Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "You can use cleaned_data to save the ModelForm.\nforms.py\nclass TestForm(forms.ModelForm):\n\n class Meta:\n model = Test\n fields = [\"email\"]\n\nAssuming, you have request method POST.\nviews.py\ndef test_view(request):\n if request.method==\"POST\":\n form=TestForm(request.POST)\n \n customer = request.user.customer\n\n if form.is_valid():\n email = form.cleaned_data['email']\n test=Test(customer=customer,email=email)\n test.save()\n\n", "You need to use a ModelForm, then save the object without commiting, edit the customer of the object, then commit.\nclass TestForm(forms.ModelForm):\n\n class Meta:\n model = Test\n fields = [\"email\", ]\n\n\ndef test_view(request):\n customer = request.user.customer #I'm not sure this line is right, but I can't see all your models\n\n if form.is_valid():\n test = form.save(commit=False)\n test.customer = customer\n test.save()\n\n" ]

[ 2, 0 ]

[]

[]

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

[ "I found out the method to do it. we need to write the logger in \".format\" way below the capturing process code.\n logger.info(\"data_capture_date:{} | case_id:{} | organization:{} | supplier_number:{} | invoice_Number:{} | document_Type:{} | invoice_Source:{} | rowcount:{}\".format(data_capture_date, case_id,organization,supplier_number,invoice_Number,document_Type,invoice_Source,rowcount))\n\nthis method shows nonetype in the logger\n" ]

[ 1 ]

[]

[]

[ "python", "xml" ]

[ "You need to put \"%s\" somewhere in the input string before string formatting. Here's more detailed explanation.\nTry this:\nfilePath= Path('.')\ntime=datetime.datetime.now()\nbot_log = \"%s Set up the file path thingy\\n\"\nwith open ('bot.log', 'a') as f:\n f.write(bot_log % datetime.datetime.now().strftime(\"%d-%b-%Y (%H:%M:%S.%f)\"))\n print(bot_log)\n\n", "It looks like you want to write three strings to your file as separate lines. I've rearranged your code to create a single list to pass to writelines, which expects an iterable:\nfilePath= Path('.')\ntime=datetime.datetime.now()\nbot_log = [\"\",\"Set up the file path thingy\"]\nwith open ('bot.log', 'a') as f:\n bot_log.append(datetime.datetime.now().strftime(\"%d-%b-%Y (%H:%M:%S.%f)\"))\n f.writelines('\\n'.join(bot_log))\n print(bot_log[0])\n\nEDIT: From the comments the desire is to prepend the timestamp to the message and keep it on the same line. I've used f-strings as I prefer the clarity they provide:\nimport datetime\nfrom pathlib import Path\n\nfilePath = Path('.')\n\nwith open('bot.log', 'a') as f:\n time = datetime.datetime.now()\n msg = \"Set up the file path thingy\"\n f.write(f\"\"\"{time.strftime(\"%d-%b-%Y (%H:%M:%S.%f)\")} {msg}\\n\"\"\")\n\nYou could also look at the logging module which does a lot of this for you.\n" ]

[ 2, 1 ]

[]

[]

[ "python", "python_datetime" ]

[ "Presumably, setting class attributes like this circumvents the usual housekeeping done by a Keras Layer (such as registering variables, sub-layers etc.), so you should avoid doing this. Rather do something like this:\nclass VggBlock(tf.keras.Model):\n def __init__(self, filters, repetitions):\n super(VggBlock, self).__init__() \n self.repetitions = repetitions\n \n self.conv_layers = [Conv2D(filters=filters, kernel_size=(3, 3), padding='same', activation='relu') for _ in range(repetitions)]\n self.max_pool = MaxPool2D(pool_size=(2, 2))\n \n def call(self, inputs):\n x = inputs\n for layer in self.conv_layers:\n x = layer(x)\n return self.max_pool(x)\n\n\ntest_block = VggBlock(64, 2)\ntemp_inputs = Input(shape=(224, 224, 3))\ntest_block(temp_inputs)\ntest_block.summary()\n\nHere, we use a list to store the layers, and can still do it using a loop. This prints\nModel: \"vgg_block_2\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n conv2d_4 (Conv2D) multiple 1792 \n \n conv2d_5 (Conv2D) multiple 36928 \n \n max_pooling2d_2 (MaxPooling multiple 0 \n 2D) \n \n=================================================================\nTotal params: 38,720\nTrainable params: 38,720\nNon-trainable params: 0\n_________________________________________________________________\n\nIf you don't need to have the layers given explicitly in the summary, you can use Sequential to simplify the call method:\nclass VggBlock(tf.keras.Model):\n def __init__(self, filters, repetitions):\n super(VggBlock, self).__init__() \n self.repetitions = repetitions\n \n self.conv_layers = tf.keras.Sequential([Conv2D(filters=filters, kernel_size=(3, 3), padding='same', activation='relu') for _ in range(repetitions)])\n self.max_pool = MaxPool2D(pool_size=(2, 2))\n \n def call(self, inputs):\n x = self.conv_layers(inputs)\n return self.max_pool(x)\n\n\ntest_block = VggBlock(64, 2)\ntemp_inputs = Input(shape=(224, 224, 3))\ntest_block(temp_inputs)\ntest_block.summary()\n\nwhich is functionally identical, but displays this summary which you might not want:\nModel: \"vgg_block_3\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n sequential (Sequential) (None, 224, 224, 64) 38720 \n \n max_pooling2d_3 (MaxPooling multiple 0 \n 2D) \n \n=================================================================\nTotal params: 38,720\nTrainable params: 38,720\nNon-trainable params: 0\n_________________________________________________________________\n\n" ]

[ 2 ]

[]

[]

[ "model", "python", "subclass", "tensorflow" ]

[ "You could do something like below, where you check every single condition you're looking for.\nfrom pathlib import Path\nfor filename in glob.glob(imagepath): #assuming gif\n path = Path(filename)\n head = os.path.split(filename)[1]\n if head.startswith('11'):\n os.rename(filename, os.path.join(path.parent, head.replace('11', 'AA')))\n elif head.startswith('33'):\n os.rename(filename, os.path.join(path.parent, head.replace('33', 'BB')))\n elif head.startswith('44'):\n os.rename(filename, os.path.join(path.parent, head.replace('44', 'CC')))\n\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "I solved my problem changing only 1 line of code. We need to declare datatype of array. Here is the code:\narr2=np.array(arr2.reshape(353,616,3), dtype=np.uint8)\n\nHowever, I found a new function cv2.imencode()\nThis function is exactly what I wanted and it is really faster than my code. So, I am going to use this function.\nThanks to all your answers!\nHere is an example of it:\nimport cv2\nimport numpy as np\nimport time\n\nimg= cv2.imread(\"resim/tfm.jpg\")\n\nimg=cv2.imencode(\".jpg\", img)[1]\nprint(img)\nbyte_img = img.tobytes()\nprint(len(byte_img))\n\nrecvd_img=np.array(bytearray(byte_img), dtype=\"uint8\")\nrecvd_img=cv2.imdecode(recvd_img, cv2.IMREAD_COLOR)\ncv2.imshow(\"sa\", recvd_img)\n\n" ]

[ 0 ]

[]

[]

[ "cv2", "image", "numpy", "python", "sockets" ]

[ "Some observations:\n\nEither l is updated and r is set to one more, or l is not updated and r does not diminish. This means that the while condition is always satisfied. The only way to exit the loop is via the break. This means the loop header could also have been written as while True:\n\nThe r index visits the same index multiple times, as r = l + 1 generally decreases its value The algorithm produces all possible pairs of [l, r] where l < r. There are n(n-1)/2 of those pairs, so the algorithm has a complexity of O(n²).\n\n\nThis complexity is not optimal -- as you know. We can get a \"feel\" of that inefficiency by realising the following: when we have found the optimal r for a given l, it makes no sense to look at lesser values of r when l has been increased. Surely that optimal r for that previous l is still going to be the optimal one for the next value of l. Only when that optimum was at l + 1 we need a \"fresh\" one. So your algorithm is wasting time on values of r that we already know cannot be optimal.\nSimilarly, when the price at l is greater than a previous one, there is no way that the optimal r (for that l) will improve the best profit we already had. So then there should be no reason at all to have r iterate over the rest of the array: it will be fruitless.\nOptimal algorithm\nYou have already mentioned Kadane's algorithm (based on day-to-day price differences)\nI find the following one-pass algorithm quite intuitive:\nWhen at a certain index during that single pass, let's assume you have correctly identified the following, using only the part of the list that was already processed:\n\nThe best profit in that list. If this sub list would be all there was, this would be the final answer.\n\nThe minimum price in that list. This is a good candidate to buy and try to make even better profits when going further.\n\n\nThen, when reading the next price from the list, there are the following cases:\n\nMaybe that price is high enough to improve on the best profit (considering a buy at the minimum price we had so far, and selling at this current price): if so, we just have to update that best profit.\n\nMaybe that price is lower than the minimum price we opted for. In that case we can forget about the previous minimum we had, since any future improvement we could get by using that previous minimum price would immediately be improved by taking the current price as buying price instead, so we don't need the previous minimum price anymore.\n\nIn all other cases we have a price that is non interesting, nor as buying price, nor as selling price.\n\n\nAfter this step we are again sure we have determined the best profit for the range of prices that was processed, knowing also the minimum price in that range.\nSo by induction this process will give the correct answer.\nCode:\nclass Solution:\n def maxProfit(self, prices: List[int]) -> int:\n maxprofit = 0\n minprice = prices[0]\n for price in prices:\n if price < minprice:\n minprice = price\n elif price - minprice > maxprofit:\n maxprofit = price - minprice # buy at minprice & sell at price\n \n return maxprofit\n\n" ]

[ 0 ]

[]

[]

[ "algorithm", "data_structures", "python" ]

[ "You can remove emojis from your data columns using the following code\ndf.astype(str).apply(lambda x: x.str.encode('ascii', 'ignore').str.decode('ascii'))\n\n" ]

[ 0 ]

[]

[]

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

[ "You can try this code. Since you mentioned that your time_taken attribute looks like this: 02h 10m. I have written an example code which you can try out.\nimport pandas as pd\n\n# initializing example time data\ntime_taken = ['1h 10m', '2h 20m', '3h 30m', '4h 40m', '5h 50m']\n\n#inserting the time data into a pandas DataFrame\ndata = pd.DataFrame(time_taken, columns = ['time_taken'])\n\n# see how the data looks like\nprint(data)\n\n# initializing \"Hours\" and \"Minutes\" columns\"\n# and assigning the value 0 to both for now.\ndata['Hours'] = 0\ndata['Minutes'] = 0\n\n# when I ran this code, the data type for the elements \n# in time_taken column was numpy.int64\n# so we convert it into string type\n\ndata['time_taken'] = data['time_taken'].apply(str)\n\n# loop through the elements to split into Hours and minutes\nfor i in range(len(data)):\n temp = data.iat[i,0]\n hours, minutes = temp.split() # use python .split() function for strings\n data.iat[i,1] = hours.translate({ord('h'): None})\n data.iat[i,2] = minutes.translate({ord('m'): None})\n\n\n# the correct data is here\nprint(data)\n\n" ]

[ 0 ]

[]

[]

[ "duration", "pandas", "python", "timedelta", "valueerror" ]

[ "as i know, you can use cross_validate() as alternative of StratifiedKFold with multiple output. You can define cross validation technique with StratifiedKFold and scoring metrics as your preference. You can check link below for more detail !\nhttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html\n" ]

[ 0 ]

[]

[]

[ "feature_selection", "k_fold", "python" ]

[ "I have referred the sample code from the official documentation. After reproducing from my end, this was working fine after adding get_user_token() in the graph.py. Below is the complete code in my graph.py.\nimport json\nfrom configparser import SectionProxy\nfrom azure.identity import DeviceCodeCredential, ClientSecretCredential\nfrom msgraph.core import GraphClient\n\nclass Graph:\n settings: SectionProxy\n device_code_credential: DeviceCodeCredential\n user_client: GraphClient\n client_credential: ClientSecretCredential\n app_client: GraphClient\n\n def __init__(self, config: SectionProxy):\n self.settings = config\n client_id = self.settings['clientId']\n tenant_id = self.settings['authTenant']\n graph_scopes = self.settings['graphUserScopes'].split(' ')\n\n self.device_code_credential = DeviceCodeCredential(client_id, tenant_id = tenant_id)\n self.user_client = GraphClient(credential=self.device_code_credential, scopes=graph_scopes)\n \n def get_user_token(self):\n a=\"Return from get_user_token\"\n return a\n\nRESULTS:\n\n" ]

[ 0 ]

[]

[]

[ "azure", "microsoft_graph_api", "python" ]

[ "As @Grismar pointed your function is not returning anything so just add return leap to the end.\ndef is_leap(year):\n leap= False\n\n if (year % 400 == 0) and (year % 100 == 0):\n leap = True\n elif (year % 4 ==0) and (year % 100 != 0):\n leap=True\n else:\n pass\n return leap\n\nyear = int(input())\nprint(is_leap(year))\n\n" ]

[ 0 ]

[]

[]

[ "function", "python" ]

[ "Create dictionary with all joined lists first and then call DataFrame is fastest and recommended way, check this:\nd = {'A': some_list_1a + some_list_2a, \n 'B': some_list_1b + some_list_2b,\n 'C': some_list_1c + some_list_2c}\ndataframe = pd.DataFrame(data=d)\n\nIf need append dict of list in loop:\nfrom collections import defaultdict\n\nd = defaultdict(list)\n\n#some loop\nfor x in iter:\n d[col_name].append(sublist)\n \ndataframe = pd.DataFrame(data=d)\n\n", "append() function is used to append rows of other dataframe to the end of the given dataframe, returning a new dataframe object. Columns not in the original dataframes are added as new columns and the new cells are populated with NONE value.\n" ]

[ 1, 0 ]

[]

[]

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

[ "You should be good with writing s in range(k) - the sum depends on the index k from the outer loop.\nTo make this a bit easier to read and comprehend, you might want to switch around the for loops like this:\nfor i in M:\n for j in N:\n for k in N:\n mdl2.addConstr(\n T[i, j, k]\n >= quicksum(\n p[l] * y[i, l, s] + s[l] * x[i, l, s] for l in N for s in range(k)\n )\n - d[j]\n - 100000 * (1 - x[i, j, k])\n )\n\n\nThis assumes that M is range(m) and N is range(n). And please note that I am using addConstr because there is just a single constraint in every iteration of the triple loop.\nIn any case, you should write out the LP file of the problem and check whether what you are modeling actually corresponds to the mathematical formulation.\n" ]

[ 1 ]

[]

[]

[ "constraints", "gurobi", "linear_programming", "optimization", "python" ]

[ "\nI have the following data in my 2 tables t1 and t2.\n\n\n\nThe following is how I have the data in my dataframe:\n\n\n\nI have used the following code to get the desired output. First, I have created 2 columns containing respective queries:\n\nfrom pyspark.sql.functions import lit,col,concat_ws \ndf = df.withColumn('count_of_nulls',concat(lit('select * from '),concat_ws('.',*['Database','Table']),lit(' where isnull('),col('Column'),lit(')')))\n\ndf = df.withColumn('no_of_rows',concat(lit('select * from '),concat_ws('.',*['Database','Table'])))\n\n\n\nNow, I converted this dataframe to pandas dataframe on spark API to perform respective operations using loops and update the dataframe:\n\npdf = df.to_pandas_on_spark()\n#pdf\n\nnull_count = []\ntotal_count = []\nfor i in pdf['count_of_nulls'].to_numpy():\n null_count.append(spark.sql(i).count())\n\n\nfor i in pdf['no_of_rows'].to_numpy():\n total_count.append(spark.sql(i).count())\n \nprint(null_count,total_count)\npdf['count_of_nulls'] = null_count\npdf['no_of_rows'] = total_count\n#pdf\n\n\n\nI converted it back to pyspark dataframe and then calculated percentage.\n\ndf = pdf.to_spark()\ndf.withColumn('percentage_of_nulls', col('count_of_nulls')/col('no_of_rows')*100).show()\n\n\n" ]

[ 0 ]

[]

[]

[ "azure_databricks", "database", "databricks", "null", "python" ]

[ "x0 = np.array([...]) # suitably shaped numpy array of your init values\nres = opt.minimize(Lf, x0, args=(u_init, sigma_init, pi_init), method='L-BFGS-B')\n\nMay be you can try calling like this\n" ]

[ 0 ]

[]

[]

[ "optimization", "python", "scipy", "scipy_optimize_minimize" ]

[ "There are several issues in your code.\n\nyou return too early (in the loop)\nyou add +1 instead of the value\nyou try to append to each loop (do it only in the end)\nthe order of the odd/even values depends on the input data (first one seen of odd/even will be first)\n\nOther \"minor\" issue:\n\ndon't loop over the indices, loop over the values\n\nclass Solution(object):\n\n def calculate_odd_even(self, li):\n even = 0\n odd = 0\n\n sum_num = []\n for x in li:\n if x % 2 == 0:\n even += x\n else:\n odd += x\n \n sum_num.append(odd)\n sum_num.append(even)\n return sum_num\n\n\nif __name__ == \"__main__\":\n p = Solution()\n lit = [1, 2, 4, 5]\n print(p.calculate_odd_even(lit))\n\nMore simple variant:\nclass Solution(object):\n\n def calculate_odd_even(self, li):\n sum_num = [0, 0]\n for x in li:\n sum_num[1-x%2] += x # use sum_num[x%2] += x for even/odd order\n return sum_num\n\n\nif __name__ == \"__main__\":\n p = Solution()\n lit = [1, 2, 4, 5]\n print(p.calculate_odd_even(lit))\n\n" ]

[ 2 ]

[]

[]

[ "list", "python" ]

[ "You could use PyPMML to apply PMML in Python, for example:\nfrom pypmml import Model\n\nmodel = Model.fromFile('DecisionTreeIris.pmml')\nresult = model.predict({\n \"Sepal_Length\" : 5.1,\n \"Sepal_Width\" : 3.5,\n \"Petal_Length\" : 1.4,\n \"Petal_Width\" : 0.2\n})\n\nFor more info about other PMML libraries, be free to see:\nhttps://github.com/autodeployai\n", "After some research I found the solution to this: the 'openscoring' library.\nUsing it is very simple:\nimport subprocess\nfrom openscoring import Openscoring\nimport numpy as np\n\np = subprocess.Popen('java -jar openscoring-server-executable-1.4.3.jar',\n shell=True)\n\nos = Openscoring(\"http://localhost:8080/openscoring\")\n\n# Deploying a PMML document DecisionTreeIris.pmml as an Iris model: \nos.deployFile(\"Iris\", \"DecisionTreeIris.pmml\")\n\n# Evaluating the Iris model with a data record:\narguments = {\n \"Sepal_Length\" : 5.1,\n \"Sepal_Width\" : 3.5,\n \"Petal_Length\" : 1.4,\n \"Petal_Width\" : 0.2\n}\nresult = os.evaluate(\"Iris\", arguments)\nprint(result)\n\nThis returns the value of the target variable in a dictionary. There is no need to go outside of Python to use PMML models anymore (you just have to run the server with Java, which can be done with Python as well as I showed above).\n", "Isn't it like trying to host H2O models in python apps? Looks like a bridge from python to Java is required here too. Such bridges are not stable at all, been there, tested them. Just a general suggestion: do not mix languages between ML algos and apps code, train in python, serve in python, re-validate also in python. Legacy R and H2O models can be always re-fitted in python.\n" ]

[ 5, 1, 0 ]

[]

[]

[ "pmml", "prediction", "python", "python_3.x", "xml" ]

[ "After you train a model, you have store the weights of that model in a file. If you don't do this, you don't keep the trained models if you run your program (or any other) again.\nYou can store the model weights during training by using the ModelCheckpoint callback. https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint\nYou can load the weights before inference (prediction) using model.load_weights(...)\nhttps://www.tensorflow.org/tutorials/keras/save_and_load\nTo clarify why your results are random. You keep initializing a new model with random weights and then directly try to predict the answer with the untrained model.\n[EDIT]\nThe reason your untrained model keeps giving the opposite results, may be due to the inputs combined with the weight initialization. The weight initialization is random, but there is some \"smarts\" behind the initialization to prevent dead connections. Here you can find more details on all weight initializers: https://www.tensorflow.org/api_docs/python/tf/keras/initializers\nGood Luck!\n", "U need to fix the random generator seeds for both tensorflow and sklearn.\nTry the following:\ntf.random.set_seed(42)\n\nand\nskf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=42)\n\n\nThis will allow you to replicate your results.\n", "It's completely normal, when you create a new model, its weights are initialized as random, so the prediction will change each time you run this code.\n" ]

[ 1, 1, 0 ]

[]

[]

[ "keras", "machine_learning", "prediction", "python", "tensorflow" ]

[ "Try this\nimport maya.OpenMaya as om\n\ndef resize_image(source_image, output_image, width, height):\n\n image = om.MImage()\n image.readFromFile(source_image)\n\n image.resize( width, height )\n image.writeToFile(output_image, 'png')\n\n\nresizeImage('<source_image.png>','<output_image.png>', 300, 300)\n\n", "I have used iconTextButton to set an image with different width and height.\nUse adjustableColumn=False to set width/height of your image.\nYou can also try iconTextStaticLabel cmd.\nHope this will work for you..\nimport maya.cmds as cmds\n\nif cmds.window('Test',exists=True):\n cmds.deleteUI('Test')\n \ncmds.window('Test',title='Test Image', bgc=[0.266, 0.266, 0.266], widthHeight=[600,400], sizeable = True)\nparentLayout = cmds.columnLayout(adjustableColumn=False)\ncmds.iconTextButton(style=\"iconOnly\",image1='Capture.png',p=parentLayout,w=150,h=150)\ncmds.setParent('..')\ncmds.showWindow('Test')\n\n" ]

[ 1, 0 ]

[]

[]

[ "maya", "python" ]

[ "This is an alternative approach for your MWE:\nmain_list = [\n [\n [34.39460533995712, 47.84539466004288],\n [33.095772478005635, 46.50422752199436],\n [36.66750709361337, 44.44360401749775],\n [33.33459042563053, 42.14689105585095],\n [36.638367322851444, 43.62250224236595],\n [36.465767572400296, 49.200899094266376],\n [32.220702473831686, 42.65929752616831],\n [34.31937169660605, 41.14216676493242],\n [31.198269305510344, 42.801730694489656],\n [31.216878962221035, 40.6092079943007],\n [28.465488368524227, 38.793770890735026],\n [34.50342917911651, 45.32990415421682],\n ]\n]\n\nres_list = main_list.copy()\nfor i, main_i in enumerate(main_list):\n for j, main_i_j in enumerate(main_i):\n res_list[i][j] = [main_i_j[1] - main_i_j[0]]\n\n", "Use numpy for this, it makes dealing with multidimensional arrays so much easier and it is far more efficient (because it is written in C). To start off, you have to install numpy. Then, in your code, import it. The convention is to import it as np:\nimport numpy as np\n\nNext we want to turn your list of lists into a numpy array. If your different ran_lists have different dimensions, you will have to treat them independently, which is what I'll do here. (If they all have the same shape, you can treat it as one 3-d array.) :\nlist_of_lists2 = []\nfor sub_list in lists_of_lists1:\n new_sub_list = np.array(sub_list)\n new_sub_list = new_sub_list[:,1] - new_sub_list[:,0]\n list_of_lists2.append(new_sub_list.tolist())\n\nHere I have turned the numpy arrays back into nested lists but if you want to continue to do calculations with them it's probably better to keep them as numpy arrays.\n" ]

[ 0, 0 ]

[ "This should work. We first get each element of the list of lists (a list with length 2) and then append the difference between the latter lists (i) elements.\nlist = [[1, 23], [3, 2], [32, 213], [2321, 23]]\nres_list = []\nfor i in list:\n res_list.append((i[1]-i[0]))\n\nprint(res_list)\n\n" ]

[ -1 ]

[ "for_loop", "list", "python" ]

[ "AWS Lambda code: create zip from files by ext in bucket/filePath.\n\ndef createZipFileStream(bucketName, bucketFilePath, jobKey, fileExt, createUrl=False):\n response = {} \n bucket = s3.Bucket(bucketName)\n filesCollection = bucket.objects.filter(Prefix=bucketFilePath).all() \n archive = BytesIO()\n\n with zipfile.ZipFile(archive, 'w', zipfile.ZIP_DEFLATED) as zip_archive:\n for file in filesCollection:\n if file.key.endswith('.' + fileExt): \n with zip_archive.open(file.key, 'w') as file1:\n file1.write(file.get()['Body'].read()) \n\n archive.seek(0)\n s3.Object(bucketName, bucketFilePath + '/' + jobKey + '.zip').upload_fileobj(archive)\n archive.close()\n\n response['fileUrl'] = None\n\n if createUrl is True:\n s3Client = boto3.client('s3')\n response['fileUrl'] = s3Client.generate_presigned_url('get_object', Params={'Bucket': bucketName,\n 'Key': '' + bucketFilePath + '/' + jobKey + '.zip'},\n ExpiresIn=3600)\n\n return response\n \n\n", "Do not write to disk, stream to and from S3\nStream the Zip file from the source bucket and read and write its contents on the fly using Python back to another S3 bucket.\nThis method does not use up disk space and therefore is not limited by size.\nThe basic steps are:\n\nRead the zip file from S3 using the Boto3 S3 resource Object into a BytesIO buffer object\nOpen the object using the zipfile module\nIterate over each file in the zip file using the namelist method\nWrite the file back to another bucket in S3 using the resource meta.client.upload_fileobj method\n\nThe Code\nPython 3.6 using Boto3\ns3_resource = boto3.resource('s3')\nzip_obj = s3_resource.Object(bucket_name=\"bucket_name_here\", key=zip_key)\nbuffer = BytesIO(zip_obj.get()[\"Body\"].read())\n\nz = zipfile.ZipFile(buffer)\nfor filename in z.namelist():\n file_info = z.getinfo(filename)\n s3_resource.meta.client.upload_fileobj(\n z.open(filename),\n Bucket=bucket,\n Key=f'{filename}'\n )\n\nNote: AWS Execution time limit has a maximum of 15 minutes so can you process your HUGE files in this amount of time? You can only know by testing.\n", "The /tmp/ directory is limited to 512MB for AWS Lambda functions.\nIf you search StackOverflow, you'll see some code from people who have created Zip files on-the-fly without saving files to disk. It becomes pretty complicated.\nAn alternative would be to attach an EFS filesystem to the Lambda function. It takes a bit of effort to setup, but the cost would be practically zero if you delete the files after use and you'll have plenty of disk space so your code will be more reliable and easier to maintain.\n", "# For me below code worked for single file in Glue job to take single .txt file form AWS S3 and make it zipped and upload back to AWS S3. \nimport boto3\nimport zipfile\nfrom io import BytesIO\nimport logging\nlogger = logging.getLogger()\n\ns3_client = boto3.client('s3')\ns3_resource= boto3.resource('s3')\n\n# ZipFileStream function declaration\nself._createZipFileStream(\n bucketName=\"My_AWS_S3_bucket_name\",\n bucketFilePath=\"My_txt_object_prefix\", \n bucketfileobject=\"My_txt_Object_prefix + txt_file_name\",\n zipKey=\"My_zip_file_prefix\")\n\n# ZipFileStream function Defination\ndef _createZipFileStream(self, bucketName: str, bucketFilePath: str, bucketfileobject: str, zipKey: str, ) -> None:\n try:\n obj = s3_resource.Object(bucket_name=bucketName, key=bucketfileobject)\n archive = BytesIO()\n\n with zipfile.ZipFile(archive, 'w', zipfile.ZIP_DEFLATED) as zip_archive:\n with zip_archive.open(zipKey, 'w') as file1:\n file1.write(obj.get()['Body'].read()) \n\n archive.seek(0)\n\n s3_client.upload_fileobj(archive, bucketName, bucketFilePath + '/' + zipKey + '.zip')\n archive.close()\n \n # If you would like to delete the .txt after zipped from AWS S3 below code will work. \n self._delete_object(\n bucket=bucketName, key=bucketfileobject)\n\n except Exception as e:\n logger.error(f\"Failed to zip the txt file for {bucketName}/{bucketfileobject}: str{e}\")\n\n# Delete AWS S3 funcation defination.\ndef _delete_object(bucket: str, key: str) -> None:\n try:\n logger.info(f\"Deleting: {bucket}/{key}\")\n S3.delete_object(\n Bucket=bucket,\n Key=key\n )\n except Exception as e:\n logger.error(f\"Failed to delete {bucket}/{key}: str{e}\")`enter code here`\n\n" ]

[ 4, 3, 0, 0 ]

[]

[]

[ "amazon_web_services", "aws_lambda", "python" ]

[ "You need something like this:\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\ndriver = webdriver.Chrome()\ndriver.get(\"https://google.com\")\n\n# Get all the elements available with tag name 'a'\nelements = driver.find_elements(By.TAG_NAME, 'a')\nfor e in elements:\n print(e.get_attribute('href'))\n\n\n" ]

[ 0 ]

[]

[]

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

[ "Use GroupBy.agg for aggregate frozenset, then count values by Series.value_counts and last create DataFrame with join for strings from frozensets:\ndf1 = (df.groupby('transaction_no')['category']\n .agg(frozenset)\n .value_counts()\n .rename(lambda x: ', '.join(sorted(x)))\n .rename_axis('bucket')\n .reset_index(name='count'))\nprint (df1)\n bucket count\n0 M, N 2\n1 F, G 1\n2 A, B, G 1\n\nAnother idea:\ndf1 = (df.groupby('transaction_no')['category']\n .agg(lambda x: ', '.join(sorted(set((x)))))\n .value_counts()\n .rename_axis('bucket')\n .reset_index(name='count')\n )\nprint (df1)\n bucket count\n0 M, N 2\n1 F, G 1\n2 A, B, G 1\n\n" ]

[ 2 ]

[]

[]

[ "combinations", "group_by", "pandas", "python" ]

[ "I think you cannot check for dictionary equality. To solve you problem you can try the following:\nvar_1_list = []\nvar_2_list = []\nvar_dict = {'var_1_list': [],\n 'var_2_list': []}\ndata_dicts = [data_dict_var_1, data_dict_var_2]\nfor i, data_dict in enumerate(data_dicts):\n all_rois = np.array([\n data_dict[\"interoception\"],\n data_dict[\"exteroception\"],\n data_dict[\"cognitive\"]\n ])\n all_rois = np.hstack(all_rois)\n \n var_dict[f'var_{i+1}_list'].append(all_rois)\n\n" ]

[ 1 ]

[]

[]

[ "dictionary", "python", "valueerror" ]

[ "You can use the built in urlparse to query out the password from a url. It is available in both Python 2 and 3, but under different locations.\nPython 2 import urlparse\nPython 3 from urllib.parse import urlparse\nExample\nfrom urllib.parse import urlparse\n\nparsed = urlparse(\"https://user:[email protected]/path?key=value#hash\")\nparsed.password # 'password'\n\nreplaced = parsed._replace(netloc=\"{}:{}@{}\".format(parsed.username, \"???\", parsed.hostname))\nreplaced.geturl() # 'https://user:[email protected]/path?key=value#hash'\n\nSee also this question: Changing hostname in a url\n", "from urllib.parse import urlparse\n\ndef redact_url(url: str) -> str:\n url_components = urlparse(url)\n if url_components.username or url_components.password:\n url_components = url_components._replace(\n netloc=f\"{url_components.username}:???@{url_components.hostname}\",\n )\n\n return url_components.geturl()\n\n", "The pip module already have an internal utility function which does exactly this.\n>>> from pip._internal.utils.misc import redact_auth_from_url\n>>> \n>>> redact_auth_from_url(\"https://user:[email protected]/path?key=value#hash\")\n'https://user:****@example.com/path?key=value#hash'\n>>> redact_auth_from_url.__doc__\n'Replace the password in a given url with ****.'\n\nThis will provide the expected result even if the url does not contain username or password.\n>>> redact_auth_from_url(\"https://example.com/path?key=value#hash\") \n'https://example.com/path?key=value#hash'\n\n" ]

[ 15, 1, 0 ]

[]

[]

[ "python", "url_parsing" ]

[ "You can sort your data without the conversion to datetime, since the date/time components already appear in sorted order (year, month, etc.). So a np.sort(data, axis=0) should do:\nimport numpy as np\n\ndata = np.array(\n [[2015, 1, 1, 23, 4, 59],\n [2015, 4, 30, 23, 5, 1],\n [2015, 1, 1, 23, 5, 25],\n [2015, 2, 15, 58,5, 0],\n [2015, 5, 20, 50, 27, 37],\n [2015, 6, 21, 25, 27, 29]])\n\nnp.sort(data, axis=0)\narray([[2015, 1, 1, 23, 4, 0],\n [2015, 1, 1, 23, 5, 1],\n [2015, 2, 15, 23, 5, 25],\n [2015, 4, 20, 25, 5, 29],\n [2015, 5, 21, 50, 27, 37],\n [2015, 6, 30, 58, 27, 59]])\n\n" ]

[ 0 ]

[]

[]

[ "arrays", "date", "datetime", "python", "sorting" ]

[ "If you don't want to loop (python loop), use numpy:\nimport numpy as np\n\ndata_matrix = [\n[2,4,5,6,4],\n[5,6,5,6,4],\n]\n\nout = np.median(data_matrix, axis=1).tolist()\n\nOutput: [4.0, 5.0]\n" ]

[ 2 ]

[]

[]

[ "performance", "python" ]

[ "Please post the error you get. From the data I think you need to pass x=\"hour\" and not x=\"hours\". Also try\ndf.hour = pd.to_datetime(df.hour)\n\n" ]

[ 0 ]

[]

[]

[ "dataframe", "python" ]

[]

[]

[ "Check out the roman library, pip install roman the info is here on their gitgub. Assuming that you are simply trying to optimize a roman numeral converter.\nAlso, check twitter, you'll find help there with regards to recommendations for help on your code. I'm learning that StackOverflow has a set of rules that discourages asking for help, on a site built around... asking for help..\n" ]

[ -1 ]

[ "python" ]

[ "Try:\n'ConnectionProperties': {\n 'USER_NAME': 'your_user_name',\n 'PASSWORD' : 'your_user_password'\n }\n\nCaution: Above is not tested. Its based on Glue Boto3 documentation from here.\n", "So it supposed to be like this.\n 'USERNAME': username,\n 'PASSWORD': password\n },\n 'PhysicalConnectionRequirements': PhysicalConnectionRequirements\n }\n )\n\n\n", "You have to add all of the details for the update_connection:\n glue_client = boto3.client('glue')\n glue_client.update_connection(\n Name='Redshift-Rosko',\n ConnectionInput={\n 'Name': 'Redshift-Rosko',\n 'ConnectionType': 'JDBC',\n 'ConnectionProperties': {\n \"USERNAME\": username,\n \"PASSWORD\": password,\n \"JDBC_ENFORCE_SSL\": \"true\",\n \"JDBC_CONNECTION_URL\": \"jdbc:redshift://...:5439/rosko_db\",\n \"KAFKA_SSL_ENABLED\": \"false\"\n },\n 'PhysicalConnectionRequirements': {\n 'SubnetId': 'subnet-......',\n 'SecurityGroupIdList': [\n 'sg-......',\n 'sg-......',\n ],\n \"AvailabilityZone\": \"eu-central-1a\"\n }\n }\n )\n\n" ]

[ 0, 0, 0 ]

[]

[]

[ "amazon_web_services", "aws_cli", "aws_glue", "boto3", "python" ]

[ "You can combine a Categorical and seaborn.boxplot:\nimport seaborn as sns\n\ndf = pd.DataFrame({'X': [1,1,1,1,1,3,3,3,3,3],\n 'Y': [1,2,3,4,5,6,7,8,9,10]\n })\ndf['X'] = pd.Categorical(df['X'], categories=[1, 2, 3])\n\nsns.boxplot(data=df, x='X', y='Y')\n\nOutput:\n\nannotating the missing categories:\nax = sns.boxplot(data=df, x='X', y='Y')\n\n# positions of the categories in the X-axis\ncats = {c: i for i,c in enumerate(df['X'].cat.categories)}\nmissing = set(df['X'].cat.categories)-set(df['X'])\n# {2}\n\n# mid-point of the Y-axis\ny_pos = np.mean(ax.get_ylim())\n\nfor x in missing:\n ax.annotate('N/A', (cats[x], y_pos), ha='center')\n\nOutput:\n\n" ]

[ 2 ]

[]

[]

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

[ "You can perform equality comparison on the entire DataFrame:\ndf[df.eq(var1).any(1)]\n\n", "You should using isin , this is return the column , is want row check cold' answer :-) \ndf.isin(['bal1']).any()\nA False\nB True\nC False\nCLASS False\ndtype: bool\n\nOr \ndf[df.isin(['bal1'])].stack() # level 0 index is row index , level 1 index is columns which contain that value \n0 B bal1\n1 B bal1\ndtype: object\n\n", "You can try the code below:\nimport pandas as pd\nx = pd.read_csv(r\"filePath\")\nx.columns = x.columns.str.lower().str.replace(' ', '_')\ny = x.columns.values\nz = y.tolist()\nprint(\"Note: It take Case Sensitive Values.\")\nkeyWord = input(\"Type a Keyword to Search: \")\ntry:\n for k in range(len(z)-1):\n l = x[x[z[k]].str.match(keyWord)]\n print(l.head(10))\n k = k+1\nexcept:\n print(\"\")\n\n", "This is a solution which will return the actual column you need.\ndf.columns[df.isin(['Yes']).any()]\n\n" ]

[ 60, 30, 2, 0 ]

[]

[]

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

[ "Yes, that is a valid strategy\nfor i in range(N):\n Sol_Ts = solve_ivp(dTsdt,t[[i,i+1]],[Ts0],args=(A[i],B[i],C[i],))\n Ts_arr.append(Sol_Ts.y.copy())\n time_arr.append(Sol_Ts.t.copy)\n Ts0 = Sol_Ts.y[:,-1]\n\nTs_arr = np.concatenate(Ts_arr, axis=1)\ntime_arr = np.concatenate(time_arr)\n\nYou could also integrate over the full interval using the sympy.interpolate.interp1d function generator for interpolation functions of various types, here you would use the \"zero-order hold\", the piece-wise constant extension of the given function table.\n" ]

[ 0 ]

[]

[]

[ "equation_solving", "ode", "python" ]

[ "For Doing this kind of repetitive task we can use snippets in VSCode.\nStep 1 : Hit > shift+ctrl+p open command palette.\nStep 2 : Select Snippets: Configure User Snippets\nStep 3 : Select Python\nStep 4 : paste below code in python.json file. change prefix value. like \"prefix\": \"hedwin\" so now when you type hedwin vscode will paste our code snippet\n\"\": {\n \"prefix\": \"\",\n \"body\": [\n \"import sys\",\n \"import matplotlib as mpl\",\n \"sys.append('C:\\\\\\\\MyPackages')\",\n \"\",\n \"rc_fonts = {\",\n \" \\\"text.usetex\\\": True,\",\n \" 'font.size': 20,\",\n \" 'text.latex.preamble': r\\\"\\\\usepackage{bm}\\\",\",\n \"}\",\n \"mpl.rcParams.update(rc_fonts)\"\n ],\n \"description\": \"\"\n}\n\nFor making snippet : snippet generator\n", "You can use the extension File Templates.\nSave a file python.py with your start content in the default template location. (see extension page, you have to set it in the settings)\nThen use Ctrl+N and select Python, give it a name and the file is created.\nYou can add variables and snippets to customize the instantiation.\n" ]

[ 0, 0 ]

[]

[]

[ "python", "visual_studio_code" ]

[ "When you click on 'next page' you're performing a GET request, and not a POST request. Which means it will go into the else block, which has no filtering but just returns all the Scrapper objects.\nYou're better off including the from_date and to_date in a GET request and not using a POST request.\nIf you're using a form you can simply set the method:\n<form method=\"GET\" action=\"...\" />\n", "Shared method\ndef paginate(request,obj,total=25):\n paginator = Paginator(obj,total) \n try:\n page = int(request.GET.get('page', 1))\n except:\n page = 1\n\n try:\n obj_list = paginator.page(page)\n except(EmptyPage,InvalidPage):\n obj_list = paginator.page(paginator.num_pages)\n return obj_list\n\nView\nusers = UserInfo.objects.all()\ndata = {\n 'users': paginate(request,users,15),\n}\nreturn render(request,self.template_name,data)\n\nHTML\n{% include './pagination.html' with obj=users %}\n\npagination.html file\n {% if obj.paginator.num_pages > 1 %}\n <div class=\"text-center\">\n <ul class=\"pagination\">\n \n <li class=\"{% if not obj.has_previous %} disabled{% endif %}\">\n \n\n {% if obj.has_previous %}\n <a data-page=\"{{obj.previous_page_number}}\" href=\"?page={{obj.previous_page_number}}{% for key, value in request.GET.items %}{% if key != 'page' %}&{{ key }}={{ value }}{% endif %}{% endfor %}\" aria-label=\"{% trans 'Previous' %}\" tabindex=\"-1\"> &laquo; </a>\n {% else %}\n <a data-page=\"0\" class=\"\" href=\"javascript:void(0);\" tabindex=\"-1\">&laquo;</a>\n {% endif %}\n </li>\n \n {% for i in obj.paginator.page_range %}\n {% if obj.number == i %}\n <li class=\"active\">\n <a data-page=\"0\" class=\"\" href=\"javascript:void(0)\">{{ i }}</a>\n </li>\n {% elif i > obj.number|add:'-5' and i < obj.number|add:'5' %}\n <li class=\"\"><a data-page=\"{{i}}\" class=\"\" href=\"?page={{i}}{% for key, value in request.GET.items %}{% if key != 'page' %}&{{ key }}={{ value }}{% endif %}{% endfor %}\">{{ i }}</a>\n </li>\n {% endif %}\n {% endfor %}\n\n <li class=\"{% if not obj.has_next %} disabled{% endif %}\">\n {% if obj.has_next %}\n <a data-page=\"{{ obj.next_page_number }}\" class=\"\" href=\"?page={{obj.next_page_number}}{% for key, value in request.GET.items %}{% if key != 'page' %}&{{ key }}={{ value }}{% endif %}{% endfor %}\" tabindex=\"-1\">&raquo;</a>\n {% else %}\n <a data-page=\"0\" class=\"\" href=\"javascript:void(0);\" tabindex=\"-1\">&raquo;</a>\n {% endif %}\n </li>\n </ul>\n </div>\n{% endif %}\n\n" ]

[ 0, 0 ]

[]

[]

[ "django", "python" ]

[ "A note regarding the title (\"Activate Conda environment inside R script\"), just as you activate python env before executing your the python script, R environment should be activated before invoking the R script.\nSetting up 2 conda enviruonments and using conda run for executing Python script and invoking R from Python:\nConda envs\n# base python env:\nconda create -n conda_python -c conda-forge python\n# base r env + httr:\nconda create -n conda_r -c conda-forge r-base r-httr\n\nTest scripts\n# testpy.py\n\nimport os\nimport subprocess\nprint('CONDA_PREFIX (py) :', os.environ['CONDA_PREFIX'])\n# subprocess.call('conda run -n conda_r Rscript testr.R', shell=True)\n# or\nsubprocess.call(['conda', 'run', '-n', 'conda_r', 'Rscript', 'testr.R'] )\n\n# testr.R\n\nmessage(\"-- R --\")\nmessage(\"CONDA_PREFIX (R) : \", Sys.getenv(\"CONDA_PREFIX\"))\nmessage(\"R .libPaths() : \", .libPaths())\nresp <- httr::GET(\"https://api.stackexchange.com/2.3/info?site=stackoverflow\")\nmessage('Questions in SO : ', httr::content(resp, as =\"parsed\")$items[[1]]$total_questions)\n\nExecuting testpy.py\n(when conda_python is not currently activated environment)\n# conda run -n conda_python python testpy.py\nCONDA_PREFIX (py) : /home/marguslt/miniconda3/envs/conda_python\n-- R --\nCONDA_PREFIX (R) : /home/marguslt/miniconda3/envs/conda_r\nR .libPaths() : /home/marguslt/miniconda3/envs/conda_r/lib/R/library\nQuestions in SO : 23249251\n\nAs an alternative, using conda nested activation :\npy script replaced with:\n# testpy_noconda.py\n\nimport os\nimport subprocess\nprint('CONDA_PREFIX (py) :', os.environ['CONDA_PREFIX'])\nsubprocess.call(['Rscript', 'testr.R'] )\n\nActivating both environments with --stack option and running testpy_noconda.py:\n(base): conda activate conda_python\n(conda_python): conda activate --stack conda_r\n(conda_r): python testpy_noconda.py\nCONDA_PREFIX (py) : /home/marguslt/miniconda3/envs/conda_r\n-- R --\nCONDA_PREFIX (R) : /home/marguslt/miniconda3/envs/conda_r\nR .libPaths() : /home/marguslt/miniconda3/envs/conda_r/lib/R/library\nQuestions in SO : 23249325\n\nEnvironment:\n(conda_r): env | grep CONDA_\nCONDA_EXE=/home/marguslt/miniconda3/bin/conda\nCONDA_PYTHON_EXE=/home/marguslt/miniconda3/bin/python\nCONDA_SHLVL=3\nCONDA_PREFIX=/home/marguslt/miniconda3/envs/conda_r\nCONDA_DEFAULT_ENV=conda_r\nCONDA_PROMPT_MODIFIER=(conda_r)\nCONDA_PREFIX_1=/home/marguslt/miniconda3\nCONDA_PREFIX_2=/home/marguslt/miniconda3/envs/conda_python\nCONDA_STACKED_3=true\n\n" ]

[ 1 ]

[]

[]

[ "conda", "python", "r" ]

[ "My first instinct would be to refactor the nested loop into a function and use return to break out. \n", "Here's another approach that is short. The disadvantage is that you can only break the outer loop, but sometimes it's exactly what you want.\nfor a in xrange(10):\n for b in xrange(20):\n if something(a, b):\n # Break the inner loop...\n break\n else:\n # Continue if the inner loop wasn't broken.\n continue\n # Inner loop was broken, break the outer.\n break\n\nThis uses the for / else construct explained at: Why does python use 'else' after for and while loops?\nKey insight: It only seems as if the outer loop always breaks. But if the inner loop doesn't break, the outer loop won't either. \nThe continue statement is the magic here. It's in the for-else clause. By definition that happens if there's no inner break. In that situation continue neatly circumvents the outer break.\n", "PEP 3136 proposes labeled break/continue. Guido rejected it because \"code so complicated to require this feature is very rare\". The PEP does mention some workarounds, though (such as the exception technique), while Guido feels refactoring to use return will be simpler in most cases.\n", "First, ordinary logic is helpful. \nIf, for some reason, the terminating conditions can't be worked out, exceptions are a fall-back plan. \nclass GetOutOfLoop( Exception ):\n pass\n\ntry:\n done= False\n while not done:\n isok= False\n while not (done or isok):\n ok = get_input(\"Is this ok? (y/n)\")\n if ok in (\"y\", \"Y\") or ok in (\"n\", \"N\") : \n done= True # probably better\n raise GetOutOfLoop\n # other stuff\nexcept GetOutOfLoop:\n pass\n\nFor this specific example, an exception may not be necessary. \nOn other other hand, we often have \"Y\", \"N\" and \"Q\" options in character-mode applications. For the \"Q\" option, we want an immediate exit. That's more exceptional.\n", "I tend to agree that refactoring into a function is usually the best approach for this sort of situation, but for when you really need to break out of nested loops, here's an interesting variant of the exception-raising approach that @S.Lott described. It uses Python's with statement to make the exception raising look a bit nicer. Define a new context manager (you only have to do this once) with:\nfrom contextlib import contextmanager\n@contextmanager\ndef nested_break():\n class NestedBreakException(Exception):\n pass\n try:\n yield NestedBreakException\n except NestedBreakException:\n pass\n\nNow you can use this context manager as follows:\nwith nested_break() as mylabel:\n while True:\n print \"current state\"\n while True:\n ok = raw_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\": raise mylabel\n if ok == \"n\" or ok == \"N\": break\n print \"more processing\"\n\nAdvantages: (1) it's slightly cleaner (no explicit try-except block), and (2) you get a custom-built Exception subclass for each use of nested_break; no need to declare your own Exception subclass each time.\n", "Introduce a new variable that you'll use as a 'loop breaker'. First assign something to it(False,0, etc.), and then, inside the outer loop, before you break from it, change the value to something else(True,1,...). Once the loop exits make the 'parent' loop check for that value. Let me demonstrate:\nbreaker = False #our mighty loop exiter!\nwhile True:\n while True:\n if conditionMet:\n #insert code here...\n breaker = True \n break\n if breaker: # the interesting part!\n break # <--- !\n\nIf you have an infinite loop, this is the only way out; for other loops execution is really a lot faster. This also works if you have many nested loops. You can exit all, or just a few. Endless possibilities! Hope this helped!\n", "First, you may also consider making the process of getting and validating the input a function; within that function, you can just return the value if its correct, and keep spinning in the while loop if not. This essentially obviates the problem you solved, and can usually be applied in the more general case (breaking out of multiple loops). If you absolutely must keep this structure in your code, and really don't want to deal with bookkeeping booleans...\nYou may also use goto in the following way (using an April Fools module from here):\n#import the stuff\nfrom goto import goto, label\n\nwhile True:\n #snip: print out current state\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\": goto .breakall\n if ok == \"n\" or ok == \"N\": break\n #do more processing with menus and stuff\nlabel .breakall\n\nI know, I know, \"thou shalt not use goto\" and all that, but it works well in strange cases like this.\n", "To break out of multiple nested loops, without refactoring into a function, make use of a \"simulated goto statement\" with the built-in StopIteration exception:\ntry:\n for outer in range(100):\n for inner in range(100):\n if break_early():\n raise StopIteration\n\nexcept StopIteration: pass\n\nSee this discussion on the use of goto statements for breaking out of nested loops.\n", "keeplooping = True\nwhile keeplooping:\n # Do stuff\n while keeplooping:\n # Do some other stuff\n if finisheddoingstuff():\n keeplooping = False\n\nor something like that.\nYou could set a variable in the inner loop, and check it in the outer loop immediately after the inner loop exits, breaking if appropriate. I kind of like the GOTO method, provided you don't mind using an April Fool's joke module - it’s not Pythonic, but it does make sense.\n", "This isn't the prettiest way to do it, but in my opinion, it's the best way.\ndef loop():\n while True:\n #snip: print out current state\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\": return\n if ok == \"n\" or ok == \"N\": break\n #do more processing with menus and stuff\n\nI'm pretty sure you could work out something using recursion here as well, but I don't know if that's a good option for you.\n", "Keep looping if two conditions are true.\nI think this is a more Pythonic way:\ndejaVu = True\n\nwhile dejaVu:\n while True:\n ok = raw_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\" or ok == \"n\" or ok == \"N\":\n dejaVu = False\n break\n\n", "\nThere is no way to do this from a language level. Some languages have\n a goto others have a break that takes an argument, python does not.\nThe best options are:\n\nSet a flag which is checked by the outer loop, or set the outer\n loops condition.\nPut the loop in a function and use return to break out of all the loops at once.\nReformulate your logic.\n\n\nCredit goes to Vivek Nagarajan, Programmer since 1987\n\nUsing Function \ndef doMywork(data):\n for i in data:\n for e in i:\n return \n\nUsing flag\nis_break = False\nfor i in data:\n if is_break:\n break # outer loop break\n for e in i:\n is_break = True\n break # inner loop break\n\n", "Factor your loop logic into an iterator that yields the loop variables and returns when done -- here is a simple one that lays out images in rows/columns until we're out of images or out of places to put them:\ndef it(rows, cols, images):\n i = 0\n for r in xrange(rows):\n for c in xrange(cols):\n if i >= len(images):\n return\n yield r, c, images[i]\n i += 1 \n\nfor r, c, image in it(rows=4, cols=4, images=['a.jpg', 'b.jpg', 'c.jpg']):\n ... do something with r, c, image ...\n\nThis has the advantage of splitting up the complicated loop logic and the processing...\n", "There is a hidden trick in the Python while ... else structure which can be used to simulate the double break without much code changes/additions. In essence if the while condition is false, the else block is triggered. Neither exceptions, continue or break trigger the else block. For more information see answers to \"Else clause on Python while statement\", or Python doc on while (v2.7).\nwhile True:\n #snip: print out current state\n ok = \"\"\n while ok != \"y\" and ok != \"n\":\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"n\" or ok == \"N\":\n break # Breaks out of inner loop, skipping else\n\n else:\n break # Breaks out of outer loop\n\n #do more processing with menus and stuff\n\nThe only downside is that you need to move the double breaking condition into the while condition (or add a flag variable). Variations of this exists also for the for loop, where the else block is triggered after loop completion.\n", "An easy way to turn multiple loops into a single, breakable loop is to use numpy.ndindex\nfor i in range(n):\n for j in range(n):\n val = x[i, j]\n break # still inside the outer loop!\n\nfor i, j in np.ndindex(n, n):\n val = x[i, j]\n break # you left the only loop there was!\n\nYou do have to index into your objects, as opposed to being able to iterate through the values explicitly, but at least in simple cases it seems to be approximately 2-20 times simpler than most of the answers suggested.\n", "In this case, as pointed out by others as well, functional decomposition is the way to go. Code in Python 3:\ndef user_confirms():\n while True:\n answer = input(\"Is this OK? (y/n) \").strip().lower()\n if answer in \"yn\":\n return answer == \"y\"\n\ndef main():\n while True:\n # do stuff\n if user_confirms():\n break\n\n", "Another way of reducing your iteration to a single-level loop would be via the use of generators as also specified in the python reference\nfor i, j in ((i, j) for i in A for j in B):\n print(i , j)\n if (some_condition):\n break\n\nYou could scale it up to any number of levels for the loop\nThe downside is that you can no longer break only a single level. It's all or nothing.\nAnother downside is that it doesn't work with a while loop. I originally wanted to post this answer on Python - `break` out of all loops but unfortunately that's closed as a duplicate of this one \n", "I'd like to remind you that functions in Python can be created right in the middle of the code and can access the surrounding variables transparently for reading and with nonlocal or global declaration for writing.\nSo you can use a function as a \"breakable control structure\", defining a place you want to return to:\ndef is_prime(number):\n\n foo = bar = number\n\n def return_here():\n nonlocal foo, bar\n init_bar = bar\n while foo > 0:\n bar = init_bar\n while bar >= foo:\n if foo*bar == number:\n return\n bar -= 1\n foo -= 1\n\n return_here()\n\n if foo == 1:\n print(number, 'is prime')\n else:\n print(number, '=', bar, '*', foo)\n\n\n>>> is_prime(67)\n67 is prime\n>>> is_prime(117)\n117 = 13 * 9\n>>> is_prime(16)\n16 = 4 * 4\n\n", "By using a function:\ndef myloop():\n for i in range(1,6,1): # 1st loop\n print('i:',i)\n for j in range(1,11,2): # 2nd loop\n print(' i, j:' ,i, j)\n for k in range(1,21,4): # 3rd loop\n print(' i,j,k:', i,j,k)\n if i%3==0 and j%3==0 and k%3==0:\n return # getting out of all loops\n\nmyloop()\n\nTry running the above codes by commenting out the return as well.\nWithout using any function:\ndone = False\nfor i in range(1,6,1): # 1st loop\n print('i:', i)\n for j in range(1,11,2): # 2nd loop\n print(' i, j:' ,i, j)\n for k in range(1,21,4): # 3rd loop\n print(' i,j,k:', i,j,k)\n if i%3==0 and j%3==0 and k%3==0:\n done = True\n break # breaking from 3rd loop\n if done: break # breaking from 2nd loop\n if done: break # breaking from 1st loop\n\nNow, run the above codes as is first and then try running by commenting out each line containing break one at a time from the bottom.\n", "Try using an infinite generator.\nfrom itertools import repeat\ninputs = (get_input(\"Is this ok? (y/n)\") for _ in repeat(None))\nresponse = (i.lower()==\"y\" for i in inputs if i.lower() in (\"y\", \"n\"))\n\nwhile True:\n #snip: print out current state\n if next(response):\n break\n #do more processing with menus and stuff\n\n", "# this version uses a level counter to choose how far to break out\n\nbreak_levels = 0\nwhile True:\n # snip: print out current state\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\":\n break_levels = 1 # how far nested, excluding this break\n break\n if ok == \"n\" or ok == \"N\":\n break # normal break\n if break_levels:\n break_levels -= 1\n break # pop another level\nif break_levels:\n break_levels -= 1\n break\n\n# ...and so on\n\n", "# this version breaks up to a certain label\n\nbreak_label = None\nwhile True:\n # snip: print out current state\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\":\n break_label = \"outer\" # specify label to break to\n break\n if ok == \"n\" or ok == \"N\":\n break\n if break_label:\n if break_label != \"inner\":\n break # propagate up\n break_label = None # we have arrived!\nif break_label:\n if break_label != \"outer\":\n break # propagate up\n break_label = None # we have arrived!\n\n#do more processing with menus and stuff\n\n", "Solutions in two ways\nWith an example: Are these two matrices equal/same? \nmatrix1 and matrix2 are the same size, n, two-dimensional matrices.\nFirst solution, without a function\nsame_matrices = True\ninner_loop_broken_once = False\nn = len(matrix1)\n\nfor i in range(n):\n for j in range(n):\n\n if matrix1[i][j] != matrix2[i][j]:\n same_matrices = False\n inner_loop_broken_once = True\n break\n\n if inner_loop_broken_once:\n break\n\n\nSecond solution, with a function\nThis is the final solution for my case.\ndef are_two_matrices_the_same (matrix1, matrix2):\n n = len(matrix1)\n for i in range(n):\n for j in range(n):\n if matrix1[i][j] != matrix2[i][j]:\n return False\n return True\n\n", "Here's an implementation that seems to work:\nbreak_ = False\nfor i in range(10):\n if break_:\n break\n for j in range(10):\n if j == 3:\n break_ = True\n break\n else:\n print(i, j)\n\nThe only draw back is that you have to define break_ before the loops.\n", "What I would personally do is use a boolean that toggles when I am ready to break out the outer loop. For example\nwhile True:\n #snip: print out current state\n quit = False\n while True:\n ok = input(\"Is this ok? (y/n)\")\n if ok.lower() == \"y\":\n quit = True\n break # this should work now :-)\n if ok.lower() == \"n\":\n quit = True\n break # This should work too :-)\n if quit:\n break\n #do more processing with menus and stuff\n\n", "My reason for coming here is that i had an outer loop and an inner loop like so:\nfor x in array:\n for y in dont_use_these_values:\n if x.value==y:\n array.remove(x) # fixed, was array.pop(x) in my original answer\n continue\n\n do some other stuff with x\n\nAs you can see, it won't actually go to the next x, but will go to the next y instead.\nwhat i found to solve this simply was to run through the array twice instead:\nfor x in array:\n for y in dont_use_these_values:\n if x.value==y:\n array.remove(x) # fixed, was array.pop(x) in my original answer\n continue\n\nfor x in array:\n do some other stuff with x\n\nI know this was a specific case of OP's question, but I am posting it in the hope that it will help someone think about their problem differently while keeping things simple.\n", "probably little trick like below will do if not prefer to refactorial into function\nadded 1 break_level variable to control the while loop condition\nbreak_level = 0\n# while break_level < 3: # if we have another level of nested loop here\nwhile break_level < 2:\n #snip: print out current state\n while break_level < 1:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok == \"y\" or ok == \"Y\": break_level = 2 # break 2 level\n if ok == \"n\" or ok == \"N\": break_level = 1 # break 1 level\n\n", "You can define a variable( for example break_statement ), then change it to a different value when two-break condition occurs and use it in if statement to break from second loop also. \nwhile True:\n break_statement=0\n while True:\n ok = raw_input(\"Is this ok? (y/n)\")\n if ok == \"n\" or ok == \"N\": \n break\n if ok == \"y\" or ok == \"Y\": \n break_statement=1\n break\n if break_statement==1:\n break\n\n", "Trying to minimal changes to the OP's question, I just added a flag before breaking the 1st for loop and check that flag on the outer loop to see if we need to brake once again.\nbreak_2 = False\nwhile True:\n # Snip: print out current state\n if break_2: break\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok.lower() == \"y\": break_2 = True\n if break_2: break\n if ok.lower() == \"n\": break\n # Do more processing with menus and stuff\n\n", "I came across this recently and, wanting to avoid a duplicate return statement, which can conceal logical errors, looked at @yak's idea. This works well within nested for loops but is not very elegant. An alternative is to check for the condition before the next loop:\nb = None\nfor a in range(10):\n if something(a, b): # should never = True if b is None\n break\n for b in range(20):\n pass\n\nThis might not work everywhere but is adaptable and, if required, has the advantage of allowing the condition to be duplicated rather than a potential result.\n", "while True:\n # Snip: print out current state\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n if ok.lower() == \"y\": \n break_2 = True\n if ok.lower() == \"n\": \n break\n if break_2:\n break\n\n", "If you just need to test an edge case inside a complex nest of for loops, you can throw in a 1/0 to raise an exception. I promise I won't tell anyone. This comes in handy when you quickly want to test a single iteration of a deeply nested for loop, and you don't want to track down a large amount of break statements or comment out a significant amount of code.\nYes, you could wrap it in a function and use return, but in some contexts that can be infeasibly cumbersome.\nexample for entry level programmers:\nfor i in first_iter:\n for j in second_iter:\n for k in third_iter:\n print(i_want_to_run_this_once_and_stop_executing(i,j,k))\n 1/0\n code_that_takes_a_long_time()\n expensive_code()\n\nin big jupyter notebook scripts that do some heavy data pre-processing, this is especially handy.\n" ]

[ 698, 450, 178, 149, 63, 63, 46, 42, 21, 13, 12, 9, 8, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 0, 0, 0, 0 ]

[ "Hopefully this helps:\nx = True\ny = True\nwhile x == True:\n while y == True:\n ok = get_input(\"Is this ok? (y/n)\") \n if ok == \"y\" or ok == \"Y\":\n x,y = False,False #breaks from both loops\n if ok == \"n\" or ok == \"N\": \n break #breaks from just one\n\n", "break for outer and inner while loops:\nwhile True:\n while True:\n print('Breaks inner \"while\" loop')\n break # Here\n print('Breaks outer \"while\" loop')\n break # Here\n\nOr, break for outer and inner while loops with if statement:\nwhile True:\n while True:\n if True:\n print('Breaks inner \"while\" loop')\n break # Here\n print('Breaks outer \"while\" loop')\n break # Here\n\nOutput:\nBreaks inner \"while\" loop\nBreaks outer \"while\" loop\n\nbreak for outer and inner for loops:\nfor _ in iter(int, 1):\n for _ in iter(int, 1):\n print('Breaks inner \"for\" loop')\n break # Here\n print('Breaks outer \"for\" loop')\n break # Here\n\nOr, break for outer and inner for loops with if statement:\nfor _ in iter(int, 1):\n for _ in iter(int, 1):\n if True:\n print('Breaks inner \"for\" loop')\n break # Here\n print('Breaks outer \"for\" loop')\n break # Here\n\nOutput:\nBreaks inner \"for\" loop\nBreaks outer \"for\" loop\n\n", "Similar like the one before, but more compact.\n(Booleans are just numbers)\nbreaker = False #our mighty loop exiter!\nwhile True:\n while True:\n ok = get_input(\"Is this ok? (y/n)\")\n breaker+= (ok.lower() == \"y\")\n break\n\n if breaker: # the interesting part!\n break # <--- !\n\n", "Since this question has become a standard question for breaking into a particular loop, I would like to give my answer with example using Exception. \nAlthough there exists no label named breaking of loop in multipally looped construct, we can make use of User-defined Exceptions to break into a particular loop of our choice. Consider the following example where let us print all numbers upto 4 digits in base-6 numbering system: \nclass BreakLoop(Exception):\n def __init__(self, counter):\n Exception.__init__(self, 'Exception 1')\n self.counter = counter\n\nfor counter1 in range(6): # Make it 1000\n try:\n thousand = counter1 * 1000\n for counter2 in range(6): # Make it 100\n try:\n hundred = counter2 * 100\n for counter3 in range(6): # Make it 10\n try:\n ten = counter3 * 10\n for counter4 in range(6):\n try:\n unit = counter4\n value = thousand + hundred + ten + unit\n if unit == 4 :\n raise BreakLoop(4) # Don't break from loop\n if ten == 30: \n raise BreakLoop(3) # Break into loop 3\n if hundred == 500:\n raise BreakLoop(2) # Break into loop 2\n if thousand == 2000:\n raise BreakLoop(1) # Break into loop 1\n\n print('{:04d}'.format(value))\n except BreakLoop as bl:\n if bl.counter != 4:\n raise bl\n except BreakLoop as bl:\n if bl.counter != 3:\n raise bl\n except BreakLoop as bl:\n if bl.counter != 2:\n raise bl\n except BreakLoop as bl:\n pass\n\nWhen we print the output, we will never get any value whose unit place is with 4. In that case, we don't break from any loop as BreakLoop(4) is raised and caught in same loop. Similarly, whenever ten place is having 3, we break into third loop using BreakLoop(3). Whenever hundred place is having 5, we break into second loop using BreakLoop(2) and whenver the thousand place is having 2, we break into first loop using BreakLoop(1).\nIn short, raise your Exception (in-built or user defined) in the inner loops, and catch it in the loop from where you want to resume your control to. If you want to break from all loops, catch the Exception outside all the loops. (I have not shown this case in example).\n", "The way I solve this is by defining a variable that is referenced to determine if you break to the next level or not. In this example, this variable is called 'shouldbreak'.\nVariable_That_Counts_To_Three=1\nwhile 1==1:\n shouldbreak='no'\n Variable_That_Counts_To_Five=0\n while 2==2:\n Variable_That_Counts_To_Five+=1\n print(Variable_That_Counts_To_Five)\n if Variable_That_Counts_To_Five == 5:\n if Variable_That_Counts_To_Three == 3:\n shouldbreak='yes'\n break\n print('Three Counter = ' + str(Variable_That_Counts_To_Three))\n Variable_That_Counts_To_Three+=1\n if shouldbreak == 'yes':\n break\n\nprint('''\nThis breaks out of two loops!''')\n\nThis gives a lot of control over how exactly you want the program to break, allowing you to choose when you want to break and how many levels to go down.\n" ]

[ -1, -1, -3, -3, -4 ]

[ "break", "control_flow", "nested_loops", "python" ]

[ "The function to_timestamp returns a string to a timestamp, with the format yyyy-MM-dd HH:mm:ss.\nThe second argument is used to define the format of the DateTime in the string you are trying to parse.\nYou can see a couple of examples in the official documentation.\n", "\nThe code should be like this, just look at the single 'd' part here, and this is tricky in many cases.\ndata= data.withColumn('date', to_timestamp(col('date'), 'yyyy/MM/d'))\n\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "I see two thing going wrong here:\n\nYou never check for the type \"list\", which you would need to iterate over\nOnce you are iterating over the list, you will get dictionaries again, of which you need to extract the \"quantities\" before you try to sum them.\n\nI would approach it differently: create an empty output dictionary. Then dig into the source (iterating roughly as you do), and check whether the \"quantity\" key exists inside the current level. Then check if the corresponding key exists, and add the quantity to the result dictionary.\n", "Your functions fails when it gets here\nif not isinstance(n[key], str):\n current_sum = current_sum + n[key]\n\nand n[key] is :\n[{'name': 'pencil', 'quantity': 5}, {'name': 'cam', 'quantity': 11}, {'name': 'powder', 'quantity': 51}]\n\nThis can be fixed with a simple loop\ndef recursive_sum(n):\ncurrent_sum = 0\nfor key in n:\n if not isinstance(n[key], dict):\n if not isinstance(n[key], str):\n for i in n[key]:\n current_sum = current_sum + i[\"quantity\"]\n \n else:\n current_sum = current_sum + recursive_sum(n[key])\nreturn current_sum\n\n" ]

[ 1, 0 ]

[]

[]

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

[ "Not sure if that's what you need, but you can call .__dict__ on a class and then check if the name of your function is present as a key in the returned dictionary.\nclass B:\n def foo(self):pass\n\n B.__dict__\nmappingproxy({'__module__': '__main__', 'foo': <function B.foo at 0x7f4861baee80>, '__dict__': <attribute '__dict__' of 'B' objects>, '__weakref__': <attribute '__weakref__' of 'B' objects>, '__doc__': None})\n\nBut from the description it seems you have multiple @classmethod, which is most often used to declare customized initializers (sometimes wrongly called \"constructors\"), otherwise maybe get rid of the class and just declare \"normal\" functions.\n" ]

[ 0 ]

[]

[]

[ "class", "python", "python_class" ]

[ "The __range lookup [Django-doc] expects a 2-tuple with the from and to datetime, so:\ndef index(request):\n if request.method == 'POST':\n from_date = request.POST.get('from_date')\n f_date = datetime.datetime.strptime(from_date, '%Y-%m-%d')\n to_date = request.POST.get('to_date')\n t_date = datetime.datetime.strptime(to_date, '%Y-%m-%d')\n get_records_by_date = Scrapper.objects.filter(\n some_date_field__range=(f_date, t_date)\n )\n # …\nI would however advise to work with a form to process and clean the input, and not use a global variable: global state, especially in a webserver is a very bad idea.\n", "You can put dates in the filter to find our range in between the dates.\nimport datetime\n\nfrom_date=datetime.datetime.today().date()-datetime.timedelta(days=29)\nto_date=datetime.datetime.today().date()\nSomemodel.objects.filter(date_created__gte=from_date, date_created__lte=to_date)\n\n" ]

[ 1, 1 ]

[]

[]

[ "django", "python" ]

[ "It seems that the best solution for your problem is to convert your dataframe to a numpy array and afterwards save it.\nnp.savez(file, df.to_numpy())\n\nfile has to be a file, in which you want to save your data and df is the dataframe in which you have your data.\n" ]

[ 1 ]

[]

[]

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

[ "Your code is already doing that - captures all success and failure in two separate list and return them as dictionary with keys succeded and failed:\n# Define a dictionary - 2 keys \"succeded\" and \"failed\" with empty list\nresult = { \"succeded\": [], \"failed\": []}\n\n# append test values to empty list\nresult[\"succeded\"].append(\"success-uuid\") #from future.result() - success\nresult[\"failed\"].append(\"failed-uuid\") #from future.result() - failure\n\n\nresult[\"succeded\"].append(\"868716387\") #from future.result() - success\nresult[\"failed\"].append(\"97234692369\") #from future.result() - failure\n\nprint(result)\n\nOutput:\n{'succeded': ['success-uuid', '868716387'], 'failed': ['failed-uuid', '97234692369']}\n\n" ]

[ 0 ]

[]

[]

[ "google_cloud_pubsub", "python" ]

[ "Short answer: It's not possible, (at least the present libraries are not able to do it.) The feature importance of linear SVMs could be found out but not for a nonlinear SVMs, the reason being that, when the SVM is non-linear the dataset is mapped into a space of higher dimension, which is quite different from the parent dataset and the hyperplane is obtained and this high dimensional data and hence the property is changed from that of the parent dataset and hence it is not possible to find the feature importance of this SVM in relation to the parent dataset features. \n", "An N x N kernel result is not invertible, only traceable!\nPlease check, if you do or can use Gradients.\nThose should normally trace the calculations.\nFor the importance you need the trace after an impulse response I guess.\nThus, if you input a bunch of ones.\nI am not that deep into the implementation of SciKit-Learn and if it ever makes sense to attempt to get access to the traces.\nBut at that point, you traced the response back to the features, it should give you the importance.\nNevertheless any gradient descent is not specifically made to directly trace the inputs rather than the parameters which lead to a specific output.\nYou have to find those back-propagated parameters of your kernel w.r.t. the response (The gradients of the kernel params given the response itself).\nAs, because this may be even impossible or is absolutely complex, I would refer to anything which can alternatively bring good results.\nSuch as kernels between the different dimensions of your samples instead of between each of your individual samples.\nOr some response functions, which give a good dynamic scaling of you features.\n", "You can't directly extract the feature importance of a SVM. But, you can use the permutation_importance from sklearn to get it.\nHere is an example:\nfrom sklearn.svm import SVC\nfrom sklearn.inspection import permutation_importance\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nsvm = SVC(kernel='poly')\nsvm.fit(X, Y)\n\nperm_importance = permutation_importance(svm, X, Y)\n\n# Making the sum of feature importance being equal to 1.0,\n# so feature importance can be understood as percentage\nperm_importance_normalized = perm_importance.importances_mean/perm_importance.importances_mean.sum()\n\n# Feature's name (considering your X a DataFrame)\nfeature_names = X.columns\nfeatures = np.array(feature_names)\n\n# Sort to plot in order of importance\nsorted_idx = perm_importance_normalized.argsort()\n\n# Plotting\nplt.figure(figsize=(13,5))\nplt.title('Feature Importance',fontsize=20)\nplt.barh(features[sorted_idx], perm_importance_normalized[sorted_idx], color='b', align='center')\nplt.xlabel('Relative Importance', fontsize=15)\nplt.xticks(fontsize=15)\nplt.yticks(fontsize=15)\n\nfor index, value in enumerate(perm_importance_normalized[sorted_idx]):\n plt.text(value, index,\n str(round(value,2)), fontsize=15)\n\nplt.show()\n\n\n" ]

[ 1, 0, 0 ]

[]

[]

[ "machine_learning", "python", "scikit_learn", "svm" ]

[ "Since your question does not present any data, I have combined the reference example with another example to confirm the events.\nI searched the plotly community for a solution and identified examples that would solve the issue.\nThe way to do this is to add a graph object choropleth map to the graph object and then add an express graph.\nOne issue is that the specified colormap is not valid. We are currently investigating but may not be able to reach a solution. I believe it is compatible with the solution to your question.\nimport plotly.express as px\nimport plotly.graph_objects as go\n\npx.set_mapbox_access_token(open(\"mapbox_api_key.txt\").read())\n\n# fig for data\ndf_election = px.data.election()\ngeojson = px.data.election_geojson()\n\n# fig2 for data\ndf_car = px.data.carshare()\ndf_car['peak_hour2'] = df_car['peak_hour']*20\n\nfig = go.Figure()\nfig.add_trace(go.Choroplethmapbox(geojson=geojson,\n z=df_election[\"Bergeron\"],\n colorscale='greens',\n locations=df_election[\"district\"],\n featureidkey=\"properties.district\",\n colorbar_x=1.12,\n colorbar_title='election'\n ))\nfig.update_layout(mapbox_style=\"open-street-map\",\n mapbox_center={\"lat\": 45.5517, \"lon\": -73.7073},\n mapbox_zoom=10)\n\nmap_scatter = px.scatter_mapbox(df_car,\n lat=\"centroid_lat\",\n lon=\"centroid_lon\",\n color=\"peak_hour\",\n size=\"car_hours\",\n color_continuous_scale=px.colors.sequential.Blues,\n size_max=15,\n zoom=9)\nfig.add_traces(list(map_scatter.select_traces()))\n\nfig.update_layout(coloraxis={'colorbar': {'title': {'text': 'peak_hour'}}})\nfig.update_layout(autosize=True, height=600, margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\nfig.show()\n\n\n" ]

[ 2 ]

[]

[]

[ "plotly", "python" ]

[ "In a lot of shells <<< denotes a here string and is a way to pass standard input to commands. <<< is used for strings, e.g.\n$ python3 <<< 'print(\"hi there\")'\nhi there\n\nIt passes the word on the right to the standard input of the command on the left.\nwhereas << denotes a here document, e.g.\ncommand <<MultiLineDoc \nStandard Input\nThat\n Streches many\nLines and preserves \n indentation and \n\nlinebreaks\n\nwhich is useful for passing many arguments to a command, \ne.g. passing text to a program and preserving its indentation.\nThe beginning and ending _MultiLineDoc_ delimiter can be named any way wanted, \nit can be considered the name of the document. \nImportant is that it repeats identically at \nboth beginning and end and nowhere else in the \ndocument, everything between that delimiter is passed.\nMultiLineDoc\n\n< is used for passing the contents of a file, e.g. command < filename.txt\nAs for your example with <<< :\nYou could do the same with | but that's only OK if all your variables are defined in what you are passing. If you do have other variables that you have defined in your environment and which you wish to cross reference you would use a here-string as in your example, that lets you reference other variables within the content you are passing.\nPlease see: https://en.wikipedia.org/wiki/Here_document\nhttps://linuxhint.com/bash-heredoc-tutorial/\n", "In Bash, zsh (and some other shells) <<< is the here string operator. The code you’ve posted is roughly equivalent to\necho \"$PYCODE\" | python3\n\n" ]

[ 1, 0 ]

[]

[]

[ "linux", "python", "python_3.x", "shell" ]

[ "You can use two different div class and apply different styles to each div particular div. eg.\n<div class=one></div>\n<div class=two></div>\n\nand then in the css file\n.one {\n background-color: yellow;\n}\n\n.two {\n background-color: blue;\n}\n\n" ]

[ 1 ]

[]

[]

[ "css", "html", "python" ]

[ "You can try to use df.reset_index() before using boolean indexing with idxmin:\ndf = df.reset_index().loc[df['Fitness Value'].idxmin()]\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "genetic_algorithm", "genetic_programming", "pandas", "python" ]

[ "I had a similar question when I started using it. Here's my experience:\n\nFixtures can be set to autouse=True, i.e., trigger automatically that may not be possible with an inline call. This is useful in some cases.\nFixtures add readability, at least for me. Looking at the signature of the test, one can figure out what initialisations are a pre-requisite for a given test. In that sense, it also help keep the test and it's initialisation isolated.\n\n", "\nWhat's advantage of a (default) function-scope fixture without\n teardown code? Why not just call the function at the beginning of the\n test?\n\nSaving vertical space.\nConsider something like this, where you have more than one fixture per test:\nimport pytest\n\n\[email protected]\ndef value1():\n return 1\n\[email protected]\ndef value2():\n return 2\n\[email protected]\ndef value3():\n return 3\n\n\ndef test_values(value1, value2, value3):\n assert value1 == 1\n assert value2 == 2\n assert value3 == 3\n\nIf we were to do this your way:\ndef test_values():\n v1 = value1()\n v2 = value2()\n v3 = value3()\n\n assert v1 == 1\n assert v2 == 2\n assert v3 == 3\n\nThat's three extra lines of code. Not a big deal, but then what if you had 10 tests that needed value1, value2 and value3? Now you have 30 extra lines of vertical space for basically no reason. \nObviously, both of our examples are overly simplified (I could just have done the call and assert inline), but I think it's straightforward to see how this could have an impact with real code.\n", "I believe that one of the most important advantage of function-scoped fixtures is consistency. It is much more readable and logical if all your fixtures (no matter of they scope or tear-down code) are used in exactly same way.\nAlso if in some point in future you'll decided to change the scope of this fixture or to add some tear-down code to it then you would not need to change any test cases, only the code of the fixture.\n", "Look at this answer:\nhttps://stackoverflow.com/a/67636207/11277611\nI'm not so liking fixtures, but one advatage - it's reusability.\nIf you have some function with long computtion - you can define it once in fixture and reuse it every time in the tests.\nSurely you can do this with a regular function, IMO, it's a kind of test.\n" ]

[ 5, 1, 1, 1 ]

[]

[]

[ "pytest", "python" ]

[ "If you just want to avoid long line or line continuation character, I usually choose to use parentheses:\nf_string = (f\"The result of {a} + {b} is \"\n f\"{some_long_complicated_function(a, b)}\")\n\n" ]

[ 0 ]

[]

[]

[ "f_string", "pylint", "python", "string.format" ]

[ "the key is the configuration which can either be modified using the config.ini file or the config object.\non my windows system the config file is in C:\\users<username>.kivy\\config.ini\nand you may have to change Windows Explorer to show hidden items if you want to see the .kivy directory.\ninside that configuration file, the borderless property being set to 1 could cause this behavior so set this to 0. The curios thing is that by default this should have been 0 so I don't know why it would have changed.\n[graphics]\nborderless = 0\n\nand you may also be interested in these properties\nwidth = 1200\nheight = 600\nfullscreen = 0\n\n" ]

[ 0 ]

[ "from kivy import Config\nConfig.set(\"graphics\", \"fullscreen\", \"0\")\n\nRTFM -> go here\n" ]

[ -2 ]

[ "kivy", "pycharm", "python" ]

[ "It is because you can't make a list lower or upper case. You have to make the elements in the list lower or upper case.\nFor example:\nres_lower = [item.lower() for item in res]\nprint(res_lower)\n\nOr in one line:\nprint([item.lower() for item in res])\n\nInstead of:\nprint(res.lower())\n\nIf you want to print each element of the list individually, use a for loop:\nfor item in res:\n print(item.lower())\n\nGood Luck!\n" ]

[ 1 ]

[]

[]

[ "anaconda", "jupyter_notebook", "list", "python", "python_3.x" ]

[ "You can't directly with resample, you should instead groupby.agg after temporarily resetting the index:\n(df.reset_index()\n .groupby(df.index.to_period('M'))\n .agg({'DATE': 'last', 'value': 'last'})\n .set_index('DATE')\n)\n\nOutput:\n value\nDATE \n2005-12-29 1162.635\n2006-01-04 1211.699\n\n", "Example\ndata = {'2005-12-27': 1161.707,\n '2005-12-28': 1164.143,\n '2005-12-29': 1162.635,\n '2006-01-02': 1171.709,\n '2006-01-03': 1184.69,\n '2006-01-04': 1211.699}\ns = pd.Series(data)\ns.index = pd.to_datetime(s.index)\n\noutput(s):\n2005-12-27 1161.707\n2005-12-28 1164.143\n2005-12-29 1162.635\n2006-01-02 1171.709\n2006-01-03 1184.690\n2006-01-04 1211.699\ndtype: float64\n\nCode\ns.groupby(s.index.to_period('M')).tail(1)\n\noutput:\n2005-12-29 1162.635\n2006-01-04 1211.699\ndtype: float64\n\nIf s is not sorted by time order, sort index\n" ]

[ 2, 2 ]

[]

[]

[ "pandas", "python", "resample" ]

[ "Found the answer thanks to nick in the comments.\nAn example of usage of PyRun_String: https://schneide.blog/2011/10/10/embedding-python-into-cpp/, and extracting list variables from python script https://docs.python.org/3/c-api/list.html\nThe final frankenstein:\nPyObject *main = PyImport_AddModule(\"__main__\");\nPyObject *globalDictionary = PyModule_GetDict(main);\nPyObject *localDictionary = PyDict_New();\nPyRun_String(\"a=[0, 1, 2, 3, 4, 5]\", Py_file_input, globalDictionary, localDictionary);\n \nPyObject *result = PyDict_GetItemString(localDictionary, \"a\");\n\ndouble a[6];\nfor (int i = 0; i < PyList_Size(result); i++) {\n a[i] = PyFloat_AsDouble(PyList_GetItem(result, i));\n}\n\n" ]

[ 1 ]

[]

[]

[ "c++", "python", "python_3.11", "python_embedding" ]

[ "To avoid quadratic complexity, better first create a real dictionary (yours are lists of dictionaries), then update:\ntmp = {d['name']: d for d in dict2}\n\nfor d in dict1:\n d.update(tmp.get(d['name'], {}))\n\nprint(dict1)\n\nOutput:\n[{'id': 1.0, 'name': 'aa', 'dtype': 'StringType'},\n {'id': 4.0, 'name': 'bb', 'dtype': 'StringType'},\n {'id': 2.0, 'name': 'cc', 'dtype': 'StringType'}]\n\nIntermediate tmp:\n{'aa': {'name': 'aa', 'dtype': 'StringType'},\n 'bb': {'name': 'bb', 'dtype': 'StringType'},\n 'xx': {'name': 'xx', 'dtype': 'StringType'},\n 'cc': {'name': 'cc', 'dtype': 'StringType'}}\n\nIf you want a copy (rather that modifying dict1 in place):\ntmp = {d['name']: d for d in dict2}\nmerged_dict = [d|tmp.get(d['name'], {}) for d in dict1]\n\n", "You can use pandas and try following:\nimport pandas as pd\ndf1 = pd.DataFrame(dict1)\ndf2 = pd.DataFrame(dict2)\nres = df1.merge(df2, on=['name'])\n\nThe output:\n id name dtype\n0 1.0 aa StringType\n1 4.0 bb StringType\n2 2.0 cc StringType\n\nIf you need a dictionary, you can convert merged result pd.DataFrame() to dict.\nres.to_dict('records')\n\nFinal output is:\n[\n {'id': 1.0, 'name': 'aa', 'dtype': 'StringType'}, \n {'id': 4.0, 'name': 'bb', 'dtype': 'StringType'}, \n {'id': 2.0, 'name': 'cc', 'dtype': 'StringType'}\n]\n\n" ]

[ 1, 0 ]

[]

[]

[ "dictionary", "python" ]

[ "You can achieve in a simple way, a grouped last():\ndf.groupby('Name',as_index=False).last()\n\nprints:\n Name ParamA ParamB ParamC ParamD\n0 A 1.0 NaN 3.0 6.0\n\nNo need for apply.\n" ]

[ 0 ]

[]

[]

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

[ "You can try installing nbextension for suggestions in jupyter notebook.\nfor more info plz Click here\n" ]

[ 0 ]

[]

[]

[ "autocomplete", "jedi", "jupyter", "python" ]

[ "You are almost there with ColumnTransformer and OneHotEncoder, refer to examples here (https://www.geeksforgeeks.org/prediction-using-columntransformer-onehotencoder-and-pipeline/) as well as their respective docs to get it working. Also when you say it doesn't work, please share what the error was.\nUse OneHotEncoder for nominal cat features, and OrdinalEncoder for ordinal cat features.\nThere is a somewhat easier option of using pandas.get_dummies() (but typically is only used in notebooks and EDAs, rather than in a production environment) which is simpler syntactically.\nThe lines of code you used for LabelEncoder initially, you can also just apply OneHotEncoder the same way, without having to use ColumnTransformer. So that could work for you as well.\n" ]

[ 0 ]

[]

[]

[ "column_tansformer", "machine_learning", "pandas", "python", "scikit_learn" ]

[ "You should use chardet which articulates encoding readings. Then add the read_Csv part in for loop.\nimport chardet\nfor name in glob.glob('file_directory/*'):\n with open(name, 'r') as f:\n heading_rows = [next(f) for _ in range(5)]\n #print(re.findall(pattern = ' \\w+ ', string = heading_rows[0])[0])\n\n# to escape errors\n try:\n city = re.findall(pattern = ' \\w+ ', string = heading_rows[0])[0].strip()\n except IndexError:\n pass\n\n numbers_list = [re.findall(pattern='\\d+', string=row) for row in heading_rows if 'cm' and 'kg' in row.lower()][0]\n\n height, weight, age = [int(numbers_list[i]) for i in range(3)]\n\n with open(name, 'rb') as file:\n encodings = chardet.detect(file.read())[\"encoding\"]\n df = pd.read_csv(name,sep='\\s+|;|,', engine='python', encoding=encodings, skiprows=1,comment='cm', index_col=None, names=list('ABCDEF'))\n\n\n df.to_csv(name+'.csv',index=False)\n\n", "Try:\nimport re\nimport pandas as pd\n\n\ntext = \"\"\"\\\nPerson:?,?;F dob. ? MT: ? Z:C NewYork Mon.:S St.?\n\n144 cm/35 Kg/5 YearsOld\n\n\n45,34,22,26,0\n78,74,82,11,0\n\"\"\"\n\npat = re.compile(\n r\"(?sim)Z:C (\\S+).*(\\d+)\\s*cm\\D+(\\d+)\\s*kg\\D+(\\d+).*?((?:^[\\d,]+\\n)+)\"\n)\n\nm = pat.search(text)\nif m:\n city, height, weight, age, data = m.groups()\n all_data = []\n for row in data.splitlines():\n all_data.append(\n list(map(int, row.split(\",\"))) + [height, weight, age, city]\n )\n\ndf = pd.DataFrame(\n all_data,\n columns=[\"A\", \"B\", \"C\", \"D\", \"E\", \"height\", \"weight\", \"age\", \"city\"],\n)\nprint(df)\n\nPrints:\n A B C D E height weight age city\n0 45 34 22 26 0 4 35 5 NewYork\n1 78 74 82 11 0 4 35 5 NewYork\n\n" ]

[ 1, 0 ]

[]

[]

[ "pandas", "python", "python_re" ]

[ "You can use json.dump arguments like json.dump(data, indent=2).\nThe second (\"ideal\") format is not a valid JSON, so it's (AFAIK) achievable only using some other string processing methods (if it's a typo, the JSON format might be valid, however it's not possible to change format using json.dump arguments and it will require few lines of Python code in order to change the json structure).\nEDIT: Also there is more possible using json.dump options, some sorting etc. See docs here.\n", "You can achieve this using below line of code\njson.dump(data, f,indent=4,sort_keys=True)\n", "you already have a few answers but here is the full python code\nimport json\n\njson_string='SOME_JSON_HERE'\n\ntry:\n parsed_json=json.loads(json_string)\n out=(json.dumps(parsed_json, indent=4,sort_keys=False))\n print(out)\nexcept Exception as e:\n print(repr(e))\n\n" ]

[ 2, 0, 0 ]

[]

[]

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

[ "You can reverse your y-axis:\nplt.gca().invert_yaxis()\n\n" ]

[ 1 ]

[]

[]

[ "machine_learning", "matplotlib", "pandas", "plot", "python" ]

[ "You are looking for the explode Pandas method\ndf.explode(['col3', 'col4', 'col5']) should do the trick\n" ]

[ 1 ]

[]

[]

[ "dataframe", "flatten", "functional_programming", "list", "python" ]

[ "numpy.flip() should do the job\narray = numpy.flip(array)\n\nreturns\n[[ 0.95] [-0.85]]\n\n", "You can do this by using flip() function.\nimport numpy as np \nl=[12,45,10,78,100]\nm=np.flip(l)\nprint(m)\n\nAlternatively, you can also go this approach.\nm=l[::-1]\nprint(m)\n\nYou can find something informative here.\nHappy Coding!\n" ]

[ 0, 0 ]

[]

[]

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

[ "It worked using code like this (I'm using python 3):\nfrom warnings import filterwarnings\nfilterwarnings(\"ignore\")\n\n", "This is apparently fixed in https://github.com/secdev/scapy/pull/3645 and will be included in Scapy 2.5.0+ (use the github version in the meantime)\n", "A more general solution (if you only want to ignore the CryptographyDeprecationWarning) but keep the rest of the warnings:\nimport warnings\nfrom cryptography.utils import CryptographyDeprecationWarning\nwarnings.filterwarnings(\"ignore\", category=CryptographyDeprecationWarning)\n\nfrom scapy.all import *\n\n" ]

[ 2, 2, 1 ]

[]

[]

[ "python", "scapy", "warnings" ]

[ "This problem is only specific to VS Code's debugger and it is happening for wrong path in PYTHONPATH variable. Hence, this problem will not happen if you run it from shell.\nIn your case, you need to add a new attribute named env in the launch.json configuration, which will add environment variable. In there you need to update the PYTHONPATH, because manage.py is not in the root folder of the project:\n\"configurations\": [\n {\"env\": {\n \"PYTHONPATH\": \"${workspaceRoot}\\\\appname\"\n },\n \"name\": \"Python: Django\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"program\": \"${workspaceFolder}\\\\appname\\\\manage.py\",\n \"args\": [\n \"runserver_plus\"\n ],\n \"django\": true,\n \"justMyCode\": false\n }\n ]\n}\n\n" ]

[ 3 ]

[ "try this\n{\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"Python: Django\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"python\": \"${workspaceFolder}/venv/Scripts/python.exe\",\n \"program\": \"${workspaceFolder}/manage.py\",\n \"args\": [\n \"runserver\",\n ],\n \"justMyCode\": false,\n \"django\": true\n }\n ]\n}\n\n", "manage.py is located in the base directory of the project.\nSo\n \"program\": \"${workspaceFolder}\\\\manage.py\",\n\n" ]

[ -1, -1 ]

[ "django", "python", "visual_studio_code", "vscode_debugger", "werkzeug" ]

[ "Use DataFrame.rename with dictionary:\ndf2 = df2.rename(columns=dict(zip(df1.word, df1.merged)))\n\n" ]

[ 1 ]

[]

[]

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

[ "First, we don't need StramingHTTPResponse for sending image data at all ...\nFor this, first, ensure you have a Django version with 3.x and Python 3.7+.\nThen, install django-channels third party package.\nConfigure the ASGI application as follows:\nimport os\nfrom channels.auth import AuthMiddlewareStack\nfrom channels.routing import ProtocolTypeRouter, URLRouter\nfrom django.core.asgi import get_asgi_application\nimport .routing\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'myproject.settings')\n\napplication = ProtocolTypeRouter({\n \"http\": get_asgi_application(),\n \"websocket\": AuthMiddlewareStack(\n URLRouter(\n app.routing.websocket_urlpatterns\n )\n )\n})\n\nThen You need to set ASGI_APPLICATION constant in the settings.py file:\nASGI_APPLICATION = \"myproject.asgi.application\"\n\nAfter that, just create an async WebSocket consumer in the consumers.py file present in the application :\nimport json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\n\n\nclass PairingChat(AsyncWebsocketConsumer):\n\n async def connect(self):\n self.room_name = self.scope['url_route']['kwargs']['room_name']\n self.room_group_name = 'chat_%s' % self.room_name\n\n await self.channel_layer.group_add(\n self.room_group_name,\n self.channel_name\n )\n \n await self.accept()\n\n async def disconnect(self):\n\n await self.channel_layer.group_discard(\n self.room_group_name,\n self.channel_name\n )\n\n\n # Asyncwebsocket consumer can send any type of data ...\n\n async def receive(self, text_data):\n data_json = json.loads(your_data)\n message = data_json['message']\n\n await self.channel_layer.group_send(\n self.room_group_name,\n {\n 'type': '# send your data from here ...',\n 'message': message,\n 'user': self.scope['session']['name']\n }\n )\n\n\n async def chat_message(self, event):\n message = event['message']\n\n await self.send(data=json.dumps({\n 'user': event['user'],\n 'message': message,\n }))\n\nCreate a route for asyncwebsocket consumers as well ...\nfrom django.urls import re_path\nfrom . import consumers\n\nwebsocket_urlpatterns = [\n re_path(r'ws/chat1/(?P<room_name>\\w+)/$', consumers.PairingChat.as_asgi()),\n]\n\nThen, just create a WebSocket client in javascript ... and you are good to go ...\nLink for JS Websocket Create : javascript-websocket\n" ]

[ 0 ]

[]

[]

[ "django", "django_channels", "opencv", "python" ]

[ "I finally find the solution.\nI think this is because the \"build\" and \"dist\" directories that are created in the current working directory by the \"toolchain build\" process get recursively included in the project created by \"toolchain create\", which in turn confuses rsync when it's copying files around.\nSo, for me, the key was to do the \"toolchain build\" outside my application source directory tree.\nThis solved it: https://github.com/kivy/kivy-ios/issues/513\n" ]

[ 0 ]

[]

[]

[ "kivy", "python", "xcode" ]

[ "#input\nin_list = [['16', 'jun', 'jun', '18'],\n ['jun', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['sep', '17', 'mar', '18']]\n\n#output\nnp.array(in_list)[np.sort(np.unique(np.sort(in_list), axis=0, return_index=True)[1])].tolist()\n\nExplanation:\n\nnp.sort the rows of in_list in order to find the rows having the same information;\ntake the index from np.unique to find the indices of unique (sorted) rows;\nnp.sort the indices to hold the starting order in in_list;\nslice the list (after converting it in np.array) with the sorted indices to only retain the unique rows;\nconverting the result in a list (.tolist())\n\n", "Pure pythonic solution:\nres = []\nfor row in your_list:\n sorted_row = sorted(row)\n if sorted_row not in [sorted(x) for x in res]:\n res.append(row)\n\nprint(res)\n\n>>> [['16', 'jun', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['sep', '17', 'mar', '18']]\n\n", "Here is the fixed code:\nlp = [['16', 'jun', 'jun', '18'],\n ['jun', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['sep', '17', 'mar', '18']]\n\n\nprint('LP:',lp, \"\\n\")\nl=[]\ncount = 0\nfor i in lp:\n for j in lp[count+1:]:\n if set(j) != set(i):\n if i not in l:\n l.append(i)\n count += 1\nprint(l)\n\nI would advise against using this strategy as your algorithm is O(n^2) because it needs to run through every element in your list for every element in your list aka x*x times. I would use this code:\ndef remove_values_from_list(the_list, val):\n \n return [value for value in the_list if sorted(value) != sorted(val)]\n\nlp = [['16', 'jun', 'jun', '18'],\n ['jun', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['aug', '16', 'jun', '18'],\n ['sep', '17', 'mar', '18']]\n\n\nprint('LP:',lp, \"\\n\")\nl=[]\ncount = 0\noof = 5\nresult_list = []\nwhile True:\n \n if len(lp) == 1:\n break\n\n element = lp[1]\n \n lp = [element] + remove_values_from_list(lp[count+1:], element)\n result_list.append(element)\n \n\n \nprint(\"Result\")\nprint(result_list)\n\nThis code is a lot more efficient because it removes every instance of the current element in the rest of the list so it does not need to compare those copies anymore in future cases. Also that code also works if there are duplicate elements in your lists. When using set() you can not have duplicate elements because of the way sets work internally in python.\nHere is a simple script to test the speed increase: (it also checks the validity of the solutions by doing a manual O(n^2) check, but that is of course not part of the main program)\nimport random\nimport colorama\nfrom colorama import Fore, Style\nimport time\n\n\ndef generate_list_of_1000_integers(n_min, n_max, len):\n return [random.randint(n_min, n_max) for _ in range(len)]\n\ndef remove_values_from_list(the_list, val):\n #return [value for value in the_list if set(value) != set(val)]\n return [value for value in the_list if sorted(value) != sorted(val)]\ndef solution(lp):\n l=[]\n count = 0\n for i in lp:\n for j in lp[count+1:]:\n if set(j) != set(i):\n if i not in l:\n l.append(i)\n count += 1\n return l\n\ndef anothersolution(lp):\n \n count = 0\n result_list = []\n while True:\n \n if len(lp) == 1:\n break\n\n \n element = lp[1]\n \n lp = [element] + remove_values_from_list(lp[count+1:], element)\n result_list.append(element)\n \n return result_list\n\nif __name__==\"__main__\":\n where_to_pick_from = [generate_list_of_1000_integers(0,100,10) for _ in range(100)]\n\n complete_list = []\n\n how_many = int(2000)\n\n for _ in range(how_many):\n list_thing = random.choice(where_to_pick_from)\n random.shuffle(list_thing)\n complete_list.append(list_thing)\n\n\n print(complete_list)\n start = time.time()\n sol = solution(complete_list)\n print(\"First solution took \"+str(time.time() - start)+str(\" seconds.\"))\n start = time.time()\n sol2 = anothersolution(complete_list)\n print(\"Second solution took \"+str(time.time() - start)+str(\" seconds.\"))\n\n\n \n\n\n for j in range(len(sol)):\n for i in range(len(sol)):\n if set(sol[i]) == set(sol[j]) and j != i:\n print(Fore.RED+\"[-] Solution 1 invalid.\")\n exit(-1)\n for j in range(len(sol2)):\n for i in range(len(sol2)):\n if set(sol2[i]) == set(sol2[j]) and j != i:\n print(Fore.RED+\"[-] Solution 2 invalid.\")\n exit(-1)\n\n print(Fore.BLUE+str(\"[+] Solutions valid!\"))\n\nEdit: I am also a beginner programmer myself so constructive criticism is welcome.\n" ]

[ 0, 0, 0 ]

[]

[]

[ "arrays", "list", "matrix", "multidimensional_array", "python" ]

[ "if you are looping through n then the iterable parameter should be n and not row... basically one or the other.\nso change row to n (or n to row) if that is what you intended to do.\nfor n in range(5):\n self.ui.table_Sum.setItem(n, 0, QTableWidget(Sum_main[n][0])) \n\n\n" ]

[ 0 ]

[]

[]

[ "python", "qt" ]

[ "The access token is what tells Smartsheet what user (account) to use to execute the API calls. Any time you're writing code that calls an API (which requires authentication), that code needs to specify the access token corresponding to the user (account) that should be used to run the API calls.\nFirst, if you haven't already done so, you'll need to login to Smartsheet (https://app.smartsheet.com) using the login credentials of the account you want to use for API calls and generate an Access Token (i.e., API Key). You can find instructions for how to do this here: https://help.smartsheet.com/articles/2482389-generate-API-key.\nNext, once you have generated an access token via the Smartsheet app, you need to store it in the SMARTSHEET_ACCESS_TOKEN environment variable in your code. To do this:\n\nAdd this import statement to the top of your code:\n\nimport os\n\n\nAdd the following code to set the SMARTSHEET_ACCESS_TOKEN environment variable to your access token. (In the following example code, replace abc123 with your access token value.) NOTE: This line of code needs to be place before the line of code that attempts to read the value of that environment variable (smart = smartsheet.Smartsheet()).\n\n# Specify access token\nos.environ['SMARTSHEET_ACCESS_TOKEN'] = 'abc123'\n\n" ]

[ 0 ]

[]

[]

[ "api", "environment_variables", "python", "smartsheet_api", "token" ]

[ "That is indeed the way to pull a list of videos without downloading them, the issue with your query is that the URL you are using requires you to be signed in (and youtube-dl doesn't have access to your credentials)\nBoth this\n youtube-dl https://www.youtube.com/@Wondrium/videos -e\n\nand this\n youtube-dl https://www.youtube.com/@Wondrium/videos --get-title\n\nworks.\nFor example:\n\n" ]

[ 1 ]

[]

[]

[ "python", "youtube_dl" ]

[ "Your issue has nothing to do with streamlit but on scaler. When you instantiate the MinMaxScaler() with:\nscaler = MinMaxScaler()\n\nUse this scaler to fit the training data. When you have a test sample, use again this scaler to transform it. But do not fit.\nHere is a demo.\nCode\ndef demo():\n train_data = [[5.0, 16.0, 7.0, 4.0, 1.0, 0.0, 0.0, 0.0],\n [8.0, 7.0, 8.0, 1.0, 1.0, 0.0, 0.0, 0.0],\n [5.0, 9.0, 9.0, 0.0, 1.0, 0.0, 1.0, 1.0]]\n\n test_data = [[25.0, 12.0, 15.0, 0.0, 1.0, 0.0, 0.0, 0.0]] # input_submit.values\n\n # Scale, fit and transform the train data.\n min_max_scaler = MinMaxScaler()\n train_data_minmax = min_max_scaler.fit_transform(train_data)\n\n # Save the scaler to disk. import pickle\n # scaler_fn = 'scaler_project.pkl'\n # with open(scaler_fn, 'wb') as handle:\n # pickle.dump(min_max_scaler, handle)\n\n # Scale new data using scaler from distk.\n # with open(scaler_fn, 'rb') as handle:\n # loaded_scaler = pickle.load(handle)\n # test_data_loaded_minmax = loaded_scaler.transform(test_data)\n\n # Scale the single test or input data.\n test_data_minmax = min_max_scaler.transform(test_data)\n\n print(f'train_data:\\n{train_data}')\n print(f'train_data_minmax:\\n{train_data_minmax}\\n')\n\n print(f'test_data:\\n{test_data}')\n print(f'test_data_minmax:\\n{test_data_minmax}')\n\ndemo()\n\nOutput\ntrain_data:\n[[5.0, 16.0, 7.0, 4.0, 1.0, 0.0, 0.0, 0.0], [8.0, 7.0, 8.0, 1.0, 1.0, 0.0, 0.0, 0.0], [5.0, 9.0, 9.0, 0.0, 1.0, 0.0, 1.0, 1.0]]\ntrain_data_minmax:\n[[0. 1. 0. 1. 0. 0.\n 0. 0. ]\n [1. 0. 0.5 0.25 0. 0.\n 0. 0. ]\n [0. 0.22222222 1. 0. 0. 0.\n 1. 1. ]]\n\ntest_data:\n[[25.0, 12.0, 15.0, 0.0, 1.0, 0.0, 0.0, 0.0]]\ntest_data_minmax:\n[[6.66666667 0.55555556 4. 0. 0. 0.\n 0. 0. ]]\n\nNot all scaled input data values are zero.\nReference\nhttps://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range\n" ]

[ 0 ]

[]

[]

[ "python", "scaling", "scikit_learn", "streamlit" ]

[]

[]

[ "Sorry I realised to late it's a dataframe\nIn this case I would just solve it like this\ndataset = ['0000sh--_dsd', '0000sd---_dsd', '000ad-_512']\ndataset = [line.replace(\"-\", \"\") for line in dataset]\ntest1 = pd.DataFrame(dataset)\n\nIgnore my first answer below\n\nStrings in Python are immutable so you need to use this\ntest1 = test1.replace('-', '')\n\n" ]

[ -1 ]

[ "dataframe", "pandas", "python", "regex", "replace" ]

[ "What you are using is known as an \"output argument\" or \"output parameter\".\nYou alter the original object that you send to the foo.add_key method.\nAdd some print(id(D)) statements, to see that the object is indeed the same everywhere. Assignment in this case would be a self assignment as a = a.\nHere is a good discussion / explanation why there is a controversy on this technique and why it is not considered \"Clean Code\".\n" ]

[ 1 ]

[]

[]

[ "dictionary", "import", "methods", "python", "python_import" ]

[ "Use Series.sub with mapping by Series.map by another DataFrame:\n df1['admissiontime'] = pd.to_datetime(df1['admissiontime'])\n df2['datetime'] = pd.to_datetime(df2['datetime'])\n\ndf2['diff'] = df2['datetime'].sub(df2['id'].map(df1.set_index('id')['admissiontime']))\n\n" ]

[ 1 ]

[]

[]

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

[]

[]

[ "change the VARIABLE with the number of sample that you want (batch_size).\nIf you want the whole data you can make batch_size=None\ndataset = tf.keras.utils.timeseries_dataset_from_array(\ninput_data, targets, batch_size=VARIABLE, sequence_length=10000)\n\n" ]

[ -1 ]

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

[ "Here is a 1-line solution using nothing more than the standard datetime library:\n(dt.replace(day=1) + datetime.timedelta(days=32)).replace(day=1)\n\nExamples:\n>>> dt = datetime.datetime(2016, 2, 29)\n>>> print((dt.replace(day=1) + datetime.timedelta(days=32)).replace(day=1))\n2016-03-01 00:00:00\n\n>>> dt = datetime.datetime(2019, 12, 31)\n>>> print((dt.replace(day=1) + datetime.timedelta(days=32)).replace(day=1))\n2020-01-01 00:00:00\n\n>>> dt = datetime.datetime(2019, 12, 1)\n>>> print((dt.replace(day=1) + datetime.timedelta(days=32)).replace(day=1))\n2020-01-01 00:00:00\n\n", "Using dateutil you can do it the most literally possible:\nimport datetime\nfrom dateutil import relativedelta\ntoday = datetime.date.today()\n\nnext_month = today + relativedelta.relativedelta(months=1, day=1)\n\nIn English: add 1 month(s) to the today's date and set the day (of the month) to 1. Note the usage of singular and plural forms of day(s) and month(s). Singular sets the attribute to a value, plural adds the number of periods.\nYou can store this relativedelta.relativedelta object to a variable and the pass it around. Other answers involve more programming logic.\nEDIT You can do it with the standard datetime library as well, but it's not so beautiful:\nnext_month = (today.replace(day=1) + datetime.timedelta(days=32)).replace(day=1)\n\nsets the date to the 1st of the current month, adds 32 days (or any number between 31 and 59 which guarantees to jump into the next month) and then sets the date to the 1st of that month.\n", "you can use calendar to get the number of days in a given month, then add timedelta(days=...), like this:\nfrom datetime import date, timedelta\nfrom calendar import monthrange\n\ndays_in_month = lambda dt: monthrange(dt.year, dt.month)[1]\ntoday = date.today()\nfirst_day = today.replace(day=1) + timedelta(days_in_month(today))\nprint(first_day)\n\nif you're fine with external deps, you can use dateutil (which I love...)\nfrom datetime import date\nfrom dateutil.relativedelta import relativedelta\n\ntoday = date.today()\nfirst_day = today.replace(day=1) + relativedelta(months=1)\nprint(first_day)\n\n", "Extract the year and month, add 1 and form a new date using the year, month and day=1:\nfrom datetime import date\n\nnow = date(2020,12,18)\ny,m = divmod(now.year*12+now.month,12)\nnextMonth = date(y,m+1,1)\n\nprint(now,nextMonth)\n# 2020-12-18 2021-01-01\n\n", "Your way looks good yet I would have done it this way:\nimport datetime\nfrom dateutil import relativedelta\n\ndt = datetime.datetime(year=1998,\n month=12,\n day=12)\n\nnextmonth = dt + relativedelta.relativedelta(months=1)\nnextmonth.replace(day=1)\nprint(nextmonth)\n\n", "Using only python standard libraries:\nimport datetime\ntoday = datetime.date.today()\nfirst_of_next_month = return date.replace(\n day=1,\n month=date.month % 12 + 1,\n year=date.year + (date.month // 12)\n)\n\ncould be generalized to...\ndef get_first_of_month(date, month_offset=0):\n # zero based indexing of month to make math work\n month_count = date.month - 1 + month_offset\n return date.replace(\n day=1, month=month_count % 12 + 1, year=date.year + (month_count // 12)\n )\n\nfirst_of_next_month = get_first_of_month(today, 1)\n\nOther solutions that don't require 3rd party libraries include:\n\nToby Petty's answer is another good option.\nIf the exact timedelta is helpful to you,\na slight modification on Adam.Er8's answer might be convenient:\nimport calendar, datetime\n\ntoday = datetime.date.today()\ntime_until_next_month = datetime.timedelta(\n calendar.monthrange(today.year, today.month)[1] - today.day + 1\n)\nfirst_of_next_month = today + time_until_next_month\n\n\n\n", "With Zope's DateTime library a very simple solution is possible\nfrom DateTime.DateTime import DateTime\n\ndate = DateTime() # today\n\nwhile date.day() != 1:\n date += 1\n\nprint(date)\n\n", "I see so many wonderful solutions to this problem I personally was looking for a solution for getting the first and last day of the previous month when I stmbled on this question.\nBut here is a solution I like to think is quite simple and elegant:\ndate = datetime.datetime.now().date()\nsame_time_next_month = date + datetime.timedelta(days = date.day)\nfirst_day_of_next_month_from_date = same_time_next_month - datetime.timedelta(days = same_time_next_month.day - 1)\n\nHere we simply add the day of the target date to the date to get the same time of the next month, and then remove the number of days elapsed from the new date gotten.\n", "Try this, for starting day of each month, change MonthEnd(1) to MonthBegin(1):\nimport pandas as pd\nfrom pandas.tseries.offsets import MonthBegin, MonthEnd\n\ndate_list = (pd.date_range('2021-01-01', '2022-01-31', \n freq='MS') + MonthEnd(1)).strftime('%Y-%m-%d').tolist()\ndate_list\n\nOut:\n['2021-01-31',\n '2021-02-28',\n '2021-03-31',\n '2021-04-30',\n '2021-05-31',\n '2021-06-30',\n '2021-07-31',\n '2021-08-31',\n '2021-09-30',\n '2021-10-31',\n '2021-11-30',\n '2021-12-31',\n '2022-01-31']\n\n", "With python-dateutil:\nfrom datetime import date\nfrom dateutil.relativedelta import relativedelta\n\nlast day of current month:\ndate.today() + relativedelta(day=31)\n\nfirst day of next month:\ndate.today() + relativedelta(day=31) + relativedelta(days=1)\n\n" ]

[ 64, 12, 11, 2, 0, 0, 0, 0, 0, 0 ]

[]

[]

[ "python", "python_datetime" ]

[ "Since each review's rating categories start with either \"Type of Traveller\" or \"Aircraft\" followed by \"Type of Traveller\", you could split them up into a list of dictionaries with\ncr = [(k, v) for k, v in zip(category, rating)]\nsi = [i for i, (k, v) in enumerate(cr) if k == 'Type Of Traveller']\nsi = [(i - 1) if i != 0 and cr[i - 1][0] == 'Aircraft' else i for i in si]\nsplitCr = [dict(cr[start:end]) for start, end in zip(si, (si[1:] + [len(cr)]))]\n\n\n\n\nHowever, it would be better to build a single list of dictionaries as we scrape [rather than to try to zip and split lists that are not guaranteed to have consistent lengths or contents]\nbase_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\npages = 3 # 5 # 10\npage_size = 5 # 1 # 100\n\nrevList = []\navgSelRef = {\n 'rating10': '.rating-10 span[itemprop=\"ratingValue\"]',\n 'header': 'div.info:has(h1[itemprop=\"name\"])',\n 'subheader': '.review-count',\n 'reviewBody': '.skytrax-rating-mob img.skytrax-rating[alt]'\n}\nrbSel = '.body[id^=\"anchor\"]'\nrevSelRef = {\n 'rating10': '.rating-10 span[itemprop=\"ratingValue\"]',\n 'header': f'{rbSel} h2.text_header',\n 'subheader': f'{rbSel} h3.text_sub_header',\n 'reviewBody': f'{rbSel} div[itemprop=\"reviewBody\"]'\n} \n\navgAdded = False\nfor i in range(1, pages + 1): \n print(\"\", end=f\"Scraping page {i} of {pages} \")\n\n # Create URL to collect links from paginated data\n url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n\n # Collect HTML data from this page\n response = requests.get(url)\n if response.status_code != 200: \n print(f' -- !ERROR: \"{response.raise_for_status()}\"\" getting {url}')\n continue\n content = response.content\n parsed_content = BeautifulSoup(content, 'html.parser')\n\n avSoups = parsed_content.select('div.review-info')\n rvSoups = parsed_content.select(f'article[itemprop=\"review\"]:has({rbSel})')\n if avSoups and not avgAdded: rvSoups += avSoups\n for r in rvSoups:\n isAvg = r.name == 'div'\n if isAvg:\n rDets = {'reviewId': '[Average]'} \n selRef = avgSelRef.items()\n avgAdded = True\n else:\n revId = r.select_one(rbSel).get('id').replace('anchor', '', 1)\n selRef = revSelRef.items()\n rDets = {'reviewId': revId} \n \n for k, s in selRef:\n rdt = r.select_one(s) \n if rdt is None: continue\n if 'img' in s and s.endswith('[alt]'):\n rDets[k] = rdt.get('alt') \n else:\n rDets[k] = ' '.join(w for w in rdt.get_text(' ').split() if w)\n\n rhSel = 'td.review-rating-header'\n rRows = r.select(f'tr:has({rhSel} + td:is(.stars, .review-value))')\n for rr in rRows: \n k = rr.select_one(rhSel).get_text(' ').strip()\n k = k.replace(' For ', ' for ').replace(' & ', ' + ') # bit of cleanup\n if k.endswith('Staff Service'): k = 'Staff Service' # bit of cleanup\n if rr.select('td.stars'): \n rDets[f'[stars] {k}'] = len(rr.select('td.stars span.star.fill'))\n else: \n rDets[k] = rr.select_one('td.review-value').get_text().strip()\n\n revList = ([rDets] + revList) if isAvg else (revList + [rDets])\n print(' - ', len(rvSoups), 'reviews --->', len(revList), 'total reviews') \n\n\n\nYou could also view just the star ratings: \n", "You have the column values, just build the DataFrame\nEg.,\nfrom pandas import DataFrame\n\ncategory = [\"Aircraft\", 'Type of Traveller', 'Seat Type']\nrating = ['A320', 'Solo', 'Business Class']\n\n# Create the records from both list, using zip and dict calls.\ndata_dict = dict(zip(category, rating))\n\n# Build the dataframe from the dictionary.\ndf = DataFrame.from_records(data_dict, columns=category, index=[0])\n\nprint(df)\n\nLooks like this. \n" ]

[ 1, 0 ]

[]

[]

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

[ "Creating 13000 files in the current directory :\nimport json\n\nfrom tqdm import tqdm # pip install tqdm\n\nfor i in tqdm(range(13_000)):\n filename = f\"data_{i}.json\"\n data = {\"filename\": filename}\n with open(filename, \"w\") as file:\n json.dump(data, file)\n\n100%|██████████| 13000/13000 [00:01<00:00, 8183.74it/s]\n\nWhich means it ran for less than 2 seconds on my computer. tqdm is just a very simple way to see throughput.\nThe script produced files like :\n{\"filename\": \"data_0.json\"}\n\nThen reading them :\nimport json\n\nfrom tqdm import tqdm # pip install tqdm\n\nfor i in tqdm(range(13_000)):\n filename = f\"data_{i}.json\"\n with open(filename, \"rt\") as file:\n data = json.load(file)\nprint(data)\n\n100%|██████████| 13000/13000 [00:00<00:00, 16472.00it/s]\n{'filename': 'data_12999.json'}\n\nWhich means that they were all read in less than one second.\nMaybe it comes from the size of the files you read. If you have many large files, indeed it will take more time. But your disk does not seem like the only cause for the slowness.\n" ]

[ 1 ]

[]

[]

[ "file", "json", "python", "python_3.x" ]

[ "Use:\ndf = df.sort_values('PRICE')\ndf['CUMULATIVE'] = df.groupby(by=['DATE', 'PRODUCT'])['CAPACITY'].cumsum()\n\nOr:\ndf = df.sort_values(['PRICE','DATE', 'PRODUCT'])\ndf['CUMULATIVE'] = df.groupby(by=['DATE', 'PRODUCT'])['CAPACITY'].cumsum()\n\n" ]

[ 1 ]

[]

[]

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

[ "Try adding numpy to your requirements.txt, e.g. as follows:\nnumpy==1.23.4\n\nas this may be caused by a missing numpy installation, from which open-cv is depending. Important: therefore you have to add it before open-cv to the requirements!\nIf that does not work, maybe try the solution presented here: No BLAS/LAPACK libraries found when installing SciPy\n" ]

[ 0 ]

[]

[]

[ "opencv", "pip", "python" ]

[ "Why are you putting them in a list?\nPandas concat lets you combine DF's while doing the column name management for you.\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html\n" ]

[ 0 ]

[]

[]

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

[ "cursor.fetchall()\n\nreturns a list of tuples which means that x in your for loop is a tuple. You can convert tuples to dictionaries using this\ntuple_as_dict = dict(tuple)\n\n\nIf store your data as json string you first need to unpack the tuple and the convert it into a string\ndictionary = json.loads(*tuple)\n\n\njson.loads(string)\n\nalso returns a dictionary but takes strings and not tuples as argument\n" ]

[ 1 ]

[]

[]

[ "arrays", "json", "python", "string" ]

[ "You need to be sure what you mean when two values are \"similar\". If I understand you correctly you set an arbitrary threshold of 0.1, for this the code could look something like this:\nreal_list = [1.94, 4.72, 8.99, 5.37, 1.33]\nlist_2 = [1.86, 4.78, 8.91, 5.41, 1.30]\nthreshold = 0.1\nfor i in list_2:\n found = False\n for j in real_list:\n if abs(i-j) <= threshold:\n print(\"found\")\n found = True\n break\n if not found:\n print(\"not found\")\n\n", "with a bit of numpy this becomes a rather compact\nimport numpy as np\nreal_list = np.array([1.94, 4.72, 8.99, 5.37, 1.33])\nlist_2 = [1.86, 4.78, 8.91, 5.41, 1.30]\n[\"found\" if np.any(np.isclose(x,real_list,atol=0.04)) else \"not found\" for x in list_2]\n\nnp.isclose(x,real_list,atol=0.04) does the comparison of each value in list_2 against the whole vector with a given tolerance - can be atol = absolute tolerance and/or rtol: relative tolerance (https://numpy.org/doc/stable/reference/generated/numpy.isclose.html).\nnp.any then reduces the comparison of each value into a single boolean.\n" ]

[ 1, 1 ]

[ "You can do something like this:\nreal_list = [1.94, 4.72, 8.99, 5.37, 1.33]\nlist_2 = [1.86, 4.78, 8.91, 5.41, 1.30]\n\nfor x in real_list:\n for y in list_2:\n\n if abs(x-y)<0.1:\n print(\"found\",x, \"is close to\",y)\n else:\n print(\"not found\")\n\nIt will run all the itens in your first list, and all itens in your second list and compare if they are less than 0.1 away from eachother.\n" ]

[ -1 ]

[ "function", "list", "python" ]

[ "The centering matrix is\nnp.eye(n) - np.ones((n, n)) / n\n\nHere is a list of issues in your original formulation:\n\nnp.ones(n).T is the same as np.ones(n). The transpose of a 1D array is a no-op in numpy. If you want to turn a row vector into a column vector, add the dimension explicitly:\nnp.ones((n, 1))\n\nOR\nnp.ones(n)[:, None]\n\n\nThe normal definition is to subtract the column-wise mean, not the row-wise, so you will have to transpose and right-multiply the input to get row-wise operation:\nn = data.shape()[1]\n...\ndata = (centeringMatrix @ data.T).T\n\n\nYour function creates a new array for the output but does not currently return anything. You can either return the result, or perform the assignment in-place:\nreturn (centeringMatrix @ data.T).T\n\nOR\ndata[:] = (centeringMatrix @ data.T).T\n\nOR\nnp.matmul(centeringMatrix, data.T, out=data.T)\n\n\n\n" ]

[ 2 ]

[]

[]

[ "matrix_multiplication", "numpy", "python" ]

[ "I have not tried saving the attachments using win32com, so I can't tell why only a single attachment from a single file is getting saved. But I was able to save multiple attachments using msg-extractor\nimport extract_msg\n\nfor file in files:\n msg = extract_msg.Message(file)\n msg_attachment = msg.attachments\n attach_path = \"path where the files have to be saved.\"\n for attachment in msg_attachment:\n if not os.path.exists(attach_path):\n os.makedirs(attach_path)\n attachment.save(customPath=attach_path)\n\n", "I figured out a solution to extract multiple files with Win32 by including a counter:\npath = r'C:\\Users\\filepath' #change path to directory where your msg files are located\nfiles = [f for f in os.listdir(path) if '.msg' in f]\nprint (files)\ncounter=0\nfor file in files:\n outlook = win32com.client.Dispatch(\"Outlook.Application\").GetNamespace(\"MAPI\")\n msg = outlook.OpenSharedItem(os.path.join(path, file))\n att=msg.Attachments\n for i in att:\n counter +=1\n i.SaveAsFile(os.path.join(path, str(counter)+i.FileName))\n\n", "This topic is quite old now but no need to use the counter:\npath = r'C:\\Users\\filepath' #change path to directory where your msg files are located\nfiles = [f for f in os.listdir(path) if '.msg' in f]\nprint (files)\noutlook = win32com.client.Dispatch(\"Outlook.Application\").GetNamespace(\"MAPI\")\n\nfor file in files:\n msg = outlook.OpenSharedItem(os.path.join(path, file))\n for att in msg.Attachments:\n fullPath = os.path.join(path, att.FileName)\n if not os.path.isfile(fullPath):\n att.SaveAsFile(fullPath)\n\n" ]

[ 2, 0, 0 ]

[]

[]

[ "attachment", "data_extraction", "python", "vba" ]

[ "you could do it like this with less for loops as possible (always rely on database not in the for loops):\nfor id in idx:\n new_value = qs.filter(survey_id=id).first()\n data.append(new_value)\n\n" ]

[ 1 ]

[]

[]

[ "django", "python" ]

[ "Assuming you have a file called file.txt with the following contents:\nsubjects ENGLISH, MATHS, SCIENCE\n\nJoe, A, A, B\n\nDave, A, B, C\n\nWill, D, D, E\n\nTry using * unpacking:\nresults = {}\nwith open('file.txt') as file:\n _, *subjects = next(file).split(' ') # Read header row\n subjects = [s[:-1] for s in subjects] # Remove trailing comma/newline from subjects\n for line in file:\n if line != '\\n': # Skip empty lines\n name, *grades = line.strip().split(', ')\n results[name] = dict(zip(subjects, grades))\nprint(results)\n\nYou can also define the subjects in code and skip the header row completely:\nsubjects = ['ENGLISH', 'MATHS', 'SCIENCE']\nresults = {}\nwith open('file.txt') as file:\n next(file) # Skip header row since we have defined subjects in code...\n for line in file:\n if line != '\\n': # Skip empty lines\n name, *grades = line.strip().split(', ')\n results[name] = dict(zip(subjects, grades))\nprint(results)\n\nOutput:\n{'Joe': {'ENGLISH': 'A', 'MATHS': 'A', 'SCIENCE': 'B'}, 'Dave': {'ENGLISH': 'A', 'MATHS': 'B', 'SCIENCE': 'C'}, 'Will': {'ENGLISH': 'D', 'MATHS': 'D', 'SCIENCE': 'E'}}\n\n", "We can read the file by using pd.read_csv() and convert the pd to a dictionary by using: df.to_dict('index')\n" ]

[ 1, 0 ]

[ "You could convert your text file to CSV\nName, ENGLISH, MATHS, SCIENCE\n\nJoe, A, A, B\n\nDave, A, B, C\n\nWill, D, D, E\n\nThen use the pandas' library to read the CSV file and convert it into the dictionary.\n>>> import pandas as pd\n>>> pd.read_csv('file_path.csv',index_col='Name').transpose().to_dict()\n\n{'Joe': {'ENGLISH': ' A', 'MATHS': ' A', 'SCIENCE': ' B'}, 'Dave': {'ENGLISH': ' A', 'MATHS': ' B', 'SCIENCE': ' C'}, 'Will': {'ENGLISH': ' D', 'MATHS': ' D', 'SCIENCE': ' E'}}\n" ]

[ -1 ]

[ "dictionary", "python", "python_3.x", "text" ]

[ "To use an RGB color space within the terminal* the following escape sequence can be used:\n# Print Hello! in lime green text.\nprint('\\033[38;2;146;255;12mHello!\\033[0m')\n# ^\n# |\n# \\ The 38 goes here, to indicate a foreground colour.\n\n# Print Hello! in white text on a fuschia background.\nprint('\\033[48;2;246;45;112mHello!\\033[0m') \n\nExplanation:\n\\033[38;2;146;255;12mHello!\\033[0m\n^ ^ ^ ^ ^ ^ ^ ^ ^ \n| | | R G B | | |\n| | | | | | \\ Reset the colour to default\n| | | | | | \n| | | | | \\ Escape character\n| | | | |\n| | | \\ R;G;B \\ Text to print\n| | |\n| | \\ Indicate the following sequence is RGB\n| |\n| \\ Code to instruct the setting of an 8 or 24-bit foreground (text) colour\n|\n\\ Escape character\n\nThe use of 38;2 indicates an RGB (foreground) sequence is to follow. However, the use of 38;5 indicates the following (foreground) value comes from the 256-colour table.\nTo clarify what appears to be a misconception, \\033 (octal) or \\x1b (hexidecimal) corresponds to the ASCII table's ESC character, which is used here to introduce an escape sequence of terminal text colouring. Whereas the 38 is used to instruct the following 8 or 24-bit colour to be set as foreground, (after the escape sequence has been introduced). Additionally, 48 can be used to set the background colour, as demonstrated in the code example above.\n*Providing the terminal emulator supports 24-bit colour sequences. (e.g. Xterm, GNOME terminal, etc.)\nLink to the Wikipedia article which explains this topic of 24-colour (RGB) in greater depth.\n" ]

[ 1 ]

[ "Below code will give you an idea.\nprint('\\033[90m' + 'hello' + '\\033[96m' + ' there?' )\n\n" ]

[ -1 ]

[ "ansi", "python", "terminal" ]

[ "Use a simple dictionary comprehension:\noutput = {dict2[k]: v for k,v in dict1.items()}\n\nOutput:\n{'D': [741, 114, 306, 70],\n 'B': [77, 325, 505, 144],\n 'C': [937, 339, 612, 100],\n 'A': [52, 811, 1593, 350]}\n\n", "While the simple dictionary comprehension by @mozway is certainly the most straightforward and elegant solution, it rests on the assumption that the keys of dict1 are a subset of those of dict2. If not, you'll get a KeyError.\nIf that assumption does not hold, you'll need to decide for yourself, how you want to deal with the case when a key in dict1 is not present in dict2. One option is to simply discard that key-value-pair and not include it in the output dictionary.\nfrom collections.abc import Mapping\n\nKT = str\nVT = list[int]\n\ndef merge(\n keys_map: Mapping[int, KT],\n values_map: Mapping[int, VT],\n) -> dict[KT, VT]:\n output = {}\n for key, value in values_map.items():\n try:\n output[keys_map[key]] = value\n except KeyError:\n pass\n return output\n\nTest:\nif __name__ == \"__main__\":\n dict1 = {\n 5: [1, 2, 3],\n 4: [741, 114],\n 2: [77, 325],\n 3: [937, 339],\n 1: [52, 811],\n }\n dict2 = {1: 'A', 2: 'B', 3: 'C', 4: 'D'}\n print(merge(dict2, dict1))\n\nOutput:\n\n{'D': [741, 114], 'B': [77, 325], 'C': [937, 339], 'A': [52, 811]}\n\n", "You can use dict.update():\ndef mergeDictionary(dict_obj1, dict_obj2):\n dict_obj3 = dict()\n for key, val in dict_obj1.items():\n dict_obj3.update({dict_obj2[key]: val})\n return dict_obj3\n\nor:\ndef mergeDictionary(dict_obj1, dict_obj2):\n dict_obj3 = dict()\n for key, val in dict_obj1.items():\n dict_obj3[dict_obj2[key]] = val\n return dict_obj3\n\n", "The error seems to be in this line:\ndict_obj3[key] = [value , dict_obj1[key]]\n\nYou want to use the value as criteria to assign an element to your dictionary, as such:\ndict_obj3[value] = dict_obj1[key]\n\nThis code should do the trick:\ndict1={4: [741, 114, 306, 70], 2: [77, 325, 505, 144], 3: [937, 339, 612, 100], 1: [52, 811, 1593, 350]}\ndict2={1: 'A', 2: 'B', 3: 'C', 4: 'D'}\n\n# My resultant dictionary should be \n# output={D: [741, 114, 306, 70], B: [77, 325, 505, 144], C: [937, 339, 612, 100], A: [52, 811, 1593, 350]}\n\n\n# My code\n\ndef mergeDictionary(dict_obj1, dict_obj2):\n dict_obj3 = {} # {**dict_obj1, **dict_obj2}\n for key, value in dict_obj2.items():\n dict_obj3[value] = dict_obj1[key]\n return dict_obj3\n\ndict_3 = mergeDictionary(dict1, dict2)\nprint(dict_3)\n\n", "If you're using pandas in your project already, it has a method that does precisely what you're asking for:\nimport pandas as pd\n\ndf1 = pd.DataFrame(dict1)\ndf1.rename(columns=dict2)\ndf1\n\n\nOtherwise it might not be worth the while.\n" ]

[ 7, 2, 2, 1, 0 ]

[ "A dictionary comprehension takes the form {key: value for (key, value) in iterable}\n# Python code to demonstrate dictionary\n# comprehension\n\n# Lists to represent keys and values\nkeys = ['a','b','c','d','e']\nvalues = [1,2,3,4,5]\n\n# but this line shows dict comprehension here\nmyDict = { k:v for (k,v) in zip(keys, values)}\n\n# We can use below too\n# myDict = dict(zip(keys, values))\n\nprint (myDict)\n\nOutput :{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}\n" ]

[ -2 ]

[ "dictionary", "key_value", "merge", "python" ]

[ "Your main goal is to classify text (i.e., email subjects) into one or more predefined class, depending on your design (you could always choose only one department to forward the email to, or multiple if the issue is inter-disciplinary). I would suggest to first go through some tutorials on supervised learning and multi-class classification. In my experience, they usually start with images, but the underlying logic is still the same. Once you understand the main concepts, you can look into natural language processing and text classification. Also, this sounds like a relatively general problem, so you should also be able to find some pretrained models or even full solutions online.\n" ]

[ 0 ]

[]

[]

[ "artificial_intelligence", "nlp", "python" ]

[ "I ended up fixing this by uninstalling my current version of Anaconda and installing a version from a few months ago. I didn't get the \"ordinal 242\" error nor the issues with scikit-learn.\n", "I encountered the same error after letting my PC sit for 4 days unattended. Restarting the kernel solved it.\nThis probably won't work for everyone, but it might save someone a little agony.\n" ]

[ 2, 1 ]

[]

[]

[ "anaconda", "python", "scikit_learn", "scipy", "spyder" ]

[ "Looking for openssh_crypt_cpu_consumption_dos.py I stumbled upon exploit-database/exploits/linux/dos/40888.py :\n except Exception, msg:\n\nWhich looks like Python2 syntax. Edit the script, as suggested by @BhavinT, but in this way :\n- except Exception, msg:\n+ except Exception as msg:\n\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "I did some research and figured out the issue is probably with the version, try installing this specific version of statsforecast\npip install statsforecasts==0.6.0\n\nTrying loading these models after that, hopefully this should work.\n", "As of v1.0.0 of StatsForecast, the API changed to be more like sklearn, using classes instead of functions. You can find an example of the new syntax here: https://nixtla.github.io/statsforecast/examples/IntermittentData.html.\nThe new code would be\nfrom statsforecast import StatsForecast\nfrom statsforecast.models import ADIDA, IMAPA\n\nmodel = StatsForecast(df=Y_train_df, # your data\n models=[ADIDA(), IMAPA()], \n freq=freq, # frequency of your data\n n_jobs=-1)\n\nIf you want to use the old syntax, setting the version as suggested should work.\n", "If you have updated the package ..use ADIDA it will work\nsee the model list name with new packages\nADIDA(),\nIMAPA(),\n(SimpleExponentialSmoothing(0.1)),\n(TSB(0.3,0.2)),\n(WindowAverage( 6))\n\n" ]

[ 3, 1, 0 ]

[]

[]

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

[ "If you are finding a Report Designer, maybe you can look into JasperReport, which supports data sources from JSON.\nVia pyreportjasper, you can generate the reports into PDF. \n", "If you'd like to use Crystal, you can create a \"Report_Request\" table in your database and insert into that table the necessary information (Report Name, parameter values, export format, export file name, email recipient(s), etc.).\nA scheduled process can then monitor that table, trigger the generation of the report, and update a status column in that table. Your code can then redirect the user's browser to the result.\nThere are free/inexpensive 3rd-party Crystal Reports tools that can handle this type of automation, assuming you wish to avoid writing that code yourself. Ken Hamady provides a listing of 3rd-party Crystal Reports tools here.\n" ]

[ 0, 0 ]

[]

[]

[ "crystal_reports", "django", "django_rest_framework", "python", "reactjs" ]

[ "The “utf8” encoding only supports three bytes per character. The real UTF-8 encoding, which everybody uses, needs up to four bytes per character. See this article.\nSo use “utf8mb4” charset instead of “utf8”.\nThe settings.py should look as follows:\nDATABASES = { \n'default': { \n 'ENGINE': 'django.db.backends.mysql', \n 'NAME': '*****_db', \n 'USER': '******', \n 'PASSWORD': '********', \n 'HOST': 'localhost', \n 'PORT': '3306', \n 'OPTIONS': { 'init_command': 'SET storage_engine=INNODB','charset': 'utf8mb4'}\n} \n\n" ]

[ 1 ]

[]

[]

[ "django", "python" ]

[ "The issue is not that Werkzeug is blocking the setting of domain-based cookies - rather the issue is that most browsers do not support domain-limited cookies scoped to localhost (or to any other single-word domain). Rather than leaving you to debug this issue on your own (why is my session not being respected) Werkzeug detects when you are using this setup and errors out right away.\nThe closest thing that I have found for a reason is the pseudo-spec:\n\ndomain=DOMAIN_NAME\nWhen searching the cookie list for valid cookies, a comparison of the domain attributes of the cookie is made with the Internet domain name of the host from which the URL will be fetched. If there is a tail match, then the cookie will go through path matching to see if it should be sent. \"Tail matching\" means that domain attribute is matched against the tail of the fully qualified domain name of the host. A domain attribute of \"acme.com\" would match host names \"anvil.acme.com\" as well as \"shipping.crate.acme.com\".\nOnly hosts within the specified domain can set a cookie for a domain and domains must have at least two (2) or three (3) periods in them to prevent domains of the form: \".com\", \".edu\", and \"va.us\". [emphasis mine] Any domain that fails within one of the seven special top level domains listed below only require two periods. Any other domain requires at least three. The seven special top level domains are: \"COM\", \"EDU\", \"NET\", \"ORG\", \"GOV\", \"MIL\", and \"INT\".\n\nIf single-name domains were allowed a hacker could set a cookie for .com and then have that cookie transmitted by the browser to every .com domain the end user visited.\nSee also: http://daniel.haxx.se/blog/2011/04/28/the-cookie-rfc-6265/\n", "As @Markus Unterwaditzer proposed, you can fake hostnames locally to get and set the cookies associated to the domain names.\nFor this, do sudo vim /etc/hosts:\n127.0.0.1 localhost\n127.0.0.1 fakesub.fakedomain.com\n127.0.0.1 foo.bar.baz.anotherfakedomain.org\n\nThis way, you can use and set cookies for the domains and subdomains fakesub.fakedomain.com, fakedomain.com, foo.bar.baz.anotherfakedomain.org, bar.baz.anotherfakedomain.org, baz.anotherfakedomain.org and anotherfakedomain.org.\n\nI use this solution every day to locally develop websites for my company using the authentication provided by my company production website through the cookies.\n" ]

[ 5, 0 ]

[]

[]

[ "cookies", "flask", "python", "werkzeug" ]

[ "You can use:\n(pd.read_csv('textfile.txt', sep='|')\n .loc[lambda d: d['id'].notna()]\n .convert_dtypes()\n .sort_values(by='id')\n .to_csv('out.txt', sep='|', index=False)\n)\n\nout.txt:\nid|name|dataType\n1|cc|DateTime\n3|dd|DateTime\n4|bb|DateTime\n5|aa|String\n\n" ]

[ 4 ]

[]

[]

[ "pandas", "python", "text_files" ]