ]

[ 1 ]

[]

[]

[ "linear_regression", "one_hot_encoding", "python", "scikit_learn" ]

[ "The problem is addressed in this thread- https://github.com/tensorflow/tensorflow/issues/56231\nThe solution that worked for me is by creating a symlink in lib/pythonx.x/site-packages/tensorflow\nln -s ../keras/api/_v2/keras/ keras\n\n" ]

[ 0 ]

[]

[]

[ "macos", "python", "python_3.x", "tensorflow", "tensorflow2.0" ]

[ "df['TAB'] = df.apply(lambda x: df2[df2['MeetingId'] == x['MeetingId']]['TAB'].values[0], axis=1)\n\nOR\ndf.loc[df['MeetingId'].isin(df2['MeetingId']), 'TAB'] = df2['TAB']\n\nExample:\n> df\n\n MeetingId TAB\n0 123 True\n1 124 False\n\n> df2\n\n MeetingId TAB\n0 123 False\n1 124 True\n2 125 False\n\nOutput after running code above:\n> df\n\n MeetingId TAB\n0 123 False\n1 124 True\n\n" ]

[ 2 ]

[]

[]

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

[]

[]

[ "There are some mistakes in it. Like the 354th day is christmas as when I type in yes, it shows 24 days left when there is more 26 days. And if someone types no you should add-on something to that also like an elif statement. And add an else statement for the input if they type anyhting else then the answer should be, \"Please enter Yes or No\"\n" ]

[ -1 ]

[ "python", "time" ]

[ "You can do this very easily just by generating markdown slide header dynamically in each iteration of loop using display(Markdown(\"Slide header\")) along with chunk option output: asis.\n---\ntitle: \"For Loops in Quarto\"\nformat: \n revealjs:\n theme: default\ncode-fold: true\nexecute: \n echo: false\n---\n\n```{python}\n#| include: false\n\nfrom IPython.display import display, Markdown\nfrom sklearn import datasets\nimport pandas as pd\nimport plotly.express as px\n```\n\n```{python}\n#| include: false\n# Get data\niris = datasets.load_iris()\ndf = pd.DataFrame(iris['data'], columns=iris['feature_names'])\ndf['target'] = iris['target']\n```\n\n```{python}\n#| output: asis\n\nfor target in df['target'].unique():\n display(Markdown(\"## Scatter plot per species\"))\n fig = px.scatter(df, x='petal length (cm)', y='sepal length (cm)')\n fig.show()\n```\n\n\n\n\n" ]

[ 0 ]

[]

[]

[ "plotly", "python", "quarto" ]

[ "If you're loading your data from somewhere external, is may not do what you expect, since it compares object identity, not contents. (You should never use is unless you know exactly what you're doing).\nThis seems to work fine:\nfor case in [\n ['True', 'True', 'True'],\n ['False', 'False', 'False'],\n ['False', 'False', 'False'],\n ['False', 'False', 'False'],\n ['True', 'True', 'True'],\n ['True', 'True', 'False'],\n ['True', 'True', 'True'],\n ['True', 'True', 'False'],\n ['True', 'True', 'True'],\n]:\n if all(x == \"False\" for x in case[-3:]):\n result = \"SHUT_DOWN\"\n elif case[-1] == \"False\":\n result = \"WARNING\"\n else:\n result = \"OK\"\n print(case, \"==>\", result)\n\n['True', 'True', 'True'] ==> OK\n['False', 'False', 'False'] ==> SHUT_DOWN\n['False', 'False', 'False'] ==> SHUT_DOWN\n['False', 'False', 'False'] ==> SHUT_DOWN\n['True', 'True', 'True'] ==> OK\n['True', 'True', 'False'] ==> WARNING\n['True', 'True', 'True'] ==> OK\n['True', 'True', 'False'] ==> WARNING\n['True', 'True', 'True'] ==> OK\n\n" ]

[ 2 ]

[]

[]

[ "python" ]

[ "You can give print an additional parameter: sep, like this:\nprint(\"This house is $\" + str(current_price), '.', \"The change is $\" +\n str(current_price - last_months_price) + \" since last month.\", sep='')\n\nbecause the default is an empty space after the comma.\n", "Maybe try f-string injection\nprint(f\"This house is ${current_price}. The change is ${current_price - last_months_price} since last month.\")\n\n\nf-string (formatted strings) provide a way to embed expressions inside string literals, using a minimal syntax. It's a simplified way to concatenate strings with out having to explicitly call str to format data types other than strings.\nAs @Andreas noted below, you could also pass sep='' to print, but that requires you to concatenate other strings with the spaces properly formatted.\n", "print(\"This house is $\" + str(current_price), '.',' ' \"The change is $\" +\nstr(current_price - last_months_price) + \" since last month.\", sep='')\nprint(\"The estimated monthly mortgage is ${:.2f}\"'.'.format((current_price * 0.051) / 12))\n" ]

[ 2, 1, 0 ]

[]

[]

[ "python", "python_3.6", "removing_whitespace" ]

[ "Because you're looking 2 back to fill the empty spots, we skip the first 2 indeces as there's nothing before theme. Here I define a func that does this:\ndef filler(l: list):\n for i in range(2, len(l)):\n if l[i] == '':\n l[i] = l[i-2]\n return l\n\nprint(filler(['Elemnt-1' , 'Elemnt-2' , 'Elemnt-3' , '' , '' , 'Elemnt-6' , 'Elemnt-7']))\nOut:\n['Elemnt-1', 'Elemnt-2', 'Elemnt-3', 'Elemnt-2', 'Elemnt-3', 'Elemnt-6', 'Elemnt-7']\n\n", "If the missing values are consecutive ones as you showed in the example you can use this.\ndef fillList(list):\n emptyCount = list.count('')\n for item in list:\n if item == '':\n list[list.index(item)] = list[list.index(item) - emptyCount]\n return list\n\n", "Using a list comprehension to replace empty values with value of this list two positions before. Repeats until no empty values left.\nEDIT : after comments, it appears that it is not always 2 position before, but n blank elements should be filled with n elements before. Answer therefore is still missing something. Replacing lis[index-2] by lis[index-lis.count('')] would not work because it is possible to have multiple set of empty spaces\nfiller = ['']*len(lis)\nwhile '' in lis :\n filler = [lis[index-2] if value==\"\" else value for index, value in enumerate(lis)]\n print(filler)\n lis=filler\n\nprint(lis)\n\n", "You can determine the blank ranges first, then fill them with the previous items.\nlst = ['Elment-1' , 'Elment-2' , 'Elment-3' , '' , '' , 'Elment-6' , 'Elment-7', '']\nlst_ext = ['p'] + lst + ['p']\n# boundary of all blank ranges\nblank_bound = [idx for idx, (a, b) in enumerate(zip(lst_ext, lst_ext[1:])) \\\n if (a == '' or b == '') and a != b] # [3, 5, 7, 8]\n# fill each blank range\nfor l, r in zip(blank_bound[::2], blank_bound[1::2]):\n assert 2*l-r >= 0, \"no enough items before the blank item\"\n lst[l:r] = lst[2*l-r:l]\n\n" ]

[ 1, 1, 1, 1 ]

[]

[]

[ "list", "python" ]

[ "To achieve this I successfully used px.line_mapbox() function.\n" ]

[ 0 ]

[]

[]

[ "plotly_dash", "plotly_express", "python" ]

[ "my-app/ \n├─ main.py \n├─ functions.py \n├─ models/ |\n ├─ model1/ │ \n ├─ utils/ │ \n ├─ file2.py | \n ├─ file1.py |\n\nBased on this information, it looks like you can use import utils.file2 because they are in the same directory.\n" ]

[ 0 ]

[]

[]

[ "import", "init", "package", "python" ]

[ "To merge DataFrames side by side, you should use pd.concat.\nframes = []\n\nfor file in os.listdir(os.getcwd()):\n if file.endswith('.txt'):\n files = pd.read_csv(file, sep='\\t', skiprows=[1])\n frames.append(files)\n\n# axis = 0 has the same behavior of your original approach\nmaster_df = pd.concat(frames, axis = 1)\n\n" ]

[ 1 ]

[]

[]

[ "csv", "merge", "pandas", "python" ]

[]

[]

[ "First remove the spaces from your headers (for clarity), then import as CSV into Pandas:\nimport pandas as pd\ndf = pd.read_csv('file.csv')\n\nIncreasing Pcode is simple:\nf['Pcode'] = df['Pcode']+1\n\nBut your timestamps have an issue. Normally you could treat them as timestamps and add an hour, but your timestamps somehow have 51:20:12.555 , which is not a valid timestamp in any format. Is this indeed correct? If so, you would need to do some manual formatting.\nThis is a fully manual method:\ndf['test'] = df['timestamp'].apply(lambda x: x.split(':'))\ndf['test2'] = df['test'].apply(lambda x: [str(int(x[0])+1), x[1], x[2]])\ndf['timestamp2'] = df['test2'].apply(lambda x: ':'.join(x))\n\n" ]

[ -1 ]

[ "python", "python_2.7" ]

[ "Yes, you can use the \"re\" module for this.\nimport re\n\nre.findall('\\*{3}(.+?)\\*{3}', string)\n\nHere, we tell it to match substrings that consist of three asterisks, any number of any characters, then three more asterisks. Then, we use parenthesis to mark the inside characters as our \"capturing group,\" so re.findall returns only those.\nDo note that the problem is not particularly well-defined. For instance, if you see ***word***word***word***, it's unclear whether that should be interpreted as. In general, getting more advanced behavior with these types of problems can be difficult or impossible.\n" ]

[ 1 ]

[]

[]

[ "python", "python_3.x" ]

[ "This was an issue with scipy package. I downgraded the package scipy from 1.7.3 to 1.7.1 and it is working. However, the downgraded version of scipy has an issue of maximum recursion depth exceeded while getting str of an object. The second issue can be resolved by expanding the limit.\n" ]

[ 0 ]

[]

[]

[ "jupyter_notebook", "python" ]

[ "The | character has a meaning, it means or in python's terms, so you need to escape that meaning in order to use it in your stop words list. You escape that with a backslash \\ (see more here)\nHaving said that you can do:\nstop_w = [\"in\", \"&\", \"the\", \"\\|\", \"and\", \"is\", \"of\", \"a\", \"an\", \"as\", \"for\", \"was\"]\ndf.loc[~df['words'].str.contains('|'.join(stop_w))]\n\nprints:\n words frequency\n1 green energy 9\n4 electricity 5\n\n", "You can create sub_df like this:\nsub_df = df[df.words.str not in stop_w]\n\nOr get ids of rows i want to remove:\nidx = df[df.words.str in stop_w].index\ndf.drop(idx)\n\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html\n" ]

[ 1, 0 ]

[]

[]

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

[ "Use:\nlabelname.config(text = text)\n\n", "You are missing widget pack(). Replace this:\nlabel1.config(text=inputValue)\n\nto:\nlabel1.pack()\n\nand add this label1.config(text=inputValue) in retrieve_input()function.\nResult before enter input:\n\nResult after:\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "It appears that python on Windows is a PITA. So, I had your problem as well, I followed the following steps:\n\nDownload Python from python.org. (you probably already have\nthat) \nDownload PyWin32 from SourceForge.\nDownload SetupTools from python.org.\nOn your desktop or in the Start menu, right-click on My Computer then click Properties.\nIn the Advanced tab, click Environment Variables.\nIn the System Variables at the bottom, locate the PATH variable and double-click on it.\nAdd C:\\Python27\\ and C:\\Python27\\scripts at the end of the Variable Value box (assuming you installed python in the location C:\\Python27).\nClick OK in all the dialogs.\n\nIt still did not work for me, however, I was puzzled by <python_install>\\scripts and found pywin32_postinstall.py in there, running that did the trick!\n\nExecute \\scripts\\pywin32_postinstall.py\nI kept getting the following trace:\nDebugging extensions (axdebug) module does not exist - debugging is disabled..\n\nI traced it to Lib\\site-packages\\win32comext\\axscript\\client\\framework.py and commented out the trace call that printed that message ... all good.\nC:\\Users\\jdoe>type d:\\python2vbs.wsf\n<?XML Version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<?job error=\"true\" debug=\"false\"?>\n<package>\n <job>\n\n <script language=\"VBScript\">\n <![CDATA[\npublic sub vbsOutput(strText)\n wscript.echo strText & \" (from vbsOutput)\"\nend sub\n ]]>\n </script>\n\n <script language=\"Python\">\n <![CDATA[\nimport sys\nglobals.vbsOutput('python testing')\n ]]>\n </script>\n\n </job>\n</package>\n\nC:\\Users\\jdoe>cscript d:\\python2vbs.wsf\nMicrosoft (R) Windows Script Host Version 5.812\nCopyright (C) Microsoft Corporation. All rights reserved.\n\npython testing (from vbsOutput)\n\n", "The original answer posted by @thecarpy has good information. There is one more thing to add. If you have multiple versions of Python installed, you need to make sure that the one which matches the version you have registered as the scripting engine is the first python.exe found in your search path. So putting it at the end of the path (as recommended in the previous answer) might not always work. I had a little more hair before I figured this out. \n" ]

[ 1, 0 ]

[]

[]

[ "python", "pywin32", "wsh" ]

[ "If pricing = hour_detail then obj[\"pricing\"] must be a list as it can have multiple value.\nYou have to create a new list in your loop for time_instance in timing: and append every time time_instance[\"day_name\"] == pricing_instance[\"day_name\"].\nExemple:\ndata = []\n\nfor time_instance in timing:\n current_hour_detail = []\n for line in hours_detail:\n if line[\"day_name\"] == time_instance[\"day_name\"]:\n current_hour_detail.append(line)\n \n data.append({\n \"timing\": time_instance,\n \"pricing\": current_hour_detail\n })\n\nOr smaller way\ndata = []\n\nfor time_instance in timing:\n current_hour_detail = [line for line in hours_detail if line[\"day_name\"] == time_instance[\"day_name\"]]\n data.append({\n \"timing\": time_instance,\n \"pricing\": current_hour_detail\n })\n\nWhatch out tho, this solution is in O(n*m) time complexity, by first transforming your list hours_detail by a hashtable {day_name: [peak_hour1, peak_hour2]} you can reduce it in O(n+m)\n", "This should answer you question:\n...\ndata = []\nfor time_instance in timing:\n obj = {\"timing\": time_instance}\n # use a list to store all your pricing\n obj[\"hour_detail\"] = []\n for pricing_instance in pricing:\n if time_instance[\"day_name\"] == pricing_instance[\"day_name\"]:\n obj[\"hour_detail\"].append(pricing_instance)\n # add 'obj' after the loop\n data.append(obj)\nprint(data)\n\n" ]

[ 1, 0 ]

[]

[]

[ "python", "python_3.x" ]

[ "It means that Celery can't find the implementation of the task my_app.tasks.my_task when it was called. Some possible solutions you may want to look at:\nPossible Solution 1:\nYou probably haven't configured correctly either:\n\nCelery imports e.g. celery_app.conf.update(imports=['my_app.tasks']) or celery_app.conf.imports = ['my_app.tasks']\nOr Celery include (example) e.g. celery_app = Celery(..., include=['my_app.tasks'])\n\nNote: If in a Django application, this can be skipped if already using celery_app.autodiscover_tasks() since the tasks are automatically discovered in the location ./<app_name>/tasks.py\nPossible Solution 2:\nIf you are only importing my_app e.g. celery_app.conf.update(imports=['my_app']) then I assume you have a file my_app/__init__.py Make sure that inside that file, it imports the task my_app.tasks.my_task along with my_app.tasks.other_task so that the celery app knows that such task exists.\n# Contents of my_app/__init__.py\nfrom my_app.tasks import (\n my_task,\n other_task,\n)\n\nPossible Solution 3:\nIn case the my_task was just newly added (whereas other_task was already an old existing task), you might not have restarted the celery worker yet to see the new task. Try restarting the worker.\n", "Another solution can be adding a name param in the shared_task decorator, i.e.,\n@shared_task(queue='celery', name='other_task')\ndef other_task():\n \"\"\" WORKS \"\"\"\n ...\n\n@shared_task(queue='celery', name='my_task')\ndef my_task():\n \"\"\" DOES NOT WORK \"\"\"\n ...\n\nI see this in a full demo project https://github.com/melikesofta/django-dynamic-periodic-tasks, and the related code can be find here.\n", "I could solve the error by just restarting Celery. I use Celery 5.1.2:\ncelery -A core worker --pool=solo -l info\n\n" ]

[ 2, 0, 0 ]

[]

[]

[ "celery", "celery_task", "django", "python", "python_3.x" ]

[ "As mentioned in the comments, you can currently use the streamlit-aggrid component to do this (guide on how to do this here). We're in the process of revamping st.dataframe, and the ability to edit the values is part of that (should be released in the next few months).\n" ]

[ 2 ]

[]

[]

[ "pandas", "python", "streamlit" ]

[ "Better solution 1D:\nWe can use a convolution:\naout = ((np.convolve(a,[1,1,1],mode='same')>1)&(a>0)).astype(a.dtype)\n# aout = array([0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0])\n\nBetter solution 2D:\nfrom scipy.signal import convolve2d\n\na = np.array([[1, 1, 0, 0, 0, 0, 1, 0, 0, 1],\n [1, 0, 1, 0, 1, 0, 0, 0, 1, 1]])\n\naout = ((convolve2d(a,np.ones((1,3)),mode='same')>1)&(a>0)).astype(a.dtype)\n\n#aout = array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n# [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]])\n\nWhy a changed ?\nAnd to understand why a is updated in your process, you need to understand the difference between a copy and a view.\nFrom the documentation:\n\nView\nIt is possible to access the array differently by just changing\ncertain metadata like stride and dtype without changing the data\nbuffer. This creates a new way of looking at the data and these new\narrays are called views. The data buffer remains the same, so any\nchanges made to a view reflects in the original copy. A view can be\nforced through the ndarray.view method.\nCopy\nWhen a new array is created by duplicating the data buffer as\nwell as the metadata, it is called a copy. Changes made to the copy do\nnot reflect on the original array. Making a copy is slower and\nmemory-consuming but sometimes necessary. A copy can be forced by\nusing ndarray.copy.\n\nOr np.split() return a view not a copy of a, so aout is still pointing to the same data buffer as a, if you change aout you change a.\nBenchmarking\nimport numpy as np\n\na = np.random.randint(0,2,(1000000,))\n\ndef continuous_split(a):\n idx = np.where(np.roll(a,1)!=a)[0][1:]\n aout = np.split(a,idx)\n sumseg = [aa.sum() for aa in aout]\n idx2 = np.where( (np.array(sumseg)>0) & (np.array(sumseg)<2) )\n [aout[ai].fill(0) for ai in idx2[0]]\n return aout\n \ndef continuous_conv(a):\n return ((np.convolve(a,[1,1,1],mode='same')>1)&(a>0)).astype(a.dtype)\n\n%timeit continuous_split(a)\n%timeit continuous_conv(a)\n\nnp.split() solution:\n668 ms ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n\nnp.convolve() solution:\n7.63 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n\n" ]

[ 3 ]

[]

[]

[ "numpy", "python" ]

[ "You could do something like this:\n#get a list of files and directories in the current directory\nfile_list = os.listdir('.')\n\n#produce a list of candidate files (files ending with `md5`). Perhaps there is a more elegant way of doing this, but it works. \ncandidates = [x for x in file_list if x.split('.')[-1] == 'md5']\n\n#iterate the file_list and check to see if there is a match in candidates (after removing the `.md5` portion of the name). \nprint([file for file in file_list if file in ['.'.join(candidate.split('.')[:-1]) for candidate in candidates]] + candidates)\n\nThere are a million ways to go about doing this so feel free to tinker.\n" ]

[ 0 ]

[]

[]

[ "python" ]