Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "Are you sure you want to collect your data and then have to process them using python ?\nWith df = spark.sql(query), depending on the answer :\nYES (python solution)\nout = df.collect()\n\nlist(map(lambda x: datetime.datetime.strftime(x.device_date, \"%Y-%m-%d\"), out))\n\n['2019-09-25', '2019-09-17', '2020-01-08']\n\n# OR\n\nlist(map(str, (x.device_date for x in out)))\n['2019-09-25', '2019-09-17', '2020-01-08']\n\nNO (Spark solution)\nfrom pyspark.sql import functions as F\n\ndf.select(F.date_format(\"device_date\", \"yyyy-MM-dd\").alias(\"device_date\")).collect()\n \n[Row(device_date='2019-09-25'),\n Row(device_date='2019-09-17'),\n Row(device_date='2020-01-08')]\n\n\nThe spark version can also be done directly in SQL :\nquery = \"select date_format(device_date, 'yyyy-MM-dd') as date_format from device\"\n\nspark.sql(query).collect()\n\n[Row(date_format='2019-09-25'),\n Row(date_format='2019-09-17'),\n Row(date_format='2020-01-08')]\n\n", "I suggest you use the date_format function beforehand.\nHere is the documentation, but basically:\n>>> from pyspark.sql.functions import date_format\n>>> df.select(date_format('device_date', 'YYYY-mm-dd').alias('date')).collect()\n[Row(date='2015-04-08')]\n\nAlso please be careful, it seems like your column name: \"device_date \" has a space at the end. That could be making your life harder.\n" ]

[ 2, 1 ]

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[]

[ "apache_spark", "pyspark", "python" ]

[ "If I understand your question correctly, one possible solution might be like this.\nThe tree nodes store their parents in order to construct the messy ├───\nand └─── before directory/file names.\nOutput:\nx1\n├── x2\n│ └── x3\n├── x4\n└── x5\n └── x6\n\nCode:\nclass TreeNode:\n def __init__(self, name, parent):\n self.parent = parent\n self.name = name\n self.children = []\n\n def add_child(self, node):\n self.children.append(node)\n return node\n\n def print(self, is_root):\n pre_0 = \" \"\n pre_1 = \"│ \"\n pre_2 = \"├── \"\n pre_3 = \"└── \"\n\n tree = self\n prefix = pre_2 if tree.parent and id(tree) != id(tree.parent.children[-1]) else pre_3\n\n while tree.parent and tree.parent.parent:\n if tree.parent.parent and id(tree.parent) != id(tree.parent.parent.children[-1]):\n prefix = pre_1 + prefix\n else:\n prefix = pre_0 + prefix\n\n tree = tree.parent\n\n if is_root:\n print(self.name)\n else:\n print(prefix + self.name)\n\n for child in self.children:\n child.print(False)\n\ndef find_and_insert(parent, edges):\n # Terminate if there is no edge\n if not edges:\n return\n \n # Find a child with the name edges[0] in the current node\n match = [tree for tree in parent.children if tree.name == edges[0]]\n \n # If there is already a node with the name edges[0] in the children, set \"pointer\" tree to this node. If there is no such node, add a node in the current tree node then set \"pointer\" tree to it\n tree = match[0] if match else parent.add_child(TreeNode(edges[0], parent))\n \n # Recursively process the following edges[1:]\n find_and_insert(tree, edges[1:])\n\npaths = [\"x1/x2\", \"x1/x2/x3\", \"x1/x4\", \"x1/x5/x6\"]\n\nroot = TreeNode(\"x1\", None)\n\nfor path in paths:\n find_and_insert(root, path.split(\"/\")[1:])\n\nroot.print(True)\n\n", "bigtree is a Python tree implementation that integrates with Python lists, dictionaries, and pandas DataFrame.\nFor this scenario, there are three parts to this,\n\nDefine a new Node class that does the inheritance of data from parent nodes\nConstruct tree using the path list and Node class we defined earlier (1 line of code!)\nAdd in the data dictionary that maps the node name to data (1 line of code!)\n\nfrom bigtree import Node, list_to_tree, print_tree, add_dict_to_tree_by_name\n\n# Define new Node class\nclass NodeInherit(Node):\n @property\n def data(self):\n if self.is_root:\n return self._data\n return self.parent.data + self._data\n\n# Construct tree using the path list\npaths = [\"x1/x2\", \"x1/x2/x3\", \"x1/x4\", \"x1/x5/x6\"]\nroot = list_to_tree(paths, node_type=NodeInherit)\n\n# Add in the data dictionary\nd = {\"x1\": [[1,2], [3,4]], \"x2\": [[5,6], [7,8]], \"x3\": [[9]], \"x4\": [[5,6]], \"x5\": [[5]], \"x6\": [[6]]}\nd2 = {k: {\"_data\": v} for k, v in d.items()} # minor input format change\nroot = add_dict_to_tree_by_name(root, d2)\n\n# Check tree structure with data\nprint_tree(root, attr_list=[\"data\"])\n\nThis results in output,\nx1 [data=[[1, 2], [3, 4]]]\n├── x2 [data=[[1, 2], [3, 4], [5, 6], [7, 8]]]\n│ └── x3 [data=[[1, 2], [3, 4], [5, 6], [7, 8], [9]]]\n├── x4 [data=[[1, 2], [3, 4], [5, 6]]]\n└── x5 [data=[[1, 2], [3, 4], [5]]]\n └── x6 [data=[[1, 2], [3, 4], [5], [6]]]\n\nYou can also export the data out to dictionary or pandas DataFrame format besides printing it out to console.\nSource/Disclaimer: I'm the creator of bigtree ;)\n" ]

[ 1, 0 ]

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[]

[ "python", "tree" ]

[ "We can use df.index.repeat, then set the qty to 1 for all rows.\ndf = df.loc[df.index.repeat(df['qty'])].reset_index(drop=True)\ndf['qty'] = 1\n\nOutput:\n Name qty Price\n0 Apple 1 3.5\n1 Apple 1 3.5\n2 Apple 1 3.5\n3 Avocado 1 1.5\n4 Avocado 1 1.5\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "division", "python" ]

[ "I do see you are defining db_cursor = self.db_connection inside the readFromDatabase() class method, so you are attempting to use a connection object as a cursor object (you are getting the error because of it when running db_cursor.execute(...)).\nBased on your code the right definition would be:\n# reference: https://dev.mysql.com/doc/connector-python/en/connector-python-api-mysqlconnection-cursor.html\ndb_cursor = self.db_connection.cursor()\n\nEventually, you need to close the cursor, I suggest having a look at the mysql-connector-python documentation to learn how you can open/close a cursor, create different cursor types, and learn more about the API in general.\n" ]

[ 2 ]

[]

[]

[ "attributeerror", "mysql", "mysql_connector_python", "python" ]

[ "I suppose positive.content.lower() is a sentence or a paragraph.\nSo why don't we split it for every space with:\nword_list = (positive.content.lower() + \" \").split(\" \")\nif \"ed\" in word_list and \"positivity\" in word_list:\n await positive.channel.send(random.choice(positivity))\n\nThe added space at the end there is to ensure we have at least one space, so that we don't get a list of letters instead of a list of words.\n" ]

[ 0 ]

[]

[]

[ "detection", "discord", "list", "python", "string" ]

[ "1-There is a way to call same function for multiple widgets. That is by using lambda you pass widget itself to function.\n2-As you call it from button call, there is no where to return the value. you can use global variables which is not adviced, or you can use classes.\n3-Listboxes like treeviews, has some major differences from other widgets. So if you wanna use it like that you should bind events.\nfrom tkinter import *\n\nwindow= Tk()\nwindow.geometry(\"300x300\")\n\ndef getEntry(entry):\n result= entry.get()\n print(result)\n\ndef getTree(et):\n tree = et.widget\n index = int(tree.curselection()[0])\n value = tree.get(index)\n print(value)\n\na = Spinbox(window, from_=0, to=10) \na.configure(command=lambda:getEntry(a))\na.pack()\n\nb = Spinbox(window, from_=0, to=10) \nb.configure(command=lambda:getEntry(b))\nb.pack()\n\nc = Spinbox(window, from_=0, to=10) \nc.configure(command=lambda:getEntry(c))\nc.pack()\n\n\nTrees= Listbox(window)\nTrees.insert(1, \"Tree1\")\nTrees.insert(2, \"Tree2\")\nTrees.pack()\nTrees.bind('<<ListboxSelect>>',getTree)\n\n\nbouton_close= Button(window, text=\"Fermer\", command=window.quit) \nbouton_close.pack()\n\nwindow.mainloop()\n\n" ]

[ 0 ]

[]

[]

[ "get", "python", "tkinter" ]

[ "You are trying to access the variable namesource outside of the function hello.\nYou can't do that.\nYou can access the variable name outside of the function hello because it is a parameter of the function. You can fix it by making a global variable.\napp = Flask(__name__)\napi = Api(app)\n\nnamesource = None\n\nclass Users(Resource):\n @app.route('/users/&lt;string:name&gt;/')\n def hello(name):\n global namesource\n namesource = request.args.get('name')\n return \"Hello {}!\".format(name)\n\nprint(namesource)\n\napi.add_resource(Users, name='users')\n# For Running our Api on Localhost\nif __name__ == '__main__':\n app.run(debug=True)\n\n" ]

[ 1 ]

[]

[]

[ "api", "flask", "python" ]

[ "You may use pandas' copy method with the deep argument set to True:\ndf_freq = new_df.copy(deep=True)\n\nSetting deep=True (which is the default parameter) ensures that modifications to the data or indices of the copy do not impact the original dataframe.\n" ]

[ 1 ]

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[]

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

[ "By default, PyTest expects test classes to be named like SomethingTest and modules like test_something. You can fine-tune the test discovery process as described here: Changing standard (Python) test discovery\n", "There are various naming conventions to follow\n\nFile name: Should start with test_\nClass name: Should start with Test (your class name is sauce_test_one over here which would not be detected)\nClass module name: Should start with test_ (your class module name is test_saucee2e which is good)\n\n" ]

[ 0, 0 ]

[]

[]

[ "pycharm", "pytest", "python", "selenium_webdriver" ]

[ "I may be way out of line but... why don't you just copy it?\nClass words\n\ndef __init__(self, text):\n self.text = text\n self.original = text\n \n def use(self): # sets the text as an empty string\n self.text = \"\"\n\n def reset(self): # revert empty string back to the original text\n self.text = self.original\n\n\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "datasets.FashionMNIST returns (image, target) where target is index of the target class. So if you want to take the mean you need to extract just the image.\nimages = torch.vstack([pair[0] for pair in train_dataset])\n\nimages should now be of shape (N, H, W) and you can do whatever you want from there.\nAnother solution as noted by OP is to use train_dataset.data to directly access the data.\n" ]

[ 1 ]

[]

[]

[ "mnist", "python", "pytorch", "tensor", "torchvision" ]

[ "You can use pd.Series.rank with method='dense'\ndf['ID'] = df['ID'].rank(method='dense').astype(int)\n\n" ]

[ 1 ]

[]

[]

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

[ "Many things to talk about.\nFirst you are making a dictionary, here, not a list of dictionaries.\nSecondly, a dictionary is made up of key-value pairs, not single values.\nSo for your example, if we wanted the keys to be i and the values to be empty strings we'd do like so:\nreviewers_dicts = {}\nfor i in reviewers_list:\n reviewers_dicts[i]=\"\"\nprint(reviewers_dict)\n\nAnd if you actually wanted a list of dicts, then you would do this:\nlist_of_dicts = []\nfor i in reviewers_list:\n list_of_dicts.append({i:\"\"})\nprint(list_of_dicts )\n\n" ]

[ 0 ]

[]

[]

[ "dictionary", "iteration", "python" ]

[ "Just replace this\nprediction = pd.DataFrame([text]).to_csv('prediction.csv')\n\nWith this\nprediction = pd.DataFrame([text]).to_csv('prediction.csv', sep=\";\")\n\n" ]

[ 3 ]

[]

[]

[ "keras", "python", "tensorflow" ]

[ "The following code worked as suggested in the comments:\nimport ast\n\n# create an instance of the API class\napi_instance = cloudmersive_convert_api_client.ConvertDocumentApi(cloudmersive_convert_api_client.ApiClient(configuration))\n\n# Convert Document to PDF\nif (os.stat(input_file).st_size != 0): #api does not work on empty files\n \n try:\n api_response = api_instance.convert_document_ppt_to_pdf(input_file) #ONLY DIFFERENCE\n \n os.remove(input_file) \n output_file=os.path.splitext(input_file)[0]+\".pdf\"\n \n data = ast.literal_eval(api_response)\n \n with open(output_file, 'wb') as binary_file:\n binary_file.write(data)\n \n print(input_file, 'was processed by ConvertDocumentPptToPdf.')\n \n except ApiException as e:\n print(input_file, 'was not processed.')\n\n" ]

[ 0 ]

[]

[]

[ "api", "arrays", "character_encoding", "pdf_generation", "python" ]

[ "You need to split the path like:\na = path.split('/')[-1]\nb = path.split('/')[-2]\n\n" ]

[ 0 ]

[]

[]

[ "python", "string" ]

[ "# regex to extract text between [p] and [/p] tags\nregex = r'\\[p\\](.*?)\\[/p\\]'\ntest_text = '[p]I am a test paragraph[/p]'\n\n# extract text between [p] and [/p] tags\nlist_of_results = re.findall(regex, test_text)\n\n", "import re\ntext = '[p]I am a test paragraph[/p]'\nparm = re.findall(r'\\[p](.*?)\\[/p]', text)[0] \nprint(parm)\n\nGives #\nI am a test paragraph\n\nor simply uisng rfind\ntext = '[p]I am a test paragraph[/p]'\n\nstart = '[p]'\nend = '[/p]'\n\nprint (text[text.find(start)+len(start):text.rfind(end)])\n\nAlso Gives #\nI am a test paragraph\n\n" ]

[ 0, 0 ]

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[]

[ "bbcode", "python", "regex" ]

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[ "a = input(\"First statement : \")\nb = input(\"Second statement : \")\nc = input(\"Third statement : \")\nal = a.split()\nbl = b.split()\ncl = c.split()\ncounte = 0\ncounter = 0\nletter = 0\nbll = \"\"\nvowels = 0\nans = 0\nansb = 0\nansc = 0\nprint(f\"The words in first statement is {len(al)}\")\nprint(f\"The words in second statement is {len(bl)}\")\nprint(f\"The words in third statement is {len(cl)}\")\nprint(f\"Total words {len(al)+len(bl)+len(cl)}\")\nfor i in al:\n g = len(i)\n letter += g\n for vow in range(0, len(i)):\n if i[vow] == 'a' or i[vow] == 'i' or i[vow] == 'e' or i[vow] == 'o' or i[vow] == 'u':\n vowels += 1\n\n if i[0] == 'e':\n counte += 1\n if i[g-1]+i[g-2] == \"re\":\n counter += 1\n\nfor j in bl:\n h = len(j)\n letter += h\n for vow in range(0, len(j)):\n if j[vow] == 'a' or j[vow] == 'i' or j[vow] == 'e' or j[vow] == 'o' or j[vow] == 'u':\n vowels += 1\n if j[0] == 'e':\n counte += 1\n if j[h-1]+j[h-2] == \"re\":\n counter += 1\nfor k in al:\n u = len(k)\n letter += u\n for vow in range(0, len(k)):\n if k[vow] == 'a' or k[vow] == 'i' or k[vow] == 'e' or k[vow] == 'o' or k[vow] == 'u':\n vowels += 1\n if k[0] == 'e':\n counte += 1\n if k[u-1]+k[u-2] == \"re\":\n counter += 1\nprint(f\"The total letters is {letter}\")\nprint(f\"The words starting with e is {counte}\")\nprint(f\"The words ending with er is {counter}\")\n\n# Reversing the second statements\nrevb = b[::-1]\nprint(f\"The second statement reversed : {revb}\")\n# Third statement reverse word by word\nfor words in bl:\n words = words[::-1]\n bll += words+\" \"\nprint(f\"The third statement reversed word by word : {bll}\")\nprint(f\"The total number of vowels is {vowels}\")\nli = ['0', '1', '3', '2', '4', '5', '6', '7', '8', '9']\nfor gh in al:\n for ff in range(len(gh)):\n if gh[ff] in li:\n ans = 967\n print(\"Numbers are there in first statement \")\n break\nfor ghk in bl:\n for ffd in range(len(ghk)):\n if ghk[ffd] in li:\n ansb = 9673\n print(\"Numbers are there in second statement \")\n break\nfor ghks in cl:\n for fffd in range(len(ghks)):\n if ghks[fffd] in li:\n ansc = 96733\n print(\"Numbers are there in third statement \")\n break\nif ans == 0:\n print(\" No Numbers are there in first statement \")\nif ansb == 0:\n print(\" No Numbers are there in second statement \")\nif ansc == 0:\n print(\" No Numbers are there in third statement \")\n\n#Code is Completed\n\n" ]

[ -1 ]

[ "python" ]

[ "You have to loop the list of keys and update the \"current value\" on each step.\nval = mydict\n\ntry:\n for key in Uselection:\n val = val[key]\nexcept KeyError:\n handle non-existing keys here\n\nAnother, more 'posh' way to do the same (not generally recommended):\nfrom functools import reduce\n\nval = reduce(dict.get, Uselection, mydict)\n\n" ]

[ 3 ]

[]

[]

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

[ "This can also happen if your Python version is too new. Pytorch currently does not support past 3.7.9.\nFigured out from: https://stackoverflow.com/a/58902298/5090928\n", "This is the latest command for pytorch.\npip install torch===1.4.0 torchvision===0.5.0 -f https://download.pytorch.org/whl/torch_stable.html\n\n", "I had some difficulties with this as well. The steps I had to do was:\nInstall the latest version of PyTorch:\n pip3 install torch===1.3.1 torchvision===0.4.2 -f \n https://download.pytorch.org/whl/torch_stable.html\n\nMake sure you are installing with 64bit python version; otherwise, it won't work\n", "I finally managed to solve this problem thanks to John Red' comment and serg06 answer. Here's what I've done :\n\nInstall Python 3.7.9 and not newer.\nBUT make sure to install 64bits python\n\nEvery other combination failed for me.\n", "I had this same issue while installing standfordnlp in my windows 10 system.\nInstalling torch before installing stanfordnlp worked out for me.\nI have installed torch from pytorch official website.\n", "If you have python 3.7 already installed along with newer versions then can use below command to install torch using python 3.7\npy -3.7 -m pip install torch\n\nBut also note that you have to execute the python program using py -3.7\npy -3.7 program_name.py\n\n", "For people visiting this questions with slightly newer versions of python and pytorch, I had Python 3.8.3 32-bit and even though the pytorch page states that:\n\nCurrently, PyTorch on Windows only supports Python 3.7-3.9;\n\nInstalling Python 3.9.13 64-bit instead of Python 3.8.3 32-bit solved it for me.\nAfter that, I used the install script generator and ran\npython -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n... and it started downloading.\n", "torch and torchvision need python 3.8.x ... so in your CLI run\npython --version\n\nto get the python version.\nmake sure that your environment has python 3.8.x, otherwise, create another virtual environment with anaconda\nconda create -n myenv python==3.8 anaconda\nconda activate myenv\n\nThen install torch and torchvision by this command\npip install torch===1.5.0 torchvision===0.6.0 -f https://download.pytorch.org/whl/torch_stable.html\n\n", "I went heaven and earth for this problem and here is what it turned out to be:\n1- i had python 3.10\n2- the python.exe in my virtual environemnt linked to python310\ni uninstalled the python3.10 , then went to delete the paths in system environemnts variables (go to windows search, type this thing, you get a window, click on environment variables, and find a word called paths, click edit) ....\\python310\\ (i named as such when initially installed, you probs have another name) and also this ...python310\\Scripts\\\ndelete them\ngo to https://www.python.org/downloads/release/python-3711/ , istall pythion 3.7 , after that go back to system env. variables thingy, .. add the paths that ends with ...\\python37\\ , and ...\\python37\\Scripts\\ (make you sure you end the paths with \"\")\nthen go to new command prompt, type python , you should get Python 3.7.0 ...\ncd to your virtual environment path script (mine looked like this C:\\Users...\\python_ver\\python_projects\\root_environment\\Scripts>) , activate to the name of whatever you called, for me i typed: activate tf.\ntype python again, if you have python 3.7 as a result, you good to go ... if you still seeing python 3.10 ... then you probs get some error saying no python in ....\\python310\\python.exe ... so:\ngo the folder that you saved python310 (the path shown in the last step), make sure all folders of your python 3.7 go in there.\ntype python in cmd in the same virtual envrioment path by the cursor ... to check you running pythong 3.7 ...\nonce python 3.7 is your default .. run the blood Clot for pytorch https://pytorch.org/ to install pytorch\nthanks to all the guys on this\n", "Use 64-Bit Python. PyTorch doesn't work with 32-Bit Python. I had the same issue.\n", "I tried every possible command for Windows, but nothing worked. I also tried using Pycharm package installation, everything throws the same error.\nFinally installed Pytorch using Anaconda.\n", "I want to pip install \" torch>=1.4.0, torchvision>=0.5.0 \", but in a conda env with python=3.0, this is not right.\nI tried create a new conda env with python=3.7, and pip install \" torch>=1.4.0, torchvision>=0.5.0 \" again, it is ok.\n", "For previous versions please use the snippets from the PyTorch website; https://pytorch.org/get-started/previous-versions/\nAs an example, this will turn into an error since the cudatoolkit versions are not listed in pip;\n!pip install torch==1.10.0+cu111\n\n\nERROR: Could not find a version that satisfies the requirement torch==1.10.0+cu111 (from versions: 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0, 1.2.0, 1.3.0, 1.3.1, 1.4.0, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.1, 1.10.2, 1.11.0) ERROR: No matching distribution found for torch==1.10.0+cu111\n\nFor the same Torch version and cudatoolkit, you can use the following code instead;\n!pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html\n\n\nimport torch\nTORCH_VERSION = \".\".join(torch.__version__.split(\".\")[:2])\nCUDA_VERSION = torch.__version__.split(\"+\")[-1]\nprint(\"torch: \", TORCH_VERSION, \"; cuda: \", CUDA_VERSION)\n\n\nResult:\n\ntorch: 1.10 ; cuda: cu111\n\n", "This is for Deep Reinforcement Learning: Chapter-6: 02_dnq_pong.py:\nI did not verify the above solutions. I found it tedious to change the existing library version.\nSo, as mentioned by someone in GIT, you just have to change two variables in your loss function.\nhttps://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On/issues/90\nChange the variables to below:\nactions_v = torch.tensor(actions).to(device, dtype=torch.int64)\ndone_mask = torch.tensor(dones).to(device, dtype=torch.bool)\nYou could make similar changes to the variables which cause the error, in your respective programs.\n", "I had the same issue with Python 3.8.2 which is currently supported by PyTorch. I updated pip from version 19.2.3 to 22.3.1 and my issue was solved:\npip install --upgrade pip\n" ]

[ 78, 44, 13, 10, 5, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0 ]

[ "follow the link: https://pytorch.org/\nand set your system requirement in QUICK START LOCALLY SECTION\n\n" ]

[ -4 ]

[ "python" ]

[ "downgrade your django version to 3.0.5\n", "I had the same problem, I fixed it by installing Pytz.\nDo :\n\npip install pytz\n\n" ]

[ 0, 0 ]

[]

[]

[ "django", "djongo", "python" ]

[ "#main.py\nfrom flask import Flask\n\napp = Flask(__name__)\n\[email protected]('/sth/')\ndef hi():\n return 'HI\\n'\n\nif __name__ == \"__main__\":\n app.run()\n\nIn another terminal you do an easy request e.g.\nimport requests\n\nr = requests.get('http://127.0.0.1:5000/sth/')\n\nassert r.status_code == 200\n\nOr you do it the Flask native way:\ntest_example.py\[email protected]()\ndef app():\n app = create_app()\n app.config.update({\n \"TESTING\": True,\n })\n\n # other setup can go here\n\n yield app\n\n # clean up / reset resources here\n\n\[email protected]()\ndef client(app):\n return app.test_client()\n\n\n\ndef yourtest(client):\n response = client.get(\"/sth/\")\n assert response.request.path == \"/index\"\n assert response.status_code == 200\n\nusing this command in your CLI\npytest test_example.py::yourtest\nI didn't test it yet.\nMy sources:\nhttps://docs.pytest.org/en/7.1.x/how-to/usage.html\nhttps://flask.palletsprojects.com/en/2.2.x/testing/\n", "I believe your issue is with how you're creating a new app in your unit test when you call app = Flask(__name__).\nIf you import your app variable into your unit test (I can't say how to do this exactly without seeing your project layout) but the below code should work if they're in the same directory.\nfrom main import app\n\nclass TestSthView:\n def test_sth_returns_ok(self):\n c = app.test_client()\n resp = c.get('/sth/')\n\n assert resp.request.path == '/sth/'\n assert resp.status_code == 200\n\n" ]

[ 1, 0 ]

[]

[]

[ "flask", "python" ]

[ "You just need to make the elements of tuples either an integer or slices. (if your requirement is string)\n" ]

[ 0 ]

[]

[]

[ "discord.py", "pycord", "python", "python_3.x", "sqlite" ]

[ "python -m pip install tensorflow-macos==2.9.0\nApple has not updated tensorflow-deps to 2.10 now.\n" ]

[ 0 ]

[]

[]

[ "apple_m1", "macos", "miniconda", "python", "tensorflow" ]

[ "Here is my solution:\ndf.loc[((df['Name'] == 'Melissa') | (df['Name'] == 'John')), 'Gender'] = 'X'\n\nAnd output\n Name Age Birth City Gender\n0 Jane 23 London F\n1 Melissa 45 Paris X\n2 John 35 Toronto X\n3 Matt 64 Atlanta M\n\n", "A possible solution:\ndf['Gender'] = df['Gender'].mask(df['Name'].isin(['Melissa', 'John']), other='X')\n\nOutput:\n Name Age Birth City Gender\n0 Jane 23 London F\n1 Melissa 45 Paris X\n2 John 35 Toronto X\n3 Matt 64 Atlanta M\n\n" ]

[ 3, 2 ]

[]

[]

[ "pandas", "python" ]

[ "When creating a parquet dataset with Mutiple files, All the files should have matching schema. In your case, when you split the csv file into Mutiple parquet files, you will have to include the csv headers in each chunk to create a valid parquet file.\nNote that parquet is a compressed format (with a high compression ratio). Parquet data will be much smaller than the csv data. On top of that, applications that read parquet file usually prefer fewer large parquet file and not many small parquet files.\n", "An easy way to write a partitioned parquet file is with dask.dataframe. You could even read in the data with dask.dataframe.read_csv and then you don't have to do any conversion:\nimport dask.dataframe\n\n# here, the block size will determine the partition boundaries, which will\n# be preserved in the parquet file. So if you have a 5 GB file, this would\n# write 50 partitions:\ndf = dask.dataframe.read_csv(fileName, blocksize=\"100MB\")\ndf.to_parquet(fileName.replace(\".csv\", \".parquet\"))\n\n" ]

[ 2, 1 ]

[]

[]

[ "csv", "parquet", "python" ]

[ "I solved this by updating the Raspberry Pi to a 64-Bit installation.\n" ]

[ 0 ]

[]

[]

[ "docker", "opencv", "python", "raspberry_pi" ]

[ "Sure thing! We have docs available for the Elastic APM Python Agent. Just add elastic-apm to your requirements.txt and then follow the onboarding instructions for whichever framework you're using. Our agent and the Elastic Stack are both fully open source.\nYou can also start a no-credit-card-required trial with Elastic Cloud here if you don't want to run the Elastic Stack yourself.\n" ]

[ 0 ]

[]

[]

[ "amazon_web_services", "elastic_apm", "monitor", "performance", "python" ]

[ "Try to add path to environment variable.\n\nC:\\Users\\username\\AppData\\Local\\Programs\\Microsoft VS Code\\bin\n\ncheck the path of your VS Code bin folder\n" ]

[ 0 ]

[]

[]

[ "conda", "path", "python", "visual_studio_code" ]

[ "Try using random.sample() which samples without replacement:\n>>> import random\n>>> random.sample(range(1, 101), k=20)\n[98, 47, 29, 50, 19, 5, 97, 12, 35, 81, 13, 89, 16, 20, 71, 11, 24, 78, 56, 85]\n>>> random.sample(range(1, 101), k=20)\n[36, 41, 47, 69, 57, 98, 73, 54, 89, 86, 8, 79, 38, 17, 90, 65, 78, 30, 77, 23]\n>>> random.sample(range(1, 101), k=20)\n[89, 71, 38, 58, 3, 64, 66, 88, 51, 30, 80, 43, 33, 44, 26, 73, 37, 98, 19, 22]\n\n" ]

[ 0 ]

[]

[]

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

[ "Your result_tables are not timezone-aware, therefore it causes an error on merge.\nIn case you except all of your data in UTC timezone, you can change the code to this, and it will not cause an error:\n result_table = pd.DataFrame(columns=['timestamp'])\n result_table['timestamp'] = pd.to_datetime(result_table['timestamp'], utc=True)\n\n result_table2 = pd.DataFrame(columns=['timestamp'])\n result_table2['timestamp'] = pd.to_datetime(result_table['timestamp'], utc=True)\n\n" ]

[ 0 ]

[]

[]

[ "dataframe", "merge", "pandas", "python", "python_datetime" ]

[ "Let's mentally step through your code,\nb and d start as [0], one element lists.\nfor i in time.index:\n b[0] = time['Pending'][i] # I'm guessing `b` is now `[36]`\n j = d # j is d, not a copy\n k= d + b # k is [0,36] - list addition is join\n for j in k: # this use of j overrides the previous assignment\n # j is going to be 0, and then 36; 0 may be a valid row index; 36?\n df['Date_time'][j] = time['Date_time'][j]\n df['Station'][j] = time['Station'][j]\n df['Pending'][j] = time['Pending'][j]\n d[0] = j+1 # I expect d to be [37]\n\nI won't continue, but that should give you an idea of what you need to track when doing iterations like this. When you get errors like this, make sure you know what the variable are. The error indicates that some how d became None, and b a number, not a list. It isn't obvious from the code how that happened.\n" ]

[ 0 ]

[]

[]

[ "list", "loops", "numpy", "pandas", "python" ]

[ "As an alternative to the answer from @DizietAsahi, you can use spines in a similar way to the matplotlib example posted here.\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax1 = fig.add_subplot(111)\nax2 = ax1.twiny()\n\n# Add some extra space for the second axis at the bottom\nfig.subplots_adjust(bottom=0.2)\n\nX = np.linspace(0,1,1000)\nY = np.cos(X*20)\n\nax1.plot(X,Y)\nax1.set_xlabel(r\"Original x-axis: $X$\")\n\nnew_tick_locations = np.array([.2, .5, .9])\n\ndef tick_function(X):\n V = 1/(1+X)\n return [\"%.3f\" % z for z in V]\n\n# Move twinned axis ticks and label from top to bottom\nax2.xaxis.set_ticks_position(\"bottom\")\nax2.xaxis.set_label_position(\"bottom\")\n\n# Offset the twin axis below the host\nax2.spines[\"bottom\"].set_position((\"axes\", -0.15))\n\n# Turn on the frame for the twin axis, but then hide all \n# but the bottom spine\nax2.set_frame_on(True)\nax2.patch.set_visible(False)\n\n# as @ali14 pointed out, for python3, use this\n# for sp in ax2.spines.values():\n# and for python2, use this\nfor sp in ax2.spines.itervalues():\n sp.set_visible(False)\nax2.spines[\"bottom\"].set_visible(True)\n\nax2.set_xticks(new_tick_locations)\nax2.set_xticklabels(tick_function(new_tick_locations))\nax2.set_xlabel(r\"Modified x-axis: $1/(1+X)$\")\nplt.show()\n\n\n", "I think you have to create a second Axes with 0 height (and hide the yaxis) to have a second xaxis that you can place wherever you like.\nfor example:\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax1 = fig.add_axes((0.1,0.3,0.8,0.6)) # create an Axes with some room below\n\nX = np.linspace(0,1,1000)\nY = np.cos(X*20)\n\nax1.plot(X,Y)\nax1.set_xlabel(r\"Original x-axis: $X$\")\n\n\n# create second Axes. Note the 0.0 height\nax2 = fig.add_axes((0.1,0.1,0.8,0.0))\nax2.yaxis.set_visible(False) # hide the yaxis\n\nnew_tick_locations = np.array([.2, .5, .9])\n\ndef tick_function(X):\n V = 1/(1+X)\n return [\"%.3f\" % z for z in V]\n\nax2.set_xticks(new_tick_locations)\nax2.set_xticklabels(tick_function(new_tick_locations))\nax2.set_xlabel(r\"Modified x-axis: $1/(1+X)$\")\nplt.show()\n\n\n", "Not really an answer to the question, but it took me quite long time until I figured out how to do the same with logscale. There are a bunch of strange behaviours in that case. Here's my code to apply some simple scaling to the original y axis:\ndef set_scaled_y_axis(ax, label1, label2, scale):\n #define the minor and major ticks\n #might give an error for too small or large exponents (e.g. 1e-20 or 1e+20)\n log_ticks_major=[]\n log_ticks_minor=[]\n tick_labels=[]\n for k in range(-15,16,1):\n log_ticks_major.append(10**k)\n tick_labels.append(\"10$^{\"+f\"{k}\"+\"}$\")\n for kk in range(2,10):\n log_ticks_minor.append(kk*10**k)\n\n log_ticks_major=np.array(log_ticks_major)\n log_ticks_minor=np.array(log_ticks_minor)\n\n #update the original label\n ax.set_ylabel(label2)\n\n # make a twin axis and set the position\n # to make the same with x axis you need \"ax.twiny()\" instead\n ax22 = ax.twinx()\n ax22.yaxis.set_ticks_position(\"left\")\n ax22.yaxis.set_label_position(\"left\")\n ax22.spines[\"left\"].set_position((\"axes\", -0.15))\n \n # draw only the left y axis\n ax22.xaxis.set_visible(False)\n\n # set the log scale for the 2nd axis\n ax22.set_yscale(\"log\")\n ax22.set_yticks(log_ticks_minor/scale, minor=True) # set minor ticks\n ax22.set_yticks(log_ticks_major/scale) # set normal(/major?) ticks\n ax22.set_yticklabels(tick_labels) #must be after \"ax22.set_yticks(log_ticks_major/scale)\"\n ax22.tick_params('y', which=\"minor\", labelleft=False) #some \"random\" minor tick labels would appear\n \n # set the 2nd y axis label\n ax22.set_ylabel(label1)\n\n # set the limits of the 2nd y axis to be the same as the 1st one\n ax22.set_ylim(ax.get_ylim())\n\n" ]

[ 25, 6, 0 ]

[]

[]

[ "matplotlib", "python" ]

[ "I found the problem is caused by this line from my example code above:\nself.top.bind_all(\"<Control-o>\", lambda event : self.menu())\n\nThis line always gives the last new created toplevel the binding. All previous created toplevel windows loose the binding in this moment. So this line must be removed. Instead the binding must be created new at any time when a toplevel window gets the focus. This can be done in this way:\nself.top.bind(\"<FocusIn>\",\n lambda event:\n self.top.bind_all(\"<Control-o>\", lambda event : self.menu()))\n\n" ]

[ 0 ]

[]

[]

[ "focus", "python", "tkinter" ]

[ "\nCan you make it better?\n\nOnly you know what better means.\n\ncan you reduce the size even more?\n\nYes. The way you're storing coordinates now you have a resolution of ~10μm (1e-5m). That seems excessively precise. If you can live with around a 1 meter resolution you can break up the coordinate values into increments of 63356 values and use a single uint16 or int16 to store each (4 bytes per coordinate pair).\nimport numpy as np\n\nINCREMENTS_PER_DEGREE_LONG = np.iinfo(np.uint16).max / 360.0\nINCREMENTS_PER_DEGREE_LAT = (np.iinfo(np.int16).max -\n np.iinfo(np.int16).min) / 180.0\n\ndef longitude_to_increments(longitude_deg):\n assert longitude_deg >= 0 and longitude_deg < 360\n return np.uint16(longitude_deg * INCREMENTS_PER_DEGREE_LONG)\n\ndef increments_to_longitude(increments: np.uint16):\n return increments / INCREMENTS_PER_DEGREE_LONG\n\n# similar for latitude but use a np.int16 for [-90, 90] degree range\n\n" ]

[ 0 ]

[]

[]

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

[ "You left a few questions unanswered (see comments). You also did not provide your own code and any unexpected output/error you got with it, so we have nothing to work with/fix. This is bad form.\nBut I found this to be a fun exercise, so here is what I came up with:\nfrom typing import TypedDict\n\n\nclass Record(TypedDict):\n label: str\n headings: bool\n key: int\n\n\ndef remove_duplicates(records: list[Record]) -> list[Record]:\n # First, decide which records (by index) _not_ to remove.\n # Map labels to 2-tuples of (index, headings boolean):\n keep: dict[str, tuple[int, bool]] = {}\n for idx, record in enumerate(records):\n label, headings = record[\"label\"], record[\"headings\"]\n # We keep it, if this is the first time we see that label OR\n # we did encounter it, but this record's `headings` value is `False`,\n # whereas the previous one was `True`:\n if label not in keep or (not headings and keep[label][1]):\n keep[label] = (idx, headings)\n # Combine all indices we want to keep into one set for easy lookup:\n keep_indices = {idx for idx, _ in keep.values()}\n # Iterate over all record indices in reverse order\n # and pop the corresponding records if necessary:\n removed = []\n for idx in reversed(range(len(records))):\n if idx not in keep_indices:\n removed.append(records.pop(idx))\n return removed\n\nThe original list is mutated in-place, but a new list is created and returned from the removed dictionaries/duplicates. The algorithm creates a few helper-datastructures sacrificing a bit of memory, but should be fairly efficient in terms of time, i.e. approximately O(n) with n being the number of records.\nTo test it:\n...\n\ndef main() -> None:\n from pprint import pprint\n records = [\n {\"label\": \"x\", \"headings\": False, \"key\": 300},\n {\"label\": \"x\", \"headings\": True, \"key\": 301},\n {\"label\": \"x\", \"headings\": False, \"key\": 302},\n {\"label\": \"x\", \"headings\": False, \"key\": 303},\n {\"label\": \"y\", \"headings\": False, \"key\": 304},\n {\"label\": \"y\", \"headings\": True, \"key\": 305},\n {\"label\": \"z\", \"headings\": True, \"key\": 306},\n {\"label\": \"z\", \"headings\": True, \"key\": 307},\n ]\n removed = remove_duplicates(records) # type: ignore[arg-type]\n print(\"remaining:\")\n pprint(records)\n removed.reverse()\n print(\"removed:\")\n pprint(removed)\n\n\nif __name__ == \"__main__\":\n main()\n\nOutput:\n\nremaining:\n[{'headings': False, 'key': 300, 'label': 'x'},\n {'headings': False, 'key': 304, 'label': 'y'},\n {'headings': True, 'key': 306, 'label': 'z'}]\nremoved:\n[{'headings': True, 'key': 301, 'label': 'x'},\n {'headings': False, 'key': 302, 'label': 'x'},\n {'headings': False, 'key': 303, 'label': 'x'},\n {'headings': True, 'key': 305, 'label': 'y'},\n {'headings': True, 'key': 307, 'label': 'z'}]\n\n" ]

[ 0 ]

[]

[]

[ "dictionary", "duplicates", "key", "list", "python" ]

[ "Have you tried installing Pandas in the following way:\npip install pyspark[pandas_on_spark]\n\nIf the pip is not discoverable by bash, maybe try to active your Python environment first (whether virtualenv, conda or anything else).\n" ]

[ 0 ]

[]

[]

[ "apache_spark", "pandas", "pyspark", "python" ]

[ "Yup, it was a file association problem, thanks for the answers.\n" ]

[ 0 ]

[]

[]

[ "python", "windows" ]

[ "Finally I figure it out, \nNow I just need to combine this with my main code, I hope this code will help to other people too... \nfrom pystray import MenuItem as item\nimport pystray\nfrom PIL import Image\nimport tkinter as tk\n\nwindow = tk.Tk()\nwindow.title(\"Welcome\")\n\ndef quit_window(icon, item):\n icon.stop()\n window.destroy()\n\ndef show_window(icon, item):\n icon.stop()\n window.after(0,window.deiconify)\n\ndef withdraw_window(): \n window.withdraw()\n image = Image.open(\"image.ico\")\n menu = (item('Quit', quit_window), item('Show', show_window))\n icon = pystray.Icon(\"name\", image, \"title\", menu)\n icon.run()\n\nwindow.protocol('WM_DELETE_WINDOW', withdraw_window)\nwindow.mainloop()\n\n", "Thanks to Oshers solution, I adapted it into my own project.\nOne issue I fixed was that you could only hide the main window once, then the loop would crash. With this solution, it has no limit.\nimport tkinter as tk\nfrom PIL import Image\n\nimport pystray\n\n\nclass Gui():\n\n def __init__(self):\n self.window = tk.Tk()\n self.image = Image.open(\"./assets/icons/ready.png\")\n self.menu = (\n pystray.MenuItem('Show', self.show_window),\n pystray.MenuItem('Quit', self.quit_window)\n )\n self.window.protocol('WM_DELETE_WINDOW', self.withdraw_window)\n self.window.mainloop()\n\n\n def quit_window(self):\n self.icon.stop()\n self.window.destroy()\n\n\n def show_window(self):\n self.icon.stop()\n self.window.protocol('WM_DELETE_WINDOW', self.withdraw_window)\n self.window.after(0, self.window.deiconify)\n\n\n def withdraw_window(self):\n self.window.withdraw()\n self.icon = pystray.Icon(\"name\", self.image, \"title\", self.menu)\n self.icon.run()\n\n\nif __name__ in '__main__':\n Gui()\n\n" ]

[ 25, 1 ]

[]

[]

[ "python", "systray", "tkinter" ]

[ "asyncio does not support asynchronous file operations, you can see the asyncio wiki for further explanation\naiofiles allows you to read files asynchronously by delegating their operations to a separate thread pool\n" ]

[ 0 ]

[]

[]

[ "json", "python", "python_asyncio" ]

[ "At least on Windows, global variables of the main process are not available in the worker processes. The Pool class supports to call an initializer function on each worker to receive such variables (if their data can be pickled) and set them.\nHere this can be done like:\ndef initializer(ext_dfs):\n global dfs\n dfs = ext_dfs\n\n\ndef mp_dosomething():\n with Pool(8, initializer, (dfs,)) as pool:\n # Do work\n\n" ]

[ 0 ]

[]

[]

[ "multiprocessing", "python" ]

[ "I was able to access the value using request.form['id'] after removing the JSON.stringify() function.\nfunction post_order_items() {\n my_data = {id:{{ order.order_id }}, name:\"test\"};\n $.ajax({\n type: \"POST\",\n url: \"{{ url_for('update_order_items') }}\",\n data: my_data,\n dataType: \"json\",\n success: function(data, textStatus) {\n if (data.redirect) {\n // data.redirect contains the string URL to redirect to\n window.location.href = data.redirect;\n } else {\n console.log('no redirect')\n } \n }\n });\n}\n\n\n" ]

[ 0 ]

[]

[]

[ "ajax", "flask", "json", "python" ]

[ "Because in 1st and 2nd you are swapping values between temp and A[3]\nBut not doing it for A[0]\njust do\nA[0] = temp\nAs you have don in 3rd\n\"temp\" is a variable outside the list.\nbe carefull\n" ]

[ 0 ]

[]

[]

[ "partitioning", "python", "reference", "sharedpreferences", "swap" ]

[ "I recommend anytree (I am the author).\nExample:\nfrom anytree import Node, RenderTree\n\nudo = Node(\"Udo\")\nmarc = Node(\"Marc\", parent=udo)\nlian = Node(\"Lian\", parent=marc)\ndan = Node(\"Dan\", parent=udo)\njet = Node(\"Jet\", parent=dan)\njan = Node(\"Jan\", parent=dan)\njoe = Node(\"Joe\", parent=dan)\n\nprint(udo)\nNode('/Udo')\nprint(joe)\nNode('/Udo/Dan/Joe')\n\nfor pre, fill, node in RenderTree(udo):\n print(\"%s%s\" % (pre, node.name))\nUdo\n├── Marc\n│ └── Lian\n└── Dan\n ├── Jet\n ├── Jan\n └── Joe\n\nprint(dan.children)\n(Node('/Udo/Dan/Jet'), Node('/Udo/Dan/Jan'), Node('/Udo/Dan/Joe'))\n\nanytree has also a powerful API with:\n\nsimple tree creation\nsimple tree modification\npre-order tree iteration\npost-order tree iteration\nresolve relative and absolute node paths\nwalking from one node to an other.\ntree rendering (see example above)\nnode attach/detach hookups\n\n", "Python doesn't have the quite the extensive range of \"built-in\" data structures as Java does. However, because Python is dynamic, a general tree is easy to create. For example, a binary tree might be:\nclass Tree:\n def __init__(self):\n self.left = None\n self.right = None\n self.data = None\n\nYou can use it like this:\nroot = Tree()\nroot.data = \"root\"\nroot.left = Tree()\nroot.left.data = \"left\"\nroot.right = Tree()\nroot.right.data = \"right\"\n\nIf you need an arbitrary number of children per node, then use a list of children:\nclass Tree:\n def __init__(self, data):\n self.children = []\n self.data = data\n\nleft = Tree(\"left\")\nmiddle = Tree(\"middle\")\nright = Tree(\"right\")\nroot = Tree(\"root\")\nroot.children = [left, middle, right]\n\n", "A generic tree is a node with zero or more children, each one a proper (tree) node. It isn't the same as a binary tree, they're different data structures, although both shares some terminology.\nThere isn't any builtin data structure for generic trees in Python, but it's easily implemented with classes.\nclass Tree(object):\n \"Generic tree node.\"\n def __init__(self, name='root', children=None):\n self.name = name\n self.children = []\n if children is not None:\n for child in children:\n self.add_child(child)\n def __repr__(self):\n return self.name\n def add_child(self, node):\n assert isinstance(node, Tree)\n self.children.append(node)\n# *\n# /|\\\n# 1 2 +\n# / \\\n# 3 4\nt = Tree('*', [Tree('1'),\n Tree('2'),\n Tree('+', [Tree('3'),\n Tree('4')])])\n\n", "You can try:\nfrom collections import defaultdict\ndef tree(): return defaultdict(tree)\nusers = tree()\nusers['harold']['username'] = 'hrldcpr'\nusers['handler']['username'] = 'matthandlersux'\n\nAs suggested here: https://gist.github.com/2012250\n", "class Node:\n \"\"\"\n Class Node\n \"\"\"\n def __init__(self, value):\n self.left = None\n self.data = value\n self.right = None\n\nclass Tree:\n \"\"\"\n Class tree will provide a tree as well as utility functions.\n \"\"\"\n\n def createNode(self, data):\n \"\"\"\n Utility function to create a node.\n \"\"\"\n return Node(data)\n\n def insert(self, node , data):\n \"\"\"\n Insert function will insert a node into tree.\n Duplicate keys are not allowed.\n \"\"\"\n #if tree is empty , return a root node\n if node is None:\n return self.createNode(data)\n # if data is smaller than parent , insert it into left side\n if data < node.data:\n node.left = self.insert(node.left, data)\n elif data > node.data:\n node.right = self.insert(node.right, data)\n\n return node\n\n\n def search(self, node, data):\n \"\"\"\n Search function will search a node into tree.\n \"\"\"\n # if root is None or root is the search data.\n if node is None or node.data == data:\n return node\n\n if node.data < data:\n return self.search(node.right, data)\n else:\n return self.search(node.left, data)\n\n\n\n def deleteNode(self,node,data):\n \"\"\"\n Delete function will delete a node into tree.\n Not complete , may need some more scenarion that we can handle\n Now it is handling only leaf.\n \"\"\"\n\n # Check if tree is empty.\n if node is None:\n return None\n\n # searching key into BST.\n if data < node.data:\n node.left = self.deleteNode(node.left, data)\n elif data > node.data:\n node.right = self.deleteNode(node.right, data)\n else: # reach to the node that need to delete from BST.\n if node.left is None and node.right is None:\n del node\n if node.left == None:\n temp = node.right\n del node\n return temp\n elif node.right == None:\n temp = node.left\n del node\n return temp\n\n return node\n\n def traverseInorder(self, root):\n \"\"\"\n traverse function will print all the node in the tree.\n \"\"\"\n if root is not None:\n self.traverseInorder(root.left)\n print(root.data)\n self.traverseInorder(root.right)\n\n def traversePreorder(self, root):\n \"\"\"\n traverse function will print all the node in the tree.\n \"\"\"\n if root is not None:\n print(root.data)\n self.traversePreorder(root.left)\n self.traversePreorder(root.right)\n\n def traversePostorder(self, root):\n \"\"\"\n traverse function will print all the node in the tree.\n \"\"\"\n if root is not None:\n self.traversePostorder(root.left)\n self.traversePostorder(root.right)\n print(root.data)\n\n\ndef main():\n root = None\n tree = Tree()\n root = tree.insert(root, 10)\n print(root)\n tree.insert(root, 20)\n tree.insert(root, 30)\n tree.insert(root, 40)\n tree.insert(root, 70)\n tree.insert(root, 60)\n tree.insert(root, 80)\n\n print(\"Traverse Inorder\")\n tree.traverseInorder(root)\n\n print(\"Traverse Preorder\")\n tree.traversePreorder(root)\n\n print(\"Traverse Postorder\")\n tree.traversePostorder(root)\n\n\nif __name__ == \"__main__\":\n main()\n\n", "There aren't trees built in, but you can easily construct one by subclassing a Node type from List and writing the traversal methods. If you do this, I've found bisect useful. \nThere are also many implementations on PyPi that you can browse. \nIf I remember correctly, the Python standard lib doesn't include tree data structures for the same reason that the .NET base class library doesn't: locality of memory is reduced, resulting in more cache misses. On modern processors it's usually faster to just bring a large chunk of memory into the cache, and \"pointer rich\" data structures negate the benefit. \n", "I implemented a rooted tree as a dictionary {child:parent}. So for instance with the root node 0, a tree might look like that:\ntree={1:0, 2:0, 3:1, 4:2, 5:3}\n\nThis structure made it quite easy to go upward along a path from any node to the root, which was relevant for the problem I was working on.\n", "Greg Hewgill's answer is great but if you need more nodes per level you can use a list|dictionary to create them: And then use method to access them either by name or order (like id)\nclass node(object):\n def __init__(self):\n self.name=None\n self.node=[]\n self.otherInfo = None\n self.prev=None\n def nex(self,child):\n \"Gets a node by number\"\n return self.node[child]\n def prev(self):\n return self.prev\n def goto(self,data):\n \"Gets the node by name\"\n for child in range(0,len(self.node)):\n if(self.node[child].name==data):\n return self.node[child]\n def add(self):\n node1=node()\n self.node.append(node1)\n node1.prev=self\n return node1\n\nNow just create a root and build it up:\nex:\ntree=node() #create a node\ntree.name=\"root\" #name it root\ntree.otherInfo=\"blue\" #or what ever \ntree=tree.add() #add a node to the root\ntree.name=\"node1\" #name it\n\n root\n /\nchild1\n\ntree=tree.add()\ntree.name=\"grandchild1\"\n\n root\n /\n child1\n /\ngrandchild1\n\ntree=tree.prev()\ntree=tree.add()\ntree.name=\"gchild2\"\n\n root\n /\n child1\n / \\\ngrandchild1 gchild2\n\ntree=tree.prev()\ntree=tree.prev()\ntree=tree.add()\ntree=tree.name=\"child2\"\n\n root\n / \\\n child1 child2\n / \\\ngrandchild1 gchild2\n\n\ntree=tree.prev()\ntree=tree.goto(\"child1\") or tree=tree.nex(0)\ntree.name=\"changed\"\n\n root\n / \\\n changed child2\n / \\\n grandchild1 gchild2\n\nThat should be enough for you to start figuring out how to make this work\n", "class Tree(dict):\n \"\"\"A tree implementation using python's autovivification feature.\"\"\"\n def __missing__(self, key):\n value = self[key] = type(self)()\n return value\n\n #cast a (nested) dict to a (nested) Tree class\n def __init__(self, data={}):\n for k, data in data.items():\n if isinstance(data, dict):\n self[k] = type(self)(data)\n else:\n self[k] = data\n\nworks as a dictionary, but provides as many nested dicts you want.\nTry the following:\nyour_tree = Tree()\n\nyour_tree['a']['1']['x'] = '@'\nyour_tree['a']['1']['y'] = '#'\nyour_tree['a']['2']['x'] = '$'\nyour_tree['a']['3'] = '%'\nyour_tree['b'] = '*'\n\nwill deliver a nested dict ... which works as a tree indeed.\n{'a': {'1': {'x': '@', 'y': '#'}, '2': {'x': '$'}, '3': '%'}, 'b': '*'}\n\n... If you have already a dict, it will cast each level to a tree:\nd = {'foo': {'amy': {'what': 'runs'} } }\ntree = Tree(d)\n\nprint(d['foo']['amy']['what']) # returns 'runs'\nd['foo']['amy']['when'] = 'now' # add new branch\n\nIn this way, you can keep edit/add/remove each dict level as you wish.\nAll the dict methods for traversal etc, still apply.\n", "If someone needs a simpler way to do it, a tree is only a recursively nested list (since set is not hashable) :\n[root, [child_1, [[child_11, []], [child_12, []]], [child_2, []]]]\n\nWhere each branch is a pair: [ object, [children] ]\nand each leaf is a pair: [ object, [] ]\nBut if you need a class with methods, you can use anytree.\n", "I've implemented trees using nested dicts. It is quite easy to do, and it has worked for me with pretty large data sets. I've posted a sample below, and you can see more at Google code\n def addBallotToTree(self, tree, ballotIndex, ballot=\"\"):\n \"\"\"Add one ballot to the tree.\n\n The root of the tree is a dictionary that has as keys the indicies of all \n continuing and winning candidates. For each candidate, the value is also\n a dictionary, and the keys of that dictionary include \"n\" and \"bi\".\n tree[c][\"n\"] is the number of ballots that rank candidate c first.\n tree[c][\"bi\"] is a list of ballot indices where the ballots rank c first.\n\n If candidate c is a winning candidate, then that portion of the tree is\n expanded to indicate the breakdown of the subsequently ranked candidates.\n In this situation, additional keys are added to the tree[c] dictionary\n corresponding to subsequently ranked candidates.\n tree[c][\"n\"] is the number of ballots that rank candidate c first.\n tree[c][\"bi\"] is a list of ballot indices where the ballots rank c first.\n tree[c][d][\"n\"] is the number of ballots that rank c first and d second.\n tree[c][d][\"bi\"] is a list of the corresponding ballot indices.\n\n Where the second ranked candidates is also a winner, then the tree is \n expanded to the next level. \n\n Losing candidates are ignored and treated as if they do not appear on the \n ballots. For example, tree[c][d][\"n\"] is the total number of ballots\n where candidate c is the first non-losing candidate, c is a winner, and\n d is the next non-losing candidate. This will include the following\n ballots, where x represents a losing candidate:\n [c d]\n [x c d]\n [c x d]\n [x c x x d]\n\n During the count, the tree is dynamically updated as candidates change\n their status. The parameter \"tree\" to this method may be the root of the\n tree or may be a sub-tree.\n \"\"\"\n\n if ballot == \"\":\n # Add the complete ballot to the tree\n weight, ballot = self.b.getWeightedBallot(ballotIndex)\n else:\n # When ballot is not \"\", we are adding a truncated ballot to the tree,\n # because a higher-ranked candidate is a winner.\n weight = self.b.getWeight(ballotIndex)\n\n # Get the top choice among candidates still in the running\n # Note that we can't use Ballots.getTopChoiceFromWeightedBallot since\n # we are looking for the top choice over a truncated ballot.\n for c in ballot:\n if c in self.continuing | self.winners:\n break # c is the top choice so stop\n else:\n c = None # no candidates left on this ballot\n\n if c is None:\n # This will happen if the ballot contains only winning and losing\n # candidates. The ballot index will not need to be transferred\n # again so it can be thrown away.\n return\n\n # Create space if necessary.\n if not tree.has_key(c):\n tree[c] = {}\n tree[c][\"n\"] = 0\n tree[c][\"bi\"] = []\n\n tree[c][\"n\"] += weight\n\n if c in self.winners:\n # Because candidate is a winner, a portion of the ballot goes to\n # the next candidate. Pass on a truncated ballot so that the same\n # candidate doesn't get counted twice.\n i = ballot.index(c)\n ballot2 = ballot[i+1:]\n self.addBallotToTree(tree[c], ballotIndex, ballot2)\n else:\n # Candidate is in continuing so we stop here.\n tree[c][\"bi\"].append(ballotIndex)\n\n", "If you are already using the networkx library, then you can implement a tree using that.\n\nNetworkX is a Python package for the creation, manipulation, and study\nof the structure, dynamics, and functions of complex networks.\n\nAs 'tree' is another term for a (normally rooted) connected acyclic graph, and these are called 'arborescences' in NetworkX.\nYou may want to implement a plane tree (aka ordered tree) where each sibling has a unique rank and this is normally done via labelling the nodes.\nHowever, graph language looks different from tree language, and the means of 'rooting' an arborescence is normally done by using a directed graph so, while there are some really cool functions and corresponding visualisations available, it would probably not be an ideal choice if you are not already using networkx.\nAn example of building a tree:\nimport networkx as nx\nG = nx.Graph()\nG.add_edge('A', 'B')\nG.add_edge('B', 'C')\nG.add_edge('B', 'D')\nG.add_edge('A', 'E')\nG.add_edge('E', 'F')\n\nThe library enables each node to be any hashable object, and there is no constraint on the number of children each node has.\n", "I've published a Python 3 tree implementation on my site: https://web.archive.org/web/20120723175438/www.quesucede.com/page/show/id/python_3_tree_implementation\nHere's the code:\nimport uuid\n\ndef sanitize_id(id):\n return id.strip().replace(\" \", \"\")\n\n(_ADD, _DELETE, _INSERT) = range(3)\n(_ROOT, _DEPTH, _WIDTH) = range(3)\n\nclass Node:\n\n def __init__(self, name, identifier=None, expanded=True):\n self.__identifier = (str(uuid.uuid1()) if identifier is None else\n sanitize_id(str(identifier)))\n self.name = name\n self.expanded = expanded\n self.__bpointer = None\n self.__fpointer = []\n\n @property\n def identifier(self):\n return self.__identifier\n\n @property\n def bpointer(self):\n return self.__bpointer\n\n @bpointer.setter\n def bpointer(self, value):\n if value is not None:\n self.__bpointer = sanitize_id(value)\n\n @property\n def fpointer(self):\n return self.__fpointer\n\n def update_fpointer(self, identifier, mode=_ADD):\n if mode is _ADD:\n self.__fpointer.append(sanitize_id(identifier))\n elif mode is _DELETE:\n self.__fpointer.remove(sanitize_id(identifier))\n elif mode is _INSERT:\n self.__fpointer = [sanitize_id(identifier)]\n\nclass Tree:\n\n def __init__(self):\n self.nodes = []\n\n def get_index(self, position):\n for index, node in enumerate(self.nodes):\n if node.identifier == position:\n break\n return index\n\n def create_node(self, name, identifier=None, parent=None):\n\n node = Node(name, identifier)\n self.nodes.append(node)\n self.__update_fpointer(parent, node.identifier, _ADD)\n node.bpointer = parent\n return node\n\n def show(self, position, level=_ROOT):\n queue = self[position].fpointer\n if level == _ROOT:\n print(\"{0} [{1}]\".format(self[position].name,\n self[position].identifier))\n else:\n print(\"\\t\"*level, \"{0} [{1}]\".format(self[position].name,\n self[position].identifier))\n if self[position].expanded:\n level += 1\n for element in queue:\n self.show(element, level) # recursive call\n\n def expand_tree(self, position, mode=_DEPTH):\n # Python generator. Loosly based on an algorithm from 'Essential LISP' by\n # John R. Anderson, Albert T. Corbett, and Brian J. Reiser, page 239-241\n yield position\n queue = self[position].fpointer\n while queue:\n yield queue[0]\n expansion = self[queue[0]].fpointer\n if mode is _DEPTH:\n queue = expansion + queue[1:] # depth-first\n elif mode is _WIDTH:\n queue = queue[1:] + expansion # width-first\n\n def is_branch(self, position):\n return self[position].fpointer\n\n def __update_fpointer(self, position, identifier, mode):\n if position is None:\n return\n else:\n self[position].update_fpointer(identifier, mode)\n\n def __update_bpointer(self, position, identifier):\n self[position].bpointer = identifier\n\n def __getitem__(self, key):\n return self.nodes[self.get_index(key)]\n\n def __setitem__(self, key, item):\n self.nodes[self.get_index(key)] = item\n\n def __len__(self):\n return len(self.nodes)\n\n def __contains__(self, identifier):\n return [node.identifier for node in self.nodes\n if node.identifier is identifier]\n\nif __name__ == \"__main__\":\n\n tree = Tree()\n tree.create_node(\"Harry\", \"harry\") # root node\n tree.create_node(\"Jane\", \"jane\", parent = \"harry\")\n tree.create_node(\"Bill\", \"bill\", parent = \"harry\")\n tree.create_node(\"Joe\", \"joe\", parent = \"jane\")\n tree.create_node(\"Diane\", \"diane\", parent = \"jane\")\n tree.create_node(\"George\", \"george\", parent = \"diane\")\n tree.create_node(\"Mary\", \"mary\", parent = \"diane\")\n tree.create_node(\"Jill\", \"jill\", parent = \"george\")\n tree.create_node(\"Carol\", \"carol\", parent = \"jill\")\n tree.create_node(\"Grace\", \"grace\", parent = \"bill\")\n tree.create_node(\"Mark\", \"mark\", parent = \"jane\")\n\n print(\"=\"*80)\n tree.show(\"harry\")\n print(\"=\"*80)\n for node in tree.expand_tree(\"harry\", mode=_WIDTH):\n print(node)\n print(\"=\"*80)\n\n", "Hi you may give itertree a try (I'm the author).\nThe package goes in the direction of anytree package but with a bit different focus. The performance on huge trees (>100000 items) is much better and it deals with iterators to have effective filter mechanism.\n>>>from itertree import *\n>>>root=iTree('root')\n\n>>># add some children:\n>>>root.append(iTree('Africa',data={'surface':30200000,'inhabitants':1257000000}))\n>>>root.append(iTree('Asia', data={'surface': 44600000, 'inhabitants': 4000000000}))\n>>>root.append(iTree('America', data={'surface': 42549000, 'inhabitants': 1009000000}))\n>>>root.append(iTree('Australia&Oceania', data={'surface': 8600000, 'inhabitants': 36000000}))\n>>>root.append(iTree('Europe', data={'surface': 10523000 , 'inhabitants': 746000000}))\n>>># you might use __iadd__ operator for adding too:\n>>>root+=iTree('Antarktika', data={'surface': 14000000, 'inhabitants': 1100})\n\n>>># for building next level we select per index:\n>>>root[0]+=iTree('Ghana',data={'surface':238537,'inhabitants':30950000})\n>>>root[0]+=iTree('Niger', data={'surface': 1267000, 'inhabitants': 23300000})\n>>>root[1]+=iTree('China', data={'surface': 9596961, 'inhabitants': 1411780000})\n>>>root[1]+=iTree('India', data={'surface': 3287263, 'inhabitants': 1380004000})\n>>>root[2]+=iTree('Canada', data={'type': 'country', 'surface': 9984670, 'inhabitants': 38008005}) \n>>>root[2]+=iTree('Mexico', data={'surface': 1972550, 'inhabitants': 127600000 })\n>>># extend multiple items:\n>>>root[3].extend([iTree('Australia', data={'surface': 7688287, 'inhabitants': 25700000 }), iTree('New Zealand', data={'surface': 269652, 'inhabitants': 4900000 })])\n>>>root[4]+=iTree('France', data={'surface': 632733, 'inhabitants': 67400000 }))\n>>># select parent per TagIdx - remember in itertree you might put items with same tag multiple times:\n>>>root[TagIdx('Europe'0)]+=iTree('Finland', data={'surface': 338465, 'inhabitants': 5536146 })\n\nThe created tree can be rendered:\n>>>root.render()\niTree('root')\n └──iTree('Africa', data=iTData({'surface': 30200000, 'inhabitants': 1257000000}))\n └──iTree('Ghana', data=iTData({'surface': 238537, 'inhabitants': 30950000}))\n └──iTree('Niger', data=iTData({'surface': 1267000, 'inhabitants': 23300000}))\n └──iTree('Asia', data=iTData({'surface': 44600000, 'inhabitants': 4000000000}))\n └──iTree('China', data=iTData({'surface': 9596961, 'inhabitants': 1411780000}))\n └──iTree('India', data=iTData({'surface': 3287263, 'inhabitants': 1380004000}))\n └──iTree('America', data=iTData({'surface': 42549000, 'inhabitants': 1009000000}))\n └──iTree('Canada', data=iTData({'surface': 9984670, 'inhabitants': 38008005}))\n └──iTree('Mexico', data=iTData({'surface': 1972550, 'inhabitants': 127600000}))\n └──iTree('Australia&Oceania', data=iTData({'surface': 8600000, 'inhabitants': 36000000}))\n └──iTree('Australia', data=iTData({'surface': 7688287, 'inhabitants': 25700000}))\n └──iTree('New Zealand', data=iTData({'surface': 269652, 'inhabitants': 4900000}))\n └──iTree('Europe', data=iTData({'surface': 10523000, 'inhabitants': 746000000}))\n └──iTree('France', data=iTData({'surface': 632733, 'inhabitants': 67400000}))\n └──iTree('Finland', data=iTData({'surface': 338465, 'inhabitants': 5536146}))\n └──iTree('Antarktika', data=iTData({'surface': 14000000, 'inhabitants': 1100}))\n\nE.g. Filtering can be done like this:\n>>>item_filter = Filter.iTFilterData(data_key='inhabitants', data_value=iTInterval(0, 20000000))\n>>>iterator=root.iter_all(item_filter=item_filter)\n>>>for i in iterator:\n>>> print(i)\niTree(\"'New Zealand'\", data=iTData({'surface': 269652, 'inhabitants': 4900000}), subtree=[])\niTree(\"'Finland'\", data=iTData({'surface': 338465, 'inhabitants': 5536146}), subtree=[])\niTree(\"'Antarktika'\", data=iTData({'surface': 14000000, 'inhabitants': 1100}), subtree=[])\n\n", "Another tree implementation loosely based off of Bruno's answer:\nclass Node:\n def __init__(self):\n self.name: str = ''\n self.children: List[Node] = []\n self.parent: Node = self\n\n def __getitem__(self, i: int) -> 'Node':\n return self.children[i]\n\n def add_child(self):\n child = Node()\n self.children.append(child)\n child.parent = self\n return child\n\n def __str__(self) -> str:\n def _get_character(x, left, right) -> str:\n if x < left:\n return '/'\n elif x >= right:\n return '\\\\'\n else:\n return '|'\n\n if len(self.children):\n children_lines: Sequence[List[str]] = list(map(lambda child: str(child).split('\\n'), self.children))\n widths: Sequence[int] = list(map(lambda child_lines: len(child_lines[0]), children_lines))\n max_height: int = max(map(len, children_lines))\n total_width: int = sum(widths) + len(widths) - 1\n left: int = (total_width - len(self.name) + 1) // 2\n right: int = left + len(self.name)\n\n return '\\n'.join((\n self.name.center(total_width),\n ' '.join(map(lambda width, position: _get_character(position - width // 2, left, right).center(width),\n widths, accumulate(widths, add))),\n *map(\n lambda row: ' '.join(map(\n lambda child_lines: child_lines[row] if row < len(child_lines) else ' ' * len(child_lines[0]),\n children_lines)),\n range(max_height))))\n else:\n return self.name\n\nAnd an example of how to use it:\ntree = Node()\ntree.name = 'Root node'\ntree.add_child()\ntree[0].name = 'Child node 0'\ntree.add_child()\ntree[1].name = 'Child node 1'\ntree.add_child()\ntree[2].name = 'Child node 2'\ntree[1].add_child()\ntree[1][0].name = 'Grandchild 1.0'\ntree[2].add_child()\ntree[2][0].name = 'Grandchild 2.0'\ntree[2].add_child()\ntree[2][1].name = 'Grandchild 2.1'\nprint(tree)\n\nWhich should output:\n\n Root node \n / / \\ \nChild node 0 Child node 1 Child node 2 \n | / \\ \n Grandchild 1.0 Grandchild 2.0 Grandchild 2.1\n\n", "Treelib is convenient for the task as well. Documentation can be found treelib.\nfrom treelib import Node, Tree\ntree = Tree() # creating an object\ntree.create_node(\"Harry\", \"harry\") # root node \ntree.create_node(\"Jane\", \"jane\", parent=\"harry\") #adding nodes\ntree.create_node(\"Bill\", \"bill\", parent=\"harry\")\ntree.create_node(\"Diane\", \"diane\", parent=\"jane\")\ntree.create_node(\"Mary\", \"mary\", parent=\"diane\")\ntree.create_node(\"Mark\", \"mark\", parent=\"jane\")\ntree.show()\n\nHarry\n├── Bill\n└── Jane\n ├── Diane\n │ └── Mary\n └── Mark\n\n", "bigtree is a Python tree implementation that integrates with Python lists, dictionaries, and pandas DataFrame. It is pythonic, making it easy to learn and extendable to many types of workflows.\nThere are various components to bigtree, namely\n\nConstructing Trees from list, dictionary, and pandas DataFrame\nTraversing Tree\nModifying Tree (shift/copy nodes)\nSearch Tree\nHelper methods (clone tree, prune tree, get the difference between two treess)\nExport tree (print to console, export tree to dictionary, pandas DataFrame, image etc.)\nOther tree structures: Binary Trees!\nOther graph structured: Directed Acyclic Graphs (DAGs)!\n\nWhat more can I say... ah yes it is also well-documented.\nSome examples:\nfrom bigtree import list_to_tree, tree_to_dict, tree_to_dot\n\n# Create tree from list, print tree\nroot = list_to_tree([\"a/b/d\", \"a/c\"])\nprint_tree(root)\n# a\n# ├── b\n# │ └── d\n# └── c\n\n# Query tree\nroot.children\n# (Node(/a/b, ), Node(/a/c, ))\n\n# Export tree to dictionary / image\ntree_to_dict(root)\n# {\n# '/a': {'name': 'a'},\n# '/a/b': {'name': 'b'},\n# '/a/b/d': {'name': 'd'},\n# '/a/c': {'name': 'c'}\n# }\n\ngraph = tree_to_dot(root, node_colour=\"gold\")\ngraph.write_png(\"tree.png\")\n\n\nSource/Disclaimer: I'm the creator of bigtree ;)\n" ]

[ 367, 146, 68, 45, 39, 17, 16, 10, 9, 9, 7, 7, 6, 2, 1, 1, 0 ]

[ "If you want to create a tree data structure then first you have to create the treeElement object. If you create the treeElement object, then you can decide how your tree behaves. \nTo do this following is the TreeElement class:\nclass TreeElement (object):\n\ndef __init__(self):\n self.elementName = None\n self.element = []\n self.previous = None\n self.elementScore = None\n self.elementParent = None\n self.elementPath = []\n self.treeLevel = 0\n\ndef goto(self, data):\n for child in range(0, len(self.element)):\n if (self.element[child].elementName == data):\n return self.element[child]\n\ndef add(self):\n\n single_element = TreeElement()\n single_element.elementName = self.elementName\n single_element.previous = self.elementParent\n single_element.elementScore = self.elementScore\n single_element.elementPath = self.elementPath\n single_element.treeLevel = self.treeLevel\n\n self.element.append(single_element)\n\n return single_element\n\nNow, we have to use this element to create the tree, I am using A* tree in this example.\nclass AStarAgent(Agent):\n# Initialization Function: Called one time when the game starts\ndef registerInitialState(self, state):\n return;\n\n# GetAction Function: Called with every frame\ndef getAction(self, state):\n\n # Sorting function for the queue\n def sortByHeuristic(each_element):\n\n if each_element.elementScore:\n individual_score = each_element.elementScore[0][0] + each_element.treeLevel\n else:\n individual_score = admissibleHeuristic(each_element)\n\n return individual_score\n\n # check the game is over or not\n if state.isWin():\n print('Job is done')\n return Directions.STOP\n elif state.isLose():\n print('you lost')\n return Directions.STOP\n\n # Create empty list for the next states\n astar_queue = []\n astar_leaf_queue = []\n astar_tree_level = 0\n parent_tree_level = 0\n\n # Create Tree from the give node element\n astar_tree = TreeElement()\n astar_tree.elementName = state\n astar_tree.treeLevel = astar_tree_level\n astar_tree = astar_tree.add()\n\n # Add first element into the queue\n astar_queue.append(astar_tree)\n\n # Traverse all the elements of the queue\n while astar_queue:\n\n # Sort the element from the queue\n if len(astar_queue) > 1:\n astar_queue.sort(key=lambda x: sortByHeuristic(x))\n\n # Get the first node from the queue\n astar_child_object = astar_queue.pop(0)\n astar_child_state = astar_child_object.elementName\n\n # get all legal actions for the current node\n current_actions = astar_child_state.getLegalPacmanActions()\n\n if current_actions:\n\n # get all the successor state for these actions\n for action in current_actions:\n\n # Get the successor of the current node\n next_state = astar_child_state.generatePacmanSuccessor(action)\n\n if next_state:\n\n # evaluate the successor states using scoreEvaluation heuristic\n element_scored = [(admissibleHeuristic(next_state), action)]\n\n # Increase the level for the child\n parent_tree_level = astar_tree.goto(astar_child_state)\n if parent_tree_level:\n astar_tree_level = parent_tree_level.treeLevel + 1\n else:\n astar_tree_level += 1\n\n # create tree for the finding the data\n astar_tree.elementName = next_state\n astar_tree.elementParent = astar_child_state\n astar_tree.elementScore = element_scored\n astar_tree.elementPath.append(astar_child_state)\n astar_tree.treeLevel = astar_tree_level\n astar_object = astar_tree.add()\n\n # If the state exists then add that to the queue\n astar_queue.append(astar_object)\n\n else:\n # Update the value leaf into the queue\n astar_leaf_state = astar_tree.goto(astar_child_state)\n astar_leaf_queue.append(astar_leaf_state)\n\nYou can add/remove any elements from the object, but make the structure intect. \n" ]

[ -3 ]

[ "data_structures", "python", "tree" ]

[ "(With \"list of XML beautifulsoup tag elements\" in variable xTagList,) you could try something like this\nbsParser = 'html.parser' # 'xml' # \n# xTagList = [BeautifulSoup(str(x), bsParser) for x in xTagList] # should fix some formatting\nwCont_xstrs = ['\\n'.join([\n str(d) for d in x.descendants if hasattr(d, 'find_all') \n and not d.find_all() and d.get_text().strip()\n]) for x in xTagList]\n\nto get html/xml string.\n\nwith bsParser = 'xml', wCont_xstrs looks like\n[\n<DateFormattedForTHForm>07/01/2022</DateFormattedForTHForm>\n<DateFormattedForTHForm>07/01/2023</DateFormattedForTHForm>\n<FormDescription>Notification Of Settlement</FormDescription>\n<FormNumber>WC 99 06 04</FormNumber>\n,\n<DisplayName>Mallesham Yamulla</DisplayName>\n<FEINOrSSN>123-45-6789</FEINOrSSN>\n<formsMaskedSSN_and_NoMaskFEIN>**-***-8834</formsMaskedSSN_and_NoMaskFEIN>\n<AddressLine1>A</AddressLine1>\n<AddressLine123>B</AddressLine123>\n<CityStateZip>ENID, OK 73703</CityStateZip>\n<Country>IND</Country>\n]\n\n[btw, if your xml had namespaces (as well formed xmls usually do), they would be lost after using xml parser. Using html parser will preserve namespaces, but there will be another issue as you will see below.]\n\nwith bsParser = 'html.parser' (and probably any other parser other than xml), wCont_xstrs looks like\n[\n<dateformattedforthform>07/01/2022</dateformattedforthform>\n<dateformattedforthform>07/01/2023</dateformattedforthform>\n<formdescription>Notification Of Settlement</formdescription>\n<formnumber>WC 99 06 04</formnumber>\n,\n<displayname>Mallesham Yamulla</displayname>\n<feinorssn>123-45-6789</feinorssn>\n<formsmaskedssn_and_nomaskfein>**-***-8834</formsmaskedssn_and_nomaskfein>\n<addressline1>A</addressline1>\n<addressline123>B</addressline123>\n<citystatezip>ENID, OK 73703</citystatezip>\n<country>IND</country>\n]\n\n(notice how capitalization has been lost from tag names)\n\nIf you want a list bs4 objects, you can do something like\nwCont_xtags = [BeautifulSoup(x, bsParser) for x in wCont_xstrs]\n\nUNLESS you're using bsParser = 'xml', because then you need to wrap them in some tag first like\nwCont_xtags = [BeautifulSoup(f'<Entry>{x}</Entry>', bsParser).Entry for x in wCont_xstrs]\n\n" ]

[ 1 ]

[]

[]

[ "beautifulsoup", "python" ]

[ "Without your data I can't replicate your problem, and probably wouldn't want to do so even with such a large array.\nBut I'll try to illustrate what I expect to happen when constructing a matrix this way. From another question I have a small matrix in a Ipython session:\nIn [60]: Mx\nOut[60]: \n<1x3 sparse matrix of type '<class 'numpy.intc'>'\n with 2 stored elements in Compressed Sparse Row format>\nIn [61]: Mx.A\nOut[61]: array([[0, 1, 2]], dtype=int32)\n\nnonzero returns the coo format indices, row, col\nIn [62]: Mx.nonzero()\nOut[62]: (array([0, 0], dtype=int32), array([1, 2], dtype=int32))\n\nThe csr attributes are:\nIn [63]: Mx.data,Mx.indices,Mx.indptr\nOut[63]: \n(array([1, 2], dtype=int32),\n array([1, 2], dtype=int32),\n array([0, 2], dtype=int32))\n\nNow lets make a new matrix, using the attributes of Mx. Assuming you constructed your indptr, indices, and data correctly this should imitate what you've done:\nIn [64]: newM = sparse.csr_matrix((Mx.data, Mx.indices, Mx.indptr)) \nIn [65]: newM.A\nOut[65]: array([[0, 1, 2]], dtype=int32)\n\ndata matches between the two matrices:\nIn [68]: Mx.data==newM.data\nOut[68]: array([ True, True])\n\nid of the data don't match, but their bases do. See my recent answer to see why this is relevant\nhttps://stackoverflow.com/a/74543855/901925\nIn [75]: id(Mx.data.base), id(newM.data.base)\nOut[75]: (2255407394864, 2255407394864)\n\nThat means changes to newA will appear in Mx:\nIn [77]: newM[0,1] = 100\nIn [78]: newM.A\nOut[78]: array([[ 0, 100, 2]], dtype=int32)\nIn [79]: Mx.A\nOut[79]: array([[ 0, 100, 2]], dtype=int32)\n\nfuller test\nLet's try a small scale test of your code:\nIn [92]: data = np.array([[1.23,2],[3],[]],object); ind = np.array([[1,2],[3],[]],object)\n ...: indptr = np.empty(4) \n ...: indptr[0] = 0\n ...: for i in range(1, 4):\n ...: indptr[i] = indptr[i-1] + len(data[i-1])\n ...: data = np.concatenate(data).ravel() \n ...: ind = np.concatenate(ind).ravel() # same than above\n\nIn [93]: data,ind,indptr\nOut[93]: (array([1.23, 2. , 3. ]), array([1., 2., 3.]), array([0., 2., 3., 3.]))\n\nAnd the sparse matrix:\nIn [94]: X = sparse.csr_matrix((data, ind, indptr), shape=(3,3)) \nIn [95]: X\nOut[95]: \n<3x3 sparse matrix of type '<class 'numpy.float64'>'\n with 3 stored elements in Compressed Sparse Row format>\n\ndata matches:\nIn [96]: X.data\nOut[96]: array([1.23, 2. , 3. ])\n\nIn [97]: data == X.data\nOut[97]: array([ True, True, True])\n\nand is infact a view:\nIn [98]: data[1]+=.23; data\nOut[98]: array([1.23, 2.23, 3. ]) \nIn [99]: X.A\nOut[99]: \narray([[0. , 1.23, 2.23],\n [0. , 0. , 0. ],\n [3. , 0. , 0. ]])\n\noops\nI made an error in specifying the X shape:\nIn [110]: X = sparse.csr_matrix((data, ind, indptr), shape=(3,4))\n\nIn [111]: X.A\nOut[111]: \narray([[0. , 1.23, 2.23, 0. ],\n [0. , 0. , 0. , 3. ],\n [0. , 0. , 0. , 0. ]])\n\nIn [112]: X.data\nOut[112]: array([1.23, 2.23, 3. ])\n\nIn [113]: X.nonzero()\nOut[113]: (array([0, 0, 1], dtype=int32), array([1, 2, 3], dtype=int32))\n\nIn [114]: X[X.nonzero()]\nOut[114]: matrix([[1.23, 2.23, 3. ]])\n\nIn [115]: data\nOut[115]: array([1.23, 2.23, 3. ])\n\nIn [116]: data == X[X.nonzero()]\nOut[116]: matrix([[ True, True, True]])\n\n", "Depending on the type of the values you store in the matrix, numpy.float64 or numpy.int64, perhaps, the following post might answer your question: https://github.com/scipy/scipy/issues/13329#issuecomment-753541268\nIn particular, the comment \"Apparently I don't get an error when data is a numpy array rather than a list.\" suggests that having data as numpy.array rather than a list could solve your problem.\nHopefully, this at least sets you on the right track.\n" ]

[ 1, 0 ]

[]

[]

[ "matrix", "numpy", "python", "scipy", "sparse_matrix" ]

[ "Not sure if this will help but given you're talking about filtering I'd assume you mean things like convolutions. Numpy and Scipy help a lot and provide various ways of applying these and work well with images from Pillow.\nFor example:\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\n\nimport numpy as np\nfrom scipy import signal\n\nimage_req = requests.get(\"https://wallpapercave.com/dwp1x/wp1816238.jpg\")\nimage_req.raise_for_status()\n\nimage = Image.open(BytesIO(image_req.content))\n\n# create gaussian glur of a given standard deviation\nsd = 3\nfilt = np.outer(*2*[signal.windows.gaussian(int(sd*5)|1, sd)])\nfilt /= filt.sum()\n\n# interpret image as 3d array\narr = np.array(image)\n\n# apply it to each channel independently, this loop runs in ~0.1 seconds\nfor chan in range(3):\n arr[:,:,chan] = signal.oaconvolve(arr[:,:,chan], filt, mode='same')\n\n# array back into image for display in notebook\nImage.fromarray(arr)\n\nThis produces an image like:\n\n" ]

[ 1 ]

[]

[]

[ "ipycanvas", "jupyter_notebook", "pixel", "python", "python_imaging_library" ]

[ "You can look at the Django framework for how they create a \"slug\" from arbitrary text. A slug is URL- and filename- friendly.\nThe Django text utils define a function, slugify(), that's probably the gold standard for this kind of thing. Essentially, their code is the following.\nimport unicodedata\nimport re\n\ndef slugify(value, allow_unicode=False):\n \"\"\"\n Taken from https://github.com/django/django/blob/master/django/utils/text.py\n Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated\n dashes to single dashes. Remove characters that aren't alphanumerics,\n underscores, or hyphens. Convert to lowercase. Also strip leading and\n trailing whitespace, dashes, and underscores.\n \"\"\"\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value.lower())\n return re.sub(r'[-\\s]+', '-', value).strip('-_')\n\nAnd the older version:\ndef slugify(value):\n \"\"\"\n Normalizes string, converts to lowercase, removes non-alpha characters,\n and converts spaces to hyphens.\n \"\"\"\n import unicodedata\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n value = unicode(re.sub('[-\\s]+', '-', value))\n # ...\n return value\n\nThere's more, but I left it out, since it doesn't address slugification, but escaping.\n", "You can use list comprehension together with the string methods.\n>>> s\n'foo-bar#baz?qux@127/\\\\9]'\n>>> \"\".join(x for x in s if x.isalnum())\n'foobarbazqux1279'\n\n", "This whitelist approach (ie, allowing only the chars present in valid_chars) will work if there aren't limits on the formatting of the files or combination of valid chars that are illegal (like \"..\"), for example, what you say would allow a filename named \" . txt\" which I think is not valid on Windows. As this is the most simple approach I'd try to remove whitespace from the valid_chars and prepend a known valid string in case of error, any other approach will have to know about what is allowed where to cope with Windows file naming limitations and thus be a lot more complex. \n>>> import string\n>>> valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n>>> valid_chars\n'-_.() abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\n>>> filename = \"This Is a (valid) - filename%$&$ .txt\"\n>>> ''.join(c for c in filename if c in valid_chars)\n'This Is a (valid) - filename .txt'\n\n", "What is the reason to use the strings as file names? If human readability is not a factor I would go with base64 module which can produce file system safe strings. It won't be readable but you won't have to deal with collisions and it is reversible.\nimport base64\nfile_name_string = base64.urlsafe_b64encode(your_string)\n\nUpdate: Changed based on Matthew comment.\n", "There is a nice project on Github called python-slugify: \nInstall:\npip install python-slugify\n\nThen use:\n>>> from slugify import slugify\n>>> txt = \"This\\ is/ a%#$ test ---\"\n>>> slugify(txt)\n'this-is-a-test'\n\n", "Just to further complicate things, you are not guaranteed to get a valid filename just by removing invalid characters. Since allowed characters differ on different filenames, a conservative approach could end up turning a valid name into an invalid one. You may want to add special handling for the cases where:\n\nThe string is all invalid characters (leaving you with an empty string)\nYou end up with a string with a special meaning, eg \".\" or \"..\"\nOn windows, certain device names are reserved. For instance, you can't create a file named \"nul\", \"nul.txt\" (or nul.anything in fact) The reserved names are:\nCON, PRN, AUX, NUL, COM1, COM2, COM3, COM4, COM5, COM6, COM7, COM8, COM9, LPT1, LPT2, LPT3, LPT4, LPT5, LPT6, LPT7, LPT8, and LPT9\n\nYou can probably work around these issues by prepending some string to the filenames that can never result in one of these cases, and stripping invalid characters.\n", "Just like S.Lott answered, you can look at the Django Framework for how they convert a string to a valid filename. \nThe most recent and updated version is found in utils/text.py, and defines \"get_valid_filename\", which is as follows:\ndef get_valid_filename(s):\n s = str(s).strip().replace(' ', '_')\n return re.sub(r'(?u)[^-\\w.]', '', s)\n\n( See https://github.com/django/django/blob/master/django/utils/text.py )\n", "This is the solution I ultimately used:\nimport unicodedata\n\nvalidFilenameChars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n\ndef removeDisallowedFilenameChars(filename):\n cleanedFilename = unicodedata.normalize('NFKD', filename).encode('ASCII', 'ignore')\n return ''.join(c for c in cleanedFilename if c in validFilenameChars)\n\nThe unicodedata.normalize call replaces accented characters with the unaccented equivalent, which is better than simply stripping them out. After that all disallowed characters are removed.\nMy solution doesn't prepend a known string to avoid possible disallowed filenames, because I know they can't occur given my particular filename format. A more general solution would need to do so.\n", "In one line:\nvalid_file_name = re.sub('[^\\w_.)( -]', '', any_string)\n\nyou can also put '_' character to make it more readable (in case of replacing slashs, for example)\n", "Keep in mind, there are actually no restrictions on filenames on Unix systems other than \n\nIt may not contain \\0 \nIt may not contain /\n\nEverything else is fair game. \n\n$ touch \"\n> even multiline\n> haha\n> ^[[31m red ^[[0m\n> evil\"\n$ ls -la \n-rw-r--r-- 0 Nov 17 23:39 ?even multiline?haha??[31m red ?[0m?evil\n$ ls -lab\n-rw-r--r-- 0 Nov 17 23:39 \\neven\\ multiline\\nhaha\\n\\033[31m\\ red\\ \\033[0m\\nevil\n$ perl -e 'for my $i ( glob(q{./*even*}) ){ print $i; } '\n./\neven multiline\nhaha\n red \nevil\n\nYes, i just stored ANSI Colour Codes in a file name and had them take effect. \nFor entertainment, put a BEL character in a directory name and watch the fun that ensues when you CD into it ;) \n", "You could use the re.sub() method to replace anything not \"filelike\". But in effect, every character could be valid; so there are no prebuilt functions (I believe), to get it done.\nimport re\n\nstr = \"File!name?.txt\"\nf = open(os.path.join(\"/tmp\", re.sub('[^-a-zA-Z0-9_.() ]+', '', str))\n\nWould result in a filehandle to /tmp/filename.txt.\n", ">>> import string\n>>> safechars = bytearray(('_-.()' + string.digits + string.ascii_letters).encode())\n>>> allchars = bytearray(range(0x100))\n>>> deletechars = bytearray(set(allchars) - set(safechars))\n>>> filename = u'#ab\\xa0c.$%.txt'\n>>> safe_filename = filename.encode('ascii', 'ignore').translate(None, deletechars).decode()\n>>> safe_filename\n'abc..txt'\n\nIt doesn't handle empty strings, special filenames ('nul', 'con', etc).\n", "Why not just wrap the \"osopen\" with a try/except and let the underlying OS sort out whether the file is valid?\nThis seems like much less work and is valid no matter which OS you use.\n", "Another issue that the other comments haven't addressed yet is the empty string, which is obviously not a valid filename. You can also end up with an empty string from stripping too many characters.\nWhat with the Windows reserved filenames and issues with dots, the safest answer to the question “how do I normalise a valid filename from arbitrary user input?” is “don't even bother try”: if you can find any other way to avoid it (eg. using integer primary keys from a database as filenames), do that.\nIf you must, and you really need to allow spaces and ‘.’ for file extensions as part of the name, try something like:\nimport re\nbadchars= re.compile(r'[^A-Za-z0-9_. ]+|^\\.|\\.$|^ | $|^$')\nbadnames= re.compile(r'(aux|com[1-9]|con|lpt[1-9]|prn)(\\.|$)')\n\ndef makeName(s):\n name= badchars.sub('_', s)\n if badnames.match(name):\n name= '_'+name\n return name\n\nEven this can't be guaranteed right especially on unexpected OSs — for example RISC OS hates spaces and uses ‘.’ as a directory separator.\n", "Though you have to be careful. It is not clearly said in your intro, if you are looking only at latine language. Some words can become meaningless or another meaning if you sanitize them with ascii characters only.\nimagine you have \"forêt poésie\" (forest poetry), your sanitization might give \"fort-posie\" (strong + something meaningless)\nWorse if you have to deal with chinese characters.\n\"下北沢\" your system might end up doing \"---\" which is doomed to fail after a while and not very helpful. So if you deal with only files I would encourage to either call them a generic chain that you control or to keep the characters as it is. For URIs, about the same.\n", "I realise there are many answers but they mostly rely on regular expressions or external modules, so I'd like to throw in my own answer. A pure python function, no external module needed, no regular expression used. My approach is not to clean invalid chars, but to only allow valid ones.\ndef normalizefilename(fn):\n validchars = \"-_.() \"\n out = \"\"\n for c in fn:\n if str.isalpha(c) or str.isdigit(c) or (c in validchars):\n out += c\n else:\n out += \"_\"\n return out \n\nif you like, you can add your own valid chars to the validchars variable at the beginning, such as your national letters that don't exist in English alphabet. This is something you may or may not want: some file systems that don't run on UTF-8 might still have problems with non-ASCII chars.\nThis function is to test for a single file name validity, so it will replace path separators with _ considering them invalid chars. If you want to add that, it is trivial to modify the if to include os path separator.\n", "If you don't mind installing a package, this should be useful:\nhttps://pypi.org/project/pathvalidate/\nFrom https://pypi.org/project/pathvalidate/#sanitize-a-filename:\n\nfrom pathvalidate import sanitize_filename\n\nfname = \"fi:l*e/p\\\"a?t>h|.t<xt\"\nprint(f\"{fname} -> {sanitize_filename(fname)}\\n\")\nfname = \"\\0_a*b:c<d>e%f/(g)h+i_0.txt\"\nprint(f\"{fname} -> {sanitize_filename(fname)}\\n\")\n\nOutput\nfi:l*e/p\"a?t>h|.t<xt -> filepath.txt\n_a*b:c<d>e%f/(g)h+i_0.txt -> _abcde%f(g)h+i_0.txt\n\n\n", "I liked the python-slugify approach here but it was stripping dots also away which was not desired. So I optimized it for uploading a clean filename to s3 this way:\npip install python-slugify\n\nExample code:\ns = 'Very / Unsafe / file\\nname hähä \\n\\r .txt'\nclean_basename = slugify(os.path.splitext(s)[0])\nclean_extension = slugify(os.path.splitext(s)[1][1:])\nif clean_extension:\n clean_filename = '{}.{}'.format(clean_basename, clean_extension)\nelif clean_basename:\n clean_filename = clean_basename\nelse:\n clean_filename = 'none' # only unclean characters\n\nOutput:\n>>> clean_filename\n'very-unsafe-file-name-haha.txt'\n\nThis is so failsafe, it works with filenames without extension and it even works for only unsafe characters file names (result is none here).\n", "Answer modified for python 3.6\nimport string\nimport unicodedata\n\nvalidFilenameChars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\ndef removeDisallowedFilenameChars(filename):\n cleanedFilename = unicodedata.normalize('NFKD', filename).encode('ASCII', 'ignore')\n return ''.join(chr(c) for c in cleanedFilename if chr(c) in validFilenameChars)\n\n", "Not exactly what OP was asking for but this is what I use because I need unique and reversible conversions:\n# p3 code\ndef safePath (url):\n return ''.join(map(lambda ch: chr(ch) if ch in safePath.chars else '%%%02x' % ch, url.encode('utf-8')))\nsafePath.chars = set(map(lambda x: ord(x), '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+-_ .'))\n\nResult is \"somewhat\" readable, at least from a sysadmin point of view.\n", "When confronted with the same problem I used python-slugify.\nUsage was also suggested by Shoham but, as therealmarv pointed out, by default python-slugify also converts dots.\nThis behaviour can be overruled by including dots into the regex_pattern argument.\n> filename = \"This is a väryì' Strange File-Nömé.jpeg\"\n> pattern = re.compile(r'[^-a-zA-Z0-9.]+')\n> slugify(filename,regex_pattern=pattern) \n'this-is-a-varyi-strange-file-nome.jpeg'\n\nNote that the regex pattern was copied from the\nALLOWED_CHARS_PATTERN_WITH_UPPERCASE\nglobal variable within the slugify.py file of the python-slugify package and extended with \".\"\nKeep in mind that special characters like .() must be escaped with \\.\nIf you want to preserve uppercase letters use the lowercase=False argument.\n> filename = \"This is a väryì' Strange File-Nömé.jpeg\"\n> pattern = re.compile(r'[^-a-zA-Z0-9.]+')\n> slugify(filename,regex_pattern=pattern, lowercase=False) \n'This-is-a-varyi-Strange-File-Nome.jpeg'\n\nThis worked using Python 3.8.4 and python-slugify 4.0.1\n", "Most of these solutions don't work.\n'/hello/world' -> 'helloworld'\n'/helloworld'/ -> 'helloworld'\nThis isn't what you want generally, say you are saving the html for each link, you're going to overwrite the html for a different webpage.\nI pickle a dict such as:\n{'helloworld': \n (\n {'/hello/world': 'helloworld', '/helloworld/': 'helloworld1'},\n 2)\n }\n\n2 represents the number that should be appended to the next filename.\nI look up the filename each time from the dict. If it's not there, I create a new one, appending the max number if needed.\n", "Yet another answer for Windows specific paths, using simple replacement and no funky modules:\nimport re\n\ndef check_for_illegal_char(input_str):\n # remove illegal characters for Windows file names/paths \n # (illegal filenames are a superset (41) of the illegal path names (36))\n # this is according to windows blacklist obtained with Powershell\n # from: https://stackoverflow.com/questions/1976007/what-characters-are-forbidden-in-windows-and-linux-directory-names/44750843#44750843\n #\n # PS> $enc = [system.Text.Encoding]::UTF8\n # PS> $FileNameInvalidChars = [System.IO.Path]::GetInvalidFileNameChars()\n # PS> $FileNameInvalidChars | foreach { $enc.GetBytes($_) } | Out-File -FilePath InvalidFileCharCodes.txt\n\n illegal = '\\u0022\\u003c\\u003e\\u007c\\u0000\\u0001\\u0002\\u0003\\u0004\\u0005\\u0006\\u0007\\u0008' + \\\n '\\u0009\\u000a\\u000b\\u000c\\u000d\\u000e\\u000f\\u0010\\u0011\\u0012\\u0013\\u0014\\u0015' + \\\n '\\u0016\\u0017\\u0018\\u0019\\u001a\\u001b\\u001c\\u001d\\u001e\\u001f\\u003a\\u002a\\u003f\\u005c\\u002f' \n\n output_str, _ = re.subn('['+illegal+']','_', input_str)\n output_str = output_str.replace('\\\\','_') # backslash cannot be handled by regex\n output_str = output_str.replace('..','_') # double dots are illegal too, or at least a bad idea \n output_str = output_str[:-1] if output_str[-1] == '.' else output_str # can't have end of line '.'\n\n if output_str != input_str:\n print(f\"The name '{input_str}' had invalid characters, \"\n f\"name was modified to '{output_str}'\")\n\n return output_str\n\nWhen tested with check_for_illegal_char('fas\\u0003\\u0004good\\\\..asd.'), I get:\nThe name 'fas♥♦good\\..asd.' had invalid characters, name was modified to 'fas__good__asd'\n\n", "UPDATE\nAll links broken beyond repair in this 6 year old answer.\nAlso, I also wouldn't do it this way anymore, just base64 encode or drop unsafe chars. Python 3 example:\nimport re\nt = re.compile(\"[a-zA-Z0-9.,_-]\")\nunsafe = \"abc∂éåß®∆˚˙©¬ñ√ƒµ©∆∫ø\"\nsafe = [ch for ch in unsafe if t.match(ch)]\n# => 'abc'\n\nWith base64 you can encode and decode, so you can retrieve the original filename again.\nBut depending on the use case you might be better off generating a random filename and storing the metadata in separate file or DB.\nfrom random import choice\nfrom string import ascii_lowercase, ascii_uppercase, digits\nallowed_chr = ascii_lowercase + ascii_uppercase + digits\n\nsafe = ''.join([choice(allowed_chr) for _ in range(16)])\n# => 'CYQ4JDKE9JfcRzAZ'\n\nORIGINAL LINKROTTEN ANSWER:\nThe bobcat project contains a python module that does just this.\nIt's not completely robust, see this post and this reply.\nSo, as noted: base64 encoding is probably a better idea if readability doesn't matter.\n\nDocs https://svn.origo.ethz.ch/bobcat/src-doc/safefilename-module.html\nSource https://svn.origo.ethz.ch/bobcat/trunk/src/bobcatlib/safefilename.py\n\n", "I'm sure this isn't a great answer, since it modifies the string it's looping over, but it seems to work alright:\nimport string\nfor chr in your_string:\n if chr == ' ':\n your_string = your_string.replace(' ', '_')\n elif chr not in string.ascii_letters or chr not in string.digits:\n your_string = your_string.replace(chr, '')\n\n", "Here, this should cover all the bases. It handles all types of issues for you, including (but not limited too) character substitution.\nWorks in Windows, *nix, and almost every other file system. Allows printable characters only.\ndef txt2filename(txt, chr_set='normal'):\n \"\"\"Converts txt to a valid Windows/*nix filename with printable characters only.\n\n args:\n txt: The str to convert.\n chr_set: 'normal', 'universal', or 'inclusive'.\n 'universal': ' -.0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n 'normal': Every printable character exept those disallowed on Windows/*nix.\n 'extended': All 'normal' characters plus the extended character ASCII codes 128-255\n \"\"\"\n\n FILLER = '-'\n\n # Step 1: Remove excluded characters.\n if chr_set == 'universal':\n # Lookups in a set are O(n) vs O(n * x) for a str.\n printables = set(' -.0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz')\n else:\n if chr_set == 'normal':\n max_chr = 127\n elif chr_set == 'extended':\n max_chr = 256\n else:\n raise ValueError(f'The chr_set argument may be normal, extended or universal; not {chr_set=}')\n EXCLUDED_CHRS = set(r'<>:\"/\\|?*') # Illegal characters in Windows filenames.\n EXCLUDED_CHRS.update(chr(127)) # DEL (non-printable).\n printables = set(chr(x)\n for x in range(32, max_chr)\n if chr(x) not in EXCLUDED_CHRS)\n result = ''.join(x if x in printables else FILLER # Allow printable characters only.\n for x in txt)\n\n # Step 2: Device names, '.', and '..' are invalid filenames in Windows.\n DEVICE_NAMES = 'CON,PRN,AUX,NUL,COM1,COM2,COM3,COM4,' \\\n 'COM5,COM6,COM7,COM8,COM9,LPT1,LPT2,' \\\n 'LPT3,LPT4,LPT5,LPT6,LPT7,LPT8,LPT9,' \\\n 'CONIN$,CONOUT$,..,.'.split() # This list is an O(n) operation.\n if result in DEVICE_NAMES:\n result = f'-{result}-'\n\n # Step 3: Maximum length of filename is 255 bytes in Windows and Linux (other *nix flavors may allow longer names).\n result = result[:255]\n\n # Step 4: Windows does not allow filenames to end with '.' or ' ' or begin with ' '.\n result = re.sub(r'^[. ]', FILLER, result)\n result = re.sub(r' $', FILLER, result)\n\n return result\n\nThis solution needs no external libraries. It substitutes non-printable filenames too because they are not always simple to deal with.\n", "Still haven't found a good library to generate a valid filename. Note that in languages like German, Norwegian or French special characters in filenames are very common and totally OK. So I ended up with my own library:\n# util/files.py\n\nCHAR_MAX_LEN = 31\nCHAR_REPLACE = '_'\n\nILLEGAL_CHARS = [\n '#', # pound\n '%', # percent\n '&', # ampersand\n '{', # left curly bracket\n '}', # right curly bracket\n '\\\\', # back slash\n '<', # left angle bracket\n '>', # right angle bracket\n '*', # asterisk\n '?', # question mark\n '/', # forward slash\n ' ', # blank spaces\n '$', # dollar sign\n '!', # exclamation point\n \"'\", # single quotes\n '\"', # double quotes\n ':', # colon\n '@', # at sign\n '+', # plus sign\n '`', # backtick\n '|', # pipe\n '=', # equal sign\n]\n\n\ndef generate_filename(\n name, char_replace=CHAR_REPLACE, length=CHAR_MAX_LEN, \n illegal=ILLEGAL_CHARS, replace_dot=False):\n ''' return clean filename '''\n # init\n _elem = name.split('.')\n extension = _elem[-1].strip()\n _length = length - len(extension) - 1\n label = '.'.join(_elem[:-1]).strip()[:_length]\n filename = ''\n \n # replace '.' ?\n if replace_dot:\n label = label.replace('.', char_replace)\n \n # clean\n for char in label + '.' + extension:\n if char in illegal:\n char = char_replace\n filename += char \n \n return filename\n\n\ngenerate_filename('nucgae zutaäer..0.1.docx', replace_dot=False)\nnucgae_zutaäer..0.1.docx\ngenerate_filename('nucgae zutaäer..0.1.docx', replace_dot=True)\nnucgae_zutaäer__0_1.docx\n" ]

[ 243, 157, 110, 108, 47, 46, 42, 20, 19, 15, 7, 6, 6, 6, 6, 6, 6, 5, 4, 2, 2, 1, 1, 0, 0, 0, 0 ]

[]

[]

[ "filenames", "python", "sanitize", "slug" ]

[ "I was able to run your code just by changing the callback function lel to:\ndef lel(sender):\n with dpg.window():\n pass\n\nand could generate many child windows. You see that I removed the tag. Tags must be unique, but you used the same one as in your main window, which is not allowed. Also note the need of the pass dummy statement at the end of an empty with block.\n" ]

[ 0 ]

[]

[]

[ "dearpygui", "python" ]

[ "import boto3\nimport pandas as pd\nfrom sagemaker import get_execution_role\n\nrole = get_execution_role()\nbucket='my-bucket'\ndata_key = 'train.csv'\ndata_location = 's3://{}/{}'.format(bucket, data_key)\n\npd.read_csv(data_location)\n\n", "In the simplest case you don't need boto3, because you just read resources.\nThen it's even simpler:\nimport pandas as pd\n\nbucket='my-bucket'\ndata_key = 'train.csv'\ndata_location = 's3://{}/{}'.format(bucket, data_key)\n\npd.read_csv(data_location)\n\nBut as Prateek stated make sure to configure your SageMaker notebook instance to have access to s3. This is done at configuration step in Permissions > IAM role\n", "If you have a look here it seems you can specify this in the InputDataConfig. Search for \"S3DataSource\" (ref) in the document. The first hit is even in Python, on page 25/26.\n", "You could also access your bucket as your file system using s3fs \nimport s3fs\nfs = s3fs.S3FileSystem()\n\n# To List 5 files in your accessible bucket\nfs.ls('s3://bucket-name/data/')[:5]\n\n# open it directly\nwith fs.open(f's3://bucket-name/data/image.png') as f:\n display(Image.open(f))\n\n", "Do make sure the Amazon SageMaker role has policy attached to it to have access to S3. It can be done in IAM.\n", "You can also use AWS Data Wrangler https://github.com/awslabs/aws-data-wrangler:\nimport awswrangler as wr\n\ndf = wr.s3.read_csv(path=\"s3://...\")\n\n", "A similar answer with the f-string.\nimport pandas as pd\nbucket = 'your-bucket-name'\nfile = 'file.csv'\ndf = pd.read_csv(f\"s3://{bucket}/{file}\")\nlen(df) # print row counts\n\n", "This code sample to import csv file from S3, tested at SageMaker notebook.\nUse pip or conda to install s3fs. !pip install s3fs\nimport pandas as pd\n\nmy_bucket = '' #declare bucket name\nmy_file = 'aa/bb.csv' #declare file path\n\nimport boto3 # AWS Python SDK\nfrom sagemaker import get_execution_role\nrole = get_execution_role()\n\ndata_location = 's3://{}/{}'.format(my_bucket,my_file)\ndata=pd.read_csv(data_location)\ndata.head(2)\n\n", "There are multiple ways to read data into Sagemaker. To make the response more comprehensive i am adding details to read the data into Sagemaker Studio Notebook in memory as well as S3 mounting options.\nThough Notebooks are not recommend for data intensive modeling and are more used for prototyping based on my experience, there are multiple ways the data can be read into it.\nIn Memory Based Options\n\nBoto3\nS3FS\n\nBoth Boto3 and S3FS can also be used in conjunction with python libraries like Pandas to read the data in memory as well as can also be used to\ncopy the data to local instance EFS.\nMount Options\n\nS3FS-Fuse (https://github.com/s3fs-fuse/s3fs-fuse)\nGoofy (https://github.com/kahing/goofys)\n\nThese two options provide a mount like behaviour where the data appears to be in as if the local directory for higher IO operations. Both of these options have their pros and cons.\n" ]

[ 57, 42, 11, 10, 5, 4, 2, 0, 0 ]

[]

[]

[ "amazon_s3", "amazon_sagemaker", "amazon_web_services", "machine_learning", "python" ]

[ "Consider using numpy for your matrices. You can then use np.sort to do what you want:\nnp.sort(matrix, axis=0)\n\n", "If you didn't want to use numpy (though you should), you could do:\nfrom collections import Counter\n\ntest = [[1,0,1,1,0,1,0],\n[0,0,0,1,0,0,0],\n[1,0,1,1,1,1,1],\n[0,1,1,0,1,1,0],\n[1,1,0,1,0,0,1] ]\n\nnew_version = [[] for _ in test] # create an empty list to append data to\nfor count, item in enumerate(test[0]): # go through the length of one of the list of lists for their length # assuming that all lists are of equal length\n frequency = Counter([x[count] for x in test]) # get frequency count for the column\n for count_inside, item_inside in enumerate(test): \n # to add the values depending on their frequency distribution in the column\n value = 0 if 0 in frequency and count_inside < frequency[0] else 1\n new_version[count_inside].append(value)\n \nprint(new_version)\n \n\n", "Not as readable as the numpy approach, but if you want to use the list-approach you could\n\nTranspose the matrix by using the zip(*matrix) approach.\nSort the resulting rows (which are columns of the original matrix)\nTranspose back.\n\nYou can do it in one line:\n[row for row in zip(*[sorted(column) for column in zip(*matrix)])]\n\n" ]

[ 2, 1, 1 ]

[]

[]

[ "matrix", "python" ]

[ "Ok, so you just need to define your own pd.date_range, then build a new MultiIndex to get the daily data for each user and use pd.DataFrame.reindex.\ndf[\"dt\"] = pd.to_datetime(df[\"dt\"])\ndf = df.set_index([\"user\", \"dt\"])\n\ndaily_idx = pd.date_range(start=\"2016-01-01\", end=\"2016-01-31\", freq=\"D\")\n\nnew_idx = pd.MultiIndex.from_product(\n [df.index.get_level_values(\"user\").unique(), daily_idx], names=[\"user\", \"daily\"]\n)\nout = df.reindex(new_idx, fill_value=0).reset_index()\nprint(out)\n\n user daily val price\n0 a 2016-01-01 0 0.0\n1 a 2016-01-02 0 0.0\n2 a 2016-01-03 0 0.0\n3 a 2016-01-04 0 0.0\n4 a 2016-01-05 1 1.0\n5 a 2016-01-06 0 0.0\n6 a 2016-01-07 0 0.0\n7 a 2016-01-08 33 2.0\n8 a 2016-01-09 0 0.0\n9 a 2016-01-10 0 0.0\n10 a 2016-01-11 0 0.0\n11 a 2016-01-12 0 0.0\n12 a 2016-01-13 0 0.0\n13 a 2016-01-14 0 0.0\n14 a 2016-01-15 0 0.0\n15 a 2016-01-16 0 0.0\n16 a 2016-01-17 0 0.0\n17 a 2016-01-18 0 0.0\n18 a 2016-01-19 0 0.0\n19 a 2016-01-20 0 0.0\n20 a 2016-01-21 0 0.0\n21 a 2016-01-22 0 0.0\n22 a 2016-01-23 0 0.0\n23 a 2016-01-24 0 0.0\n24 a 2016-01-25 0 0.0\n25 a 2016-01-26 0 0.0\n26 a 2016-01-27 0 0.0\n27 a 2016-01-28 0 0.0\n28 a 2016-01-29 0 0.0\n29 a 2016-01-30 0 0.0\n30 a 2016-01-31 0 0.0\n31 b 2016-01-01 0 0.0\n32 b 2016-01-02 0 0.0\n33 b 2016-01-03 0 0.0\n34 b 2016-01-04 0 0.0\n35 b 2016-01-05 0 0.0\n36 b 2016-01-06 0 0.0\n37 b 2016-01-07 0 0.0\n38 b 2016-01-08 0 0.0\n39 b 2016-01-09 0 0.0\n40 b 2016-01-10 2 1.0\n41 b 2016-01-11 0 0.0\n42 b 2016-01-12 0 0.0\n43 b 2016-01-13 0 0.0\n44 b 2016-01-14 0 0.0\n45 b 2016-01-15 1 1.0\n46 b 2016-01-16 0 0.0\n47 b 2016-01-17 0 0.0\n48 b 2016-01-18 0 0.0\n49 b 2016-01-19 0 0.0\n50 b 2016-01-20 0 0.0\n51 b 2016-01-21 0 0.0\n52 b 2016-01-22 0 0.0\n53 b 2016-01-23 0 0.0\n54 b 2016-01-24 0 0.0\n55 b 2016-01-25 0 0.0\n56 b 2016-01-26 0 0.0\n57 b 2016-01-27 0 0.0\n58 b 2016-01-28 0 0.0\n59 b 2016-01-29 0 0.0\n60 b 2016-01-30 0 0.0\n61 b 2016-01-31 0 0.0\n62 c 2016-01-01 0 0.0\n63 c 2016-01-02 0 0.0\n64 c 2016-01-03 0 0.0\n65 c 2016-01-04 0 0.0\n66 c 2016-01-05 0 0.0\n67 c 2016-01-06 0 0.0\n68 c 2016-01-07 0 0.0\n69 c 2016-01-08 0 0.0\n70 c 2016-01-09 0 0.0\n71 c 2016-01-10 0 0.0\n72 c 2016-01-11 0 0.0\n73 c 2016-01-12 0 0.0\n74 c 2016-01-13 0 0.0\n75 c 2016-01-14 0 0.0\n76 c 2016-01-15 0 0.0\n77 c 2016-01-16 5 2.0\n78 c 2016-01-17 0 0.0\n79 c 2016-01-18 0 0.0\n80 c 2016-01-19 6 4.2\n81 c 2016-01-20 0 0.0\n82 c 2016-01-21 0 0.0\n83 c 2016-01-22 5 5.5\n84 c 2016-01-23 0 0.0\n85 c 2016-01-24 0 0.0\n86 c 2016-01-25 0 0.0\n87 c 2016-01-26 0 0.0\n88 c 2016-01-27 0 0.0\n89 c 2016-01-28 0 0.0\n90 c 2016-01-29 0 0.0\n91 c 2016-01-30 0 0.0\n92 c 2016-01-31 0 0.0\n\n", "You can use:\ndf['dt'] = pd.to_datetime(df['dt'])\n\n(df.set_index('dt')\n .groupby('user', as_index=False)\n .apply(lambda d: d.reindex(pd.date_range(d.index.min(), d.index.max()),\n fill_value=0\n ))\n .reset_index(-1)\n)\n\nIf you want to round to month start/end:\n(df.set_index('dt')\n .groupby('user', as_index=False)\n .apply(lambda d: d.reindex(pd.date_range(d.index.min()-pd.offsets.MonthBegin(1),\n d.index.max()+pd.offsets.MonthEnd(1)\n ).rename('id'),\n fill_value=0)\n )\n .reset_index('id')\n)\n\nOutput:\n id user val price\n0 2016-01-01 0 0 0.0\n0 2016-01-02 0 0 0.0\n0 2016-01-03 0 0 0.0\n0 2016-01-04 0 0 0.0\n0 2016-01-05 a 1 1.0\n.. ... ... ... ...\n2 2016-01-27 0 0 0.0\n2 2016-01-28 0 0 0.0\n2 2016-01-29 0 0 0.0\n2 2016-01-30 0 0 0.0\n2 2016-01-31 0 0 0.0\n\n[93 rows x 4 columns]\n\n" ]

[ 2, 2 ]

[]

[]

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

[ "@tree.command(name=\"ban\", description=\"Ban someone\")\nasync def _ban(ctx, user: discord.Member):\n await user.ban(reason=\"NO reason\")\n await ctx.send(f'Banned {user}! Reason: {reason}') \n\nIf you are only using discord.py, which i assume so, user: discord.Member is enough. options = [app_commands.choices( isn't a thing.\nRead the docs on @discord.app_commands.CommandTree.command\nThe real use of app_commands.choices looks like this:\[email protected](name='command')\n@app_commands.choices(option=[\n app_commands.Choice(name='option1', value='1'),\n app_commands.Choice(name='option2', value='2'),\n])\nasync def command(interaction: discord.Interaction, option: app_commands.Choice[str]):\n if option.value == '1':\n #chosen option was option1\n elif option.value == '2':\n #chosen option was option2\n\n" ]

[ 1 ]

[]

[]

[ "discord.py", "python" ]

[ "with .config you can adjust Attributes after you placed it.\nNow you can adjust the window size with the len() of your string.\nI hope it helps.\nimport tkinter as tk\n\ndef adjust_Window(canvas, text):\n\n canvas.config(width=len(text)*50)\n\ndef main():\n root = tk.Tk()\n\n canvas = tk.Canvas(root, width=500, height=500)\n canvas.pack()\n\n adjust_Window(canvas, \"i hope it helps <3\")\n\n root.mainloop()\n\n\nif __name__ == '__main__':\n main()\n\n", "Easier way to do this:\nfrom tkinter import *\nfrom tkinter import ttk\n\n# Create an instance of tkinter frame or window\nwin=Tk()\n\n# Set the size of the window\nwin.geometry(\"700x350\")\n\ndef update_width():\n l.configure(text='Hotel Raj Kundra and Family Resturant', background='blue', foreground='white', font=(\"Calibri,8,itlaic\"))\n \n\n# Create a frame\nframe=Frame(win, background=\"skyblue3\", width=700, height=250)\nframe.pack()\n\n# Add a button in the main window\nttk.Button(win, text=\"Update\", command=update_width).pack()\nl = ttk.Label(win, background='red', text=\"so how it will automatically adjust in this size of window without wrapped the text. just fit in the window siz\" , font=(\"Calibri,32,Bold\"))\nl.pack()\n \nwin.mainloop()\n\nResult widest:\n\nResult to shrink:\n\n" ]

[ 0, 0 ]

[]

[]

[ "font_size", "python", "tkinter" ]

[ "bigtree is a Python tree implementation that integrates with Python lists, dictionaries, and pandas DataFrame.\nFor this scenario, there is a built-in dataframe_to_tree method which does this for you.\nimport pandas as pd\nfrom bigtree import dataframe_to_tree, print_tree\n\n# I changed the dataframe to path column and count column\n# I also added a `root` over here since trees must start from same root\npath_data = pd.DataFrame([\n [\"root/A/A2/A3\", 10],\n [\"root/A/B2/A3\", 12],\n [\"root/A/B2/B3\", 14],\n [\"root/B/A2/A3\", 20],\n [\"root/B/B2/A3\", 25],\n],\n columns=[\"Path\", \"Count\"]\n)\nroot = dataframe_to_tree(path_data)\nprint_tree(root, attr_list=[\"Count\"])\n\nThis results in output,\nroot\n├── A\n│ ├── A2\n│ │ └── A3 [Count=10]\n│ └── B2\n│ ├── A3 [Count=12]\n│ └── B3 [Count=14]\n└── B\n ├── A2\n │ └── A3 [Count=20]\n └── B2\n └── A3 [Count=25]\n\nSource/Disclaimer: I'm the creator of bigtree ;)\n" ]

[ 0 ]

[]

[]

[ "dataframe", "python", "tree" ]

[ "At this line surface=self.surface you're trying to access a variable that does not exist in this scope. self has only been defined within the context of the various functions of your classes, and python doesn't know about it outside of those functions.\nIf you have an instance of Rectangle called for example rect, you can refer to its member function surface as rect.surface, or you can evaluate that function's value by calling rect.surface().\nThe key to understanding this is to know that objects can have many names. By convention, within the object we refer to the instance by the name self. Outside of the object, this would be confusing so we use names that tell us what object we're referring to. (Just as you might refer to yourself as \"me\", but you'd be confused if others used that same word to refer to you!)\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "Unless you want to manually install packages/modules like pandas you should first install a package manager like PIP, or Anaconda if you have not done so already.\nOnce you install follow instructions of either to setup the package manager.\nUsing PIP you should be entering the following command once installed correctly:\n$ pip install package_name\n$ pip3 install package_name\n\nIf you are using Anaconda, Miniconda, or any other package manager follow the instructions here\nNOTE: Before asking a question, check if there are any similar questions that have already been answered\n" ]

[ 0 ]

[]

[]

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

[ "User.name.property.columns[0].type.length\n\nNote, that SQLAlchemy supports composite properties, that's why columns is a list. It has single item for simple column properties.\n", "This should work (tested on my machine) :\nprint user.columns.name.type.length\n\n", "I was getting errors when fields were too big so I wrote a generic function to trim any string down and account for words with spaces. This will leave words intact and trim a string down to insert for you. I included my orm model for reference.\nclass ProductIdentifierTypes(Base):\n __tablename__ = 'prod_id_type'\n id = Column(Integer, primary_key=True, autoincrement=True)\n name = Column(String(length=20))\n description = Column(String(length=100))\n\ndef trim_for_insert(field_obj, in_str) -> str:\n\n max_len = field_obj.property.columns[0].type.length\n if len(in_str) <= max_len:\n return in_str\n \n logger.debug(f'Trimming {field_obj} to {max_len} max length.')\n \n trim_str = in_str[:(max_len-1)]\n \n if ' ' in trim_str[:int(max_len*0.9)]:\n return(str.join(' ', trim_str.split(' ')[:-1]))\n \n return trim_str\n\ndef foo_bar():\n from models.deals import ProductIdentifierTypes, ProductName\n \n _str = \"Foo is a 42 year old big brown dog that all the kids call bar.\"\n \n print(_str)\n \n print(trim_for_insert(ProductIdentifierTypes.name, _str))\n \n _str = \"Full circle from the tomb of the womb to the womb of the tomb we come, an ambiguous, enigmatical incursion into a world of solid matter that is soon to melt from us like the substance of a dream.\"\n \n print(_str)\n \n print(trim_for_insert(ProductIdentifierTypes.description, _str))```\n\n", "If you have access to the class:\nTableClass.column_name.type.length\n\nIf you have access to an instance, you access the Class using the __class__ dunder method.\ntable_instance.__class__.column_name.type.length\n\nSo in your case:\n# Via Instance\nuser.__class__.name.type.length\n# Via Class\nUser.name.type.length\n\nMy use case is similar to @Gregg Williamson\nHowever, I implemented it differently:\ndef __setattr__(self, attr, value):\n column = self.__class__.type\n if length := getattr(column, \"length\", 0):\n value = value[:length]\n super().__setattr__(name, value)\n\n" ]

[ 22, 3, 0, 0 ]

[]

[]

[ "python", "sqlalchemy" ]

[]

[]

[ "by setting end parameter you can set whatever will be after it printed. it is next line command by default \"\\n\" so everytime it prints it gets to the next line\nimport time\nimport random\nmaybe = range(5)\nprint(\"GIVE ME AN ANSWER!!!\")\ntime.sleep(1)\nfor x in maybe:\n print(random.choice(\"1\" \"0\"),end=\" \")\n time.sleep(0.1)\n print(random.choice(\"1\" \"0\"),end=\" \")\n time.sleep(0.1)\n print(random.choice(\"1\" \"0\"),end=\" \")\n time.sleep(0.1)\n print(random.choice(\"1\" \"0\"),end=\" \")\n time.sleep(0.1)\n print(random.choice(\"1\" \"0\"),end=\" \")\n time.sleep(0.1)\n print(\" \")\n\noutput is:\n1 1 0 1 1 \n0 1 1 1 0 \n0 1 0 1 0 \n1 0 0 1 0 \n1 0 0 0 1 \n\n", "import random\nimport time\n\nfor x in range(10):\n print(random.randint(0,1),end=' ')\n time.sleep(0.1)\nprint()\n\n" ]

[ -1, -1 ]

[ "python", "replit" ]

[ "Calculate the cumulative sum grouping by Suppler and divide by the Supplier Total Revenue.\nThen find each Supplier Revenue Threshold by getting the minimum Cumulative Revenue Percentage under 90%.\nThen you can get the list of SKUs by Supplier and calculate the coverage.\nimport pandas as pd\n\ndf = pd.DataFrame([\n ['UNILEVER', '1111', 10000], \n ['UNILEVER', '2222', 50000], \n ['UNILEVER', '3333', 500], \n ['PEPSICO', '1313', 680], \n ['PEPSICO', '2424', 10000], \n ['PEPSICO', '2323', 450], \n], columns=['Supplier', 'SKU', 'Net Revenue'])\n\ntotal_revenue_by_supplier = df.groupby(df['Supplier']).sum().reset_index()\ntotal_revenue_by_supplier.columns = ['Supplier', 'Total Revenue']\n\ndf = df.sort_values(['Supplier', 'Net Revenue'], ascending=[True, False])\n\ndf['cumsum'] = df.groupby(df['Supplier'])['Net Revenue'].transform(pd.Series.cumsum)\n\ndf = df.merge(total_revenue_by_supplier, on='Supplier')\n\ndf['cumpercentage'] = df['cumsum'] / df['Total Revenue']\n\nmin_before_threshold = df[df['cumpercentage'] >= 0.9][['Supplier', 'cumpercentage']].groupby('Supplier').min().reset_index()\nmin_before_threshold.columns = ['Supplier', 'Revenue Threshold']\n\ndf = df.merge(min_before_threshold, on='Supplier')\n\ndf = df[df['cumpercentage'] <= df['Revenue Threshold']][['Supplier', 'SKU', 'Net Revenue']]\n\ndf\n\n" ]

[ 1 ]

[]

[]

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

[ "As suspected it's just byte order, I get a sensible looking image when running the following code in a Jupyter notebook:\nimport numpy as np\nfrom PIL import Image\n\n# open as big-endian, convert to native order, then reshape as appropriate\nraw = np.fromfile(\n './Sequence 1_000021.dat', dtype='>u2'\n).astype('uint16').reshape((24, 1024, 1024))\n\n# display inline\nImage.fromarray(raw[1,:,:])\n\n" ]

[ 1 ]

[]

[]

[ "geospatial", "image", "numpy", "python", "python_imaging_library" ]

[ "You can sort your dataframe using sort_values based on 'ID' and 'label in ascending and descending order respectively.\nThen you can calculate a grouped difference using diff on your columns, which would calculate the difference between the last and first row of each group (last - new) and populate the last row, leaving the first row with NaN.\nThe last thing to do is to fill those resulted NaN (which are the first rows of each group):\ndf_sorted = df.sort_values(by=['ID','label'],ascending=[True,False])\ndf_sorted.groupby('ID').diff().assign(label=np.nan).fillna(df_sorted).astype(int)\n\nprints:\n Feature_1; Feature_2; Feature_3; label\n0 10 3 4 1\n1 -1 -2 -2 0\n2 100 30 40 1\n3 -10 -20 -20 0\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "Seems like your error is at index=searchDB[\"AllKeywords\"]\nClean up your variables\nimport pandas as pd\nfrom elasticsearch import Elasticsearch\n\nes = Elasticsearch(\"http://localhost:9200\")\n\ndf = pd.read_csv('')\nkeywords = df[\"AllKeywords\"].str.split(', ')\nexploded_keywords = searchDB.explode()\n\nfor i in range(len(df)):\n keywordToSearch = df.loc[i, \"H\"]\n res = es.search(index=df[\"AllKeywords\"], body={\"from\":0, \"size\":0, \"query\":{\"match\":{\"sentence\": keywordToSearch}}})\n\n" ]

[ 0 ]

[]

[]

[ "elasticsearch", "pandas", "python" ]

[ "To improve the image quality after rotating the image, you can try using different interpolation methods when rotating the image. The rotate function in skimage.transform has a mode parameter that allows you to specify the interpolation method to use. The default value is constant, which means that it uses a constant value (specified by the cval parameter) for pixels outside the boundaries of the input image.\nYou can try using a different interpolation method, such as bilinear or bicubic, which can provide better results in some cases. For example, the following code uses the bilinear interpolation method:\nfrom skimage.transform import rotate\nimage = cv2.imread('122.png')\nrotated = rotate(image, 34, cval=1, resize=True, mode='bilinear')\n\n\nIn addition to using a different interpolation method, you can also try using a different threshold value when applying thresholding to the rotated image. The cv2.threshold function has a threshold parameter that specifies the threshold value to use. By default, it is set to 0, which means that all pixels with a value less than or equal to 0 will be set to 0 (black) and all other pixels will be set to the maximum value specified by the maxval parameter (100 in your case).\nret, thresh_hold = cv2.threshold(rotated, 127, 255, cv2.THRESH_BINARY)\nplt.imshow(thresh_hold)\n\nAlternatively, you can use the cv2.THRESH_BINARY_INV flag instead of cv2.THRESH_BINARY to invert the thresholding result, so that pixels with a value less than or equal to 127 are set to 255 (white) and all other pixels are set to 0 (black). The following code shows how to do this:\nret, thresh_hold = cv2.threshold(rotated, 127, 255, cv2.THRESH_BINARY_INV)\nplt.imshow(thresh_hold)\n\n", "One way to approach that is to simply antialias the image in Python/OpenCV.\nTo do that one simply converts to grayscale. Then blurs the image, then applies a stretch of the image.\nAdjust the blur sigma to change the antialiasing.\nInput:\n\nimport cv2\nimport numpy as np\nimport skimage.exposure\n\n# load image\nimg = cv2.imread('122.png')\n\n# convert to gray\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# blur threshold image\nblur = cv2.GaussianBlur(gray, (0,0), sigmaX=2, sigmaY=2, borderType = cv2.BORDER_DEFAULT)\n\n# stretch so that 255 -> 255 and 127.5 -> 0\nresult = skimage.exposure.rescale_intensity(blur, in_range=(127.5,255), out_range=(0,255)).astype(np.uint8)\n\n# save output\ncv2.imwrite('122_antialiased.png', result)\n\n# Display various images to see the steps\ncv2.imshow('result', result)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\nResult:\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "I don't think there is a better way of doing this, but if the b parameter is not going to be used in foo() then I wouldn't have it in there.\ndef bar(a=0, b=0, c=0, d=0):\n print(a,b,c,d)\n\ndef foo(d=0):\n bar(d=d)\n\nfoo(1)\n#(0,0,0,1)\n\n", "Any answer here is subjective but I'm not going to submit to close the question because I think this is valuable for anyone learning Python and professional practice.\nWith that said, I think the way you have it is correct, if b in foo is assumed to be used in a line of code within the function after bar is called. If b isn't used, you should remove it from the parameter list.\nBut let me make another case while we're at subjectivity: specify arguments upon a function call by use of keyword arguments as much as possible.\nWhy? \nBecause when you're looking at complex code in a professional environment, it becomes much more clear what's being passed into a function and why. And in Python, explicit is preferred over implicit.\nYou will write your code once. You might correct it a couple of times. Someone will read your code to handle a production issue a thousand times.\nTake for example the below code (which is a financial model):\ndef black_scholes(spot, strike, volatility, risk_free_rate, expiry=.25):\n # do calculation and return result\n # ....\n return result\n\noption_value = black_scholes(44, 48, .08, .54, .75)\n\nPut your hand over the parameter list in the function definition. Can you tell what each of those numbers represents? \nSure, you can find the definition in the same file and compare the positional arguments, but what if black_scholes() is in another module? Now it's a tiny bit tougher. What if we expanded on this and added a higher-leveled wrapper around black_scholes() and that's what we had to debug to get to this?\nNow, let me show you the function call with keyword arguments:\nresult = black_scholes(spot=44, strike=48, volatility=.08, risk_free_rate=.54, expiry=.75)\n\nThis now becomes much more clear, and we can anticipate what the expected result should be. We also just saved a lot of time on reading the code and have a result to compare with (given the expected) from stepping over the function in the debugger instead of having to go into it and reading it line by line.\n", "It's not specific to Python, but what is asked looks much like a classical code smell, which are mostly language agnostic.\nIf you have the same fields appearing frequently in function signatures, you should probably make them an object.\nhttps://refactoring.guru/fr/smells/data-clumps\n" ]

[ 0, 0, 0 ]

[]

[]

[ "named_parameters", "python" ]

[ "Final answer to my issue was to use the global tag to call the variable outside of the function into it.\nvariable = \"te\"\ndef onStart():\n global variable\n variable = variableEntry.get()\n\nwhen reading about globals for python i misunderstood it as a declarative global and not a call global\n" ]

[ 0 ]

[]

[]

[ "pypdf2", "python" ]

[ "To get the number of votes for a specific item, you would use:\nvote_count = Item.objects.filter(votes__contest=contestA).count()\n\nIf you wanted a break down of the distribution of votes in a particular contest, I would do something like the following:\ncontest = Contest.objects.get(pk=contest_id)\nvotes = contest.votes_set.select_related()\n\nvote_counts = {}\n\nfor vote in votes:\n if not vote_counts.has_key(vote.item.id):\n vote_counts[vote.item.id] = {\n 'item': vote.item,\n 'count': 0\n }\n\n vote_counts[vote.item.id]['count'] += 1\n\nThis will create dictionary that maps items to number of votes. Not the only way to do this, but it's pretty light on database hits, so will run pretty quickly.\n", "Another way of doing this would be using Aggregation. You should be able to achieve a similar result using a single query. Such as this:\nfrom django.db.models import Count\n\nItem.objects.values(\"contest\").annotate(Count(\"id\"))\n\nI did not test this specific query, but this should output a count of the items for each value in contests as a dictionary.\n", "Use related name to count votes for a specific contest\nclass Item(models.Model):\n name = models.CharField()\n\nclass Contest(models.Model);\n name = models.CharField()\n\nclass Votes(models.Model):\n user = models.ForeignKey(User)\n item = models.ForeignKey(Item)\n contest = models.ForeignKey(Contest, related_name=\"contest_votes\")\n comment = models.TextField()\n\n>>> comments = Contest.objects.get(id=contest_id).contest_votes.count()\n\n", "You can use len() to get the count of contestA's votes:\ncurrent_vote = len(Item.objects.filter(votes__contest=contestA))\n\n" ]

[ 181, 23, 1, 0 ]

[]

[]

[ "count", "django", "django_queryset", "python", "python_3.x" ]

[ "I recommend to use EmailMultiAlternatives instead of EmailMessage\nIn this case you can attack file without any problems\nemail_message = EmailMultiAlternatives(\n subject=\"subject\",\n to=\"to_email\",\n)\nhtml_email: str = loader.render_to_string(template_name, context)\nemail_message.attach_alternative(html_email, 'text/html')\nif file_path:\n email_message.attach_file(file_path, mimetype=\"file mimetype\")\nemail_message.send()\n\nSo you need to create EmailMultiAlternatives instance and via attach_file() method add a path to file.\nAlso need to provide mimetype of the file. For example for pdf file will be mimetype=\"application/pdf\"\n" ]

[ 0 ]

[]

[]

[ "django", "python" ]

[ "Lastly, I found the solution. It was about the python folder into the path /usr/local/bin/python\nYou could check it by the following command: where python\nIn my case this folder is missing, perhaps I deleted it after the upgrade of the python3.\nThat was a mistake both folders should exist on this path!\nIf you type: where python you should receive: /usr/local/bin/python\nIf you type: where python3 you should receive: /usr/local/bin/python3\nIn order to fix the error, I installed python again by using the brew install pyenv\nthis suggestion helps me to install it properly.\nIn the end, in order to eliminate all errors I installed the Python six package by using the command:\npip install --ignore-installed six\n", "Try doing it with the python virtual environment. Following are the steps.\n\nCreate a virtual environment.\nActivate the virtual environment.\nRun the pip install command with the virtual environment active.\n\nImplement as follows:\n\nuse correct version of Python when creating VENV\n\npython3 -m venv venv\n\nactivate on Unix or MacOS\n\nsource venv/bin/activate\n\nactivate on Windows (cmd.exe)\n\nvenv\\Scripts\\activate.bat\n\nactivate on Windows (PowerShell)\n\n'venv\\Scripts\\Activate.ps1'\n\ninstall the required package in the virtual environment\n\npython3 -m pip install --upgrade pip\npython3 -m pip install six\nNote: This works only when you are in the virtual environment.\n" ]

[ 0, 0 ]

[]

[]

[ "apple_m1", "homebrew", "nativescript", "python" ]

[ "It appears the user nginx is running as (nginx?) is missing privileges to read the local file /home/ubuntu/virtualenv/myapp/myapp/homelaunch/static/img/templated/home/img.png. You probably wanna check file permissions as well as permissions on the directories in the hierarchy.\n", "MacOs El Capitan: At the top of nginx.conf write user username group_name\nMy user name is Kamil so i write:\nuser Kamil staff;\n\n(word 'staff' is very important in macOS). This do the trick. After that you don't need to change any permission in your project folder and files.\n", "The minimum fix that worked for me is:\nsudo chmod -R 664 /home/ubuntu/virtualenv/myapp/myapp/homelaunch/static/\nsudo chmod -R a+X /home/ubuntu/virtualenv/myapp/myapp/homelaunch/static/\n\n(BTW, in my case the static folder is called collected_static)\n", "It seems the web server user doesn't have read permissions to the static files.\nYou can solve this in 2 ways:\n\n(easiest, safer) run the nginx as you app user instead of default nginx user. To do this, add the following in nginx.conf\nuser your_app_user\n\nReplace your_app_user with appropriate unix username for your app. In this case the your_app_user already has necessary permissions to the static content.\nAnother way would be to to grant permissions for the web server user to the static dir.\n\n", "Try specifying a user at the top of your nginx.conf, above the server section. \nuser www-data;\n\n", "The best solution in that case would be to add www-data to username group:\ngpasswd -a www-data username\nFor your changes to work, restart nginx\nnginx -s reload \n", "I had the same issue no long ago. It might be a combination of factors. I found how to fix 403 access denied by replacing the user in the nginx.conf file.\n\nI deployed my website on an ubuntu server using Digital Ocean.\nI created a new user on my new ubuntu server and give admin priviliges\n\n adduser newuser\n\n usermod -aG sudo newuser \n\n\nI updated my new server and installed few packages\n\n sudo apt update\n\n sudo apt install python3-pip python3-dev libpq-dev postgresql postgresql-contrib nginx curl \n\n\nI followed all this beautiful instruction on how to deploy your site on Digital Ocean\nSince I changed the user and I ssh into my new server using this new user, I need to replace the user on the nginx.conf. By default nginx.conf user is www-data:\n\n user www-data;\n\n worker_processes auto;\n\n pid /run/nginx.pid;\n\nThen I replaced with my sudo user and solved my problem. \n user newuser;\n\n worker_processes auto;\n\n pid /run/nginx.pid;\n\n\nThen I restart nginx, gunicorn and postgresql(even if the last one it is not really necessary)\n\n sudo systemctl restart nginx \n\n sudo systemctl restart gunicorn\n\n sudo systemctl restart postgresql\n\nAnd tada.. :) no more issue.\n", "Fix 403 error with Django static files on Ubuntu server.\n\nRun this -> gpasswd -a www-data your_proj_username\n\nReload nginx -> nginx -s reload\n\nCheck chmod for your dirs: /home, /home/proj_dir, /home/proj_dir/static\n\n\n\nRun this - stat --format '%a' /home . Result must be 755\nRun this - stat --format '%a' /home/your_proj_dir/static . Result must be 755\nRun this - stat --format '%a' /home/your_proj_dir . Result must be 750\n\n\nIf you have different values you can try to change this:\n\n\nsudo chmod 755 /home\nsudo chmod 755 /home/your_proj_dir/static\nsudo chmod 750 /home/your_proj_dir\n\n\nReload you project-server. This solve all permission errors\n\n" ]

[ 27, 27, 9, 9, 8, 4, 1, 0 ]

[ "After hours upon hours following so many articles, I ran across :\nhttp://nicholasorr.com/blog/2008/07/22/nginx-engine-x-what-a-pain-in-the-bum/\nwhich had a comment to chmod the whole django app dir, so I did:\nsudo chmod -R myapp\n\nThis fixed it. Unbelievable!\nThanks to those who offered solutions to fix this. \n" ]

[ -6 ]

[ "configuration", "django", "nginx", "python" ]

[]

[]

[ "Try to using anaconda\nthere you can specify version of python while creating virtual environment\nNo need to uninstall base python\nconda create -n envname python=x.x anaconda\n\n" ]

[ -2 ]

[ "archlinux", "binaries", "executable", "python" ]

[ "You can try the following:\ndef get_value(d, key_list):\n for key in key_list:\n d = d[key]\n return d\n\n\ndef set_value(d, key_list, value):\n res = d\n *keys, last_key = key_list\n\n for key in keys:\n d = d[key]\n\n d[last_key] = value\n return res\n\n\nperson_dict = [{\"person\": {\"first_name\": \"John\", \"age_of_children\": [1, 8, 13]}}]\nkey_tree = [0, \"person\", \"first_name\"]\n\nprint(get_value(person_dict, key_tree))\nprint(set_value(person_dict, key_tree, \"John2\"))\n\noutput:\nJohn\n[{'person': {'first_name': 'John2', 'age_of_children': [1, 8, 13]}}]\n\nFor getting values just use get_value, it's pretty simple. In set_value you need to iterate until before the last key, so that you can assign new value to the last object. After the for-loop, d is your last container(dict or list or whatever object who can is subscriptable) object, you can update the last_key value with the value of value. res = d line is needed because you need to have a reference to the most outer container otherwise after the for-loop you have only the last inner container.\n", "person_dict['person'].update({'first_name':'Ahmad'})\n\nor\nperson_dict[key_tree[0]].update({key_tree[1]: 'Ahmad'})\n\n" ]

[ 1, 0 ]

[]

[]

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

[ "An aside for users that land on this page: I could not reproduce the error that the OP posted. json.dumps() would be very highly unlikely to output \"bad json\". This was merely an attempt to help out the poster.\nSplitting The Strings Into Lists\nI am assuming per your comment that you mean that you want to take your strings, for example, this line\nkey2_3=value2_3_1,value2_3_2,value2_3_3\nand break these values up into \"key2_3\": [\"value2_3_1\", \"value2_3_2\", \"value2_3_3\"].\nTo do so, you'd have to make the following adjustment to the code provided to you:\ndef as_dict(self):\n if len(self.children) > 1:\n return {self.text: [node.as_dict() for node in self.children]}\n elif len(self.children) == 1:\n return {self.text: self.children[0].as_dict()}\n else:\n return self.text.split(\",\") # was self.text\n\n\nDictionaries of Dictionaries Instead of Lists\nTo make the output dictionary a dictionary of dictionaries with node base values of lists, ie {k1: {k2: [1, 2, 3]}}, and of the like, we have to make 2 changes.\n\nUpdate the as_dict method to use {}\ninstead of [].\nInclude a function to compress keys.\n\nWhen I was doing this, I had a hard time outputting the correct data structure... it'd look basically like this, {k1: {k1: {k2: {k2: value}}}}. This becomes obvious when you don't run the d = compress(root.as_dict()['root']) (d = root.as_dict()['root']) function in the code. So the code went from\ndef as_dict(self):\n if len(self.children) > 1:\n return {self.text: [node.as_dict() for node in self.children]}\n elif len(self.children) == 1:\n return {self.text: self.children[0].as_dict()}\n else:\n return self.text.split(\",\") if \",\" in self.text else self.text\n\nto\ndef as_dict(self):\n if len(self.children) > 1:\n return {self.text: {node.text: node.as_dict() for node in self.children}}\n elif len(self.children) == 1:\n return {self.text: self.children[0].as_dict()}\n else:\n return self.text.split(\",\") if \",\" in self.text else self.text\n\n, then I included the compress function\n# for merging like sub keys and values\ndef compress(dictionary):\n if isinstance(dictionary, dict):\n for k, v in dictionary.items():\n if isinstance(v, dict):\n if k in v.keys():\n dictionary[k] = dictionary[k].pop(k)\n compress(dictionary[k])\n compress(k)\n return dictionary\n\n\nFull Code\nIf you put the below in a file and run it from the command line, it should work 100%. Otherwise its probably a problem with anaconda or version of python (though that doesn't really seem likely).\nfrom io import StringIO\nimport json\n\n# for merging like sub keys and values\ndef compress(dictionary):\n if isinstance(dictionary, dict):\n for k, v in dictionary.items():\n if isinstance(v, dict):\n if k in v.keys():\n dictionary[k] = dictionary[k].pop(k)\n compress(dictionary[k])\n compress(k)\n return dictionary\n\n# helper method to convert equals sign to indentation for easier parsing\ndef convertIndentation(inputString):\n indentCount = 0\n indentVal = \" \"\n for position, eachLine in enumerate(inputString):\n if \"=\" not in eachLine:\n continue\n else:\n strSplit = eachLine.split(\"=\", 1)\n #get previous indentation\n prevIndent = inputString[position].count(indentVal)\n newVal = (indentVal * (prevIndent + 1)) + strSplit[1]\n inputString[position] = strSplit[0] + '\\n'\n inputString.insert(position+1, newVal)\n flatList = \"\".join(inputString)\n return flatList\n\n\n\n# helper class for node usage\nclass Node:\n def __init__(self, indented_line):\n self.children = []\n self.level = len(indented_line) - len(indented_line.lstrip())\n self.text = indented_line.strip()\n def add_children(self, nodes):\n childlevel = nodes[0].level\n while nodes:\n node = nodes.pop(0)\n if node.level == childlevel: # add node as a child\n self.children.append(node)\n elif node.level > childlevel: # add nodes as grandchildren of the last child\n nodes.insert(0,node)\n self.children[-1].add_children(nodes)\n elif node.level <= self.level: # this node is a sibling, no more children\n nodes.insert(0,node)\n return\n def as_dict(self):\n if len(self.children) > 1:\n return {self.text: {node.text: node.as_dict() for node in self.children}}\n elif len(self.children) == 1:\n return {self.text: self.children[0].as_dict()}\n else:\n return self.text.split(\",\") if \",\" in self.text else self.text\n\nif __name__ == \"__main__\":\n\n s = \"\"\"\n key1=value1\n key2\n key2_1=value2_1\n key2_2\n key2_2_1\n key2_2_1_1=value2_2_1_1\n key2_3=value2_3_1,value2_3_2,value2_3_3\n key3=value3_1,value3_2,value3_3\n \"\"\"\n\n fh = StringIO(s)\n fileContent = fh.readlines()\n fileParse = convertIndentation(fileContent)\n # convert equals signs to indentation\n root = Node('root')\n root.add_children([Node(line) for line in fileParse.splitlines() if line.strip()])\n d = compress(root.as_dict()['root'])\n # this variable is storing the json output\n jsonOutput = json.dumps(d, indent=4, sort_keys=False)\n f = StringIO(jsonOutput)\n\n # load the \"file\"\n loaded = json.load(f)\n\n print(s)\n print(jsonOutput)\n print(loaded)\n\n" ]

[ 1 ]

[]

[]

[ "arrays", "dictionary", "json", "python", "txt" ]

[ "Here is the result of my research on your case:\nYou don't supply the typing, so I had to case check to find out you use one str and one int. How do I know this?\nWell if you used 2 strs the multiplication would fail:\n>>> \"10\"*\"2\"\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nTypeError: can't multiply sequence by non-int of type 'str'\n\nAnd if you used 2 ints, h would also be an int, and your sum would fail:\n>>> str(sum([int(i) for i in 100]))\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nTypeError: 'int' object is not iterable\n\nSo as a result you must have one int and one str. But that also doesn't work since the int*str multiplication uses concatenation instead of the \"mathematical\" addition:\n>>> 10 * \"2\"\n'2222222222'\n\nMy solution suggestion is simply to:\n\nUse typing for clarity\nUse ints for the multiplication and strs for splitting the digits\n\nThis can be done simply by editing only the first two lines:\ndef superDigit(n: int, k:int) -> int:\n h=str(n*k)\n while len(h)>1:\n h=str(sum([int(i) for i in h]))\n return int(h)\n\nLet me know if this helps.\n", "Thanks to some helpful comments from @Tim Roberts, @Paul Hankin, and @yagod I was able to solve the issue. It was in fact due to time-out! All I had to do was update one line: h=str(sum([int(i) for i in n])*(k%9))\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[ "Install caffe from source then it will work.\n", "I install from the source but get this same problem.\nStill no idea what happen.\n", "Faced the same issue, while importing Caffe after installing Caffe in windows with GPU, could fix it by copying <CAFFE installation>/caffe/python/caffe/ to <Python Directory>/Lib/site-packages\nHope this will help, all the best..!\n" ]

[ 1, 0, 0 ]

[]

[]

[ "caffe", "deep_dream", "pycaffe", "python", "python_3.x" ]

[ "import pandas as pd\nimport os\n\noutput_excel = r'/home/bera/Desktop/all_excels.xlsx'\n\n#List all excel files in folder\nexcel_folder= r'/home/bera/Desktop/GIStest/excelfiles/'\nexcel_files = [os.path.join(root, file) for root, folder, files in os.walk(excel_folder) for file in files if file.endswith(\".xlsx\")]\n\nwith pd.ExcelWriter(output_excel) as writer:\n for excel in excel_files: #For each excel\n sheet_name = pd.ExcelFile(excel).sheet_names[0] #Find the sheet name\n df = pd.read_excel(excel) #Create a dataframe\n df.to_excel(writer, sheet_name=sheet_name, index=False) #Write it to a sheet in the output excel\n\n\n" ]

[ 1 ]

[]

[]

[ "excel", "pandas", "python" ]

[ "No recursion is necessary:\ndef smallestSquare(n):\n x = 1\n while isSquare(int(str(n)+str(x))) == False:\n x += 1\n return int(str(n)+str(x))\n\n", "If, given some number 'n', you are looking for the smallest perfect suqare that begins with 'n', the following is an approach that should work:\nimport math\n\ndef find_smallest_perfect_square(start: int) -> int:\n while True:\n if int(math.sqrt(start)) != math.sqrt(start):\n start += 1\n else:\n return start\n \ndef find_concatenation(n: int) -> int:\n str_n = str(n)\n while True:\n val = find_smallest_perfect_square(n)\n if str(val).startswith(str_n):\n return val\n else:\n n = val + 1\n\nTesting:\nfor i in range(10):\n print (f'The smallest perfect square that begins with {i} is {find_concatenation(i)}')\n\n# Result:\n # The smallest perfect square that begins with 0 is 0\n # The smallest perfect square that begins with 1 is 1\n # The smallest perfect square that begins with 2 is 25\n # The smallest perfect square that begins with 3 is 36\n # The smallest perfect square that begins with 4 is 4\n # The smallest perfect square that begins with 5 is 529\n # The smallest perfect square that begins with 6 is 64\n # The smallest perfect square that begins with 7 is 729\n # The smallest perfect square that begins with 8 is 81\n # The smallest perfect square that begins with 9 is 9\n\nIf what you're looking for is \"the smallest value of k\" which, when concatenated with n, yields a perfect square - the above approach is not guaranteed to give you what you want. If that's what you require, please specify.\n" ]

[ 1, 0 ]

[]

[]

[ "function", "python", "recursion" ]

[ "In the lexicographic order, 10 comes befor 8, because 1 is before 8\nYou need to convert to float to get it work like you expect\n\nat input\n stu.append(input())\n stu.append(float(input()))\n\n\nor at use\ndef sort(sub_li):\n return sorted(sub_li, key=lambda x: float(x[1]))\n\n\n\n" ]

[ 0 ]

[]

[]

[ "nested_lists", "python", "secondary_indexes", "sorting" ]

[ "For python you can use pyppeteer.\nFor javascript you can use puppeteer\nYou can find the documentation here\n" ]

[ 0 ]

[]

[]

[ "javascript", "python", "screenshot", "selenium", "undetected_chromedriver" ]

[ "Put a loop around the process, and collect into a list\nusernames = []\nnb = int(input(\"How many users do you want to create ?\"))\nfor i in range(nb):\n print(f\"Person n°{i + 1}\")\n firstName = input('What is your firstname: ').lower()\n lastName = input('What is your lastname: ').lower()\n username = username_gen(firstName, lastName)\n print(\">>\", username)\n usernames.append(username)\nprint(usernames)\n\nUsing f-string that is easier to build strings with variable\ndef username_gen(firstName, lastName):\n all_letters = firstName\n first_letters = lastName[0:3]\n return f'{all_letters}{first_letters}company.com'\n\n" ]

[ 0 ]

[]

[]

[ "arrays", "list", "methods", "python", "tuples" ]

[ "If I understand it correctly you ask how you can parse those args.\nHere is an example of argument parsing:\nparser.add_argument('--naruto', action='store_true')\nparser.add_argument('--transformers', action='store_true')\nparser.add_argument('--goku', action='store_true')\nparser.add_argument('--anime', action='store_true')\n\nargs = parser.parse_args()\n\n# You can access your args with dot notation\nif args.goku:\n print(\"goku arg was passed in\")\n\nif args.anime:\n print(\"anime arg was passed in\")\n\n" ]

[ -1 ]

[]

[]

[ "argparse", "command_line", "command_line_arguments", "python", "python_3.x" ]

[ "Similar to @mx0's suggestion (+1), rather than a square, we define a circle that encompasses the snowflake and for each successful placement, keep a list of existing positions and radii. We also use the radius to avoid drawing partial snowflakes near the edge of our window:\nfrom turtle import Screen, Turtle\nfrom random import randint, choice\n\nWIDTH, HEIGHT = 480, 320 # small for testing\n\nSF_COLORS = [\n 'yellow', 'gold', 'orange', 'red', 'violet',\n 'magenta', 'purple', 'navy', 'blue', 'skyblue',\n 'cyan', 'turquoise', 'lightgreen', 'green', 'darkgreen',\n 'white', 'BlueViolet', 'DeepSkyBlue', 'snow2', 'ForestGreen',\n 'gainsboro', 'GhostWhite', 'goldenrod',\n]\n\ndef snowflake(size):\n radius = 15 * size # circle roughly encompassing snowflake\n\n position = randint(radius - WIDTH/2, WIDTH/2 - radius), randint(radius - HEIGHT/2, HEIGHT/2 - radius)\n speedy.goto(position)\n\n trys = 0\n\n while any(speedy.distance(other_position) < (radius + other_radius) for other_position, other_radius in snowflakes):\n position = randint(radius - WIDTH/2, WIDTH/2 - radius), randint(radius - HEIGHT/2, HEIGHT/2 - radius)\n speedy.goto(position)\n\n trys += 1\n\n if trys > 100:\n return False # can't fit this snowflake, signal caller to try a different `size`\n\n snowflakes.append((position, radius))\n speedy.color(choice(SF_COLORS))\n\n speedy.penup()\n speedy.forward(10 * size)\n speedy.left(45)\n speedy.pendown()\n\n for _ in range(8):\n branch(size)\n speedy.left(45)\n\n speedy.penup()\n\n return True\n\ndef branch(size):\n length = 10.0 * size / 3\n\n for _ in range(3):\n for _ in range(3):\n speedy.forward(length)\n speedy.backward(length)\n speedy.right(45)\n\n speedy.left(90)\n speedy.backward(length)\n speedy.left(45)\n\n speedy.right(90)\n speedy.forward(length * 3)\n\nprint(\"This program creates snowflakes. Enjoy!\")\n\nn = int(input(\"How many snowflakes do you want?: \"))\n\nscreen = Screen()\nscreen.setup(WIDTH, HEIGHT)\nscreen.bgcolor('black')\n\nspeedy = Turtle()\nspeedy.speed('fastest')\n\nsnowflakes = []\n\nflakes = 0\n\nwhile flakes < n:\n sfsize = randint(1, 4)\n\n if snowflake(sfsize):\n flakes += 1\n\nspeedy.hideturtle()\nscreen.exitonclick()\n\nHowever, fitting snowflakes like this creates an issue. The user might request more snowflakes than can fit in a given size window. The code above partially addresses this by returning failure and letting the caller figure out what to do. Here, we simply try another snowflake size. Smarter code would reduce the random size range based on failure, and quit trying altogether when a size 1 snowflake fails!\n\nI've removed the restart logic to simplify my example and because I'm not convinced it works.\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x", "python_turtle", "turtle_graphics" ]

[ "The Exception iterating requests! error message means there is an Exception raised in the request iterator. I would recommend to add a try-catch clause in the client-side def data() function.\n", "I Believe this post might be bit late, But at least for those who might wonder over her looking for a resolution, hope below solves your issue, for me it did!!\nThe non blocking stub method is looking for an iterator, but the yield command or the data() method returns a Generator object which might be reason your getting that error, try passing the generator's iterator attribute - that solved the issue for me.\nThe code snippet-\ndef data():\n for i in range(1, 5):\n request = OrderRequest(orders=[SingleOrder(contract='asd',\n begin_pos=i,\n end_pos=i)])\n print(\"Visiting OrderRequest %s\" % request.contract)\n yield request\n\n\ndef run_client():\n with grpc.insecure_channel('localhost:50062') as channel:\n stub = PlaceOrderStub(channel)\n\n data1 = data()\n #Below change should resolve the issue\n response = stub.TransOrder(data1.__iter()__)\n\n" ]

[ 1, 0 ]

[]

[]

[ "grpc", "python" ]

[ "Do not mix frameworks, mixing frameworks always means some kind of undefined behavior. The frameworks may interact poorly or completely conflict with one another. Getting it to work on your system doesn't mean it will work on another system or with a different version of any of the frameworks.\nIf you use Qt, then I suggest to develop the game with Qt as well (see Qt Based Games).\n" ]

[ 1 ]

[]

[]

[ "pygame", "pyqt5", "python" ]

[ "Here is the node lambda that works to connecting to Redshift and pulling data from it.\nexports.handler = function(event, context, callback) {\n var response = {\n status: \"SUCCESS\",\n errors: [],\n response: {},\n verbose: {}\n };\n\n var client = new pg.Client(connectionString);\n client.connect(function(err) {\n if (err) {\n callback('Could not connect to RedShift ' + JSON.stringify(err));\n } else {\n client.query(sql.Sql, function(err, result) {\n client.end();\n if (err) {\n callback('Error Cleaning up Redshift' + err);\n } else {\n callback(null, ' Good ' + JSON.stringify(result));\n }\n });\n }\n });\n};\n\nHope it helps.\n", "You need to fetch the records first.\nresults = cur.fetchall()\nfor result in results:\n ...\n\n" ]

[ 2, 0 ]

[]

[]

[ "amazon_s3", "amazon_web_services", "aws_lambda", "lambda", "python" ]

[ "This is tricky with regular expressions, but quite trivial with \"parsing\":\ndef top_level_parens(s):\n stack = []\n\n for n, c in enumerate(s):\n if c in '({[':\n stack.append(n)\n elif c in ')}]':\n m = stack.pop()\n if not stack:\n yield s[m:n+1]\n\n\nresult = list(top_level_parens(your_string))\n\nAssuming parens are properly balanced, if this is not always the case, add additional checks to the \"parser\".\n", "so far the solution for me is\nre.findall(r'(\\{[^n].*?[^e]\\})|(\\[.*?\\])', message)\n\n" ]

[ 1, 0 ]

[]

[]

[ "python", "python_re" ]

[ "The problem is here:\nfor i in range(0, 51):\n kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n\nYou cannot set n_clusers to 0. It must be larger than 0.\n" ]

[ 1 ]

[]

[]

[ "jupyter_notebook", "pandas", "python" ]

[ "If your purpose is to disable logging by default and let users implement their own logging levels, it is similar to how Python packages implement logging - there is a need to use NullHandler.\nimport logging\nlogging.getLogger('foo').addHandler(logging.NullHandler())\n\nSource: The logging Documentation on how to configure logging for a Python library\nFor your other issues:\n\nsetLevel(0) does not disable the logging: I suspect there is overriding happening such that the level is overridden by some other configuration\nbasicConfig(level=0) duplicates the error: There could be inheritance happening resulting in duplicated logs, you need to set propagate to False\n\nSource: Common issues faced in logging\n" ]

[ 0 ]

[]

[]

[ "python", "python_logging", "python_module" ]

[ "The features are indeed in the same order, as you assume; see how to extract the most important feature names? and how to get feature names from explainer issues in Github.\nTo find the feature name, you simply need to access the element with the same index of the array with the names\nFor example:\nshap_values = np.array([\n 0.76437867, -0.11881508, 0.57451954, -0.41974955, -0.20982443,\n -0.38079952, -0.00986504, 0.32272505, -3.04392116, 0.00411322,\n -0.26587735, 0.02700199])\nfeatures_names = ['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation',\n 'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss',\n 'Hours per week', 'Country']\n\nfeatures_names[shap_values.argmin()] # the index 8 -> Capital Gain\nfeatures_names[shap_values.argmax()] # the index 0 -> Age\n\n", "If you find this answer useful, upvote @lucas's answer and GitHub user ba1mn's post. I'm just adding it here in case the link breaks.\nThe following function will return the feature names along with their corresponding importance in a DataFrame.\ndef global_shap_importance(model, X):\n \"\"\" Return a dataframe containing the features sorted by Shap importance\n Parameters\n ----------\n model : The tree-based model \n X : pd.Dataframe\n training set/test set/the whole dataset ... (without the label)\n Returns\n -------\n pd.Dataframe\n A dataframe containing the features sorted by Shap importance\n \"\"\"\n explainer = shap.Explainer(model)\n shap_values = explainer(X)\n cohorts = {\"\": shap_values}\n cohort_labels = list(cohorts.keys())\n cohort_exps = list(cohorts.values())\n for i in range(len(cohort_exps)):\n if len(cohort_exps[i].shape) == 2:\n cohort_exps[i] = cohort_exps[i].abs.mean(0)\n features = cohort_exps[0].data\n feature_names = cohort_exps[0].feature_names\n values = np.array([cohort_exps[i].values for i in range(len(cohort_exps))])\n feature_importance = pd.DataFrame(\n list(zip(feature_names, sum(values))), columns=['features', 'importance'])\n feature_importance.sort_values(\n by=['importance'], ascending=False, inplace=True)\n return feature_importance\n\n" ]

[ 3, 0 ]

[]

[]

[ "machine_learning", "python", "python_3.x", "shap" ]

[ "Try to use axis=1 in pd.concat:\ndf = pd.concat(dataframes, axis=1)\nprint(df)\n\nPrints:\n year q1 q2 q3 q4\n0 1 1 0 0 0\n1 1 0 1 0 0\n2 1 0 0 1 0\n3 1 0 0 0 1\n4 2 1 0 0 0\n5 2 0 1 0 0\n6 2 0 0 1 0\n7 2 0 0 0 1\n8 3 1 0 0 0\n9 3 0 1 0 0\n10 3 0 0 1 0\n11 3 0 0 0 1\n12 4 1 0 0 0\n13 4 0 1 0 0\n14 4 0 0 1 0\n15 4 0 0 0 1\n16 5 1 0 0 0\n17 5 0 1 0 0\n18 5 0 0 1 0\n19 5 0 0 0 1\n20 6 1 0 0 0\n21 6 0 1 0 0\n22 6 0 0 1 0\n23 6 0 0 0 1\n24 7 1 0 0 0\n25 7 0 1 0 0\n26 7 0 0 1 0\n27 7 0 0 0 1\n28 8 1 0 0 0\n29 8 0 1 0 0\n30 8 0 0 1 0\n31 8 0 0 0 1\n32 9 1 0 0 0\n33 9 0 1 0 0\n34 9 0 0 1 0\n35 9 0 0 0 1\n\n" ]

[ 1 ]

[]

[]

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

[ "So what you should do is:\nds = [d.text for d in datas.find_elements('tag name','option')]\n\nyou were using the improper tag locator. Options are tags not names, the 'name=' attribute (similar to class names) inside a tag element. Secondly you were looking for a singular item and then iterating over that element (as it was a single item there isn't any support for iteration over it to retrieve the text element) so had to make it .find_elements() to find all such matches.\nAnd to get the values you could do:\ndv = [d.get_attribute('value') for d in datas.find_elements('tag name','option')]\n\n" ]

[ 2 ]

[]

[]

[ "python", "selenium" ]

[ "In your treatment table you have a reference as a foreign key to owner; try putting an equivalent to 'nullable=True' or give it a default value\n", "why not put\ndoc_name = models.CharField(max_length=250, null = True) to try is working\n", "The problem was the corruption in db.sqlite3 file of the Django project. Deleting this file and redoing migrations fixed the problem.\n" ]

[ 0, 0, 0 ]

[]

[]

[ "django", "python" ]

[ "You must define the relevant variable to show where your settings.py file lives:\nexport DJANGO_SETTINGS_MODULE=mysite.settings\n\nThis is the relevant docs entry:\n\nWhen you use Django, you have to tell it which settings you’re using.\nDo this by using an environment variable, DJANGO_SETTINGS_MODULE.\nThe value of DJANGO_SETTINGS_MODULE should be in Python path syntax,\ne.g. mysite.settings. Note that the settings module should be on the\nPython import search path.\n\nIf you are using a virtualenv (which is the best practice), you can paste the relevant export command in the file <path-to-virtualenv>/bin/activate\n", "It happens to me too, I just now found unintentional.\nI changed \"configuration\" in top of PyCharm IDE so pycharm confused when try to run django code. Exactly the problem popup when I try to run server with shortcut but when I used terminal to run server, I found django code not having any problem and pycharm can't found setting.\nSo please check \"configuration\" in top pycharm's ide maybe you do same mistake :)\nI'm new in django so be calm if my answer was silly.\n\n", "I had this problem because I tried to import a django script after just typing\npython\n\ninstead of\npython manage.py shell\n\nDuh!\n", "Problem\nI had the same error with Django==3.2 and djangorestframework==3.12.4 (2021/04) when I ran unittest. In my case, the python manage.py test works properly but I cannot directly run or debug the test module which was in a specific app, and I get this error:\ndjango.core.exceptions.ImproperlyConfigured: Requested setting INSTALLED_APPS, but settings are not configured. You must either define the environment variable DJANGO_SETTINGS_MODULE or call settings.configure() before accessing settings.\n\nSolution\nFirst I added the django project root to the sys.path. Then I added DJANGO_SETTINGS_MODULE to the environment variable to address the project setting root and called the Django setup function. This is my code:\nfrom os import path, environ\nfrom sys import path as sys_path\nfrom django import setup\n\nsys_path.append(<path to django setting.py>) \nenviron.setdefault('DJANGO_SETTINGS_MODULE', 'django_project.settings')\nsetup()\n\n", "put this in top of settings.py \nthis will configure django for you \nimport os\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", __file__)\nimport django\ndjango.setup()\n\n", "I had the same error with Django 3.0.8 (july 2020) when trying to run the server, and in my case no need for the DJANGO_SETTINGS_MODULE environment variable. The solution is to use another form to run the server:\ncd <directory containing manage.py>\npython3 manage.py runserver\n\nIt works to run Django shell too :\npython3 manage.py shell\n\n", "Like raratiru answered, you need DJANGO_SETTINGS_MODULE environment variable defined with the relative pythonic path to your setting file.\nOR use your django-admin command with the settings parameter:\ndjango-admin --settings=mysite.settings dbshell\n\n", "Below code in terminal to manually inform which settings I am using worked for me:\nset DJANGO_SETTINGS_MODULE=mysite.settings\n\n", "Dear all I faced the same problem and i scrapped the web to find a solution. None of the one above solved anything.\nThe issue in my case was related to a wrong configuration of pycharm. As for someone above the app didnt have any issue when launched from the command line/ shell.\nThe issue is here:\n\nFor some reason the env variable disappeared. i added back and everything started to work again without any issue.\n" ]

[ 40, 17, 12, 5, 4, 4, 3, 0, 0 ]

[]

[]

[ "django", "mysql_python", "python" ]

[ "Stereoscopic 3D video is typically encoded in a single file using a technique called \"multi-view video coding\" (MVC). This technique allows the video to be played back on devices that support 3D playback, such as VR headsets.\nTo create a MVC video file, you can use a tool like ffmpeg. The basic process would be to use ffmpeg to encode the two separate video files for the left and right eyes into a single MVC file. This can be done using the following ffmpeg command:\nffmpeg -i left_eye.mp4 -i right_eye.mp4 -c:v mvc your_output_file.mp4\n\nThis will create a single MVC file called your_output_file.mp4 that contains the video data for both the left and right eye views.\nYou can also use Python to create MVC files by using the ffmpeg-python library, which provides a Pythonic interface to the ffmpeg command-line tools. For example, the following code will create an MVC file from two input videos:\nimport ffmpeg\n\n# Set the input and output filenames\nleft_eye = 'left_eye.mp4'\nright_eye = 'right_eye.mp4'\noutput = 'your_output_file.mp4'\n\n# Use ffmpeg to encode the MVC file\n(\n ffmpeg\n .input(left_eye)\n .input(right_eye)\n .output(output, c='mvc')\n .run()\n)\n\nThis code will create an MVC file called your_output_file.mp4 that contains the video data for both the left and right eye views.\n" ]

[ 1 ]

[]

[]

[ "bash", "python", "video", "virtual_reality" ]

[ "IIUC, you can use rapidfuzz and pandas.DataFrame.merge.\nTo give you the general logic, let's compare for example two dataframes (df_ger) with (df_us) :\nimport pandas as pd\nfrom rapidfuzz import process\n​\nout = (\n df_ger\n .assign(message_adapted = (df_ger['CUSTOMER_SUPPLIER_NAME']\n .map(lambda x: process.extractOne(x, df_us['CUSTOMER_SUPPLIER_NAME']))).str[0])\n .merge(df_us.add_suffix(\" (US)\"),\n left_on=\"message_adapted\", right_on=\"CUSTOMER_SUPPLIER_NAME (US)\", how=\"left\", indicator=\"CHECK\")\n .rename(columns= {\"CUSTOMER_SUPPLIER_NAME\": \"CUSTOMER_SUPPLIER_NAME (GE)\"})\n .drop(columns=\"message_adapted\")\n )\n\n# Output :\nprint(out)\n\n CUSTOMER_SUPPLIER_NAME (GE) CUSTOMER_SUPPLIER_NAME (US) CHECK\n0 LEUTERIS MAVROPOULOS LEFTERIS MAVROPOULOS both\n1 HARALAMPOS GEORGIOU CHARALAMPOS GEORGIOU both\n2 ATHLETIC COMPANY L.T.D. ATHLETIC COMAPANY LTD both\n3 LTK LTD LTK L.T.D. both\n4 GEORGE ANDREW GEORGE ANDREW GEORGE both\n5 BLACK POWER BLACK POWER both\n6 LOS HERMANOS HERMANOS both\n7 HILLS BROTHERS HILLS BROTHERS both\n8 AFOI KLIRU AFOI KLIROU both\n9 BOOKER HALIFA BOOKER HALIFA both\n10 MARCOS COMPANY MARCO'S COMPANY both\n\n# Edit :\nAssuming, you have a single dataframe df holding two columns and you want to calculate the score of similarity between those, you can use difflib.SequenceMatcher :\nfrom difflib import SequenceMatcher​\n​\ndf['SIMILARITY'] = (\n df.apply(lambda x: str(round(SequenceMatcher(None,\n x[0].lower(),\n x[1].lower()).ratio(),2)*100) + \"%\",\n axis=1)\n )\nprint(df)\n​\n CUSTOMER_SUPPLIER_NAME (GE) CUSTOMER_SUPPLIER_NAME (US) SIMILARITY\n0 LEUTERIS MAVROPOULOS LEFTERIS MAVROPOULOS 95.0%\n1 HARALAMPOS GEORGIOU CHARALAMPOS GEORGIOU 97.0%\n2 ATHLETIC COMPANY L.T.D. ATHLETIC COMAPANY LTD 91.0%\n3 LTK LTD LTK L.T.D. 82.0%\n4 GEORGE ANDREW GEORGE ANDREW GEORGE 79.0%\n5 BLACK POWER BLACK POWER 100.0%\n6 LOS HERMANOS HERMANOS 80.0%\n7 HILLS BROTHERS HILLS BROTHERS 100.0%\n8 AFOI KLIRU AFOI KLIROU 95.0%\n9 BOOKER HALIFA BOOKER HALIFA 100.0%\n10 MARCOS COMPANY MARCO'S COMPANY 97.0%\n\n" ]

[ 0 ]

[]

[]

[ "machine_learning", "pandas", "python" ]

[ "Instead of collecting all button elements you can make more precise locating.\nThis Xpath will give you \"Connect\" buttons only //button[contains(@aria-label,'Invite')].\nSo, instead of using this all_connect_buttons = driver.find_elements(By.TAG_NAME, 'button') you can use this:\nall_connect_buttons = driver.find_elements(By.XPATH, \"//button[contains(@aria-label,'Invite')]\")\n\nThis can also be done with CSS Selectors\nall_connect_buttons = driver.find_elements(By.CSS_SELECTOR, \"button[aria-label*='Invite']\")\n\n" ]

[ 3 ]

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[]

[ "css_selectors", "python", "selenium", "selenium_webdriver", "xpath" ]

[ "I personally use CX-Freeze to compile my executables. I have probably used it over 100 or so updates of my tools and typically the problem I run into is either related to missing file that need to be identified in the setup.py file or the fact that when it compiles the Tkinter folder it uses a capital T instead of a lower case t so after I compiling an app I have to manually update the folder to be lowercase T.\nHere is an example of the setup file.\nAs you can see below when compiling tkinter you need to ID the TK and TCL library folders in order for it to compile the listed DLL files properly.\nfrom cx_Freeze import setup, Executable\nimport os\n\n\nbase = \"Win32GUI\"\n\nos.environ['TCL_LIBRARY'] = r'C:\\Users\\user\\Desktop\\Python381\\tcl\\tcl8.6'\nos.environ['TK_LIBRARY'] = r'C:\\Users\\user\\Desktop\\Python381\\tcl\\tk8.6'\n\n\nbuild_exe_options = {'packages': ['os',\n 'json',\n 'http',\n 'email',\n 'pyodbc',\n 'openpyxl',\n 'calendar',\n 'threading',\n 'datetime',\n\n 'tkinter',\n 'tkinter.ttk',\n 'tkinter.messagebox'],\n 'excludes': ['PyQt5',\n 'PIL',\n 'numpy',\n 'pandas'], # 'urllib', # 'encodings', # 'numpy'\n\n 'include_files': [r'excel_temp.xlsx',\n r'opt_3_excel_temp.xlsx',\n r'tcoms_excel_temp.xlsx',\n r'main_config.json',\n r\"C:\\Users\\user\\Desktop\\Python381\\DLLs\\tcl86t.dll\",\n r\"C:\\Users\\user\\Desktop\\Python381\\DLLs\\tk86t.dll\"]}\n \nsetup(\n name='<GIT>',\n options={'build_exe': build_exe_options},\n version='0.57',\n description='<GIT - Global Inventory Tool!>',\n executables=[Executable(r'C:\\Users\\user\\PycharmProjects\\Py381_GIT\\MAIN.py', base=base)]\n)\n\nAfter you run the compiler you will often get an error that looks like this.\n\nThe error NoduleNotFoundError: No module named 'tkinter' is due to the odd behavior of the compiler giving the tkinter folder a Capital T like the below image in the lib folder.\n\n\nIn this case you would update the library to be a lowercase t.\n\nLet me know if you have any questions.\n" ]

[ 1 ]

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[]

[ "exe", "python", "tkinter" ]

[ "That depends on the code in main.py that runs the doctests. You can change that code to test a specific function by calling doctest.run_docstring_examples().\nWhen that code runs doctest.testmod() however, you cannot limit testing to a single function from the command line.\n", "You can accomplish this using my doctestfn package:\npip install doctestfn\n\nIt can then be used to run tests for a single function in a module as follows:\ndoctestfn myfile.py myfunction\n\n" ]

[ 1, 0 ]

[]

[]

[ "doctest", "python" ]

[ "You can convert it to a pandas Dataframe, perform filtering operations, then sum up the price.\nimport datetime\nimport pandas as pd\n\nlist_purchases = [{'id':'123','name':'Luis', 'surname':'Henderson', 'price':16000, 'date': \"(2022, 3, 12)\"},{'id':'123','name':'Luis', 'surname':'Henderson', 'price':4000, 'date': \"(2022, 12, 1)\"}]\n\n# Convert to DataFrame and datetime format\npurchase_data = pd.DataFrame(list_purchases)\npurchase_data[\"date\"] = pd.to_datetime(purchase_data[\"date\"], format=\"(%Y, %m, %d)\")\n\n# Query for first and last date (you can set as input, let me pre-define it here)\nfirst_date = datetime.datetime(2022, 3, 12)\nlast_date = datetime.datetime(2022, 12, 1)\n\n# Filter for date between first_date and last_date inclusive\npurchase_data_filter = purchase_data[((first_date <= purchase_data[\"date\"])) & (purchase_data[\"date\"] <= last_date)]\n\nresult = purchase_data_filter.price.sum()\n\n\n" ]

[ 0 ]

[]

[]

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

[ "Use pivot and seaborn.heatmap:\nimport seaborn as sns\n\nsns.heatmap(df.pivot(index='Y', columns='X', values='Z'))\n\nOutput:\n\nIF you want to handle missing coordinates:\ndf2 = (df\n .pivot(index='Y', columns='X', values='Z')\n .pipe(lambda d: d.reindex(index=range(d.index.min(), d.index.max()+1),\n columns=range(d.columns.min(), d.columns.max()+1),\n )\n )\n)\n\nsns.heatmap(df2)\n\nOutput:\n\n", "https://seaborn.pydata.org/generated/seaborn.heatmap.html\nHowever I notice your data are all numeric. You may be looking for a Z dimensional colored scatterplot rather than a true 'heatmap'. You can use the plt.scatter function of matplotlib. (X_col=x,Y_col=y,color=Z). https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\n" ]

[ 1, 0 ]

[]

[]

[ "graph", "heatmap", "matplotlib", "python", "seaborn" ]