Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "Dot product of non-NaNness and the columns (suffixed \", \") is a way of doing this:\nIn [242]: df.notna().dot(df.columns + \", \").str[:-2]\nOut[242]:\nA\nB\nC A\nD B\nE B, C\nF\nG D\nH\nI A\nJ\ndtype: object\n\nWhat's happening is that, df.notna() is a True/False dataframe; then we take the dot product of it with the column names (\", \" added). Since True is 1 and False is 0 in numeric context, the dot product behaves like a selector of column names. Then lastly we strip out the trailing \", \"s.\n", "You can use stack to benefit from the default dropping of NaN values, combined with groupby.agg:\ndf['To'] = (df\n .stack()\n .reset_index(-1)['level_1']\n .groupby(level=0).agg(','.join)\n )\n\nOutput:\n A B C D E F G H I J To\nA NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\nB NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\nC yes NaN NaN NaN NaN NaN NaN NaN NaN NaN A\nD NaN yes NaN NaN NaN NaN NaN NaN NaN NaN B\nE NaN ok ok NaN NaN NaN NaN NaN NaN NaN B,C\nF NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\nG NaN NaN NaN ok NaN NaN NaN NaN NaN NaN D\nH NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\nI yes NaN NaN NaN NaN NaN NaN NaN NaN NaN A\nJ NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n\n" ]

[ 4, 3 ]

[]

[]

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

[ "The Python list passed into the non-default constructor gets converted to a temporary SWIG proxy of a vector<double>* and that pointer is saved by the constructor into SampleClass's m_v member, but the pointer no longer exists when the constructor returns. If you create a persistent doublevector and make sure it stays in scope, the code works.\nI built the original code in the question for the following example:\n>>> import example as ex\n>>> v = ex.doublevector([1,2,3,4])\n>>> s = ex.SampleClass(v)\n\nnon default constructor!\n>>> s.m_v.size()\n4\n>>> list(s.m_v)\n[1.0, 2.0, 3.0, 4.0]\n\nAs long as v exists, s.m_v will be valid.\n" ]

[ 0 ]

[]

[]

[ "c++", "python", "swig" ]

[ "I don't know why you want to combine these df's in the first place, since the rows aren't connected at all. But if you just want to align them next to each other, you want to use pd.concat\nout = pd.concat([df1, df2], axis=1)\nprint(out)\n\n Mutations Number of Mutations Number of Bases\n0 A>T 213 A = 42239\n1 A>G 659 T = 55005\n2 A>C 281 G = 46060\n3 T>A 204 C = 45422\n4 T>C 627 NaN\n5 T>G 208 NaN\n6 C>A 351 NaN\n7 C>T 1004 NaN\n8 C>G 360 NaN\n9 G>A 1054 NaN\n10 G>T 323 NaN\n11 G>C 351 NaN\n\nIf there are different indices and you just want to have df2 at the beginning till there is no more data, then you need to use reset_index() before you concat them.\nout = pd.concat([df1.reset_index(drop=True), df2.reset_index(drop=True)], axis=1)\n\n\n" ]

[ 0 ]

[]

[]

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

[ "Based on @ggorlen's suggestion, I spotted that the select_option triggered a request, which I was able to trigger on, and validate, thus:\n with page.expect_response(lambda response: response.url) as response_info:\n assert loc[0] in response_info.value.url, f\"wrong location {response_info.value.url}, expecting {loc}\"\n\n\nFrom this page:\nhttps://playwright.dev/python/docs/network#variations\nBut note that there seemed to be a bug on that page as token in in the lambda function isn't relevant. The code checks that the query relates to the address of interest.\n" ]

[ 0 ]

[]

[]

[ "playwright", "playwright_python", "python" ]

[ "Use typing.Protocol:\nfrom typing import Protocol\n\n\nclass SupportsB(Protocol):\n b: int\n\n" ]

[ 0 ]

[]

[]

[ "python", "python_typing" ]

[ "Here's a solution to issues 1 and 2. In my version of networkx, self-loops are displayed.\nimport pandas as pd\nimport networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nd = {'grad': {0: 'CUHK', 1: 'CUHK', 2: 'CUHK', 3: 'CUHK', 4: 'CUHK', 5: 'CUHK', 6: 'CUHK', 7: 'CUHK', 8: 'CityU', 9: 'CityU', 10: 'CityU', 11: 'CityU', 12: 'CityU', 13: 'CityU', 14: 'CityU', 15: 'HKBU', 16: 'HKU', 17: 'HKU', 18: 'HKU', 19: 'HKU', 20: 'HKU', 21: 'HKU', 22: 'HKU', 23: 'HKUST', 24: 'HKUST', 25: 'HKUST', 26: 'HKUST', 27: 'HKUST', 28: 'HKUST', 29: 'HKUST', 30: 'HKUST', 31: 'Low Frequency', 32: 'Low Frequency', 33: 'Low Frequency', 34: 'Low Frequency', 35: 'Low Frequency', 36: 'Low Frequency', 37: 'Low Frequency', 38: 'Low Frequency', 39: 'PolyU', 40: 'PolyU', 41: 'PolyU', 42: 'PolyU', 43: 'PolyU', 44: 'PolyU'}, 'to': {0: 'CUHK', 1: 'CityU', 2: 'EduHK', 3: 'HKBU', 4: 'HKU', 5: 'HKUST', 6: 'LingU', 7: 'PolyU', 8: 'CityU', 9: 'EduHK', 10: 'HKBU', 11: 'HKU', 12: 'HKUST', 13: 'LingU', 14: 'PolyU', 15: 'HKBU', 16: 'CUHK', 17: 'CityU', 18: 'EduHK', 19: 'HKBU', 20: 'HKU', 21: 'HKUST', 22: 'PolyU', 23: 'CUHK', 24: 'CityU', 25: 'EduHK', 26: 'HKBU', 27: 'HKU', 28: 'HKUST', 29: 'LingU', 30: 'PolyU', 31: 'CUHK', 32: 'CityU', 33: 'EduHK', 34: 'HKBU', 35: 'HKU', 36: 'HKUST', 37: 'LingU', 38: 'PolyU', 39: 'CityU', 40: 'EduHK', 41: 'HKBU', 42: 'HKU', 43: 'LingU', 44: 'PolyU'}, 'count': {0: 13, 1: 6, 2: 3, 3: 6, 4: 5, 5: 3, 6: 2, 7: 6, 8: 4, 9: 1, 10: 5, 11: 2, 12: 1, 13: 2, 14: 7, 15: 2, 16: 2, 17: 4, 18: 3, 19: 1, 20: 17, 21: 3, 22: 9, 23: 4, 24: 2, 25: 2, 26: 4, 27: 2, 28: 4, 29: 4, 30: 6, 31: 76, 32: 73, 33: 1, 34: 16, 35: 57, 36: 46, 37: 3, 38: 69, 39: 1, 40: 2, 41: 3, 42: 1, 43: 1, 44: 23}}\n\ndata = pd.DataFrame(d)\ndata = data[data['grad']!='Low Frequency']\n\nrad = .2\nconn_style = f'arc3,rad={rad}'\n\ndef offset(d, pos, dist = rad/2, loop_shift = .2):\n for (u,v),obj in d.items():\n if u!=v:\n par = dist*(pos[v] - pos[u])\n dx,dy = par[1],-par[0]\n x,y = obj.get_position()\n obj.set_position((x+dx,y+dy))\n else:\n x,y = obj.get_position()\n obj.set_position((x,y+loop_shift))\n\nplt.figure(figsize = (20,10))\nG = nx.from_pandas_edgelist(data, source='grad',\n target='to', edge_attr='count',\n create_using=nx.DiGraph())\nweight = nx.get_edge_attributes(G, 'count')\npos = nx.shell_layout(G, scale=1)\nnx.draw_networkx_nodes(G, pos, node_size=300, node_color='lightblue')\nnx.draw_networkx_labels(G, pos=pos, font_color='red')\nnx.draw_networkx_edges(G, pos=pos, edgelist=G.edges(), edge_color='black',\n connectionstyle=conn_style)\nd = nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=weight)\noffset(d,pos)\nplt.gca().set_aspect('equal')\n\nplt.show()\n\nThe result I get:\n\nHere's what I get if I include the Low Frequency node and set rad to .1 instead of .2.\n\n\nHere's an approach that doesn't change the aspect ratio:\nimport pandas as pd\nimport networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nd = {'grad': {0: 'CUHK', 1: 'CUHK', 2: 'CUHK', 3: 'CUHK', 4: 'CUHK', 5: 'CUHK', 6: 'CUHK', 7: 'CUHK', 8: 'CityU', 9: 'CityU', 10: 'CityU', 11: 'CityU', 12: 'CityU', 13: 'CityU', 14: 'CityU', 15: 'HKBU', 16: 'HKU', 17: 'HKU', 18: 'HKU', 19: 'HKU', 20: 'HKU', 21: 'HKU', 22: 'HKU', 23: 'HKUST', 24: 'HKUST', 25: 'HKUST', 26: 'HKUST', 27: 'HKUST', 28: 'HKUST', 29: 'HKUST', 30: 'HKUST', 31: 'Low Frequency', 32: 'Low Frequency', 33: 'Low Frequency', 34: 'Low Frequency', 35: 'Low Frequency', 36: 'Low Frequency', 37: 'Low Frequency', 38: 'Low Frequency', 39: 'PolyU', 40: 'PolyU', 41: 'PolyU', 42: 'PolyU', 43: 'PolyU', 44: 'PolyU'}, 'to': {0: 'CUHK', 1: 'CityU', 2: 'EduHK', 3: 'HKBU', 4: 'HKU', 5: 'HKUST', 6: 'LingU', 7: 'PolyU', 8: 'CityU', 9: 'EduHK', 10: 'HKBU', 11: 'HKU', 12: 'HKUST', 13: 'LingU', 14: 'PolyU', 15: 'HKBU', 16: 'CUHK', 17: 'CityU', 18: 'EduHK', 19: 'HKBU', 20: 'HKU', 21: 'HKUST', 22: 'PolyU', 23: 'CUHK', 24: 'CityU', 25: 'EduHK', 26: 'HKBU', 27: 'HKU', 28: 'HKUST', 29: 'LingU', 30: 'PolyU', 31: 'CUHK', 32: 'CityU', 33: 'EduHK', 34: 'HKBU', 35: 'HKU', 36: 'HKUST', 37: 'LingU', 38: 'PolyU', 39: 'CityU', 40: 'EduHK', 41: 'HKBU', 42: 'HKU', 43: 'LingU', 44: 'PolyU'}, 'count': {0: 13, 1: 6, 2: 3, 3: 6, 4: 5, 5: 3, 6: 2, 7: 6, 8: 4, 9: 1, 10: 5, 11: 2, 12: 1, 13: 2, 14: 7, 15: 2, 16: 2, 17: 4, 18: 3, 19: 1, 20: 17, 21: 3, 22: 9, 23: 4, 24: 2, 25: 2, 26: 4, 27: 2, 28: 4, 29: 4, 30: 6, 31: 76, 32: 73, 33: 1, 34: 16, 35: 57, 36: 46, 37: 3, 38: 69, 39: 1, 40: 2, 41: 3, 42: 1, 43: 1, 44: 23}}\n\ndata = pd.DataFrame(d) \n\nrad = .1\nconn_style = f'arc3,rad={rad}'\n\ndef offset(d, pos, dist = rad/2, loop_shift = .2, asp = 1):\n for (u,v),obj in d.items():\n if u!=v:\n par = dist*(pos[v] - pos[u])\n dx,dy = par[1]*asp,-par[0]/asp\n x,y = obj.get_position()\n obj.set_position((x+dx,y+dy))\n else:\n x,y = obj.get_position()\n obj.set_position((x,y+loop_shift))\n\ndef sub(a,b):\n return a-b\n\ndef get_aspect(ax):\n # Total figure size\n figW, figH = ax.get_figure().get_size_inches()\n # Axis size on figure\n _, _, w, h = ax.get_position().bounds\n # Ratio of display units\n disp_ratio = (figH * h) / (figW * w)\n # Ratio of data units\n # Negative over negative because of the order of subtraction\n data_ratio = sub(*ax.get_ylim()) / sub(*ax.get_xlim())\n\n return disp_ratio / data_ratio\n\nplt.figure(figsize = (20,10))\nG = nx.from_pandas_edgelist(data, source='grad',\n target='to', edge_attr='count',\n create_using=nx.DiGraph())\nweight = nx.get_edge_attributes(G, 'count')\npos = nx.shell_layout(G, scale=1)\nnx.draw_networkx_nodes(G, pos, node_size=300, node_color='lightblue')\nnx.draw_networkx_labels(G, pos=pos, font_color='red')\nnx.draw_networkx_edges(G, pos=pos, edgelist=G.edges(), edge_color='black',\n connectionstyle=conn_style)\nd = nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=weight)\n\noffset(d, pos, asp = get_aspect(plt.gca()))\n\nplt.show()\n\nResulting figure:\n\n" ]

[ 1 ]

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[]

[ "networkx", "python" ]

[ "Following up on the guidance from @accdias, here is a code snippet that closes the gaps in your JSON snippet and demonstrates how to access the Objects section:\nimport json\n\njson_string = \"\"\"\n{\n \"_id\": \"635bd4bfc594743ce9b1a5a3\",\n \"dateStart\": \"2022-10-28T13:09:28.609Z\",\n \"dateFinish\": \"2022-10-28T13:10:23.698Z\",\n \"method\": \"customer.file.upsert\",\n \"request\": {\n \"Objects\": [\n {\n \"ERPId\": \"6915\",\n \"B24Id\": 403772,\n \"FileName\": \"B2B000202\",\n \"FileContent\": \"JVBERi0xLjMNJeLjz9MN\",\n \"B24EntityId\": 3334\n }\n ]\n }\n}\n\"\"\"\njson_dict = json.loads(json_string)\nprint(json_dict[\"request\"][\"Objects\"])\n\nOutput:\n[{'ERPId': '6915', 'B24Id': 403772, 'FileName': 'B2B000202', 'FileContent': 'JVBERi0xLjMNJeLjz9MN', 'B24EntityId': 3334}]\n\n" ]

[ 0 ]

[]

[]

[ "json", "python" ]

[ "Try:\npr = model.predict_classes(im.reshape((1, 1, 28, 28)))\n\nHere : first dimension comes from examples (you need to specify it even if you have only one example), second comes from channels (as it seems that you use Theano backend) and rest are spatial dimensions.\n", "It should be noted that the images must be uploaded in grayscale.\n\nLike:\n\nim = im[:,:,0]\n\nOr\n\nimport cv2\nim = cv2.imread('C:\\\\Users\\\\Divyesh\\\\Desktop\\\\mnist.png', cv2.IMREAD_GRAYSCALE)\n" ]

[ 6, 0 ]

[]

[]

[ "conv_neural_network", "keras", "machine_learning", "neural_network", "python" ]

[ "Finally I managed to solve my problem. In case anyone else encounters the same issue, I am posting a solution here.\nThis is a snipped that is taken from SQLAlchemy's website:\n\nThe model metaclass is responsible for setting up the SQLAlchemy internals when defining model subclasses. Flask-SQLAlchemy adds some extra behaviors through mixins; its default metaclass, DefaultMeta, inherits them all.\n\nBindMetaMixin: bind_key is extracted from the class and applied to the table. See Multiple Databases with Binds.\nNameMetaMixin: If the model does not specify a tablename but does specify a primary key, a name is automatically generated.\n\nYou can add your own behaviors by defining your own metaclass and creating the declarative base yourself. Be sure to still inherit from the mixins you want (or just inherit from the default metaclass).\nPassing a declarative base class instead of a simple model base class, as shown above, to base_class will cause Flask-SQLAlchemy to use this base instead of constructing one with the default metaclass.\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_sqlalchemy.model import DefaultMeta, Model\n\nclass CustomMeta(DefaultMeta):\n def __init__(cls, name, bases, d):\n # custom class setup could go here\n\n # be sure to call super\n super(CustomMeta, cls).__init__(name, bases, d)\n\n # custom class-only methods could go here\n\ndb = SQLAlchemy(model_class=declarative_base(\n cls=Model, metaclass=CustomMeta, name='Model'))\n\nYou can also pass whatever other arguments you want to declarative_base() to customize the base class as needed.\n\nIn addition to that, my initial purpose was actually to add some extra functionality to my models which are inherits from db.Model. To do that actually you do not need to use Meta classes. So, for that case, we can extend the db.Model class, as described in the same web page. This is an example of giving every model an integer primary key, or a foreign key for joined-table inheritance:\nfrom flask_sqlalchemy import Model, SQLAlchemy\nimport sqlalchemy as sa\nfrom sqlalchemy.ext.declarative import declared_attr, has_inherited_table\n\nclass IdModel(Model):\n @declared_attr\n def id(cls):\n for base in cls.__mro__[1:-1]:\n if getattr(base, '__table__', None) is not None:\n type = sa.ForeignKey(base.id)\n break\n else:\n type = sa.Integer\n\n return sa.Column(type, primary_key=True)\n\ndb = SQLAlchemy(model_class=IdModel)\n\nclass User(db.Model):\n name = db.Column(db.String)\n\nclass Employee(User):\n title = db.Column(db.String)\n\n", "Actually if you are very famillar with the mechanism of metaclass in python, the porblem is very simple. I give you a example in SqlAlchemy.\nfrom sqlalchemy.ext.declarative import as_declarative, declared_attr\n\n@as_declarative()\nclass Base():\n id = Column(Integer, primary_key=True, index=True)\n __name__: str\n @declared_attr\n def __tablename__(cls) -> str:\n return cls.__name__.lower()\n\n @property\n def url(self):\n return f'/{self.__class__.__name__.lower()}/{self.id}/'\n\nclass AnotherMetaClass(type):\n def __new__(cls, name, bases, attrs):\n pass\n # do something\n\nclass ModelMeta(Base.__class__, AnotherMetaClass):\n ...\n\nclass User(Base, metaclass=ModelMeta):\n pass\n\nThe key steps is that you should make the metaclass of Base in SqlAlchemy consistent with the metclass you design by yourself. One solution is to make a new metaclass that is the subclass of both of them.\n", "Another variant if you want metaclasses to update your classes options or methods before creating instances or set it during project initialization you can add init_subclass method to your Base.\nclass ClassMetod:\ndef __init__(self, cls):\n self._class = cls\n\n@as_declarative()\nclass Base:\n\n @declared_attr\n def __tablename__(cls) -> str:\n return cls.__name__.lower()\n\n def __init_subclass__(cls):\n super().__init_subclass__()\n\n cls.method = ClassMetod(cls)\n\n" ]

[ 3, 0, 0 ]

[]

[]

[ "metaclass", "python", "sqlalchemy", "types" ]

[ "You need to call get() inside the get_value() function\nUPDATE - I misread the code previously - this should work now.\nFYI:\n\nYou don't need an IntVar to store the value of the Entry, you can just call get() directly and do away with setting the textvariable parameter\nYou should make a habit of declaring your widgets separately from adding them to a geometry manager (i.e. pack, grid, place). The geometry manager methods always return None, so something like my_entry = tk.Entry(root).pack() will always evaluate to None, which is probably not what you want\n\nentry1 = tk.Entry(window1, width=8,fg=\"darkblue\", font=('secular one', 13))\nentry1.place(x=20, y=60)\n\n\ndef get_value():\n num_of_diners = int(entry1.get()) # get entry contents as an integer\n tk.Label(window1, text=num_of_diners, font=('secular one', 20)).place(x=300, y=510)\n\nBear in mind that if the contents of entry1 can't be cast to an int you'll get an exception. An easy way around this is:\ndef get_value():\n entry_content = entry1.get()\n if entry_content.isdigit():\n num_of_diners = int(entry_content)\n else:\n num_of_diners = 0 # fall back to 0 on conversion failure\n\n" ]

[ 1 ]

[]

[]

[ "python", "tkinter" ]

[ "Try locating the message_box inside the loop.\nAlso, you have to improve your locators.\nAnd it should be element_to_be_clickable expected condition there, not just presence_of_element_located.\nSo, please try this code:\nmessage_box_path='//footer//p'\nfor x in range(5):\n message_box=wait.until(EC.element_to_be_clickable((By.XPATH,message_box_path)))\n message_box.click()\n message_box.clear()\n message_box.send_keys(message + Keys.ENTER)\n time.sleep(0.2)\n\n" ]

[ 0 ]

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[]

[ "automation", "python", "selenium", "selenium_webdriver", "whatsapp" ]

[ "The attributes coefs_ and intercepts_ are the final ones. Indeed, by design\n\nAttributes that have been estimated from the data must always have a name ending with trailing underscore.\n\nThe starting values of such parameters are not exposed via a public attribute or method; instead, they are exploiting the _init_coef() method to Glorot-initialize parameters (https://github.com/scikit-learn/scikit-learn/blob/f3f51f9b611bf873bd5836748647221480071a87/sklearn/neural_network/_multilayer_perceptron.py#L344).\n" ]

[ 1 ]

[]

[]

[ "python", "scikit_learn" ]

[ "This is because you caught the exception MyException direct in myFunction().\nComment out out the try-except clause and try again, test should pass.\nassertRaises is used for uncaught errors. You can also re-raise in except block.\n", "The exception is handled internally, so there is no external evidence that the exception is raised.\nHowever, both MyException and do_clean_up are external names, which means you can patch them and make assertions about whether they do or do not get used. For example,\n# Make sure the names you are patching are correct\nwith unittest.mock.patch('MyException', wraps=MyException) as mock_exc, \\\n unittest.mock.patch('do_clean_up', wraps=do_clean_up) as mock_cleanup:\n myfunction()\n if mock_exc.called:\n mock_cleanup.assert_called()\n\n" ]

[ 1, 0 ]

[]

[]

[ "exception", "python", "python_unittest", "unit_testing" ]

[ "Django 3.2 introduced native support for these indexes.\nThe question as asked presently doesn't seem to have the definition of the JSONField, but assuming it is something like\nfrom django.db import models\n\nclass Facilitators(models.Model):\n foster_data = models.JSONField()\n\nTo index a particular key, you combine an F expression with a JSONField path lookup on the model's Meta indexes option:\nfrom django.contrib.postgres.fields import JSONField \n\nclass Facilitators(models.Model):\n foster_data = models.JSONField()\n class Meta:\n indexes = [\n models.Index(models.F(\"foster_data__root__level_1__level2__name\"), name=\"foster_data__root__level_1__level2__name_idx\"),\n ]\n\nThis will create a B-Tree index. If you are adding these to an existing model, be sure to makemigrations and migrate.\nSee this answer as well https://stackoverflow.com/a/74619523/\n" ]

[ 0 ]

[]

[]

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

[ "__str__ canonically doesn't accept any arguments.\nSince you're subclassing str, you probably mean\nclass X(str):\n def __str__(self):\n return self.capitalize()\n\nb = X('hello')\nprint(b.__str__()) \n# or print(str(b))\n# or print(b)\n\ni.e. to override the __str__ magic method in a way that uses the superclass str's methods to work on the \"intrinsic\" string data of the object (which exists because you're subclassing str).\nThis prints out\nHello\n\n" ]

[ 4 ]

[]

[]

[ "class", "oop", "python" ]

[ "Discord.py v2 changed the variables. Meaning that .avatar_url is now .avatar.url. .icon_url is now .icon.url. Hence meaning that member.display_avatar.url is what you're looking for.\nThis also means that stuff like .avatar_with_size(...) for example have now been changed to .avatar.with_size(...). Just for future reference.\n", "Discord.py now has different variables for display avatars etc.\nJust edit this line:\nembed.set_author(name=f\"{member.name}#{member.discriminator}\", icon_url=member.display_avatar_url)\nto\nembed.set_author(name=f\"{member.name}#{member.discriminator}\", icon_url=member.display_avatar.url)\nIf this answer has worked, please mark it as the correct answer! :)\n" ]

[ 1, 0 ]

[]

[]

[ "discord", "nextcord", "python" ]

[ "Here is one option:\ns = (df['Problem']\n .sub(df['Impact'].mul(df['Prob'])\n .groupby(df['ID']).cumsum()\n )\n)\n\nm = s.le(6).groupby(df['ID']).shift(fill_value=False)\n\ndf['Res_Problem'] = s.mask(m)\n\noutput:\n ID Problem X Impact Prob Res_Problem\n0 ID1 12 IDC1 1 2 10.0\n1 ID1 12 IDC2 2 2 6.0\n2 ID1 12 IDC3 1 0 NaN\n3 ID1 12 IDC4 1 0 NaN\n4 ID2 10 IDB1 1 2 8.0\n5 ID2 10 IDB1 1 2 6.0\n\n" ]

[ 5 ]

[]

[]

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

[ "You need to do this:\n x.columns = pd.MultiIndex.from_product([['D'], *x.columns.levels])\n\nwhere x.columns.levels gives you a Frozenlist of columns that form the MultiIndex.\nAnd then you have to unpack the list using * in order to pass list of lists to from_product.\n", "You can denote a multi-level column with a tuple. For instance:\nx[('y', 'C')] = x[('x', 'A')] + 1\nx[('x', 'D')] = 0\n\n>>> x\n x y x\n A B C D\n0 0 1 1 0\n1 2 3 3 0\n2 4 5 5 0\n3 6 7 7 0\n4 8 9 9 0\n\nAnd, of course, you can sort the columns:\nx = x.sort_index(axis=1)\n\n>>> x\n x y\n A B D C\n0 0 1 0 1\n1 2 3 0 3\n2 4 5 0 5\n3 6 7 0 7\n4 8 9 0 9\n\n" ]

[ 3, 0 ]

[]

[]

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

[ "The comment above is correct. The source of your problems is that you used this:\nmodel = pyo.ConcreteModel\n\ninstead of this:\nmodel = pyo.ConcreteModel()\n\nTo explain what happens when you do this... You have unwittingly created an \"alias\" for the function ConcreteModel, which is obviously not what you intended to do. You wanted to call the function and get a model instance back. If this concept is confusing, this below may help. The punchline of the story is: put parenthesis on that line to call the function.\nIn [14]: def make_model():\n ...: return \"I am a model object\"\n ...: \n\nIn [15]: m = make_model # this is creating an \"alias\"\n\nIn [16]: print(m)\n<function make_model at 0x1157c5e10>\n\nIn [17]: # because this \"alias\" refers to the function we can call it...\n\nIn [18]: m()\nOut[18]: 'I am a model object'\n\nIn [19]: # we really wanted to assign m to the output of the function:\n\nIn [20]: m = make_model()\n\nIn [21]: print(m)\nI am a model object\n\n" ]

[ 1 ]

[]

[]

[ "pyomo", "python" ]

[ "The str.format method was introduced in Python 3.0, and backported to Python 2.6 and later.\n", "Your example code seems to be written for Python 2.6 or later, where the str.format method was introduced.\nFor Python versions below 2.6, use the % operator to interpolate a sequence of values into a format string:\nfor x in range(1, 11):\n print '%2d %3d %4d' % (x, x*x, x*x*x)\n\n\nYou should also be aware that this operator can interpolate by name from a mapping, instead of just positional arguments:\n>>> \"%(foo)s %(bar)d\" % {'bar': 42, 'foo': \"spam\", 'baz': None}\n'spam 42'\n\nIn combination with the fact that the built-in vars() function returns attributes of a namespace as a mapping, this can be very handy:\n>>> bar = 42\n>>> foo = \"spam\"\n>>> baz = None\n>>> \"%(foo)s %(bar)d\" % vars()\n'spam 42'\n\n", "I believe that is a Python 3.0 feature, although it is in version 2.6. But if you have a version of Python below that, that type of string formatting will not work.\nIf you are trying to print formatted strings in general, use Python's printf-style syntax through the % operator. For example:\nprint '%.2f' % some_var\n\n", "Which Python version do you use?\nEdit\nFor Python 2.5, use \"x = %s\" % (x) (for printing strings)\nIf you want to print other types, see here.\n", "Although the existing answers describe the causes and point in the direction of a fix, none of them actually provide a solution that accomplishes what the question asks.\nYou have two options to solve the problem. The first is to upgrade to Python 2.6 or greater, which supports the format string construct.\nThe second option is to use the older string formatting with the % operator. The equivalent code of what you've presented would be as follows.\nfor x in range(1,11):\n print '%2d %3d %4d' % (x, x*x, x*x*x)\n\nThis code snipped produces exactly the same output in Python 2.5 as your example code produces in Python 2.6 and greater.\n" ]

[ 48, 38, 8, 7, 7 ]

[ "Use this:\nprint \"test some char {} and also number {}\".format('a', 123)\n\nresult:\n\ntest some char a and also number 123\n\n" ]

[ -1 ]

[ "format", "python" ]

[ "To use the $() in Makefiles, you have to declare a variable like this HELLO = $(shell python -c \"import sys;print('hello')\"), and now HELLO has the value of \"hello\"\nIf you want to print something on the screen, just use echo or printf in the target's script\n" ]

[ 1 ]

[]

[]

[ "android_ndk", "makefile", "ndk_build", "python" ]

[ "If you already have the bytes of the image in memory\nReturn a fastapi.responses.Response with your custom content and media_type.\nYou'll also need to muck with the endpoint decorator to get FastAPI to put the correct media type in the OpenAPI specification.\[email protected](\n \"/image\",\n\n # Set what the media type will be in the autogenerated OpenAPI specification.\n # fastapi.tiangolo.com/advanced/additional-responses/#additional-media-types-for-the-main-response\n responses = {\n 200: {\n \"content\": {\"image/png\": {}}\n }\n }\n\n # Prevent FastAPI from adding \"application/json\" as an additional\n # response media type in the autogenerated OpenAPI specification.\n # https://github.com/tiangolo/fastapi/issues/3258\n response_class=Response,\n)\ndef get_image()\n image_bytes: bytes = generate_cat_picture()\n # media_type here sets the media type of the actual response sent to the client.\n return Response(content=image_bytes, media_type=\"image/png\")\n\nSee the Response documentation.\nIf your image exists only on the filesystem\nReturn a fastapi.responses.FileResponse.\nSee the FileResponse documentation.\n\nBe careful with StreamingResponse\nOther answers suggest StreamingResponse. StreamingResponse is harder to use correctly, so I don't recommend it unless you're sure you can't use Response or FileResponse.\nIn particular, code like this is pointless. It will not \"stream\" the image in any useful way.\[email protected](\"/image\")\ndef get_image()\n image_bytes: bytes = generate_cat_picture()\n # ❌ Don't do this.\n image_stream = io.BytesIO(image_bytes)\n return StreamingResponse(content=image_stream, media_type=\"image/png\")\n\nFirst of all, StreamingResponse(content=my_iterable) streams by iterating over the chunks provided by my_iterable. But when that iterable is a BytesIO, the chunks will be \\n-terminated lines, which won't make sense for a binary image.\nAnd even if the chunk divisions made sense, chunking is pointless here because we had the whole image_bytes bytes object available from the start. We may as well have just passed the whole thing into a Response from the beginning. We don't gain anything by holding data back from FastAPI.\nSecond, StreamingResponse corresponds to HTTP chunked transfer encoding. (This might depend on your ASGI server, but it's the case for Uvicorn, at least.) And this isn't a good use case for chunked transfer encoding.\nChunked transfer encoding makes sense when you don't know the size of your output ahead of time, and you don't want to wait to collect it all to find out before you start sending it to the client. That can apply to stuff like serving the results of slow database queries, but it doesn't generally apply to serving images.\nUnnecessary chunked transfer encoding can be harmful. For example, it means clients can't show progress bars when they're downloading the file. See:\n\nContent-Length header versus chunked encoding\nIs it a good idea to use Transfer-Encoding: chunked on static files?\n\n", "I had a similar issue but with a cv2 image. This may be useful for others. Uses the StreamingResponse.\nimport io\nfrom starlette.responses import StreamingResponse\n\napp = FastAPI()\n\[email protected](\"/vector_image\")\ndef image_endpoint(*, vector):\n # Returns a cv2 image array from the document vector\n cv2img = my_function(vector)\n res, im_png = cv2.imencode(\".png\", cv2img)\n return StreamingResponse(io.BytesIO(im_png.tobytes()), media_type=\"image/png\")\n\n", "All the other answer(s) is on point, but now it's so easy to return an image\nfrom fastapi.responses import FileResponse\n\[email protected](\"/\")\nasync def main():\n return FileResponse(\"your_image.jpeg\")\n\n", "It's not properly documented yet, but you can use anything from Starlette.\nSo, you can use a FileResponse if it's a file in disk with a path: https://www.starlette.io/responses/#fileresponse\nIf it's a file-like object created in your path operation, in the next stable release of Starlette (used internally by FastAPI) you will also be able to return it in a StreamingResponse.\n", "Thanks to @biophetik's answer, with an important reminder that caused me confusion: If you're using BytesIO especially with PIL/skimage, make sure to also do img.seek(0) before returning!\[email protected](\"/generate\")\ndef generate(data: str):\n img = generate_image(data)\n print('img=%s' % (img.shape,))\n buf = BytesIO()\n imsave(buf, img, format='JPEG', quality=100)\n buf.seek(0) # important here!\n return StreamingResponse(buf, media_type=\"image/jpeg\",\n headers={'Content-Disposition': 'inline; filename=\"%s.jpg\"' %(data,)})\n\n", "The answer from @SebastiánRamírez pointed me in the right direction, but for those looking to solve the problem, I needed a few lines of code to make it work. I needed to import FileResponse from starlette (not fastAPI?), add CORS support, and return from a temporary file. Perhaps there is a better way, but I couldn't get streaming to work:\nfrom starlette.responses import FileResponse\nfrom starlette.middleware.cors import CORSMiddleware\nimport tempfile\n\napp = FastAPI()\napp.add_middleware(\n CORSMiddleware, allow_origins=[\"*\"], allow_methods=[\"*\"], allow_headers=[\"*\"]\n)\n\[email protected](\"/vector_image\")\ndef image_endpoint(*, vector):\n # Returns a raw PNG from the document vector (define here)\n img = my_function(vector)\n\n with tempfile.NamedTemporaryFile(mode=\"w+b\", suffix=\".png\", delete=False) as FOUT:\n FOUT.write(img)\n return FileResponse(FOUT.name, media_type=\"image/png\")\n\n", "My needs weren't quite met from the above because my image was built with PIL. My fastapi endpoint takes an image file name, reads it as a PIL image, and generates a thumbnail jpeg in memory that can be used in HTML like:\n<img src=\"http://localhost:8000/images/thumbnail/bigimage.jpg\">\nimport io\nfrom PIL import Image\nfrom fastapi.responses import StreamingResponse\[email protected]('/images/thumbnail/{filename}',\n response_description=\"Returns a thumbnail image from a larger image\",\n response_class=\"StreamingResponse\",\n responses= {200: {\"description\": \"an image\", \"content\": {\"image/jpeg\": {}}}})\ndef thumbnail_image (filename: str):\n # read the high-res image file\n image = Image.open(filename)\n # create a thumbnail image\n image.thumbnail((100, 100))\n imgio = io.BytesIO()\n image.save(imgio, 'JPEG')\n imgio.seek(0)\n return StreamingResponse(content=imgio, media_type=\"image/jpeg\")\n\n", "You can use a FileResponse if it's a file in disk with a path:\nimport os\n\nfrom fastapi import FastAPI \nfrom fastapi.responses import FileResponse\n\napp = FastAPI()\n\npath = \"/path/to/files\"\n\[email protected](\"/\")\ndef index():\n return {\"Hello\": \"World\"}\n\[email protected](\"/vector_image\", responses={200: {\"description\": \"A picture of a vector image.\", \"content\" : {\"image/jpeg\" : {\"example\" : \"No example available. Just imagine a picture of a vector image.\"}}}})\ndef image_endpoint():\n file_path = os.path.join(path, \"files/vector_image.jpg\")\n if os.path.exists(file_path):\n return FileResponse(file_path, media_type=\"image/jpeg\", filename=\"vector_image_for_you.jpg\")\n return {\"error\" : \"File not found!\"}\n\n", "You can do something very similar in FastAPI\nfrom fastapi import FastAPI, Response\n\napp = FastAPI()\n\[email protected](\"/vector_image/\")\nasync def image_endpoint():\n # img = ... # Create the image here\n return Response(content=img, media_type=\"image/png\")\n\n", "If when following the top answer and you are attempting to return a BytesIO object like this in your Response\n buffer = BytesIO(my_data)\n\n # Return file\n return Response(content=buffer, media_type=\"image/jpg\")\n\nYou may receive an error that looks like this (as described in this comment)\nAttributeError: '_io.BytesIO' object has no attribute 'encode'\n\nThis is caused by the render function in Response which explicitly checks for a bytes type here. Since BytesIO != bytes it attempts to encode the value and fails.\nThe solution is to get the bytes value from the BytesIO object with getvalue()\n buffer = BytesIO(my_data)\n\n # Return file\n return Response(content=buffer.getvalue(), media_type=\"image/jpg\")\n\n" ]

[ 67, 66, 37, 34, 17, 13, 4, 3, 1, 0 ]

[]

[]

[ "api", "fastapi", "python" ]

[ "Well, Google's own documentation states that the code I was using would automatically close the subscription because of the with block. For some reason I kept overlooking that comment in the code.\nThis resolved my issue:\n def unsubscribe(self, subscription_id):\n subscriber = self.subscriber\n\n subscription_path = subscriber.subscription_path(self.project_id, subscription_id)\n\n subscriber.delete_subscription(request={\"subscription\": subscription_path})\n\n return True\n\n" ]

[ 0 ]

[]

[]

[ "google_cloud_pubsub", "python" ]

[ "Once you have the ID of the message you want to edit, just go:\nawait msg.edit(content=\"Edit\")\n\nObviously, edit content to what you want. Also remember, you can only edit messages you have sent, so make sure the bot has sent that message.\n" ]

[ 0 ]

[]

[]

[ "discord", "python" ]

[ "You might have to change soup.find in dictionary to get the data you want.\nimport json\n\ndictionary = {\n \"title\": soup.find(\"title\"),\n \"date\": soup.find(\"date\")\n}\n\njson_object = json.dumps(dictionary, indent=4)\n\nwith open(\"saveFile.json\", \"w\") as outfile:\n outfile.write(json_object)\n\n", "As it looks like you are parsing some very simple HTML data, I think you could simply use xmltodict package for this:\ndata = []\nfor txt in txt_files:\n with open(txt, \"r\") as file:\n data += [xmltodict.parse(file.read())]\n\njson_str = json.dumps(data)\n\n\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "As your dataframe is not clean (the 2 first rows are a multiindex column name), you can first create the inner dataframe before melting it :\nnew_df = pd.DataFrame(df.iloc[2:,1:]).set_index(df.iloc[2:,0])\nnew_df.columns = pd.MultiIndex.from_frame(df.iloc[:2,1:].T)\nnew_df.melt(ignore_index=False).reset_index()\n\n" ]

[ 0 ]

[]

[]

[ "database", "dataframe", "pandas", "pandas_melt", "python" ]

[ "If you want to get all classes but one for example, you can loop through all element and choose the ones you keep:\nfor p in soup.find_all(\"p\", \"review_comment\"):\n if p.find(class_=\"something-archived\"):\n continue\n # p is now a wanted p\n\nsource: Excluding unwanted results of findAll using BeautifulSoup\n" ]

[ 1 ]

[]

[]

[ "beautifulsoup", "python" ]

[ "np.take(x, x[:, 1].astype(int).argsort(), 0)\nYou may just cast the values for sorting. The overall result of you np.take() will remain as strings.\narray([['0', '0'],\n ['1', '1'],\n [',', '2'],\n ['4', '3'],\n ['.', '4'],\n ['2', '5'],\n ['5', '6'],\n ['er014', '7'],\n ['ww', '8'],\n ['*', '9'],\n ['7', '10'],\n ['8', '11']], dtype='<U5')\n\n" ]

[ 1 ]

[]

[]

[ "np.argsort", "numpy", "python" ]

[ "Just make questions_list a set:\nquestions_list = set()\n\nSets don't allow a value more than once.\n", "You can work with a single query with:\nsubject = QuestionSubject.objects.get(id=request.POST.get('subject'))\nquestion_topics = QuestionTopic.objects.filter(question_subject=subject)\nquestions_list = [\n question\n for topic in question_topics\n for question in Question.objects.filter(\n question_subject=subject, question_topic=topic\n ).order_by('?')[: topic.exam_questions_num]\n]\nThis will make O(n) queries with n the number of topics.\nIf the number of questions is not that large, you can do this with two queries with:\nfrom random import sample\n\nsubject = QuestionSubject.objects.get(id=request.POST.get('subject'))\nquestion_topics = QuestionTopic.objects.filter(\n question_subject=subject\n).prefetch_related('question_set')\nquestions_list = [\n question\n for topic in question_topics\n for question in sample(topic.question_set.all(), topic.exam_question_num)\n]\n" ]

[ 1, 1 ]

[]

[]

[ "django", "python" ]

[ "You can try:\nimport re\nimport requests\nimport urllib.parse\nfrom bs4 import BeautifulSoup\n\nurl = \"https://www.pigiame.co.ke/cars\"\n\nheaders = {\n \"User-Agent\": \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:107.0) Gecko/20100101 Firefox/107.0\",\n}\n\nsoup = BeautifulSoup(requests.get(url, headers=headers).content, \"html.parser\")\n\nlinks = [\n item.a[\"href\"] for item in soup.select(\".listings-cards__list-item:has(a)\")\n]\n\nfor link in links:\n soup = BeautifulSoup(requests.get(link).content, \"html.parser\")\n data = soup.select_one(\".btn-whatsapp\")[\"href\"]\n data = urllib.parse.unquote(data)\n\n phone = re.search(r\"phone=(.*?)&\", data).group(1)\n print(\"{:<20} {}\".format(phone, link))\n\nPrints:\n+254723099904 https://www.pigiame.co.ke/listings/nissan-note-5253578\n+254722935411 https://www.pigiame.co.ke/listings/honda-freed-7-seater-2015-5291221\n+254722763845 https://www.pigiame.co.ke/listings/2006-bmw-x3-2500cc-petrol-155000kms-5241375\n+254722710833 https://www.pigiame.co.ke/listings/mazda-mpv-2006-5273382\n+254713193417 https://www.pigiame.co.ke/listings/landrover-109-very-clean-accident-free-5282118\n+254708467397 https://www.pigiame.co.ke/listings/landcrusser-prado-tx-fully-loaded-with-sunroof-2016-model-5304294\n+254708467397 https://www.pigiame.co.ke/listings/landcrusser-prado-tx-fully-loaded-with-sunroof-5304293\n+254708467397 https://www.pigiame.co.ke/listings/mistubishi-canter-2016-model-3tones-5304291\n+254708467397 https://www.pigiame.co.ke/listings/hillux-revolution-2016-model-fully-loaded-5304288\n+254708467397 https://www.pigiame.co.ke/listings/toyota-prado-2016-model-fully-loaded-with-sunroof-5304285\n+254708467397 https://www.pigiame.co.ke/listings/toyota-prado-txl-diesel-fuel-2017-model-5304283\n+254769333436 https://www.pigiame.co.ke/listings/toyota-fielder2015kdkbelow-50000km-5304279\n+254708467397 https://www.pigiame.co.ke/listings/subaru-forester-sti-turbo-2016-model-5304280\n+254769333436 https://www.pigiame.co.ke/listings/honda-fit2015kdkbelow-60000km-5304276\n+254769333436 https://www.pigiame.co.ke/listings/honda-vezel2015kdklow-milleage-5304273\n\n" ]

[ 0 ]

[]

[]

[ "beautifulsoup", "css_selectors", "html", "python", "web_scraping" ]

[ "\nActually, detailed paragraph is visualized in the html dom and click on thereviews just go down to the page and the jab can be done by scrolling button too.The subject matter is that clicking on reviews will not work rather will create exception.\n\nThe big problem is to select the virtual games paragraph html nodes perfectly becase all the p nodes are in the same level thats why whatever selection it grabs the entire paragraph.So the split method generates the right output here.\n\n\nWorking code:\nfrom selenium import webdriver\nimport time\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\noptions = webdriver.ChromeOptions()\noptions.add_argument(\"--no-sandbox\")\noptions.add_argument(\"--disable-gpu\")\noptions.add_argument(\"--window-size=1920x1080\")\noptions.add_argument(\"--disable-extensions\")\ndriver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))\n \nURL = 'https://www.askgamblers.com/online-casinos/countries/uk'\ndriver.get(URL)\ntime.sleep(2)\n\nurls= []\ndata = []\npage_links =driver.find_elements(By.XPATH, \"//div[@class='card__desc']//a[starts-with(@href, '/online')]\")\nfor link in page_links:\n href=link.get_attribute(\"href\")\n urls.append(href)\n #print(href)\n\nfor url in urls:\n driver.get(url)\n time.sleep(1)\n\n soup = BeautifulSoup(driver.page_source,\"lxml\")\n\n try:\n paragraph=soup.select_one('section[class=\"review-text richtext\"]').get_text(' ',strip=True).split('Virtual Games')[1].split('Live Casino')[0]\n #print(paragraph)\n except:\n #print('empty')\n pass\n data.append({'Virtual Games':paragraph})\nprint(data)\n\nOutput:\n[{'Virtual Games': ' Players who enjoy video slots have a large selection to choose from here with titles from gaming suppliers like NetEnt, Microgaming and Play’n GO. The casino is home to some of the newest and popular games in the market with a range of themes to suit all. Titles include the Starburst Slot , Aloha! Cluster Pays Slot , Moon Princess Slot and Birds on a Wire Slot . When it comes to table games members will be able to enjoy some blackjack and \nroulette games including Roulette Pro and Blackjack Solo. There is also a small selection of video pokers for players who enjoy betting with cards such as Jacks or Better. In addition to that, members can pick from multiple Scratch \ncard games for added entertainment. '}, {'Virtual Games': ' Bet365 Casino gives players a full range of online casino games to choose from, including a wide variety of table games, arcades, online slots, video poker and several live dealer choices. Players can find their favourite games under both the Casino and Games tab in the site’s header. Slots lovers have an immense selection of game options at their disposal. Among our favourites are Age of the Gods slot , Immortal Romance slot , to name a few. Those looking for table games will also have a massive collection of roulette, blackjack, baccarat and other casino games to choose from. In addition to this, poker lovers will also find a \ngreat offer that will suit their taste, including Jacks or Better, Joker Poker, and Deuces Wild. '}, {'Virtual Games': ' Players who open the casino lobby will be greeted with lots of video slots and there is plenty of variety to cater for all kinds of people. In fact, they have a game library of over 1000+ games from some of the best providers in the industry. The lobby is easy to use and players can easily find their favourite game by using the search facility and can filter the games by each individual provider too. Titles here include the Book of Dead Slot , Gonzo’s Quest Slot , Bonanza Slot , Immortal Romance Slot and Mega Moolah Slot . There are table games to enjoy here as well, especially handy for players who enjoy card and dice games. These include variations of blackjack, roulette and other traditional games. Players who like card games can try the casinos selection of video poker games and these include Jacks or Better and All Aces. There are also some ‘Other’ games available in the lobby too for anyone who is looking for something different. '}, {'Virtual Games': ' Slot lovers will find a good variety of both bonus video slots and classic fruity-style games at Trada Casino. These include titles from both Microgaming and PariPlay, including popular favourites like Immortal Romance , Avalon II , Tomb Raider , Big Bad Wolf , Rabbit in the Hat , Jurassic Park , and many more variants. Trada Casino also has an exceptional variety of bingo , keno , scratch games , and specialties from various providers. Bingo players can choose from several different gaming styles and themes with titles like Mayan Bingo, Super Bonus Bingo, and Samba Bingo. Those who love to scratch can try to win big with games like Whack a Jackpot , Slam Funk , and Turtley Awesome . Table game enthusiasts can choose from several different roulette and blackjack variants at Trada Casino, such as Classic Blackjack, Spanish Blackjack, Vegas Strip Blackjack, American \nRoulette, Premier Roulette Diamond, and French Roulette. Trada Casino’s selection of video pokers from Microgaming is comprehensive and covers practically every main variety of the game, with many offered in both single-hand and multi-hand versions. Available variants include Aces & Faces, Deuces Wild, Double Double Bonus, Louisiana Double, Tens \nor Better, Joker Poker, and Jacks or Better. '}, {'Virtual Games': \" PlayFrank Casino is home to an extensive selection of slots . Players will find hit titles from Betsoft, Microgaming, NYX Interactive, Play'n Go, and NetEnt. Some \nof the most famous ones include Aliens slot , Starburst slot , Mr. Vegas slot , Foxin’ Wins Again slot , At the Movies slot , and Chimney Sweep slot . Also, the casino hosts numerous of the world’s most popular progressive slots like Mega Fortune Slot and Hall of Gods, with jackpots often in the multi-million range. In addition to its live table \ngames , PlayFrank Casino provides players with a number of virtual table game favourites. These include several variants of European and French Roulette; blackjack games like Single Deck Blackjack and Double Exposure Blackjack; multiple types of Punto Banco/Baccarat; and many other favourites. Play Frank Casino’s video poker selection contains many classic and modern variants from both NetEnt and GamesOS. Among these are All American Poker, Jacks or Better, Deuces Wild, Joker Wild, Shockwave Poker, and Coliseum Poker, among others. Live Games PlayFrank Casino features an extensive live dealer room powered by Evolution Gaming. Players here can enjoy round-the-clock table game excitement in dozens of different live rooms. The game selection includes a surprising number of Live Blackjack, Live Roulette, \nand Live Baccarat variants, all dealt to professional standards and broadcast in real-time. Mobile Gaming PlayFrank \nCasino makes satisfying mobile players a key priority. The site’s HTML5 interface is designed to make it easy to play on virtually any device, from a home computer to smartphones and tablets . Players can enjoy dozens of their favourite slots and table games on the go. No app download is necessary. Support The PlayFrank Casino support team is available in English, German and Norwegian. Players via the site’s live chat facility, available from 7 AM till 23 PM. \nAlso, players can reach the casino support team by email or telephone, on top of having a helpful FAQ section at their disposal, where certain previously answered questions can be found. Security and Fairness PlayFrank Casino protects players with the state-of-the-art SSL encryption. This technology is the gold standard for online safety and it is trusted by all reputable online merchants and banks. It prevents anyone from seeing sensitive data with an advanced algorithm that cannot be decoded. The games at PlayFrank Casino meet the standards of fairness required by two independent testing agencies : TST and iTech Labs . As such, players can rest assured that they will find a fair game here.\"}, {'Virtual Games': \" PlayFrank Casino is home to an extensive selection of slots . Players will find hit titles from Betsoft, Microgaming, NYX Interactive, Play'n Go, and NetEnt. Some of the most famous ones include Aliens slot , Starburst slot , Mr. Vegas slot , Foxin’ Wins Again slot , At the Movies slot , and Chimney Sweep slot . Also, \nthe casino hosts numerous of the world’s most popular progressive slots like Mega Fortune Slot and Hall of Gods, with jackpots often in the multi-million range. In addition to its live table games , PlayFrank Casino provides players with a number of virtual table game favourites. These include several variants of European and French Roulette; blackjack games like Single Deck Blackjack and Double Exposure Blackjack; multiple types of Punto Banco/Baccarat; and many other favourites. Play Frank Casino’s video poker selection contains many classic and modern variants from both \nNetEnt and GamesOS. Among these are All American Poker, Jacks or Better, Deuces Wild, Joker Wild, Shockwave Poker, and Coliseum Poker, among others. Live Games PlayFrank Casino features an extensive live dealer room powered by Evolution Gaming. Players here can enjoy round-the-clock table game excitement in dozens of different live rooms. The game selection includes a surprising number of Live Blackjack, Live Roulette, and Live Baccarat variants, all dealt to \nprofessional standards and broadcast in real-time. Mobile Gaming PlayFrank Casino makes satisfying mobile players a \nkey priority. The site’s HTML5 interface is designed to make it easy to play on virtually any device, from a home computer to smartphones and tablets . Players can enjoy dozens of their favourite slots and table games on the go. No \napp download is necessary. Support The PlayFrank Casino support team is available in English, German and Norwegian. \nPlayers via the site’s live chat facility, available from 7 AM till 23 PM. Also, players can reach the casino support team by email or telephone, on top of having a helpful FAQ section at their disposal, where certain previously answered questions can be found. Security and Fairness PlayFrank Casino protects players with the state-of-the-art SSL \nencryption. This technology is the gold standard for online safety and it is trusted by all reputable online merchants and banks. It prevents anyone from seeing sensitive data with an advanced algorithm that cannot be decoded. The games at PlayFrank Casino meet the standards of fairness required by two independent testing agencies : TST and iTech Labs . As such, players can rest assured that they will find a fair game here.\"}, {'Virtual Games': ' Slots are a big part of the Hello! Casino game library. Players will find more than 400 different video slot titles from big names like NetEnt, NYX Gaming group, Betsoft, Leander, Thunderkick, Aristocrat, all in a simple instant-play interface. \nAmong the most popular games are Steam Tower , Twin Spin , Starburst , Gonzo’s Quest , and many others. Players who \nenjoy the thrill of the tables will find several virtual card, dice, and roulette games at Hello! Casino. Among them are Multi Wheel Roulette and French Roulette, Blackjack and Pontoon, Craps, Baccarat, Sic Bo, Red Dog Progressive, \nTexas Hold’em Pro, Oasis Poker, and Caribbean Stud. Those looking for something else will find a number of scratch games and keno games in the “Lottery” section, as well as four different video poker variants under “Other” available in both single-hand and multi-hand denominations. '}, {'Virtual Games': ' As 21Prive Casino uses a number different software providers it’s no wonder that they have a rich games portfolio with over 500 games which should meet the taste of different players. Games are easily accessible and carefully categorised. However, there isn’t a search option available. 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Mobile Gaming Spinland also offers a mobile casino that can be played straight \nthrough a mobile browser, without the need to download any additional software. Support Players who need assistance \ncan enjoy 24/7 live chat with a friendly and professional support team. Questions can also be sent through email for a quick response. Security and Fairness Members can rest assured they are playing in a safe environment as the casino employs SSL encryption to protect personal and financial details. A random number generator is also utilized to ensure that all their games result in a random outcome. Furthermore, the site is closely monitored and regulated by eCogra .'}, {'Virtual Games': ' Players can enjoy loads of games here which happens to be from some of the most well \nknown games in the industry. The lobby is very user friendly due to the number of filters players can use to find their favourite games. 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Security and Fairness All sensitive areas of the 21 Casino website are encrypted with state-of-the-art technology from GlobalSign , a leading security firm. This protocol obscures private information like financial data and passwords, making it virtually \nimpossible for anyone to compromise player accounts. All of the games used at 21 Casino have been thoroughly audited by a trusted independent agency, such as TST or iTech Labs .'}, {'Virtual Games': ' Being a Microgaming casino , it provides players with an excellent selection of slot games to suit every kind of preference, with favorites like Avalon II , Immortal Romance , Thunderstruck II , Elementals , Break da Bank , Playboy , Money Mad Monkey , Starlight \nKiss and so much more. However, being a new comer to online gaming market, Conquer Casino’s list of slot games is not yet comprehensive with its 180+ games, out of which, 13 are jackpot games. Nevertheless, these should accommodate \nmost demands for choice of games. 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'}, {'Virtual Games': \" The casino lobby boasts hundreds of the industry's best video slots from some of the most talked about providers in the market like NetEnt , Microgaming and Play’n GO . Players can easily search for their favourite games which will help find them quicker but other than that other filters here are limited. Players will have to browse the lobby until they find what they want to play. Titles include the Book of Dead Slot , Bonanza Slot , Aloha! Cluster Pays Slot , Thunderstruck 2 Slot and Starburst Slot . Of course, \nthere are table games here as well for anyone looking to play something different to slots. These include popular versions of blackjack and roulette. Players who prefer card games can try their luck with the video poker games like Jacks or Better and Joker Poker. The casino lobby is also home to some other casino games like scratch cards. \"}, {'Virtual Games': ' SlotsMagic is \n\n... so on\n" ]

[ 0 ]

[]

[]

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

[ "If a generator expression is acceptable with regards to your requirements, then:\ndef check(student, classes):\n return any(student in value for value in classes.values())\n\nAnd to get a boolean for each student, you could create this function:\ndef checkall(students, classes):\n return [any(student in value for value in classes.values()) \n for student in students]\n\nFor your sample data, this would return [True, False, True].\n", "So if you want to just print is here or is not here here is an example:\nclasses = {\n \"class_A\" : [\"Mike\",\"Alice\",\"Peter\"],\n \"class_B\" : [\"Sam\",\"Robert\",\"Anna\"],\n \"class_C\" : [\"Tom\",\"Nick\",\"Jack\"]\n}\nfor line in str(classes).split(\",\"):\n if student in line:\n print(\"Student in here\")\n else:\n print(\"Student not here\")\n\n", "Since this is in a loop, you should create a set for all of the values:\nfrom itertools import chain\n\nvalues = set(chain(*classes.values()))\n\nstudents=[\"Alice\",\"Frodo\",\"Jack\"]\n\nfor student in students:\n if student in values:\n print(\"Is here\")\n else:\n print(\"Is not here\")\n\n\nThe reason is that a set lookup is a constant time lookup, and in a tight loop makes a big difference\n", "How about making the list of students in each class a set?\nThen the lookup time will be o(1) and you can loop over n classes.\nThe you can have:\nclass_to_students = {\n \"class_A\" : {\"Mike\",\"Alice\",\"Peter\"},\n \"class_B\" : {\"Sam\",\"Robert\",\"Anna\"},\n \"class_C\" : {\"Tom\",\"Nick\",\"Jack\"}\n}\nstudents=[\"Alice\",\"Frodo\",\"Jack\"]\nfor student in students:\n for class_students in class_to_students.values():\n if student in class_students:\n print(f\"{student} Is here\")\n break\n else:\n # loop was not broken out of\n print(f\"{student} Is not here\")\n\n-->\nAlice Is here\nFrodo Is not here\nJack Is here\n\nIf you exclude a solution like this, then you are stuck with your n*m solution where n is the number of classes and m is the number of students in each class.\n", "Is this something you are looking for?\nclasses = {\n \"class_A\": [\"Mike\",\"Alice\",\"Peter\"],\n \"class_B\": [\"Sam\",\"Robert\",\"Anna\"],\n \"class_C\": [\"Tom\",\"Nick\",\"Jack\"]\n}\n\nstudents = [\"Alice\", \"Frodo\", \"Jack\"]\nres = [student in class_ for class_ in classes.values() for student in students ]\nprint(res)\n\n", "\nIf allowed, I guess you'd increase performance if you delete the entry within the class, whenever you had a match.\n\nUse sorting beforehand\n\n\nstudentsInClasses=[]\nfor studentsInClass in classes.values()\n studentsInClasses+=studentsInClass\n\nstudentsInClasses = sorted(studentsInClasses)\nstudents=sorted([\"Alice\",\"Frodo\",\"Jack\"])\n\nlastMatch=0\nfor i in range(len(students)):\n student = students[i]\n try:\n lastMatch = studentsInClasses[lastMatch:].index(student)\n print(f\"{student} in class\")\n except ValueError as e:\n pass\n\n\n" ]

[ 3, 0, 0, 0, 0, 0 ]

[]

[]

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

[ "use threading and the timeout parameter for join https://docs.python.org/3/library/threading.html#threading.Thread.start\nfrom threading import Thread\nimport time\ndef A():\n while True:\n time.sleep(2)\ndef B():\n while True:\n time.sleep(1)\ndef C():\n while True:\n time.sleep(1)\n\nt_a = Thread(target=A)\nt_a.run()\nThread(target=B).run()\nThread(target=C).run()\n\nt_a.join(timeout=10)\n\n" ]

[ 2 ]

[]

[]

[ "python" ]

[ "This ended up being very straight forward. While I feel a bit silly I didn't click at first I learnt a fair bit about virtual environments in the process!\nAll that was necessary was to simply run the following once I had activated the environment (in Anaconda prompt)\njupyter notebook\n\nThe problem was that I was running a jupyter notebook from a different location hence the clash. Hopefully this helps someone else.\n", "You should be able to run jupyter notebook from any environment, and be able to select the kernel from the dropdown. No need to exit and change directory.\nconda activate py37\npython -m ipykernel install --user --name py37 --display-name \"guacamole\"\n\n# then, to test \nconda deactivate\njupyter notebook \n\n" ]

[ 0, 0 ]

[]

[]

[ "anaconda", "conda", "jupyter_notebook", "python" ]

[ "You can tell Django to use a custom model for the many to many relationship using through parameter in that field, this way:\n\nclass Player(models.Model):\n username = models.CharField(max_length = 200)\n won_games = models.IntegerField(default=0)\n\n\nclass Group(models.Model):\n id = models.CharField(max_length = 200)\n players = models.ManyToManyField(Player, related_name=\"groups\", through='Point')\n\n\nclass Point(models.Model):\n player = models.ForeignKey(Player, on_delete=models.PROTECT, related_name='points')\n group = models.ForeignKey(Group, on_delete=models.PROTECT, related_name='points')\n\n\nDjango docs include an example for this topic here: https://docs.djangoproject.com/en/4.1/topics/db/models/#extra-fields-on-many-to-many-relationships\n", "You probably need an intermediate table with an extra field. Something similar to this:\nfrom django.db import models\n\nclass Player(models.Model):\n username = models.CharField(max_length = 200)\n won_games = models.IntegerField(default=0)\n\n def __str__(self):\n return self.username\n\nclass Group(models.Model):\n name = models.CharField(max_length=128)\n players= models.ManyToManyField(Player, through='Point')\n\n def __str__(self):\n return self.name\n\nclass Point(models.Model):\n player = models.ForeignKey(Player, on_delete=models.CASCADE)\n group = models.ForeignKey(Group, on_delete=models.CASCADE)\n point = models.IntegerField()\n\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "Using Miller at command line to filter a CSV and capture all rows with time <= 00:00:30.\ncat time_select.csv\nid,time_val\n1,00:00:01\n2,00:00:02\n3,00:00:03\n4,00:00:04\n5,00:00:05\n6,00:00:06\n7,00:00:07\n8,00:00:08\n9,00:00:09\n10,00:00:10\n...\n50,00:00:50\n51,00:00:51\n52,00:00:52\n53,00:00:53\n54,00:00:54\n55,00:00:55\n56,00:00:56\n57,00:00:57\n58,00:00:58\n59,00:00:59\n\n\nmlr --csv filter 'strptime($time_val, \"%H:%M:%S\") <= strptime(\"00:00:30\", \"%H:%M:%S\")' time_select.csv > time_filtered.csv\n\ncat time_filtered.csv \nid,time_val\n1,00:00:01\n2,00:00:02\n3,00:00:03\n4,00:00:04\n5,00:00:05\n6,00:00:06\n7,00:00:07\n8,00:00:08\n9,00:00:09\n10,00:00:10\n11,00:00:11\n12,00:00:12\n13,00:00:13\n14,00:00:14\n15,00:00:15\n16,00:00:16\n17,00:00:17\n18,00:00:18\n19,00:00:19\n20,00:00:20\n21,00:00:21\n22,00:00:22\n23,00:00:23\n24,00:00:24\n25,00:00:25\n26,00:00:26\n27,00:00:27\n28,00:00:28\n29,00:00:29\n30,00:00:30\n\n\n" ]

[ 0 ]

[]

[]

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

[ "There are 2 issues with the code here:\n\nVariable data has a type of tuple, not a number.\nEach member of the tuple is a float as the TypeError indicates.\n\nYou will need to pass one value at a time to ser.write. And you will need to cast the float returned by the slider widget to an integer. Something like the following:\ndata = int(values[\"SLIDER_X\"])\nser.write(data)\n\n" ]

[ 0 ]

[]

[]

[ "pyserial", "pysimplegui", "python", "python_3.x" ]

[ "First, understand that in a chained assignment, the right-most expression is evaluated to an object. A reference to that object is then assigned to each target in sequence, from left to right. x = y = z is effectively the same as\nt = z # A new temporary variable \"t\" to hold the result of evaluating z\nx = t\ny = t\ndel t\n\nSecond, you need to understand the difference between assigning t to the subscription x[1] and to the slicing x[1:]. In the former, element 1 of x is replaced by t. In the latter, elements x[1], x[2], etc are replaced by t[0], t[1], etc, extending or shrinking x as necessary.\nThe first example is equivalent to\nt = [1,2]\nx = t\nx[1] = t\ndel t\n\nx[1] becomes a reference to x itself; list.__repr__ can detect cycles like this and represents them with .... x, x[1], x[1][1], etc are all references to the original list [1,2].\nThe second example is equivalent to\nt = [1,2]\nx = t\nx[1:] = t\ndel t\n\nIn this case, no cycles are created. x[1] is replaced by a reference to 1, not x/t itself, and x is extended to make x[2] a reference to 2. (If it helps, think of x[1:] = t as producing the same result as x = [x[0]]; x.extend(t).)\n", "x = x[1] = [1, 2]\n\nIn this case, x and x[1] are the same object. Therefore x contains itself, which is represented by ... in your output.\nx = x[1:] = [1, 2]\n\nIn this case, x is a slice, and slices are copies. so they are not the same object.\n", "Explaining the outputs:\nx = x[1] = [1, 2] # Output: [1, [...]]\n\nThe reason is that the list is not copied, but referenced.\n\nx[1] is a reference to the list [1, 2].\nWhen you assign x[1] to x, you are assigning a reference to the list [1, 2] to x.\nSo x and x[1] are both references to the same list.\nWhen you change the list through one of the references, the change appears in the other reference as well. This is called aliasing.\n\n\nx = x[1:] = [1, 2] # Output: [1, 1, 2]\n\nThe reason is that the list is copied, not referenced.\n\nx[1:] is slice of the list [1, 2].\nThe slice is a copy of the list, not a reference to the list.\nWhen you assign x[1:] to x, you are assigning a copy of the list [1, 2] to x.\nSo x and x[1:] are both copies of the same list.\nWhen you change the list through one of the references, the change does not appear in the other reference.\n\n" ]

[ 3, 0, 0 ]

[]

[]

[ "infinite_loop", "python", "variable_assignment" ]

[ "I would suggest to group by day and take mode on hours:\ndf['date_time'] = pd.to_datetime(df['date_time'])\ndf['date'] = df['date_time'].dt.day\ndf['hour'] = df['date_time'].dt.hour\ndf_groupped = df.groupby(df['date'])['hour'].agg(pd.Series.mode)\n\n", "You can use pandas.DataFrame.groupby with .dt accessors.\nTry this :\ndf_day = (\n df.assign(hour= \"Hour \" + df['date_time'].dt.hour.astype(str),\n week= \"Week\" + df['date_time'].dt.isocalendar().week.astype(str))\n .groupby(\"week\", as_index=False).agg(MostAccesHour= (\"hour\", lambda x: x.value_counts().index[0]))\n )\n \ndf_week= (\n df.assign(hour= \"Hour \" + df['date_time'].dt.hour.astype(str),\n day= df['date_time'].dt.date)\n .groupby(\"day\", as_index=False).agg(MostAccesHour= (\"hour\", lambda x: x.value_counts().index[0]))\n )\n\n# Output :\nprint(df_week)\n\n day MostAccesHour\n0 2022-11-24 Hour 19\n1 2022-11-25 Hour 0\n2 2022-11-29 Hour 20\n\nprint(df_day)\n\n week MostAccesHour\n0 Week47 Hour 0\n1 Week48 Hour 20\n\n" ]

[ 1, 1 ]

[]

[]

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

[ "Maybe this is the code you are looking for:\nimport json\n\n\nBuildings = ['nr1','nr2','nr3']\noffices = [1,3,2]\narea=[23,[67,77,94],[78,79]]\nprice=[45,[43,89,56],[54,53]]\nemployees=[56,[45,54,78],[56,89]]\n\nmyDict = dict()\n\nfor building, office in zip(Buildings, offices):\n myDict[f\"Build {building}\"] = dict()\n for i in range(office):\n myDict[f\"Build {building}\"][f\"office {i+1}\"] = dict()\n\ni = 0\nfor ar in area:\n i += 1\n j = 0\n if isinstance(ar, int):\n myDict[f\"Build nr{i}\"][f\"office {j + 1}\"][\"area\"] = f\"{ar} kvm\"\n if isinstance(ar, list):\n for element in ar:\n myDict[f\"Build nr{i}\"][f\"office {j + 1}\"][\"area\"] = f\"{element} kvm\"\n j += 1\nk = 0\nfor pr in price:\n k += 1\n j = 0\n if isinstance(pr, int):\n myDict[f\"Build nr{k}\"][f\"office {j + 1}\"][\"price\"] = pr\n if isinstance(pr, list):\n for element in pr:\n myDict[f\"Build nr{k}\"][f\"office {j + 1}\"][\"price\"] = element\n j += 1\nl = 0\nfor emp in employees:\n l += 1\n j = 0\n if isinstance(emp, int):\n myDict[f\"Build nr{l}\"][f\"office {j + 1}\"][\"employees\"] = emp\n if isinstance(emp, list):\n for element in emp:\n myDict[f\"Build nr{l}\"][f\"office {j + 1}\"][\"employees\"] = element\n j += 1\n\nprint(json.dumps(myDict, indent = 4))\n\nwhich prints:\n{\n \"Build nr1\": {\n \"office 1\": {\n \"area\": \"23 kvm\",\n \"price\": 45,\n \"employees\": 56\n }\n },\n \"Build nr2\": {\n \"office 1\": {\n \"area\": \"67 kvm\",\n \"price\": 43,\n \"employees\": 45\n },\n \"office 2\": {\n \"area\": \"77 kvm\",\n \"price\": 89,\n \"employees\": 54\n },\n \"office 3\": {\n \"area\": \"94 kvm\",\n \"price\": 56,\n \"employees\": 78\n }\n },\n \"Build nr3\": {\n \"office 1\": {\n \"area\": \"78 kvm\",\n \"price\": 54,\n \"employees\": 56\n },\n \"office 2\": {\n \"area\": \"79 kvm\",\n \"price\": 53,\n \"employees\": 89\n }\n }\n}\n\n" ]

[ 0 ]

[]

[]

[ "dictionary", "nested_lists", "python" ]

[ "I'm making an assumption based on your comment about the need for a non-comma separator to avoid conflicts with the commas in your fields that you would be okay with something like this:\nrows = [('Name,1', '20221110'), ('Name2', '20221115')]\nfor r in rows:\n print(\"|\".join(r))\n\nOutput:\nName,1|20221110\nName2|20221115\n\njoin creates a single concatenated string from a collection of strings that are separated by the specified string.\n" ]

[ 0 ]

[]

[]

[ "pymssql", "python" ]

[ "The following code works but may not be very efficient:\ndata1 = {'a':1, 'b':2}\ndata2 = {'a':3, 'b':4}\n\nlist = [data1, data2]\ndf = pd.concat([pd.DataFrame(list[i], index=[i]) for i in range(len(list))])\n\nprint(df)\n\n", "Here is a proposition using pandas.concat with pandas.DataFrame.unstack :\nlist_of_dicts = [data1, data2]\n\ndf= (\n pd.concat(dict(enumerate(map(pd.Series,\n list_of_dicts))))\n .unstack()\n )\n\n# Output :\nprint(df)\n\n a b\n0 1 2\n1 3 4\n\nNB : Try to avoid naming your variables with python built-in function names (list, dict, ..)\n" ]

[ 0, 0 ]

[]

[]

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

[]

[]

[ "Try saving it all as a text by doing\ndef text():\n#your code here\nwhile True: text()\n\nwhat that does is repeats your code even when it stops or breaks.\nalso you could try doing an if function: if it breaks, start again\nthanks!\n" ]

[ -1 ]

[ "crash", "matlab", "python", "shell", "try_catch" ]

[ "Well...there was no wheel i could find so i finally got the build working. It took about a day.\n" ]

[ 0 ]

[]

[]

[ "cryptography", "linux", "python", "raspberry_pi", "twine" ]

[ "It seems that the column CustomerName holds some NaN values, so you need to set na=False in pandas.Series.str.contains :\nTry this :\nfinal_df.loc[final_df['CustName'].str.contains('bbb', case=False, na=False), 'CustomerName'] = 'Big Boy Booty'\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "\nDoes this mean that I have to add a get_context_data method to every single view I have?\n\nNo, you don't have to do that. HomeView.get_context_data(...) is specific to the home page.\nIn your example, it looks like you want to show the news in all pages (all pages that use the base.html template). In that case, I would recommend to use a templatetag to load the news.\nSee: https://docs.djangoproject.com/en/4.1/howto/custom-template-tags/\n\nYou would do something like:\nmy_app/templatetags/news_tags.py\nfrom django import template\n\nregister = template.Library()\n\[email protected]_tag('includes/latest_news.html')\ndef latest_news(context):\n return {\n 'news': News.objects.order_by(\"-published_at\")[0:25],\n }\n\nbase.html:\n{% load static %}\n{% load news_tags %}\n\n<body>\n <div class=\"navbar\">\n <a id=\"title\" href=\"{% url 'home' %}\">home</a>\n </div>\n\n ...\n\n <div class=\"side-bar\">\n {% latest_news %}\n </div>\n {% block content %}{% endblock %}\n\n ...\n\n</body>\n\n\nTip: restart runserver after adding a new templatetag file for it to be found.\n" ]

[ 0 ]

[]

[]

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

[ "Each tuple in your list has two elements. Let's call the first one a \"key\".\nWe're going to create an empty list to fill with the tuples we want.\nLet's also create a set (already_added) containing the keys we have already added. For each tuple, we need to check if the \"key\" exists in already_added, and only add it to our result if it doesn't.\nlst = [(1, 60),\n (1, 93),\n (1, 104),\n (1, 145),\n (1, 159),\n (4, 20),\n (4, 30),\n (4, 103),\n (8, 8),\n (9, 35),\n (9, 172),\n (9, 191),\n (10, 33),\n (10, 164),\n (10, 185)]\n\nresult = []\nalready_added = set()\n\nfor item in lst:\n if item[0] not in already_added:\n result.append(item)\n already_added.add(item[0]) \n\nThis gives us the following result:\n[(1, 60), (4, 20), (8, 8), (9, 35), (10, 33)]\n\n", "test = [(1, 60),\n (1, 93),\n (1, 104),\n (1, 145),\n (1, 159),\n (4, 20),\n (4, 30),\n (4, 103),\n (8, 8),\n (9, 35),\n (9, 172),\n (9, 191),\n (10, 33),\n (10, 164),\n (10, 185)]\nseen = set()\nprint([x for x in test if x[0] not in seen and not seen.add(x[0])])\n>>>\n[(1, 60), (4, 20), (8, 8), (9, 35), (10, 33)]\n\n", "This is my simple solution:\nx=[(1, 60),\n (1, 93),\n (1, 104),\n (1, 145),\n (1, 159),\n (4, 20),\n (4, 30),\n (4, 103),\n (8, 8),\n (9, 35),\n (9, 172),\n (9, 191),\n (10, 33),\n (10, 164),\n (10, 185)]\nx.reverse()\nl={i:(i,j) for i,j in x}\nfilter_list = list(l.values())\nfilter_list.reverse()\n\nSince you care about the first occurrence, we revert the list to add the elements to a dictionary, since we use the first integer as a key, the elements will be overwritten and the last one to appear will be your first occurrence, then we keep the values and revert the list again.\n", "Use a dictionary to with key-value pairs the unique identifier of the tuple and the tuple itself as the value.\nlst = # from above\n\n# if ordering is important\nlst = sorted(lst, key=lambda p: (p[0], -p[1]))\n\n# grant unicity in 1st entry (take latest entry for each repeated key)\nlst = {x: (x, y) for x, y in lst}\n\nres = list(lst.values())\n\n" ]

[ 2, 1, 0, 0 ]

[]

[]

[ "python" ]

[ "This is a Python repository, not JavaScript...\nrequirements.txt is pip's version of npm's package.json\nSee what is PIP\nSee here for information on installing the necessary packages using pip.\nOr this Stack answer\n" ]

[ 0 ]

[]

[]

[ "django", "python" ]

[ "While you have created a new Room object, you haven't assigned it to room.\nTry\nroom = Room.objects.create(\n\n", "Your create room function should look like this\n@login_required(login_url='login')\ndef createRoom(request):\n form = RoomForm()\n topics = Topic.objects.all()\n if request.method == 'POST':\n topic_name = request.POST.get('topic')\n topic, created = Topic.objects.get_or_create(name=topic_name)\n\n room = Room.objects.create(\n host=request.user,\n topic=topic,\n name=request.POST.get('name'),\n assigned=request.user,\n status=request.POST.get('status'),\n priority=request.POST.get('priority'),\n type=request.POST.get('type'),\n description=request.POST.get('description'),\n )\n room.save() \n return redirect(\"created-room-view-function\")\n\n context = {'form': form, 'topics': topics, 'room': room}\n return render(request, 'room/room_form.html', context)\n\n" ]

[ 1, 0 ]

[]

[]

[ "django", "python" ]

[ "You get an error, because you're using .ceil() on datetime.timedelta class in your example. You need the pandas class for this. (pandas._libs.tslibs.timedeltas.Timedelta)\ndelta = pd.Timedelta(4, \"d\")\nNow, what does this .ceil(\"1d\") do?\nIf you have a delta with hours and minutes you want ceil it to days value. E.g.:\ndate1 = pd.to_datetime(1490195805, unit='s')\ndate2 = pd.to_datetime(1490597688, unit='s')\ndate2 - date1\n\nTimedelta('4 days 15:38:03')\n(date2 - date1).ceil(\"1d\")\n\nTimedelta('5 days 00:00:00')\nAs the hours are more than 12h, the date delta gets ceiled to 5 days.\n" ]

[ 0 ]

[]

[]

[ "pandas", "python", "timedelta" ]

[ "You don't need PyCrypto or PyMe, fine though those packages may be - you will have all kinds of problems building under Windows. Instead, why not avoid the rabbit-holes and do what I did? Use gnupg 1.4.9. You don't need to do a full installation on end-user machines - just gpg.exe and iconv.dll from the distribution are sufficient, and you just need to have them somewhere in the path or accessed from your Python code using a full pathname. No changes to the registry are needed, and everything (executables and data files) can be confined to a single folder if you want.\nThere's a module GPG.py which was originally written by Andrew Kuchling, improved by Richard Jones and improved further by Steve Traugott. It's available here, but as-is it's not suitable for Windows because it uses os.fork(). Although originally part of PyCrypto, it is completely independent of the other parts of PyCrypto and needs only gpg.exe/iconv.dll in order to work.\nI have a version (gnupg.py) derived from Traugott's GPG.py, which uses the subprocess module. It works fine under Windows, at least for my purposes - I use it to do the following:\n\nKey management - generation, listing, export etc.\nImport keys from an external source (e.g. public keys received from a partner company)\nEncrypt and decrypt data\nSign and verify signatures\n\nThe module I've got is not ideal to show right now, because it includes some other stuff which shouldn't be there - which means I can't release it as-is at the moment. At some point, perhaps in the next couple of weeks, I hope to be able to tidy it up, add some more unit tests (I don't have any unit tests for sign/verify, for example) and release it (either under the original PyCrypto licence or a similar commercial-friendly license). If you can't wait, go with Traugott's module and modify it yourself - it wasn't too much work to make it work with the subprocess module.\nThis approach was a lot less painful than the others (e.g. SWIG-based solutions, or solutions which require building with MinGW/MSYS), which I considered and experimented with. I've used the same (gpg.exe/iconv.dll) approach with systems written in other languages, e.g. C#, with equally painless results.\nP.S. It works with Python 2.4 as well as Python 2.5 and later. Not tested with other versions, though I don't foresee any problems.\n", "After a LOT of digging, I found a package that worked for me. Although it is said to support the generation of keys, I didn't test it. However I did manage to decrypt a message that was encrypted using a GPG public key. The advantage of this package is that it does not require a GPG executable file on the machine, and is a Python based implementation of the OpenPGP (rather than a wrapper around the executable). \nI created the private and public keys using GPG4win and kleopatra for windows\nSee my code below.\nimport pgpy\nemsg = pgpy.PGPMessage.from_file(<path to the file from the client that was encrypted using your public key>)\nkey,_ = pgpy.PGPKey.from_file(<path to your private key>)\nwith key.unlock(<your private key passpharase>):\n print (key.decrypt(emsg).message)\n\nAlthough the question is very old. I hope this helps future users.\n", "PyCrypto supports PGP - albeit you should test it to make sure that it works to your specifications.\nAlthough documentation is hard to come by, if you look through Util/test.py (the module test script), you can find a rudimentary example of their PGP support:\nif verbose: print ' PGP mode:',\nobj1=ciph.new(password, ciph.MODE_PGP, IV)\nobj2=ciph.new(password, ciph.MODE_PGP, IV)\nstart=time.time()\nciphertext=obj1.encrypt(str)\nplaintext=obj2.decrypt(ciphertext)\nend=time.time()\nif (plaintext!=str):\n die('Error in resulting plaintext from PGP mode')\nprint_timing(256, end-start, verbose)\ndel obj1, obj2\n\nFuthermore, PublicKey/pubkey.py provides for the following relevant methods:\ndef encrypt(self, plaintext, K)\ndef decrypt(self, ciphertext):\ndef sign(self, M, K):\ndef verify (self, M, signature):\ndef can_sign (self):\n \"\"\"can_sign() : bool\n Return a Boolean value recording whether this algorithm can\n generate signatures. (This does not imply that this\n particular key object has the private information required to\n to generate a signature.)\n \"\"\"\n return 1\n\n", "PyMe does claim full compatibility with Python 2.4, and I quote:\n\nThe latest version of PyMe (as of this\n writing) is v0.8.0. Its binary\n distribution for Debian was compiled\n with SWIG v1.3.33 and GCC v4.2.3 for\n GPGME v1.1.6 and Python v2.3.5,\n v2.4.4, and v2.5.2 (provided in\n 'unstable' distribution at the time).\n Its binary distribution for Windows\n was compiled with SWIG v1.3.29 and\n MinGW v4.1 for GPGME v1.1.6 and Python\n v2.5.2 (although the same binary get\n installed and works fine in v2.4.2 as\n well).\n\nI'm not sure why you say \"it doesn't seem to be compatible with Python 2.4 which I have to use\" -- specifics please?\nAnd yes it does exist as a semi-Pythonic (SWIGd) wrapper on GPGME -- that's a popular way to develop Python extensions once you have a C library that basically does the job.\nPyPgp has a much simpler approach -- that's why it's a single, simple Python script: basically it does nothing more than \"shell out\" to command-line PGP commands. For example, decryption is just:\ndef decrypt(data):\n \"Decrypt a string - if you have the right key.\"\n pw,pr = os.popen2('pgpv -f')\n pw.write(data)\n pw.close()\n ptext = pr.read()\n return ptext\n\ni.e., write the encrypted cyphertext to the standard input of pgpv -f, read pgpv's standard output as the decrypted plaintext.\nPyPgp is also a very old project, though its simplicity means that making it work with modern Python (e.g., subprocess instead of now-deprecated os.popen2) would not be hard. But you still do need PGP installed, or PyPgp won't do anything;-).\n", "M2Crypto has PGP module, but I have actually never tried to use it. If you try it, and it works, please let me know (I am the current M2Crypto maintainer). Some links:\n\nModule sources\nDemo Script\nunit tests\n\nUpdate: The PGP module does not provide ways to generate keys, but presumably these could be created with the lower level RSA, DSA etc. modules. I don't know PGP insides, so you'd have to dig up the details. Also, if you know how to generate these using openssl command line commands, it should be reasonably easy to convert that to M2Crypto calls.\n", "As other have noted, PyMe is the canonical solution for this, since it's based on GpgME, which is part of the GnuPG ecosystem.\nFor Windows, I strongly recommend to use Gpg4win as the GnuPG distribution, for two reasons:\nIt's based on GnuPG 2, which, among other things, includes gpg2.exe, which can (finally, I might add :) start gpg-agent.exe on-demand (gpg v1.x can't).\nAnd secondly, it's the only official Windows build by the GnuPG developers. E.g. it's entirely cross-compiled from Linux to Windows, so not a iota of non-free software was used in preparing it (quite important for a security suite :).\n", "To sign with only the exported public key file without a keyring.\nWith PGPy 0.5.2 (pure Python GPG RFC implementation):\nkey_fpath = './recipient-PUB.gpg'\n \nrsa_pub, _ = pgpy.PGPKey.from_file(rkey_fpath)\nrkey = rsa_pub.subkeys.values()[0] \n \ntext_message = pgpy.PGPMessage.new('my msg')\nencrypted_message = rkey.encrypt(text_message)\nprint encrypted_message.__bytes__()\n\nWith gpg 1.10.0 (gpgme Python bindings - former PyME):\nrkey_fpath = './recipient-PUB.gpg'\ncg = gpg.Context()\nrkey = list(cg.keylist(source = rkey_fpath)) \n \nciphertext, result, sign_result = cg.encrypt('my msg', recipients=rkey, sign=False, always_trust=True)\nprint ciphertext\n\nA simple benchmark in a for loop shows me that for this simple operation on my system PGPy is ~3x time faster than gpgme Python bindings (please do not take this statement as X is faster than Y: I will invite you to test in your environment).\n", "Here's a full script that will:\n\nAttempt to decrypt all the files in a given folder that were encrypted with your public key.\nWrite the new files to a specified folder.\nMove the encrypted files to a specified folder.\n\nThe script also has everything you need to create and store your own private and public keys, check out the \"First time set up\" section below.\nThe idea is that you can schedule this script to run as often as you like, and it'll automatically decrypt data found and store it for you.\nI hope this helps someone, this was a tricky project to figure out.\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ Introduction, change log and table of contents\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Purpose: This script is used to decrypt files that are passed to us from ICF.\n#\n# Change date Changed by Description\n# 2022-10-03 Ryan Bradley Initial draft\n# 2022-10-12 Ryan Bradley Cleaned up some comments and table of contents. \n#\n# Table of Contents\n# [1.0] Hard-coded variables\n# [1.1] Load packages and custom functions\n# [1.3] First time set up\n# [1.4] Define custom functions\n# [2.0] Load keys and decrypt files\n#\n# Sources used to create this script, and for further reading:\n# https://github.com/SecurityInnovation/PGPy/\n# https://stackoverflow.com/questions/1020320/how-to-do-pgp-in-python-generate-keys-encrypt-decrypt\n# https://pypi.org/project/PGPy/\n# https://betterprogramming.pub/creating-a-pgp-encryption-tool-with-python-19bae51b7fd\n# https://pgpy.readthedocs.io/en/latest/examples.html\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ [1.1] Load packages \n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nimport glob\nimport pgpy\nimport shutil\nimport io\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ [1.2] Hard-coded variables\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# Define the paths to public and private keys\npath_public_key = r'YOUR PATH HERE'\npath_private_key = r'YOUR PATH HERE'\n\n# Define paths to files you want to try decrypting\npath_original_files = r'YOUR PATH HERE'\npath_decrypted_files = r'YOUR PATH HERE'\npath_encrypted_files= r'YOUR PATH HERE'\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ [1.3] First time set up\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n# IMPORTANT WARNINGS!!!!\n# - Do NOT share your private key with anyone else. \n# - You MUST have the associated private key that is is generated along with a public key \n# if you want to be able to decrypt anything that is encryped with that public key. Do\n# not overwrite the existing keys unless you will never need any of the previously \n# encryped data. \n# - Do not generate new public and private keys unless you have a good reason to. \n#\n# The following steps will walk you through how to create and write public and private keys to\n# a network location. Be very careful where you store this information. Anyone with access\n# to your private key can decrypt anything that was encryped with your public key.\n#\n# These steps only need to be performed one time when the script is first being \n# created. They are commented out intentionally, as they shouldn't need to be performed \n# every time the script is ran. \n# \n# Here's the a link to the documentation on this topic:\n# https://pgpy.readthedocs.io/en/latest/examples.html\n\n# # Load the extra things we need to define a new key\n# from pgpy.constants import PubKeyAlgorithm, KeyFlags, HashAlgorithm, SymmetricKeyAlgorithm, CompressionAlgorithm\n \n# # Gerate a new a primary key. For this example, we'll use RSA, but it could be DSA or ECDSA as well\n# key = pgpy.PGPKey.new(PubKeyAlgorithm.RSAEncryptOrSign, 4096)\n\n# # Define a new user \n# uid = pgpy.PGPUID.new('SA_CODA_Admin', comment='Customer Strategy and Data Analytics service account.', email='[email protected]')\n\n# # Add the new user id to the key, and define all the key preferences.\n# key.add_uid(uid, usage={KeyFlags.Sign, KeyFlags.EncryptCommunications, KeyFlags.EncryptStorage},\n# hashes=[HashAlgorithm.SHA256, HashAlgorithm.SHA384, HashAlgorithm.SHA512, HashAlgorithm.SHA224],\n# ciphers=[SymmetricKeyAlgorithm.AES256, SymmetricKeyAlgorithm.AES192, SymmetricKeyAlgorithm.AES128],\n# compression=[CompressionAlgorithm.ZLIB, CompressionAlgorithm.BZ2, CompressionAlgorithm.ZIP, CompressionAlgorithm.Uncompressed]\n# , is_compressed = True)\n\n# # Write the ASCII armored public key to a network location.\n# text_file = open(path_public_key, 'w')\n# text_file.write(str(key.pubkey))\n# text_file.close()\n\n# # Write the ASCII armored private key to a network location.\n# text_file = open(path_private_key, 'w')\n# text_file.write(str(key))\n# text_file.close()\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ [1.4] Define custom functions\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\ndef file_encrypt(path_original_file, path_encrypted_file, key_public):\n \"\"\"\n A function that encrypts the content of a file at the given path and \n creates an ecryped version file at the new location using the specified\n public key.\n \"\"\"\n \n # Create a PGP file, compressed with ZIP DEFLATE by default unless otherwise specified\n pgp_file = pgpy.PGPMessage.new(path_original_file, file=True)\n \n # Encrypt the data with the public key\n encrypted_data = key_public.encrypt(pgp_file) \n \n # Write the encryped data to the encrypted destination\n text_file = open(path_encrypted_file, 'w')\n text_file.write(str(encrypted_data))\n text_file.close()\n \ndef file_decrypt(path_encrypted_file, path_decrypted_file, key_private):\n \"\"\"\n A function that decrypts the content of a file at path path and \n creates a decrypted file at the new location using the given \n private key.\n \"\"\"\n\n # Load a previously encryped message from a file\n pgp_file = pgpy.PGPMessage.from_file(path_encrypted_file)\n \n # Decrypt the data with the given private key\n decrypted_data = key_private.decrypt(pgp_file).message\n \n # Read in the bytes of the decrypted data\n toread = io.BytesIO()\n toread.write(bytes(decrypted_data)) \n toread.seek(0) # reset the pointer \n \n # Write the data to the location\n with open(path_decrypted_file, 'wb') as f:\n shutil.copyfileobj(toread, f)\n f.close()\n \n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~ [2.0] Load keys and decrypt files\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# Load your pre-generated public key from the network\nkey_public, _ = pgpy.PGPKey.from_file(path_public_key)\n\n# Load your pre-generated public key from the network\nkey_private, _ = pgpy.PGPKey.from_file(path_private_key)\n\n# Find and process any encrypted files in the landing folder\nfor file in glob.glob(path_original_files + '\\*.pgp'):\n \n # Get the path to the file we need to decrypt\n path_encrypted_file = str(file)\n \n # Extract the file name\n parts = path_encrypted_file.split('\\\\')\n str_file_name = parts[len(parts)-1]\n str_clean_file_name = str_file_name[:-4]\n \n # Extract the file exension\n str_extension = str_clean_file_name.split('.')\n str_extension = str_extension[len(str_extension) - 1]\n \n # Create the path to the new decryped file, dropping the \".pgp\" extension\n path_decrypted_file = path_decrypted_files + '\\\\' + str_clean_file_name\n \n # Create the path to the place we'll store the encryped file\n path_archived_encrypted_file = path_encrypted_files + '\\\\' + str_file_name\n \n # Decrypt the file\n try:\n file_decrypt(path_encrypted_file, path_decrypted_file, key_private)\n \n # Move the encryped file to its new location\n shutil.move(path_encrypted_file, path_archived_encrypted_file)\n except:\n print('DECRYPTION ERROR!')\n print(f'Unable to decrypt {path_encrypted_file}')\n \n# Just for reference, here's how you would call the function to encrypt a file:\n# file_encrypt(path_original_file, path_encrypted_file, key_public)\n\n" ]

[ 41, 35, 7, 3, 3, 3, 1, 0 ]

[]

[]

[ "encryption", "gnupg", "pgp", "public_key_encryption", "python" ]

[ "As pointed out by @jasonharper you changed your logic in the second example and in the process misused time in your while statement. Keeping your second example's logic in line with your first example's logic (assuming that is your goal) you would want to do something like this:\nstart = int(time.time())\nhits = 0\nwhile int(time.time()) - 2 < start:\n #COMBAT MECHANICS#\n\n" ]

[ 0 ]

[]

[]

[ "python", "replit", "time" ]

[ "demand_hist_sku_date = demand_history['Pyramid Key'] + demand_history['FCST_YR_PRD']\nazlog_3_sku_date = azlog_3['Pyramid Key']+ azlog_3['FCST_YR_PRD']\ndemand_history.loc[demand_hist_sku_date.isin(azlog_3_sku_date), 'DMD_ACTL_QTY' ] = azlog_3['DMD_ACTL_QTY']\n" ]

[ 0 ]

[]

[]

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

[ "Here's a way to do this from \"scratch\". I have the following import statements:\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy import sin,cos,pi\nfrom scipy.linalg import norm\n\nAfter your 3d plotting code, I added the following:\nazim = ax.azim*pi/180\nelev = ax.elev*pi/180\nelev *= 1.2 # this seems to improve the outcome\n\na_vec = np.array([cos(azim),sin(azim),0])\nnormal = cos(elev)*a_vec + np.array([0,0,sin(elev)])\n\nz_vec = np.array([0,0,1])\ny_comp = z_vec - (z_vec@normal)*normal\ny_comp = y_comp/norm(y_comp)\nx_comp = np.cross(y_comp,normal)\n\nproj_mat = np.vstack([x_comp,y_comp]) # build projection matrix\nproj_mat = -proj_mat # account for flipped axes\npoints_2D = points @ proj_mat.T # apply projection\n\nplt.figure()\nplt.scatter(*points_2D.T)\nplt.gca().set_aspect('equal', adjustable='box')\nplt.axis('off')\nplt.show()\n\nThe resulting points:\n\n" ]

[ 3 ]

[]

[]

[ "computer_vision", "numpy", "opencv", "pose_estimation", "python" ]

[ "I had the same use case come up! I was happy to find you can do this:\nrows, cols = fig._get_subplot_rows_columns()\n\n" ]

[ 0 ]

[]

[]

[ "plotly", "python" ]

[ "Based on the experimentation I have done thus far, plotting these diagrams as one would do in a local dev environment is not (yet?) possible.\nFor this specific case, the next best solution was to create a new GitHub Codespace from this repo: https://github.com/education/codespaces-teaching-template-py\n\nOnce the repo has been cloned into the Codespace, navigate to an existing .ipynb file or create your own.\nInside there you'll be able to run chunks of custom code and plot figures.\n\nThe big limitation I see is that the figure cannot be interacted with the same way that one would be able to on a local machine (zooming, panning, etc).\nAs always, don't forget to shut your Codespace down when you're done using it!\n" ]

[ 1 ]

[]

[]

[ "codespaces", "python" ]

[ "I will only do a portion of your homework.\nI hope it will help you to find energy to work on missing parts.\nimport struct\nimport socket\n\nCPROTO_ECODE_REQUEST, CPROTO_ECODE_SUCCESS, CPROTO_ECODE_FAIL = (0,1,2)\n\nver = 1 # version of protocol\nmid = 0 # initial value\ncid = 99 # client Id (arbitrary)\n\nsock = socket.socket( ...) # to be customized\n\ndef sendRecv( num1, op, num2):\n global mid\n ocs = (\"+\", \"-\", \"*\", \"/\").index( op)\n byte0 = ver + (ocs << 3) + (CPROTO_ECODE_REQUEST << 6)\n hdr = struct.pack( \"!BBH\", byte0, mid, cid)\n parts1 = (b'0000' + num1.encode() + b'0000').split(b'.')\n parts2 = (b'0000' + num2.encode() + b'0000').split(b'.')\n msg = hdr + parts1[0][-4:] + parts1[1][:4] + parts2[0][-4:] + parts2[1][:4]\n \n socket.send( msg) # send request\n bufr = socket.recv( 512) # get answer\n # to do:\n # complete socket_send and socket.recv\n # unpack bufr into: verr,ecr,opr,value_i, value_f\n # verify that verr, ecr, opr, are appropriate\n # combine value_i and value_f into answer\n mid += 1\n return answer\n\nresult = sendRecv( '2.47', '+', '46.234')\n\n\nThere are many elements that haven't be specified by your teacher:\n\nwhat should be the byte-ordering on the network (bigEndian or littleEndian)? The above example suppose it's bigEndian but you can easily modify the 'pack' statement to use littleEndian.\nWhat should the program do if the received packet header is invalid?\nWhat should the program do if there's no answer from server?\nPayload: how should we interpret \"4 most significant digits of fraction\"? Does that mean that the value is in ASCII? That's not specified.\nPayload: assuming the fraction is in ASCII, should it be right-justified or left-justified in the packet?\nPayload: same question for integer portion.\nPayload: if the values are in binary, are they signed or unsigned. It will have an affect on the unpacking statement.\n\nIn the program above, I assumed that:\n\nvalues are positive and in ASCII (without sign)\ninteger portion is right-justified\nfractional portion is left justified\n\nHave fun!\n" ]

[ 1 ]

[]

[]

[ "networking", "python", "python_3.x", "sockets", "udp" ]

[ "You should assign value and a to variables outside of the function. Since you are returning a tuple you should assign it like x,y=f(value). Then print(y) to just show a.\n", "As Karl Knechtel mentioned your requirements are inconsistent but the nearest you can get is:\ndef f(value):\n global other_value\n\n value = ~~~\n a = ~~~~\n other_value = value\n return a\n\nprint(f(value))\n\nBut use of global variables is not recommended.\n", "In next two solutions I expect that you are allowed to change definition of your f(...) function. First solution uses global variable, and second doesn't use any globals.\nFirst variant (below) is that you can just use a global variable to store saved value.\nTry it online!\ndef f(value):\n global saved_value\n saved_value = value\n value = 123\n a = 456\n return a\n\n# Regular usage of your function\nvalue = 789\nprint(f(value)) # Prints 456\n\n# Somewhere later in your code\n# This will print saved value\nprint(saved_value) # Prints 789\n\nOutput:\n456\n789\n\n\nSecond variant (below) without using any global variable is by using state dictionary with default {} value. This state does same thing as global variable, it saves value for later use. I changed number of arguments in your function but that is not a problem because as before everyone can call your function just as f(value), the rest of arguments are not necessary to be provided and will be considered to be equal to default values.\nTry it online!\ndef f(value, *, state = {}, return_value = False):\n if return_value:\n return state['value']\n else:\n state['value'] = value\n value = 123\n a = 456\n return a\n\n# Regular usage of your function\nvalue = 789\nprint(f(value)) # Prints 456\n\n# Somewhere later in your code\n# This will print saved value\nprint(f(None, return_value = True)) # Prints 789\n\nOutput:\n456\n789\n\n" ]

[ 1, 0, 0 ]

[ "https://stackoverflow.com/a/45972642/9184997\ndef test():\n r1 = 1\n r2 = 2\n r3 = 3\n return r1, r2, r3\n\nx,y,z = test()\nprint x\nprint y\nprint z\n\n\n> test.py \n1\n2\n3\n\n" ]

[ -1 ]

[ "global", "python", "return" ]

[ "In each iteration of your loop you access the same element at index 0.\nTo get to each individual element of your list by index you have set it to i:\nx = 1\nlst = [x,x+1,x+2]\nfor i in range(3):\n print(lst[i])\n\nI changed list to lst as the former is a reserved keyword and shouldn't be used as variable name.\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "Some of the issues:\n\nslow != a is a condition that is always False because slow is a ListNode object, and a is an int. The same problem occurs with fast != b.\nslowslow.val != a-1 is wrong, as there is nothing in the question that requires to look at the values in the list. a is an index, not a value.\nYour code will set slow to either the first node or the second node of the first list. There is no loop that guarantees that slow will reference the node before index a. The same problem exists for fast.\n\nHere is working code:\nclass Solution:\n def mergeInBetween(self, list1: ListNode, a: int, b: int, list2: ListNode) -> ListNode:\n # Find node that precedes index a in first list\n left = list1\n for i in range(a - 1):\n left = left.next\n # Find node that follows index b in first list\n right = left\n for i in range(b - a + 2):\n right = right.next\n # Find tail node of second list \n tail = list2\n while tail.next:\n tail = tail.next\n # Make the connections\n left.next = list2\n tail.next = right\n return list1 \n\n" ]

[ 0 ]

[]

[]

[ "linked_list", "python", "python_3.x", "singly_linked_list" ]

[ "With the given data, you can use the numpy and matplotlib modules to achieve the desired result.\nso, you can do something like this:\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nx = np.array(inside_humidity)\ny = np.array(outside_humidity)\n\nfig = plt.figure()\n\n# fit a curve of your choice\na, b = np.polyfit(inside_humidity, outside_humidity, 1)\ny_fit = a * x + b\n\n# scatter plot, and fitted plot (best fit used)\nplt.scatter(inside_humidity, outside_humidity)\nplt.plot(x, y_fit)\n\nplt.show()\n\nwhich gives this:\n\n" ]

[ 0 ]

[]

[]

[ "cross_correlation", "numpy", "python", "scipy", "time_series" ]

[ "I tried to work on a similar dataset, and I didn’t get any segmentation fault. Check with the subscription and deployment pattern with pricing tier. Implemented the same with the custom speech to text translator and it worked in the segmentation also.\n\nCheck with the pricing tier which is creating segmentation fault\nCheck with the subscription allowance\nCheck to train in custom speech studio and test.\n\n\nThe segmentation differs from the location to location and the pricing tier.\n\nAfter running the syntax, I didn't get any segmentation error as the pricing tier is suitable for the volume of the data.\n", "From 1.24.0 Speech SDK version (and onwards), you can stream ALAW/MULAW encoded data directly to speech service (without the need of Gstreamer) by using AudioStreamWaveFormat (https://learn.microsoft.com/en-us/python/api/azure-cognitiveservices-speech/azure.cognitiveservices.speech.audiostreamwaveformat?view=azure-python). This way there is less complexity involved (no Gstreamer).\nencoded_format = msspeech.audio.AudioStreamFormat(samples_per_second=16000, bits_per_sample=16,\n channels=1, wave_stream_format=msspeech.AudioStreamWaveFormat.MULAW)\n\n" ]

[ 0, 0 ]

[]

[]

[ "azure_cognitive_services", "python", "speech_recognition", "speech_to_text", "wav" ]

[ "See Get the row(s) which have the max value in groups using groupby\nExample:\nnew_df = df.groupby(['topic'], sort=False)['num'].max()\n\n", "You can use GroupBy.max with numeric_only=True:\nnewdf= df.groupby(\"topic\", as_index=False).max(numeric_only=True)\n\nOutput:\nprint(newdf)\n\n topic num\n0 a01 2\n1 a02 3\n2 a03 2\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "\nI just want subprocess.check_output to return an empty string if grep doesn't find anything.\n\nWell, too bad. grep considers no matches to be failure, and the whole point of the check in check_output is to check for failure, so you're explicitly asking to do things this way. Here are the relevant docs:\n\nIf the return code was non-zero it raises a CalledProcessError. The CalledProcessError object will have the return code in the returncode attribute and any output in the output attribute.\n\nAnd for grep:\nThe following exit values shall be returned:\n 0 One or more lines were selected.\n 1 No lines were selected.\n >1 An error occurred.\n\nSo, if you want to treat \"no lines\" as success, but actual errors as errors, you have to handle that 1 value differently than other non-zero values. And check_output has no idea that you want to do that.\nSo, either you have to handle the CalledProcessError, or you have to do your own checking. In other words, either this:\ntry:\n tyrs = subprocess.check_output('grep \"^A\" %s | grep TYR' % pocket_location, shell = True).split('\\n')\nexcept subprocess.CalledProcessError as e:\n if e.returncode > 1:\n raise\n tyrs = []\n\n… or this:\np = subprocess.Popen('grep \"^A\" %s | grep TYR' % pocket_location, shell=True,\n stdout=subprocess.PIPE)\noutput, _ = p.communicate()\nif p.returncode == 1: # no matches found \n tyrs = []\nelif p.returncode == 0: # matches found\n tyrs = output.split('\\n')\nelse:\n # error, do something with it\n\n", "tyrs = subprocess.check_output('grep \"^A\" %s | grep TYR || true' % pocket_location, shell = True).split('\\n')\n\n", "For future reference, if anyone is looking for a similar solution using pgrep -\nimport subprocess \nvar = subprocess.check_output(\"pgrep -af 'python3 choice_multi.py CMC0001'\", shell=True, text=True)\nvar = var.splitlines()\nvar = [x for x in var if '/bin/sh' not in x]\nprint(var )\n\nThis will come as an irritating error if you do pgrep because it also shows the PID of the temporary process being created.\nNow the /bin/sh can be different for other systems.\nAlternatively, there are two other solutions that I thought as inefficient -\n\nYou can just delete the first item in the list.\nYou can check the length of the list and scan for the above 1 only because all of them will have a minimum 1 PID.\n\nReferences -\nIs there a simple way to delete a list element by value?\n" ]

[ 14, 6, 0 ]

[]

[]

[ "grep", "python", "subprocess" ]

[ "Use combinations to find the ordered combinations of a list.\nfrom itertools import combinations\n\ndef split(lst, t, m):\n n = len(lst)\n counter = 0\n for i in range(1, m+1):\n for c in combinations(x, r=i):\n if min(c) + t >= max(c):\n counter += 1\n return counter\n\n\nx = [1, 2, 5, 9]\nt, m = 1, 3\nc = split(x, t, m)\nprint(f'{c}-sublists of at most length {m} with tolerance {t}')\n\n" ]

[ 0 ]

[]

[]

[ "list", "python", "recursion" ]

[ "numpy.array_split docs says\n\nFor an array of length l that should be split into n sections, it\nreturns l % n sub-arrays of size l//n + 1 and the rest of size l//n.\n\nAs l and n are fixed in each run, we can conclude that for every element in returned array next one will be no longer than current.\nEdit: when in doubt, as this is python, we can read the code. As you are interesting in case where indices_or_sections is integer relevant piece is:\nNsections = int(indices_or_sections)\nif Nsections <= 0:\n raise ValueError('number sections must be larger than 0.')\nNeach_section, extras = divmod(Ntotal, Nsections)\nsection_sizes = ([0] +\n extras * [Neach_section+1] +\n (Nsections-extras) * [Neach_section])\ndiv_points = _nx.array(section_sizes, dtype=_nx.intp).cumsum()\n\nwhere Ntotal is number of elements in your input array. As you can see there are Neach_section+1s followed by Neach_sectionss.\n", "No they are not sorted during the split so the groups may not be in order. I am using the qcut function to guarantee ranked groups. I tried using np.array_split however the data was not sorted in the deciles which causes issue for my project. Below is a code example of how I got ranked groups (deciles in this case).\ndf['Decile'] = pd.qcut(dev2['variable_name'], 10, labels=False)\n" ]

[ 1, 1 ]

[]

[]

[ "numpy", "python" ]

[ "Can use pd.isnull(row[1][10]) instead of if row[1][10] != None.\nExample:\ni=0\nfor row in df.iterrows():\n if pd.isnull(row[1][10]):\n df2.loc[i,:] = row[1][:]\n i=i+1\n else:\n names = row[1][10].split(',')\n for name in names:\n df2.loc[i,:] = row[1][:]\n df2.loc[i,'name'] = name\n i=i+1\n\nAlso please do give feedback about this solution.\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "# define a function called 'myRange' which takes 3 arguments,\n# stop, start and step, where start and step default to None\ndef myRange(stop,start=None,step=None):\n\n # set the value of outputList to an empty list\n outputList = []\n\n # if stop is 0, do this block of code below\n if stop == 0:\n # set start to 0\n start= 0\n # set stop to 0 (this is a little\n # redundant as we already know stop is 0 at this point)\n stop = start\n\n # set step to 1\n step = 1\n\n # return an empty Tuple\n return()\n\n\nprint(myRange(10))\n\nSo you've definitely made a start and have some code down and running and this is always a huge milestone. It'll just take some figuring out and then I'm sure you'll get it. As you don't want specific code, here are some pointers:\n\nWhat do you want the function to return? Right now it doesn't return anything. range in python returns a sequence of numbers, from start to stop, counting up by step. It goes up by 1 by default and starts at 0 by default\n\nHow would you describe the instructions for a human to perform this action? This can help you figure out what the code needs to do\n\nThe code indented under an if statement will only be run if that if statement is true\n\n\nHopefully that'll get you going in the right direction. Happy to help some more if you get stuck\n" ]

[ 0 ]

[]

[]

[ "list", "python" ]

[ "I may not understand what you need, but let's see if we can get the ball rolling.\nLet's start by using groupby_rolling and see if this gets us close to what is needed.\nFirst, let's convert the floats (kilometers) to integers (meters). That allows us to specify our period as an integer: 150i. We'll then calculate a duration (loc_end - loc_start) that we'll use in our calculations.\nAlso, we'll create a boolean (is_not_null) which will automatically be upcast to 0 or 1 in our calculations.\nThe basic idea is to allow groupby_rolling to calculate each window. We'll use a dot product to calculate our weighted values.\nSince each window will likely be more than 150 meters, we'll need to back out some overage from the last line that represents the amount over 150 (before dividing by 150)\nHere's what this would look like. (Note: I've also asked Polars to include the values it sees in each window -- as lists -- so that the results can be more easily inspected).\nwindow_size = 150\n(\n df\n .with_columns([\n (pl.col('^loc.*$') * 1_000).cast(pl.Int64),\n pl.col('readings').is_not_null().alias('is_not_null'),\n ])\n .with_column(\n (pl.col('loc_end') - pl.col('loc_start')).alias('duration'),\n )\n .groupby_rolling(\n index_column='loc_start',\n period=str(window_size) + \"i\",\n offset=\"0i\",\n closed=\"left\",\n )\n .agg([\n (\n (\n pl.col('duration').dot('is_not_null') -\n (pl.sum('duration') - window_size) * pl.col('is_not_null').last()\n ) / window_size\n ).alias('result'),\n pl.all().suffix('_val_list'),\n ])\n)\n\nshape: (7, 7)\n┌───────────┬──────────┬────────────────────┬────────────────────┬───────────────────┬──────────────────────┬───────────────────┐\n│ loc_start ┆ result ┆ loc_start_val_list ┆ loc_end_val_list ┆ readings_val_list ┆ is_not_null_val_list ┆ duration_val_list │\n│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n│ i64 ┆ f64 ┆ list[i64] ┆ list[i64] ┆ list[i64] ┆ list[bool] ┆ list[i64] │\n╞═══════════╪══════════╪════════════════════╪════════════════════╪═══════════════════╪══════════════════════╪═══════════════════╡\n│ 1000 ┆ 0.666667 ┆ [1000, 1100] ┆ [1100, 1230] ┆ [7, null] ┆ [true, false] ┆ [100, 130] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1100 ┆ 0.133333 ┆ [1100, 1230] ┆ [1230, 1300] ┆ [null, 8] ┆ [false, true] ┆ [130, 70] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1230 ┆ 0.466667 ┆ [1230, 1300, 1340] ┆ [1300, 1340, 1400] ┆ [8, null, null] ┆ [true, false, false] ┆ [70, 40, 60] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1300 ┆ 0.333333 ┆ [1300, 1340, 1400] ┆ [1340, 1400, 1500] ┆ [null, null, 5] ┆ [false, false, true] ┆ [40, 60, 100] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1340 ┆ 0.6 ┆ [1340, 1400] ┆ [1400, 1500] ┆ [null, 5] ┆ [false, true] ┆ [60, 100] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1400 ┆ 1.0 ┆ [1400, 1500] ┆ [1500, 1650] ┆ [5, 6] ┆ [true, true] ┆ [100, 150] │\n├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n│ 1500 ┆ 1.0 ┆ [1500] ┆ [1650] ┆ [6] ┆ [true] ┆ [150] │\n└───────────┴──────────┴────────────────────┴────────────────────┴───────────────────┴──────────────────────┴───────────────────┘\n\nNote: the third value in the result column (0.46667) differs from the result in your posting (0.73).\nDoes this get the ball rolling?\n" ]

[ 2 ]

[]

[]

[ "python", "python_polars" ]

[ "here is one way to do it\nNote: your starting DF has two 'P' in the DF, the expected output has three 'P'. seems to be a typo\n\n# create a temp seq based on type and i/p\n# count for 'I' and 'P' both starts from 0\n# sort the result with type and seq\n\n\nout=df.assign(seq=df.groupby(['Type','I/P']).cumcount()).sort_values(['Type','seq','I/P']).drop(columns='seq')\n\nout\n\nID Type I/P Value\n0 ID1 Primary I 8\n4 ID5 Primary P 3\n1 ID2 Primary I 3\n5 ID6 Primary I 4\n3 ID4 Secondary I 2\n2 ID3 Secondary P 6\n\n" ]

[ 2 ]

[]

[]

[ "dataframe", "multiple_columns", "pandas", "python", "sorting" ]

[ "As it seems your question has been answered in another Question\nA bit of an explanation:\nFirst we assume we work with python-version-2.5 (because I chose so - you have to replace 2.5 by your python-version)\n\nFirst there was/is the command to avoid multiple requests:\nex.g. $ python-2.5 -m pip install myfoopackage\n#you avoid them by specifying your python version (*as it didn't worked in your\ncase I assume you put the wrong python version which you can find out with $ python -V)\nSince version 0.8, Pip supports pip-{python-version}\nex.g. pip-2.5 install myfoopackage #as you can see python-<VERSION> -m is no\nlonger needed\nSince version 1.5 they changed the schema to pip<VERSION>\nex.g. pip2.5 intall mypackage\n\nTL;DR\nTRY:\n$ python -V\n$ for f in find . -type f -iname \"requirements*.txt\"; do\n pip<python_versoin> install -r $f\ndone\n\n" ]

[ 0 ]

[]

[]

[ "pip", "python" ]

[ "I'm assuming you need a min?\nSomething like (untested):\nMeeting.objects.annotate(first_date=Min('dates__start_date')).order_by('first_date')\n\n" ]

[ 0 ]

[]

[]

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

[ "So when you work with numpy arrays they have indexes, like lists. But since they're multi-dimensional you have to call more than one axis.\nIf you replace line passenger_ages.np.append(age) with passenger_ages[pass_no][x] = age then you can get this working. In this case pass_no will increment each time you add a passenger since you've implemented that functionality. You will also need to add a functionality to increment x as well.\nYou need two indexes due to the fact it's a 2d array. Think of them like X, y coordinates on a chart.\nHere's some code to get you pointed in the right direction (I'm not going to finish your school work for you lol). In order to finish this properly you'll need to change the incrementing functions to properly index on each line. Hint: I'd use if/else statements to check and see when it's time to increment pass_no since it has to stay the same for several passengers while just x increments each time, however there are about twenty ways to do this.\nSee also: numpy.asarray and numpy.reshape for another solution.\nimport numpy as np\nimport random as rd\n\npassenger_ages = np.zeros([4, 8], dtype=int)\npass_no = 0\nx = 0\n\nclass Bus:\n\n def run():\n print('==============================')\n print('Welcome to the bus simulator')\n print('==============================')\n print('What do you want to do?')\n print('1. Add passenger.')\n print('2. Print all passengers.')\n print('3. See other age options')\n choice = int(input('Make your choice: '))\n match choice:\n case 1:\n Bus.add_passenger()\n case 2:\n Bus.print_all()\n case 3:\n Bus.age_numbers()\n\n def print_all():\n print(passenger_ages)\n\n def age_numbers():\n print('What do you want to see?')\n print('1. Combined age.')\n print('2. Average age.')\n print('3. Oldest on the bus.')\n print('Youngest on the bus.')\n\n def add_passenger():\n global pass_no\n global x\n\n try:\n age = int(input('Passengers age: '))\n passenger_ages[pass_no][x] = age\n # passenger_ages.np.append(age)\n pass_no += 1\n x += 1\n print(f'You added a passenger with the age: {age}')\n again = input('Do you want to add more passengers?(J/N)')\n if again == 'j' or again == 'J':\n Bus.add_passenger()\n elif pass_no > 32:\n print('Buss is full.')\n Bus.run()\n else:\n Bus.run()\n except ValueError:\n print('Only numbers.')\n\n\nBus.run()\n\n" ]

[ 0 ]

[]

[]

[ "arrays", "numpy", "python" ]

[ "I recommend iterparse() for large xml files:\nimport timeit\nimport os, psutil\nimport datetime\n\nimport pandas as pd\nimport xml.etree.ElementTree as ET\n\nclass parse_xml:\n def __init__(self, path):\n self.xml = os.path.split(path)[1]\n print(self.xml)\n \n columns = [\"Pos_x\", \"Pos_y\", \"Pos_z\", \"Scalar_Temp\"]\n \n data = []\n for event, elem in ET.iterparse(self.xml, events=(\"end\",)):\n if elem.tag == \"Position\":\n x = elem.get(\"x\")\n y = elem.get(\"y\")\n z = elem.get(\"z\")\n if elem.tag == \"Scalar\":\n row = (x, y, z , elem.get(\"TEMP\"))\n data.append(row)\n elem.clear()\n \n df = pd.DataFrame(data, columns=columns)\n print(df)\n \ndef main():\n xml_file = r\"D:\\Daten\\Programmieren\\stackoverflow\\document.xml\"\n parse_xml(xml_file)\n\nif __name__ == \"__main__\":\n now = datetime.datetime.now()\n starttime = timeit.default_timer()\n main()\n process = psutil.Process(os.getpid())\n print('\\nFinished')\n print(f\"{now:%Y-%m-%d %H:%M}\")\n print('Runtime:', timeit.default_timer()-starttime)\n print(f'RAM: {process.memory_info().rss/1000**2} MB')\n\nOutput:\ndocument.xml\n Pos_x Pos_y Pos_z Scalar_Temp\n0 -0.176300004 -0.103100002 -0.153699994 84.192421\n1 -0.173557162 -0.103100002 -0.153699994 83.9050522\n2 -0.170814306 -0.103100002 -0.153699994 83.7506332\n\nFinished\n2022-11-29 23:51\nRuntime: 0.007375300000000029\nRAM: 55.619584 MB\n\nIf the output will be too large you can write it to a sqlite3 database with df.to_sql().\n" ]

[ 0 ]

[]

[]

[ "anaconda", "lxml", "python", "xml" ]

[ "The error means that the client is not able to reach the host for some reason. Try the following:\n\nMake sure the database is still online (they auto-shutdown after 2 hours if there is no activity by default)\nCheck that the IP Address of the host you are connecting with is added to the allow list in the SaaS UI. (see the docs)\nCheck if your host is able to reach the host and port specified in the SaaS UI (for example using telnet on port 8563). Maybe some firewall is preventing access to the database?\n\n", "So I did more digging. actually I have no idea what the issue was.\nTalend:\nI made a connection via JDBC in Talend with the help of exasol-support. The DBType Exasol in talend doesn't work for some reason, its not known if it's talend side or Exasol side, maybe this will be updated in the future. Just remember in the jdbc url type this: \"jdbc:exa:yourconnectionstring\", don't forget the \"exa\".\nPowerBI:\nI tried the connection string with fingerprint method that worked for me. Just put the fingerprint with the connection string and it should connect.\nhttps://exasol.my.site.com/s/article/PowerBI-Encryption-Fingerprint-Issue-in-Exasol-7-1?language=en_US\nDBvisualizer:\nI had a wrong in connection string.\nPython:\nI had a wrong in connection string.\nHopefully this helps someone.\n" ]

[ 0, 0 ]

[]

[]

[ "database_connection", "dbvisualizer", "exasol", "python", "talend" ]

[ "This is not possible, but the issue is tracked on the Keras repository here.\n" ]

[ 0 ]

[]

[]

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

[ "It seems to me that it's not really a scheduled query you want at all. You don't want one to run at regular intervals, you want to run a query in response to a certain event.\nNow, you've rigged up a cloud function to execute some code whenever a new file is added to a bucket. What this cloud function needs is the BigQuery python client library. Here's an example of how it's used.\nAll that remains is to wrap this code in an appropriate function and specify dependencies and permissions using the cloud functions python framework. Here is a guide on how to do that.\n" ]

[ 1 ]

[]

[]

[ "google_bigquery", "google_cloud_functions", "google_cloud_platform", "google_cloud_storage", "python" ]

[ "here is one way to do it\n\n# take a difference b/w the amount of two consecutive rows and then\n# choose rows where the difference is not zero\n\nout= df[df['amount'].diff().ne(0) ]\n\nout\n\n\nid amount\n0 1001 34.30\n2 1003 776.00\n3 1015 18.95\n7 1037 321.20\n8 1001 344.20\n9 1016 3.20\n11 1027 344.20\n\n" ]

[ 2 ]

[]

[]

[ "pandas", "python" ]

[ "let's see, we can remove the nesting using itertool.chain.from_iterable, but we also need the total length, which we can compute by making another generator to avoid looping twice, and we need to count the repetitions, which is done by a counter.\nfrom collections import Counter\nfrom itertools import chain\n\ntotal_length = 0\ndef sum_sublist_length(some_list): # to sum the lengths of the sub-lists\n global total_length\n for value in some_list:\n total_length += len(value)\n yield value\n \ncounts = Counter(chain.from_iterable(sum_sublist_length(my_list)))\nitems = [item for item in counts if counts[item]/total_length >= 0.01]\nprint(items)\n\n['amazon', 'phone', 'serious', 'mind', 'blown', 'enjoy', 'use', 'applic', 'full', 'websit', 'allow', 'quick', 'track', 'packag', 'descript', 'say', 'would', 'app', 'real', 'thing', 'show', 'ghost', 'said', 'quot', 'orang', 'ware', 'cloth', 'adiquit', 'recsmend', 'want', 'talk', 'love', 'play', 'backgammonthi', 'game', 'offer', 'varieti', 'difficulti', 'make', 'perfect', 'beginn', 'season', 'player']\n\n", "Here's another way to generate a list of elements that appear 1% or more of the time, using pandas.DataFrame:\n\nimport numpy as np\nimport pandas as pd\n\n\n# == Define `flatten` function to combine objects with multi-level nesting =======\ndef flatten(iterable, base_type=None, levels=None):\n \"\"\"Flatten an iterable with multiple levels of nesting.\n\n >>> iterable = [(1, 2), ([3, 4], [[5], [6]])]\n >>> list(flatten(iterable))\n [1, 2, 3, 4, 5, 6]\n\n Binary and text strings are not considered iterable and\n will not be collapsed.\n\n To avoid collapsing other types, specify *base_type*:\n\n >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']]\n >>> list(flatten(iterable, base_type=tuple))\n ['ab', ('cd', 'ef'), 'gh', 'ij']\n\n Specify *levels* to stop flattening after a certain level:\n\n >>> iterable = [('a', ['b']), ('c', ['d'])]\n >>> list(flatten(iterable)) # Fully flattened\n ['a', 'b', 'c', 'd']\n >>> list(flatten(iterable, levels=1)) # Only one level flattened\n ['a', ['b'], 'c', ['d']]\n\n \"\"\"\n def walk(node, level):\n if (\n ((levels is not None) and (level > levels))\n or isinstance(node, (str, bytes))\n or ((base_type is not None) and isinstance(node, base_type))\n ):\n yield node\n return\n try:\n tree = iter(node)\n except TypeError:\n yield node\n return\n else:\n for child in tree:\n yield from walk(child, level + 1)\n yield from walk(iterable, 0)\n\n\n# == Problem Solution ==========================================================\n# 1. Flatten the array into a single level list of elements, then convert it\n# to a `pandas.Series`.\nseries_array = pd.Series(list(flatten(array)))\n\n# 2. Get the total number of elements in flattened list\nelement_count = len(series_array)\n\n# 3. Use method `pandas.Series.value_counts() to count the number of times each\n# elements appears, then divide each element count by the\n# total number of elements in flattened list (`element_count`)\nelements = (\n (series_array.value_counts()/element_count)\n # 4. Use `pandas.Series.loc` to select only values that appear more than\n # 1% of the time.\n # .loc[lambda xdf: xdf['rate_count'] >= 0.01, :]\n .loc[lambda value: value >= 0.01]\n # 5. Select the elements, and convert results to a list\n .index.to_list()\n)\nprint(elements)\n['would', 'serious', 'blown', 'quot', 'orang', 'app', 'ghost', 'say', 'use', 'adiquit', 'enjoy', 'said', 'cloth', 'thing', 'applic', 'talk', 'player', 'track', 'recsmend', 'beginn', 'packag', 'allow', 'perfect', 'want', 'real', 'love', 'full', 'show', 'play', 'make', 'backgammonthi', 'mind', 'amazon', 'game', 'difficulti', 'offer', 'descript', 'websit', 'quick', 'season', 'phone', 'variety', 'ware']\n\n\n\n" ]

[ 0, 0 ]

[]

[]

[ "arrays", "python" ]

[ "If your Python dict really consists of a single key value pair, mapping of a string to a string, as you indicate, ruamel.yaml will not dump the output you display\nwith or without typ='safe')\nimport sys\nimport ruamel.yaml\n\ndata = {\n \"key\": \"Y\"\n}\nyaml = ruamel.yaml.YAML(typ='safe')\nyaml.version = (1, 1)\nyaml.default_flow_style = None\nyaml.dump(data, sys.stdout)\nyaml = ruamel.yaml.YAML()\nyaml.version = (1, 1)\nyaml.default_flow_style = None\nyaml.dump(data, sys.stdout)\n\nwhich gives:\n%YAML 1.1\n--- {key: 'Y'}\n%YAML 1.1\n--- {key: 'Y'}\n\nNot even changing the value to a boolean gets your result\nimport sys\nimport ruamel.yaml\n\ndata = dict(key=True)\nyaml = ruamel.yaml.YAML(typ='safe')\nyaml.version = (1, 1)\nyaml.default_flow_style = None\nyaml.dump(data, sys.stdout)\nyaml = ruamel.yaml.YAML()\nyaml.version = (1, 1)\nyaml.default_flow_style = None\nyaml.dump(data, sys.stdout)\n\nwhich gives:\n%YAML 1.1\n--- {key: true}\n%YAML 1.1\n--- {key: true}\n\nAlways, always, always provide a minimal program that can be cut-and-pasted to get the results that you are seeing.\nThat is how I produce my answers ( the answers are actually comming from a multidocument YAML file processed by (ryd)[https://pypi.org/project/ryd/)), and that is how you should consider providing your questions.\n" ]

[ 2 ]

[]

[]

[ "python", "ruamel.yaml" ]

[ "Did you try pre-signed POST instead? Here is the AWS Python reference for it: https://docs.aws.amazon.com/sdk-for-php/v3/developer-guide/s3-presigned-post.html\nThis will potentially workaround proxy limitations from client perspective, if any:\n\nAs a last resort, you can always try good old REST API, although I don't think the issue is in your code and neither in boto3: https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingRESTAPImpUpload.html\n", "Here is a command utilty that does exactly the same thing, you might want to give it at try and see if it works. If it does it will be easy to find the difference between your code and theirs. If it doesn't I would double check the whole process. Here is an example how to upload a file using aws commandline https://aws.amazon.com/premiumsupport/knowledge-center/s3-multipart-upload-cli/?nc1=h_ls\nActually if it does work. Ie you can replecate the upload using aws s3 commands then we need to focus on the use of persigned url. You can check how the url should look like here:\nhttps://github.com/aws/aws-sdk-js/issues/468\nhttps://github.com/aws/aws-sdk-js/issues/1603\nThis are js sdk but the guys there talk about the raw urls and parameters so you should be able to spot the difference between your urls and the urls that are working. \nAnother option is to give a try this script, it uses js to upload file using persigned urls from web browser. \nhttps://github.com/prestonlimlianjie/aws-s3-multipart-presigned-upload\nIf it works you can inspect the communication and observe the exact URLs that are being used to upload each part, which you can compare with the urls your system is generating.\nBtw. once you have a working url for multipart upload you can use the aws s3 presign url to obtain the persigned url, this should let you finish the upload using just curl to have full control over the upload process.\n", "Presigned URL Approach\nYou can study AWS S3 Presigned URLs for Python SDK (Boto3) and how to use multipart upload APIs at the following links:\n\nAmazon S3 Examples >> Presigned URLs\nPython Code Samples for Amazon S3 >> generate_presigned_url.py\nBoto3 - S3 - create_multipart_upload\nBoto3 - S3 - complete_multipart_upload\n\nTransfer Manager Approach\nBoto3 provides interfaces for managing various types of transfers with S3 to automatically manage multipart and non-multipart uploads. To ensure that multipart uploads only happen when absolutely necessary, you can use the multipart_threshold configuration parameter.\nTry out the following code for Transfer Manager approach:\nimport boto3\nfrom boto3.s3.transfer import TransferConfig\nimport botocore\nfrom botocore.client import Config\nfrom retrying import retry\nimport sysdef upload(source, dest, bucket_name):\n try:\n conn = boto3.client(service_name=\"s3\", \n aws_access_key_id=[key], \n aws_secret_access_key=[key], \n endpoint_url=[endpoint],\n config=Config(signature_version='s3')\n config = TransferConfig(multipart_threshold=1024*20, \n max_concurrency=3, \n multipart_chunksize=1024*20, \n use_threads=True)\n conn.upload_file(Filename=source, Bucket=bucket_name, \n Key=dest, Config=config)\n except Exception as e:\n raise Exception(str(e))def download(src, dest, bucket_name):\n try:\n conn = boto3.client(service_name=\"s3\", \n aws_access_key_id=[key], \n aws_secret_access_key=[key], \n endpoint_url=[endpoint],\n config=Config(signature_version='s3')\n config = TransferConfig(multipart_threshold=1024*20, \n max_concurrency=3, \n multipart_chunksize=1024*20, \n use_threads=True)\n conn.download_file(bucket=bucket_name, key=src, \n filename=dest, Config=config) \n except AWSConnectionError as e:\n raise AWSConnectionError(\"Unable to connect to AWS\")\n except Exception as e:\n raise Exception(str(e))if __name__ == '__main__': \nupload(source, dest, bucket_name)\ndownload(src, dest, bucket_name)\n\nAWS STS Approach\nYou can also follow the AWS Security Token Service (STS) approach to generate a set of temporary credentials to complete your task instead.\nTry out the following code for the AWS STS approach:\nimport json\nfrom uuid import uuid4\n\nimport boto3\n\n\ndef get_upload_credentials_for(bucket, key, username):\n arn = 'arn:aws:s3:::%s/%s' % (bucket, key)\n policy = {\"Version\": \"2012-10-17\",\n \"Statement\": [{\n \"Sid\": \"Stmt1\",\n \"Effect\": \"Allow\",\n \"Action\": [\"s3:PutObject\"],\n \"Resource\": [arn],\n }]}\n client = boto3.client('sts')\n response = client.get_federation_token(\n Name=username, Policy=json.dumps(policy))\n return response['Credentials']\n\n\ndef client_from_credentials(service, credentials):\n return boto3.client(\n service,\n aws_access_key_id=credentials['AccessKeyId'],\n aws_secret_access_key=credentials['SecretAccessKey'],\n aws_session_token=credentials['SessionToken'],\n )\n\n\ndef example():\n bucket = 'mybucket'\n filename = '/path/to/file'\n\n key = uuid4().hex\n print(key)\n\n prefix = 'tmp_upload_'\n username = prefix + key[:32 - len(prefix)]\n print(username)\n assert len(username) <= 32 # required by the AWS API\n\n credentials = get_upload_credentials_for(bucket, key, username)\n client = client_from_credentials('s3', credentials)\n client.upload_file(filename, bucket, key)\n client.upload_file(filename, bucket, key + 'bob') # fails\n\n\nexample()\n\nMinIO Client SDK for Python Approach\nYou can use MinIO Client SDK for Python which implements simpler APIs to avoid the gritty details of multipart upload.\nFor example, you can use a simple fput_object(bucket_name, object_name, file_path, content_type) API to do the need full.\nTry out the following code for MinIO Client SDK for Python approach:\nfrom minio import Minio\nfrom minio.error import ResponseError\n\ns3client = Minio('s3.amazonaws.com',\n access_key='YOUR-ACCESSKEYID',\n secret_key='YOUR-SECRETACCESSKEY')\n\n# Put an object 'my-objectname' with contents from 'my-filepath'\n\ntry: \n s3client.fput_object('my-bucketname', 'my-objectname', 'my-filepath')\nexcept ResponseError as err:\n print(err)\n\n", "Make sure that when you connect to the S3 endpoints you use proper s3 domain name (which should include region!)\nBucket name in the header name is not enough.\nThe easiest thing to debug this is just try to generate presign URL with AWS CLI with --debug option\naws s3 presign s3://your-bucket/file --expires-in 604800 --region eu-central-1 --debug\n\nand then just use it with curl\ncurl -X GET \"https://your-bucket.s3.eu-central-1.amazonaws.com/file\n\nNormally aws client can redirect based on bucket name (it contains quite a lot of logic), but http client will not so you need to talk with proper endpoints.\nIn other words, change:\n\"Host\": \"AWS_S3_BUCKET.s3.amazonaws.com\"\n\nto\n\"Host\": \"AWS_S3_BUCKET.s3.REGION.amazonaws.com\"\n\n" ]

[ 1, 1, 1, 0 ]

[]

[]

[ "amazon_s3", "amazon_web_services", "boto3", "python" ]

[ "I don't know turtle much, but I think this should work:\ndef bar():\n # do stuff here\n\n# Test this with either a 2 or a 3, idk which one it is\nturtle.onscreenclick(bar, 2, True) \n\nIt will bind the right click event to this bar function.\nTell me if it does not work!\nEdit: I had used the wrong method sorry!\nIt's actually \"onscreenclick\".\n" ]

[ 0 ]

[]

[]

[ "keyboard_events", "python", "python_3.x", "right_click" ]

[ "Finally found the solution...\ndef get_balance(*args):\n\n print('trade_helper_support.get_balance')\n _w1.balance_label['text'] = \"Text updated\"\n\n" ]

[ 0 ]

[]

[]

[ "attributes", "python" ]

[ "I assume that the score you want to compute is the current value of the tiles in a particular Player's hand. You mention the need to access an instance of Bag. I don't see where this is necessary or desirable. Once you have built up a Player with a hand, you only need to work with that instance of Player.\nThe current score of a Player's hand is an attribute specific to that Player, so it makes perfect sense to add a scoreHand() method to your Player class:\nclass Player:\n ...\n def scoreHand(self):\n return sum([tile.value for tile in self.hand])\n\nIf you want to perform operations on a Player's tiles outside of the logic for the class, you can add one or more methods to Player to provide whatever it is that you want to extract from a Player instance. Given that your Tile class is a public top-level class, you could have a method that returns the list of Tiles for the Player:\nclass Player:\n ...\n def getTiles(self):\n return self.hand\n\nIf instead, you wanted just the names/letters of a Player's tiles, you could return a list of strings:\nclass Player:\n ...\n def getLetters(self):\n return [tile.letter for tile in self.hand]\n\n" ]

[ 0 ]

[]

[]

[ "oop", "python", "python_class" ]

[ "This seems to do the trick:\ndef Calculate(arr):\n res = []\n for i, val in enumerate(arr):\n total = -val * (len(arr) - 1) + sum(arr[0:i]) + sum(arr[i+1:])\n res.append(total)\n return res\n\nWe iterate through each element and calculate the sum of differences like you described. Since the current element gets subtracted from each term we can factor it out.\n", "The operations that you are doing inside Calculate() clearly assume that the input initial_opinion is a NumPy Array, and not a list. NumPy arrays support broadcast operations, lists do not.\nSo you should call your function with the right kind of input, e.g.:\nCalculate(np.array([0.5, -0.5, 1]), 5)\n\nNow, there are other errors with your code (parentheses are not balanced, and the calculation doesn't quite make sense yet), but if you pass the right kind of data type in, you'll be able to get further with your work.\nWith a NumPy Arrays, you can add an array to another array of the same size for a direct addition, or an array to a scalar to add that scalar's value to all the elements, and all sorts of other broadcast operations are supported for vectorized computations.\n", "Use np.subtract.outer and np.ndarray.sum:\nimport numpy as np\n\ndef Calculate(arr):\n return np.subtract.outer(arr, arr).sum(axis=0)\n\ninitial_values = [0.5, -0.5, 1]\nout = Calculate(initial_values)\n# out = array([-0.5, 2.5, -2. ])\n\n" ]

[ 1, 0, 0 ]

[]

[]

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

[ "I believe your problem is with this part \"Python/Resources\".\nYou need the python-stdlib to appear in Build Phase's Copy Bundle Resources. And then do this:\nimport Python\n\nguard let stdLibPath = Bundle.main.path(forResource: \"python-stdlib\", ofType: nil) else { return }\nguard let libDynloadPath = Bundle.main.path(forResource: \"python-stdlib/lib-dynload\", ofType: nil) else { return }\nsetenv(\"PYTHONHOME\", stdLibPath, 1)\nsetenv(\"PYTHONPATH\", \"\\(stdLibPath):\\(libDynloadPath)\", 1)\nPy_Initialize()\n// we now have a Python interpreter ready to be used\n\nI've wrote an article elaborating step-by-step how to embed a Python interpreter in a MacOS / iOS app. I'll live it here for anyone in the future having trouble with this topic: https://medium.com/swift2go/embedding-python-interpreter-inside-a-macos-app-and-publish-to-app-store-successfully-309be9fb96a5\n" ]

[ 0 ]

[]

[]

[ "ios", "python", "swift", "xcode" ]

[ "You created a pd.Series which is basically a single column. If you want multiple columns, you need a whole dataframe.\nSo instead, do pd.DataFrame(predictions)\n" ]

[ 0 ]

[]

[]

[ "dataframe", "prediction", "python" ]

[ "You probably have an easier time with the glob module for finding files and f-strings for renaming. Also, for the sake of teaching modern python, I'm using the pathlib and its glob method. Try this:\nimport os\nimport pathlib\n\nsrc = pathlib.Path(\"C:/ZTD/pwv2015\")\npattern = \"2015_[0-9][0-9][0-9].txt\"\ninpaths = sorted(src.glob(pattern))\nfor outnum, inpath in enumerate(inpaths, 1):\n outpath = src / f\"2015_{outnum:03d}.txt\"\n if outpath != inpath:\n os.rename(inpath, outpath)\n\nNote that since the input and output directory are identical, we have to be careful with overwriting files. However, in this instance we \"compress\" the range, so if we go in sorted order, the input file numbers will always be greater or equal to the output file numbers, so we are okay.\nAlso, when you test this, replace os.rename with print, just to be sure ;-)\n" ]

[ 3 ]

[]

[]

[ "directory", "file_rename", "python" ]

[ "There are two issues in create_an_image():\n\nimg is a local variable, so it will be garbage collected after exiting the function. So use an instance variable self.img instead.\nyou need to use file option of PhotoImage() to specify the filename of the image\n\n def create_an_image(self, file, x, y):\n # use instance variable self.img instead of local variable img\n # use file option of PhotoImage\n self.img = PhotoImage(file=file)\n self.create_image(x, y, image=self.img, tags=('imagec',))\n\n" ]

[ 1 ]

[]

[]

[ "python", "tags", "tkinter", "tkinter_canvas" ]

[ "After I applied .flatten() all my values converted to 0\nHere it is the output\n" ]

[ 0 ]

[]

[]

[ "arrays", "data_science", "dataframe", "finance", "python" ]

[ "\nI set the go_up movement speed to 0 (\"fastest\"), however if I input\nany value here, or type out the \"fastest\", \"slowest\"... whichever\nvalue... the turtle still moves at the same rate no matter any value I\ninput into the go_up function\n\nOnce you invoke tracer(0), the turtles' speed() method is a no-op.\nOne way you can get more performance from the player to to make it like your cars in that its increment of motion increases for each level.\nAlso, the while game_is_on: is effectively a while True: loop which has no place in an event-driven world like turtle. So, let's switch to a timer event based model, along with other changes:\nfrom turtle import Screen, Turtle\nfrom random import randint, choice\n\nFINISH_LINE_Y = 280\n\nclass Player(Turtle):\n PLAYER_STARTING_POSITION = (0, -280)\n PLAYER_STARTING_MOVE_DISTANCE = 10\n PLAYER_MOVE_INCREMENT = 2\n\n def __init__(self):\n super().__init__()\n self.shape('turtle')\n self.color('green')\n self.penup()\n self.go_to_start()\n self.setheading(90)\n self.player_speed = Player.PLAYER_STARTING_MOVE_DISTANCE\n\n def go_up(self):\n self.forward(self.player_speed)\n\n def go_to_start(self):\n self.goto(Player.PLAYER_STARTING_POSITION)\n\n def is_at_finish_line(self):\n return self.ycor() > FINISH_LINE_Y\n\n def level_up(self):\n self.player_speed += Player.PLAYER_MOVE_INCREMENT\n\nclass CarManager:\n CAR_STARTING_MOVE_DISTANCE = 5\n CAR_MOVE_INCREMENT = 10\n\n CAR_COLORS = ['red', 'orange', 'yellow', 'green', 'blue', 'purple']\n\n def __init__(self):\n self.all_cars = []\n self.car_speed = CarManager.CAR_STARTING_MOVE_DISTANCE\n\n def create_car(self):\n if randint(1, 6) == 1:\n new_car = Turtle('square')\n new_car.shapesize(stretch_wid=1, stretch_len=2)\n new_car.color(choice(CarManager.CAR_COLORS))\n new_car.penup()\n random_y = randint(-250, 250)\n new_car.goto(300, random_y)\n new_car.setheading(180)\n\n self.all_cars.append(new_car)\n\n def move_cars(self):\n for car in self.all_cars:\n car.forward(self.car_speed)\n\n def level_up(self):\n self.car_speed += CarManager.CAR_MOVE_INCREMENT\n\nclass Scoreboard(Turtle):\n FONT = ('Courier', 24, 'normal')\n\n def __init__(self):\n super().__init__()\n self.level = 1\n self.hideturtle()\n self.penup()\n self.goto(-270, 270)\n self.update_scoreboard()\n\n def update_scoreboard(self):\n self.clear()\n self.write(f\"Level: {self.level}\", font=Scoreboard.FONT)\n\n def increase_level(self):\n self.level += 1\n self.update_scoreboard()\n\n def game_over(self):\n self.goto(0, 0)\n self.write(\"GAME OVER\", align='center', font=Scoreboard.FONT)\n\ngame_is_on = True\n\ndef move():\n global game_is_on\n\n if not game_is_on:\n return\n\n car_manager.create_car()\n car_manager.move_cars()\n\n for car in car_manager.all_cars:\n if car.distance(player) < 20:\n game_is_on = False\n scoreboard.game_over()\n break\n else: # no break #\n if player.is_at_finish_line():\n player.go_to_start()\n player.level_up()\n car_manager.level_up()\n scoreboard.increase_level()\n\n screen.update()\n screen.ontimer(move, 100)\n\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.tracer(0)\n\nplayer = Player()\ncar_manager = CarManager()\nscoreboard = Scoreboard()\n\nscreen.onkey(player.go_up, 'Up')\nscreen.listen()\n\nmove()\n\nscreen.mainloop()\n\nI had fun playing your (reworked) game!\n" ]

[ 2 ]

[]

[]

[ "python", "python_turtle", "turtle_graphics" ]

[ "You should include your input data as text. Screenshots are really hard to work with.\nYou can use numpy broadcasting. However, this will have an n^2 computational complexity since you are comparing every row against every other row:\nreference, type_, doc_condition = df.to_numpy().T\nmatch = (\n (type_[:, None] == \"BD\")\n & (reference[:, None] == reference)\n & (doc_condition[:, None] < doc_condition)\n)\ndf[\"COUNTIFS\"] = match.sum(axis=1)\n\nWithin the snippet above, you can read reference[:, None] as \"the Reference value of the current row; reference as \"the Reference values of all rows\". This is what enables the comparison between the current row and all other rows (including itself) by numpy.\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "you could use the built-in join() method for strings:\nlst_str = \" -> \".join(user_input.split()) + \" \\n\"\n\n", "This is the code I used. The output is without the '->' and any extra space at the end:\nuser_input = input()\nhourly_temperature = user_input.split()\n\n\nfor temp in hourly_temperature:\n lst_str = \" -> \".join(user_input.split())\n\nprint(lst_str,'')\n\n" ]

[ 0, 0 ]

[]

[]

[ "python" ]

[ "Your first attrempt would have wored had you converted the Date column to Timestamp. A scatter plot requires that both values on x- and y-axis to be numerical. When you supply strings on the x-axis, they are treated as positions [0, 1, 2, 3,...] with tick marks equal to the supplied values.\nfor df in [dfA, dfB]:\n df[\"Date\"] = pd.to_datetime(df[\"Date\"], dayfirst=True)\n\nax = dfA.plot.scatter(x=\"Date\", y=\"Q\", rot=90, label=\"A\")\ndfB.plot.scatter(x=\"Date\", y=\"Q\", rot=90, ax=ax, color=\"r\", label=\"B\")\n\n\n" ]

[ 0 ]

[]

[]

[ "datetime", "pandas", "plot", "python", "x_axis" ]

[ "you should need to use comma(,) after employeeName TEXT.\nCorrect code-\nconnection = sqlite3.connect(\"TempDatabase.db\")\n cursor = connection.cursor()\n\n sqlCommand = \"\n CREATE TABLE IF NOT EXISTS OrderTb1N1\n (\n OrderID INTEGER NOT NULL,\n dateOrdered DATE,\n customerFirstname TEXT,\n customerSurname TEXT,\n customerPhoneNumber TEXT,\n collectionDate DATE,\n flavours TEXT,\n glaze TEXT,\n toppings TEXT,\n personalisation TEXT,\n orderSize TEXT,\n price REAL,\n paymentStatus TEXT,\n employeeName TEXT,\n primary key (OrderID)\n )\n \"\n\n cursor.execute(sqlCommand)\n connection.commit()\n connection.close()\n\n" ]

[ 0 ]

[]

[]

[ "database", "python", "sqlite", "syntax", "syntax_error" ]

[ "pygame.image.rotate does not actualy exists.\nTo rotate an image, you have to do the same as to scale :\npygame.transform.rotate(surface, angle)\n\nIn your case, that would be:\nYELLOW_SPACESHIP = pygame.transform.rotate(pygame.transform.scale(YELLOW_SPACESHIP_IMAGE, (SPACESHIP_WIDTH, SPACESHIP_HEIGHT)), 90) \n\nPlease acknowledge that this function will rotate counterclockwise, and you can put negative angles to go clockwise.\nHere is the full documentation : https://www.pygame.org/docs/ref/transform.html#pygame.transform.rotate\n", "You're close, try:\nYELLOW_SPACESHIP = pygame.transform.rotate(pygame.transform.scale(\n YELLOW_SPACESHIP_IMAGE, (SPACESHIP_WIDTH, SPACESHIP_HEIGHT)), 90)\n\nrotate is a member of the module transform. Your code was calling rotate on image which is a variable that you haven't yet defined.\n" ]

[ 2, 2 ]

[]

[]

[ "attributeerror", "pygame", "python" ]

[ "have you tried to use a list? something like... seen_numbers = set()\nI know this isn't exactly what you're asking but I use lists to find duplicates or find things I want to exclude. maybe this will help...\nthis = line[92:102] + line[114:123]\nif this not in seen_numbers:\n seen_numbers.add(this)\n\nand you should be able to tweak this to remove what you want\n" ]

[ 0 ]

[]

[]

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

[ "I tested it and it works well with MONTH partition :\ndef create_table_time_partitioning_month(self):\n from google.cloud import bigquery\n client = bigquery.Client()\n project = client.project\n dataset_ref = bigquery.DatasetReference(project, 'my_dataset')\n\n table_ref = dataset_ref.table(\"my_partitioned_table\")\n schema = [\n bigquery.SchemaField(\"name\", \"STRING\"),\n bigquery.SchemaField(\"post_abbr\", \"STRING\"),\n bigquery.SchemaField(\"date\", \"DATE\"),\n ]\n table = bigquery.Table(table_ref, schema=schema)\n table.time_partitioning = bigquery.TimePartitioning(\n type_=bigquery.TimePartitioningType.MONTH,\n field=\"date\"\n )\n\n table = client.create_table(table)\n\n print(\n \"Created table {}, partitioned on column {}\".format(\n table.table_id, table.time_partitioning.field\n )\n )\n\nI used the following import :\nfrom google.cloud import bigquery\n\nAlso, check if you have the correct Python package installed in your image :\nrequirements.txt\ngoogle-cloud-bigquery==3.4.0\n\npip install -r requirements.txt\n\n" ]

[ 0 ]

[]

[]

[ "gcloud", "google_bigquery", "python" ]

[ "First update setuptools, as per https://stackoverflow.com/a/58754136/5666087\npip install -U setuptools\n\nThen use the correct package for dotenv, which is python-dotenv.\npip install python-dotenv\n\n" ]

[ 1 ]

[]

[]

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

[ "here is one way to do it, using diff\n\n# create a new col by taking difference b/w consecutive rows of DF using diff\ndf['diff_2']=df['series_2'].diff()\ndf\n\n series_1 series_2 diff_2\n0 10.1 9.60 NaN\n1 15.3 10.40 0.80\n2 16.0 11.20 0.80\n3 12.0 3.30 -7.90\n4 14.5 6.00 2.70\n5 11.8 4.00 -2.00\n6 2.3 1.94 -2.06\n7 7.7 15.44 13.50\n8 5.0 6.17 -9.27\n9 10.0 8.16 1.99\n\n", "You might want to add the following line of code:\ndf[\"diff_2\"] = df[\"series_2\"].sub(df[\"series_2\"].shift(1))\n\nto achieve your goal output:\n series_1 series_2 diff_2\n0 10.1 9.60 NaN\n1 15.3 10.40 0.80\n2 16.0 11.20 0.80\n3 12.0 3.30 -7.90\n4 14.5 6.00 2.70\n5 11.8 4.00 -2.00\n6 2.3 1.94 -2.06\n7 7.7 15.44 13.50\n8 5.0 6.17 -9.27\n9 10.0 8.16 1.99\n\nThat is a build-in pandas feature, so that should be optimized for good performance.\n" ]

[ 2, 1 ]

[]

[]

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