Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 11

[ "It looks like there may be a problem with the finally clause in your code. The finally clause is executed whether or not an exception is thrown in the try clause, and it is typically used to clean up resources, such as closing open files or network connections. In your code, the finally clause is executing even when the try clause is successful, which may cause issues.\nOne way to fix this issue is to move the code in the finally clause to after the try clause, and remove the finally clause entirely. This will ensure that the code in the finally clause is only executed if an exception is thrown in the try clause. Here is an example of how you could modify your code to fix this issue:\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport requests\nfrom bs4 import BeautifulSoup\n\nod = input('Origin Destination: ')\ndp = input('Departure Periode: ')\n#op = input('Observation Periode: ')\ntry:\n driver = webdriver.Chrome()\n driver.get(\"http://infare.net/login.aspx\")\n username= driver.find_element(By.NAME,\"loxLogin$UserName\")\n username.send_keys(\"username\")\n password= driver.find_element(By.NAME,\"loxLogin$Password\")\n password.send_keys(\"password\")\n login= driver.find_element(By.CLASS_NAME,\"LoginSplashButtonStyle\")\n login.click()\n\nexcept Exception:\n # Handle any exceptions thrown in the try clause here.\n pass\n\n# Move code from the finally clause here.\nsecure_url = 'http://infare.net/Pages/Analysis/DataDisplay.aspx'\ndriver.get(secure_url)\nreq = requests.get(secure_url)\nsoup = BeautifulSoup(req.text,'html.parser')\n\n#dropdown rute \ndropdownbox = ...\n\n" ]

[ 0 ]

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[ "forum", "html", "python", "web_scraping" ]

[ "Firstly, you are checking if street is school way early before even appending it. This causes your loop to break and the data for school direction isn't updated.\nNext, you don't need to reverse your list again in the end since it is already in the order you require. So remove [::-1]. Here's the fixed code:\ndirections = []\ncounter = 0\nwhile True:\n direction = input(\"Enter L or R for the direction: \")\n street = input(\"Enter the street name: \")\n directions.append((direction, street))\n counter += 1\n if counter == 3 or street.casefold == \"school\":\n break\n\nreversed_directions = []\nfor direction, street in directions:\n if direction == \"L\":\n direction = \"R\"\n elif direction == \"R\":\n direction = \"L\"\n reversed_directions.append((direction, street))\n\ndel reversed_directions[0]\nif directions[0][0] == \"L\": # update Left or right based on first value\n reversed_directions.insert(2, (\"R\", \"HOME\"))\nelse:\n reversed_directions.insert(2, (\"L\", \"HOME\"))\n# used f-strings for ease\nprint(f\"Original directions: {directions}\")\nprint(f\"New directions: {reversed_directions}\")\n\nInput: [(\"R\", \"JOHN\"), (\"L\", \"KING\"), (\"L\", \"SCHOOL\")]\nOutput:\n>>> Original directions: [('R', 'JOHN'), ('L', 'KING'), ('L', 'SCHOOL')]\n>>> New directions: [('R', 'KING'), ('R', 'SCHOOL'), ('L', 'HOME')]\n\n" ]

[ 0 ]

[]

[]

[ "counter", "list", "python" ]

[ "I have managed to solve my problem now. What I did was, instead of trying to put multple ax.plot() lines or putting multiple DataFrames inside ax.plot(df1, df2, df3) etc. I created a list which I put inside the For Loop. I also created a column variable to go in the \"inner loop\".\nIf using Nested Loops like this, the value that changes horizontally must go in the outer loop while the value that changes vertically must go in the inner loop.\nIn my case, the first subplot contains four different columns from the same DataFrame, the second contains four different columns for another DataFrame, and so on.\nThis is how it is implemented in the code:\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nresidual_list = (['df_168h', 'residuals_168', 'residuals_168_d', 'residuals_168_weekend'])\n\nfig = plt.figure(figsize=(10, 8))\nouter = gridspec.GridSpec(2, 2, wspace=0.1, hspace=0.1)\n\n\nfor i, s in zip(range(4), residual_list):\n inner = gridspec.GridSpecFromSubplotSpec(4, 1,\n subplot_spec=outer[i], wspace=0.1, hspace=0.1)\n\n for j, column in zip(range(4), df_168h):\n ax = plt.Subplot(fig, inner[j])\n a = ax.plot(locals()[s][column])\n t.set_ha('center')\n ax.set_xticks([])\n ax.set_yticks([])\n fig.add_subplot(ax)\n\n" ]

[ 0 ]

[]

[]

[ "python", "subplot" ]

[ "It looks like the cv.imread() function is unable to find the image file \"starry_night.jpg\" in your current directory. This is likely because the findFile() function is returning an empty string, which indicates that the file could not be found.\nTo fix this issue, you will need to make sure that the \"starry_night.jpg\" file exists in your current working directory. You can verify this by running ls in your terminal to list all files in the current directory, or by using the os.path.exists() function in Python to check if the file exists.\nOnce you have confirmed that the file exists in your current directory, you can try modifying the code to specify the full path to the image file instead of relying on the findFile() function. For example:\nimport cv2 as cv\nimport os\n\n# Replace \"/path/to/image/file\" with the full path to your \"starry_night.jpg\" file\nimg_path = \"/path/to/image/file/starry_night.jpg\"\n\n# Check if the file exists\nif not os.path.exists(img_path):\n sys.exit(\"Error: File not found.\")\n\n# Read the image file\nimg = cv.imread(img_path)\n\n# Check if the image was successfully read\nif img is None:\n sys.exit(\"Error: Could not read the image.\")\n\n# Display the image\ncv.imshow(\"Display window\", img)\n\n# Wait for a key press and save the image if \"s\" is pressed\nk = cv.waitKey(0)\n...\n\n" ]

[ 0 ]

[]

[]

[ "apple_m1", "opencv", "python" ]

[ "To write all rows in a single column, you can use a list comprehension to create a list of lists, where each inner list contains a single item. You can then write the resulting list using the writerows method of the csv module.\nHere is an example:\nimport csv\n\n# Create a list of lists, where each inner list contains a single item\nall_results = [[result] for result in all_results]\n\n# Open the file in write mode\nwith open('output.csv', 'w') as fh:\n # Create a CSV writer\n cvs_writer = csv.writer(fh)\n\n # Write the headers\n cvs_writer.writerow([\"Column 1\"])\n\n # Write the rows\n cvs_writer.writerows(all_results)\n\nThis code will write the all_results list to a CSV file, with each item in the list appearing in a separate row in the first column.\n" ]

[ 1 ]

[]

[]

[ "export_to_csv", "python" ]

[ "You can use the notnull method of a pandas dataframe to check if a column contains any non-null values, and then use the np.where function to write the appropriate value based on that check. Here is an example:\n# create a sample dataframe with some null values\ndf = pd.DataFrame({'Column1': [1, 2, None, 3],\n 'Column2': [None, 5, 6, None],\n 'Column3': [7, 8, 9, None]})\n\n# use the `notnull` method to check if a column contains any non-null values\n# and use the `np.where` function to write the appropriate value based on that check\ndf['Column4'] = np.where(df['Column1'].notnull(), 'Yes',\n np.where(df['Column2'].notnull(), 'Yes',\n np.where(df['Column3'].notnull(), 'Yes', 'No')))\n\n# the resulting dataframe should look like this:\n Column1 Column2 Column3 Column4\n0 1.0 NaN 7.0 Yes\n1 2.0 5.0 8.0 Yes\n2 NaN 6.0 9.0 Yes\n3 3.0 NaN NaN No\n\nYou can also use the notna method instead of notnull, which has the same effect. Note that the notna method was introduced in pandas version 0.24.0, so if you are using an older version you will need to use notnull instead.\nIf you want to check if a column contains any non-empty string values, you can use the str.len method along with the notnull method to check for the length of the strings in the column, and then use the np.where function to write the appropriate value based on that check. Here is an example:\n# create a sample dataframe with some null and empty string values\ndf = pd.DataFrame({'Column1': [1, 2, None, 3],\n 'Column2': [None, 5, 6, None],\n 'Column3': ['', '8', '9', None]})\n\n# use the `str.len` method along with the `notnull` method to check\n# if a column contains any non-empty string values\n# and use the `np.where` function to write the appropriate value based on that check\ndf['Column4'] = np.where(df['Column1'].notnull(), 'Yes',\n np.where(df['Column2'].notnull(), 'Yes',\n np.where((df['Column3'].str.len() > 0) & df['Column3'].notnull(), 'Yes', 'No')))\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "In the b class, you need to include the name argument in the __init__ method and pass it to the super() method as shown below:\nclass a:\n def __init__(self, name):\n self.name = name\n \nclass b(a):\n def __init__(self, name, age):\n super().__init__(name)\n self.age = age\n\nNow you can create an instance of the b class and pass the name and age arguments as follows:\nb('joe', 40)\n\n\nThis will correctly initialize the name attribute inherited from the a class and the age attribute in the b class.\n", "The arguments from the child constructor need to be passed to the parent. This is because the child constructor overrides the method (i.e. replaces the constructor method) from the parent. Calling super allows you to access the original parent constructor, which will need to be provided the appropriate arguments.\nclass a:\n def __init__(self, name):\n self.name = name\n \nclass b(a):\n def __init__(self, name, age):\n super().__init__(name)\n self.age = age\n\nAs you might notice, this means you need to write a lot of boilerplate code to plumb down the arguments (especially if there are many arguments). If this class is purely for data, then dataclasses provide a much easier and less error prone alternative.\nfrom dataclasses import dataclass\n\n@dataclass\nclass a:\n name: str\n\n@dataclass\nclass b(a):\n age: int\n\nprint(b('joe', 12))\n\nb(name='joe', age=12)\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "The error is telling you that it couldn't allocate as much VRAM as you are using. The easiest way to overcome this kind of problem is to reduce to batch-size to a number that fits on your GPU's VRAM.\n", "The error message you received tensorflow.python.framework.errors_impl.ResourceExhaustedError: failed to allocate memory [Op:AddV2] could indicate that your GPU does not have enough memory for the training job you want to run. What GPU are you using and how much vRAM does it have?\nWhen it comes to \"Out Of Memory\" (OOM) errors when training, the most straightforward thing to do is to reduce the batch_size hyperparameter.\nThere's no straightforward way to determine what the largest batch_size you can use while training that will fit your GPU's available vRAM other than trial and error. A general rule however, is to use a power of 2 (e.g. 8, 16, 32).\n", "As this implies an out-of-memory scenario, the first thing you should try is to reduce the batch size. This could also happen if you have a very large training dataset size. You can try training the model on a subset of training data and see if that helps.\n", "If you have lot of training samples you might get ResourceExhaustedError\nResourceExhaustedError from tensorflow\n\nFor example, this error might be raised if a per-user quota is\nexhausted, or perhaps the entire file system is out of space.\n\nHow to fix this error:\n\nSetting smaller batch_size when training model using fit method:\n\n\nbatch_size: Integer or None. Number of samples per gradient update.\n\n\nWhich means is higher the batch_size more memory is required while\ntraining.\n\n\nIf you are on Jupyter notebook try restarting kernel\n\n\nRestarting kernel will reset your notebook and remove all the memory allocated to variables or methods\nyou've defined!\n\n" ]

[ 7, 4, 0, 0 ]

[]

[]

[ "conv_neural_network", "deep_learning", "gpu", "python", "tensorflow" ]

[ "To make sure that the HLS stream is constantly updated, you can use the -hls_flags append_list option in the ffmpeg command that you are using to create the HLS stream. This option will make sure that the HLS playlist is constantly updated with new segments as they are added, so that the stream is always up-to-date.\nHere is an example of how you can use this option in your ffmpeg command:\ncommand = f\"ffmpeg -i {movie_path}\" \\\n f\"-c copy -map 0\" \\\n f\"-f segment -segment_time_delta 0 \" \\\n f\"-segment_list_type hls\" \\\n f\"-mov flags +faststart\" \\\n f\"-preset veryfast\" \\\n f\"-hls_playlist_type event\" \\\n f\"-hls_flags append_list\" \\\n f\"-segment_list_size 0\" \\\n f\"-segment_list {m3u8_path}\" \\\n f\"-segment_format mpegts\" \\\n f\"{movie_path.parent}/segment_%03d.ts\"\n\nYou can also use the -hls_list_size option to control how many segments are included in the HLS playlist at any given time. This can be useful if you want to limit the size of the playlist to prevent it from growing too large. For example, you can use the -hls_list_size 10 option to make sure that only the 10 most recent segments are included in the playlist.\n" ]

[ 0 ]

[]

[]

[ "ffmpeg", "http_live_streaming", "python" ]

[ "To download a file from a specific point, you can use the Range request header to specify the byte range that you want to download. For example, to download the last 100 bytes of a file, you can use the following code:\nfrom requests import get\nimport sys\n\ndef download(url, file_name, start_byte, end_byte):\n # open in binary mode\n with open(file_name, \"wb\") as f:\n print(\"Downloading %s\" % file_name)\n response = get(url, headers={'Range': f'bytes={start_byte}-{end_byte}'}, stream=True)\n total_length = response.headers.get('content-length')\n\n if total_length is None: # no content length header\n f.write(response.content)\n else:\n dl = 0\n total_length = int(total_length)\n for data in response.iter_content(chunk_size=4096):\n dl += len(data)\n f.write(data)\n done = int(50 * dl / total_length)\n sys.stdout.write(\"\\r[%s%s]\" % ('=' * done, ' ' * (50-done)) ) \n sys.stdout.flush()\n\n# download the last 100 bytes of the file\ndownload(url, file_name, -100, None)\n\nNote that not all servers support the Range header.\n" ]

[ 1 ]

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[]

[ "download", "python" ]

[ "There may be a difference between manually logging in to a website and using Selenium to login to the same website. This difference may be due to a number of factors, such as the way in which the website authenticates users, the specific actions that are performed during the login process, and the way in which the website responds to different types of input.\nOne potential difference between manual and automated login is that manual login may allow a user to enter their credentials in a more flexible way, such as using a keyboard, mouse, or other input device, whereas automated login may be more limited in terms of the input methods that are supported. Additionally, manual login may allow a user to view and interact with the website in a way that is not possible with automated login, such as clicking on buttons or links, or interacting with other elements on the page.\nAnother potential difference is that manual login may be subject to human error, such as mistyping a password or entering the wrong username, whereas automated login using Selenium can be more consistent and reliable. However, automated login may also be subject to errors, such as incorrect configuration of the Selenium script or errors in the website's authentication system.\nOverall, the differences between manual and automated login will depend on the specific website and the way in which it is designed and implemented. It is therefore important to carefully evaluate the specific requirements and constraints of the login process in order to determine the best approach for logging in to the website.\n" ]

[ 1 ]

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[]

[ "python", "selenium" ]

[ "Yes, you can use the since and until parameters in the tweepy code to collect tweets within a certain timeline. These parameters can be passed as part of the query parameter in the Cursor object when calling the Cursor.items() method.\nHere is an example of how you can use these parameters:\nimport tweepy\n\n# authentication details and other code to initialize the tweepy API client\n# ...\n\n# define the start and end dates for the timeline\nstart_date = \"2022-01-20\"\nend_date = \"2022-02-02\"\n\n# define the keyword to search for in the tweets\nkeyword = \"Messi\"\n\n# create the query string to search for tweets containing the keyword\n# within the defined timeline\nquery = f\"{keyword} since:{start_date} until:{end_date}\"\n\n# create a tweepy Cursor object to iterate over the tweets matching the query\ncursor = tweepy.Cursor(api.search, q=query)\n\n# iterate over the tweets and print the text of each tweet\nfor tweet in cursor.items():\n print(tweet.text)\n\n\nYou can find more information about the since and until parameters in the Twitter API documentation.\n" ]

[ 0 ]

[]

[]

[ "postman", "python", "tweepy", "twitter_api_v2" ]

[ "The simplest approach to solve your problem will be use set intersection(find common element from set).\nEg:\ndf1=pd.DataFrame({\"date\":['2016-03-21 20:10:00','2016-03-22 21:09:00','2016-05-03 17:05:00']})\n\ndf2=pd.DataFrame({\"date\":['2016-03-21 20:10:00','2016-03-21 21:00:00',\n '2016-03-22 21:09:00','2016-05-03 17:05:00','2017-06-01 16:10:00']})\n\nprint(len(set(df1.date) & set(df2.date))) # 3\n\nThis will just convert that specified column to python-set and find common between them.\n\nIf you want to use Pandas then you can use pandas.merge() to get the common rows based on the columns.\ndf3 = pd.merge(df1, df2)\nprint(len(df3)) # 3\n\nand count common rows using len function.\n", "You could use the isin function:\nlen(df1[df1.Date.isin(df2.Date)])\n\nOutput:\n3\n\n" ]

[ 0, 0 ]

[]

[]

[ "count", "date", "pandas", "python" ]

[ "You might need to modify the sourceFilePath variable to include the full URI of the file you want to load, including the scheme (e.g. adl:// or wasbs://) and the hostname or storage account name. For example:\nsourceFilePath = 'adl://<storage_account_name>.dfs.core.windows.net/mnt/pp-working-1/A'\n\nYou can also try using the spark.read.json method to load the JSON files, which automatically detects the schema of the JSON files and loads them as a DataFrame. This method also takes an option called recursiveFileLookup, which allows you to specify whether to recursively search for files in subdirectories. For example:\ndef json_parquet(sourceFilePath):\n df = spark.read.json(sourceFilePath, recursiveFileLookup=True)\n return df\n\nsourceFilePath = 'adl://<storage_account_name>.dfs.core.windows.net/mnt/pp-working-1/A'\ndf = json_parquet(sourceFilePath)\n\n\nAlternatively, you can use the spark.read.format method and specify the json format, along with the recursiveFileLookup and pathGlobFilter options to recursively search for JSON files in the specified directory and load them as a DataFrame. For example:\ndef json_parquet(sourceFilePath):\n df = (spark.read.format(\"json\")\n .option(\"recursiveFileLookup\", \"true\")\n .option(\"pathGlobFilter\",\"*.json\")\n .load(sourceFilePath))\n return df\n\nsourceFilePath = 'adl://<storage_account_name>.dfs.core.windows.net/mnt/pp-working-1/A'\ndf = json_parquet(sourceFilePath)\n\nI hope this helps.\n" ]

[ 0 ]

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[]

[ "azure_data_lake", "databricks", "pyspark", "python" ]

[ "One way to improve the efficiency of your method is to first create a set of all the numbers in Dict 2 and then iterate through Dict 1, adding the corresponding numbers from Dict 2 to a temporary set. Then, you can check if the temporary set is a subset of the set of all numbers from 1 to 10. If it is, you can return the selection of keys from Dict 1 that you used to create the temporary set. If it is not, you can continue iterating through Dict 1 and adding the corresponding numbers to the temporary set until it is a subset of the set of all numbers from 1 to 10.\nHere is an example of how this might look in code:\nimport random\n\n# Dict 1\ndict1 = {\n \"key1\": (\"A\", \"C\"),\n \"key2\": (\"B\", \"O\", \"P\"),\n \"key3\": (\"R\", \"T\", \"A\"),\n ...\n}\n\n# Dict 2\ndict2 = {\n \"A\": (1, 4, 7),\n \"B\": (9, 2, 3),\n \"C\": (1, 3),\n ...\n}\n\n# Set of all numbers from 1 to 10\nnumbers = set(range(1, 11))\n\n# Function to get a selection of keys from Dict 1 that covers all numbers from 1 to 10 in Dict 2\ndef get_selection(dict1, dict2, numbers):\n\n" ]

[ 0 ]

[]

[]

[ "dictionary", "python", "random", "set", "subset" ]

[ "I think you can try to run pip install tensorflow in command\n", "If you're having trouble importing a package in Python, it's possible that you haven't installed it properly or that it's not installed at all. To check if tensorflow is installed, you can try running pip freeze in your terminal. This will print out a list of all the packages that are currently installed in your environment. If tensorflow is not in that list, then you need to install it.\nIf you're not sure how to install a package in Python, you can use the pip command. For example, to install tensorflow, you would run pip install tensorflow in your terminal. Once the installation is complete, you should be able to import tensorflow in your Python code without any issues\n" ]

[ 1, 0 ]

[]

[]

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

[ "Call AssumeRole\nWhen calling AssumeRole(), a new set of credentials is returned. You can then use these credentials to create new clients, including another Security Token Service (STS) client that can be used to call AssumeRole() again.\nHere is an example:\nimport boto3\n\n# Create STS client using default credentials\n\nsts_client = boto3.client('sts')\n\n# Assume Role 1\n\nresponse1 = sts_client.assume_role(RoleArn='arn:aws:iam::111111111111:role/assume1', RoleSessionName='One')\n\ncredentials1 = response1['Credentials']\n\nrole1_session = boto3.Session(\n aws_access_key_id=credentials1['AccessKeyId'],\n aws_secret_access_key=credentials1['SecretAccessKey'],\n aws_session_token=credentials1['SessionToken'])\n\nsts_client1 = role1_session.client('sts')\n\n# Assume Role 2\n\nresponse2 = sts_client1.assume_role(RoleArn='arn:aws:iam::111111111111:role/assume2', RoleSessionName='Two')\n\ncredentials2 = response2['Credentials']\n\nrole2_session = boto3.Session(\n aws_access_key_id=credentials2['AccessKeyId'],\n aws_secret_access_key=credentials2['SecretAccessKey'],\n aws_session_token=credentials2['SessionToken'])\n\n# Use Role 2\n\ns3_client2 = role2_session.client('s3')\n\nresponse = s3_client2.list_buckets()\n\nprint(response)\n\nUse profiles\nHowever, there is an easier way to do this using profiles. You can configure the ~/.aws/config file to assume roles automatically:\n[default]\nregion = ap-southeast-2\n\n[profile role1]\nrole_arn=arn:aws:iam::111111111111:role/assume1\nsource_profile=default\n\n[profile role2]\nrole_arn=arn:aws:iam::111111111111:role/assume2\nsource_profile=role1\n\nThis is telling boto3:\n\nWhen assuming role1, use the default credentials\nWhen assuming role2, use credentials from role1\n\nAssuming both roles is then as simple as:\nimport boto3\n\nsession = boto3.Session(profile_name='role2')\ns3_client = session.client('s3')\n\nresponse = s3_client.list_buckets()\n\nprint(response)\n\nThis also works with the AWS CLI:\naws s3 ls --profile role2\n\nFor more information, see: Credentials — Boto3 documentation\n" ]

[ 0 ]

[]

[]

[ "amazon_web_services", "assume_role", "boto3", "python" ]

[ "you should read your old json content. then append new item to it. an finally write it to your json file again. use code below:\nwith open (\"hall.json\") as f:\n dct=json.load(f)\n\n#add new item to dct\ndct.update(hall_of_fame)\n\n#write new dct to json file\nwith open(\"hall.json\",\"w\") as f:\n json.dump(dct,f)\n\nhave fun :)\n", "You're overwriting the file every time you write to it. Instead, you should read the existing data from the file, append the new data to the dictionary, and then write the whole dictionary back to the file.\nHere's an example of how you can do that:\nimport json\n\n# Read the existing data from the file\nwith open(\"hall.json\", \"r\") as django:\n hall_of_fame = json.load(django)\n\n# Append the new data to the dictionary\nhall_of_fame[\"john\"] = 5\nhall_of_fame[\"ana\"] = 7\nhall_of_fame[\"mary\"] = 3\nhall_of_fame[\"jim\"] = 1\nhall_of_fame[\"willie\"] = 6\n\n# Write the updated dictionary back to the file\nwith open(\"hall.json\", \"w\") as django:\n json.dump(hall_of_fame, django)\n\nAlternatively, you can use the json.dump() method's ensure_ascii and indent parameters to make the resulting JSON file more readable. Here's an example:\nimport json\n\n# Read the existing data from the file\nwith open(\"hall.json\", \"r\") as django:\n hall_of_fame = json.load(django)\n\n# Append the new data to the dictionary\nhall_of_fame[\"john\"] = 5\nhall_of_fame[\"ana\"] = 7\nhall_of_fame[\"mary\"] = 3\nhall_of_fame[\"jim\"] = 1\nhall_of_fame[\"willie\"] = 6\n\n# Write the updated dictionary back to the file\nwith open(\"hall.json\", \"w\") as django:\n json.dump(hall_of_fame, django, ensure_ascii=False, indent=4)\n\n" ]

[ 1, 0 ]

[]

[]

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

[ "It looks like you're trying to use multiple threads to run your macros simultaneously. However, the GUI freezes because you're blocking the main thread, which is responsible for updating the GUI.\nTo fix this, you need to make sure that your macro functions are non-blocking, i.e. they don't freeze the main thread. One way to do this is to use the threading module to run each macro in a separate thread.\nHere's an example of how you could modify your code to do this:\nimport threading\n\ndef netherwart(metric_raw):\n # ...\n\ndef sugarcane(metric_raw):\n # ...\n\ndef cobblestone(metric_raw):\n # ...\n\ndef run_macro(func, metric_raw):\n thread = threading.Thread(target=func, args=(metric_raw,))\n thread.start()\n\n# To run a macro, call the run_macro() function with the name of the macro function\n# and the metric_raw argument that you want to pass to it.\nrun_macro(sugarcane, 100)\n\nThis way, each macro function will be run in a separate thread, and the main thread will be free to update the GUI. You'll need to make similar changes to the rest of your code to ensure that all blocking operations are moved to separate threads.\nI hope this helps!\n" ]

[ 0 ]

[]

[]

[ "multiprocessing", "python", "tkinter" ]

[ "If you want it to fully 'animate' down, you should add the code that keeps the pygame screen/player updating in your while loop, otherwise you're just changing the y without changing the screen. So your code would look somewhat like this:\nclock = pygame.time.Clock()\nfps = 60\nrun = True\nwhile run:\n clock.tick(fps)\n keys = pygame.key.get_pressed()\n if keys[pygame.K_a] and x > 0:\n x = x - 5\n if keys[pygame.K_d] and x < (500 - width):\n x = x + 5\n if keys[pygame.K_s]: #this is the portion that is too fast. \n while y < (500 - height):\n y = y + 5 \n player = pygame.draw.rect(screen, (player_color), (x,y,width,height)) # Make sure to update the player\n pygame.display.update() # Make sure to update the display\n player = pygame.draw.rect(screen, (player_color), (x,y,width,height))\n pygame.display.update()\n\nChanging the FPS:\nBut, if you do want to change the speed of the game loop/essentially the frames per second, you can simply change the fps variable/the clock.tick() argument. So for example:\nclock = pygame.time.Clock()\nfps = 30 # This value is the amount of frames per second\nrun = True\nwhile run:\n clock.tick(fps) # The argument (currently fps) passed into this method will change the frames per second\n keys = pygame.key.get_pressed()\n if keys[pygame.K_a] and x > 0:\n x = x - 5\n if keys[pygame.K_d] and x < (500 - width):\n x = x + 5\n if keys[pygame.K_s]: #this is the portion that is too fast. \n while y < (500 - height):\n y = y + 5 \n player = pygame.draw.rect(screen, (player_color), (x,y,width,height))\n pygame.display.update()\n\nYou can read more about the clock.tick() method here\nPlease mark my answer as accepted if it solved your issue\n" ]

[ 1 ]

[]

[]

[ "pygame", "python" ]

[ "Yes. This line:\nsts = session.client(\"sts\")\n\ntells boto3 to create a session using the default credentials.\nThe credentials can be provided in the ~/.aws/credentials file. If the code is running on an Amazon EC2 instance, boto3 will automatically use credentials associated with the IAM Role associated with the instance.\nCredentials can also be passed via Environment Variables.\nSee: Credentials — Boto3 documentation\n" ]

[ 0 ]

[]

[]

[ "amazon_iam", "amazon_web_services", "assume_role", "boto3", "python" ]

[ "Python and C# have different definitions of Mod. Python uses the mathematical definition of mod (the result is always a non-negative number) while C# returns a value with the same sign as you started with.\nYou'll notice that the difference between C#'s answer and Python's answer is precisely the modulus. In effect, both are returning the same value, but a different representation of it.\n>>> x # result returned by C#\n-4025962189194561064516553363283375014525921961557175720338529077112971710311848366234203913802556580292548393361209331638020083844074732374616343653135077799402946400533385408838985464487221381056062706959966844828188406680815976045839849337092509291814967568127589935214307295687077940893465121894442245470126915769901660325457529592729859443914551917517655171549159468233372814554103351084536838316208603471169266662039167296594675085295938849603548128017384775032304908588861826301095512209694112961080272106412321025934327013828425487280677537982063915874200399589997896633613461631025068511458444974149152947749747984549594822000420708352393424384641432718686749229405669436317664008070112083271636922781745940251494349374415266883563753139424535520085753958356376469108498554786941521255255003415937460671624180845917463097501347690828778209895285876290213489273200505952380626734459377765095025966725691306820725825499\n>>> y # result returned by Python\n1783643806175396998275362602355826387650690265345724813364371805666764467579142495237890860674783000854825016824437046690023645956675737723594580834731857259761425187634662132104996180029411374011438919474589553364998222309255272614979511868027284402170465728908528297700102876189729595563926155962569604427283291749203673030343591516627038015511719927953742781126799972560120257074290771695973286302279629131295383214811291564651109155633319576684151577295199734593114604874743329126921653504771250132941337184148762999959335547394147714802188259839801355116944682610659081543579365393513921728510730572041617697936145453462119608425988630323921319186513104423344823774870759265115372373731593225388193828408607005773987710556891304121163609340263880054616842988101393815039937434342691332663137114581535172021453932283969024301846449291944006405969946744999443455011016318658938083030075774742259090377978078691693422518308\n>>> y - x # your modulus\n5809605995369958062791915965639201402176612226902900533702900882779736177890990861472094774477339581147373410185646378328043729800750470098210924487866935059164371588168047540943981644516632755067501626434556398193186628990071248660819361205119793693985433297036118232914410171876807536457391277857011849897410207519105333355801121109356897459426271845471397952675959440793493071628394122780510124618488232602464649876850458861245784240929258426287699705312584509625419513463605155428017165714465363094021609290561084025893662561222573202082865797821865270991145082200656978177192827024538990239969175546190770645685893438011714430426409338676314743571154537142031573004276428701433036381801705308659830751190352946025482059931306571004727362479688415574702596946457770284148435989129632853918392117997472632693078113129886487399347796982772784615865232621289656944284216824611318709764535152507354116344703769998514148343807\n\n" ]

[ 1 ]

[]

[]

[ "biginteger", "c#", "javascript", "mod", "python" ]

[]

[]

[ "Make a function for inner inserting.\nTry this\na=['Groceries', 'School Fees', 'Medicines', 'Furniture']\ndef innerInsert(index, value):\n try:\n a[index].insert(index, value)\n except AttributeError:\n a[index] = []\n a[index].insert(index, value)\n\ninnerInsert(0, 10000)\ninnerInsert(0, 100)\nprint(a)\n\n\n[[100, 10000], 'School Fees', 'Medicines', 'Furniture']\n\nFor more options, I made some changes to the function\na=['Groceries', 'School Fees', 'Medicines', 'Furniture']\ndef innerInsert(l, iLstIndex, iLstValueIndex, value):\n try:\n l[iLstIndex].insert(iLstValueIndex, value)\n except AttributeError:\n l[iLstIndex] = []\n l[iLstIndex].insert(iLstValueIndex, value)\n\ninnerInsert(a, 0, 0, 10000)\ninnerInsert(a, 0, 1, 2)\ninnerInsert(a, 0, 2, 3)\ninnerInsert(a, 0, 3, 4)\ninnerInsert(a, 0, 4, 1)\ninnerInsert(a, 0, 5, 2)\ninnerInsert(a[0], 4, 2, 100)\nprint(a)\n\nOUTPUT\n[[10000, 2, 3, 4, [100], 2], 'School Fees', 'Medicines', 'Furniture']\n\n", "You can use indexing to access the first sublist and then use the list.insert() method to insert the new value at the specified index. For example:\na = ['Groceries', 'School Fees', 'Medicines', 'Furniture']\nb = [[element] for element in a]\n\n# Add the value 1000 to the first element of the first sublist\nb[0].insert(0, 1000)\n\n# The resulting nested list should be: [[1000, 'Groceries'], ['School Fees'], ['Medicines'], ['Furniture']]\nprint(b)\n\n\n" ]

[ -1, -1 ]

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

[ "You can use a clustering algorithm to group the entries in Col1 and Col2 into clusters. Then you can use the MultilabelRecall metric to calculate the recall for each cluster. This way, you don't have to specify the number of labels for each entry in Col1.\n", "If you have a large number of rows in your table, it may be inefficient to calculate the cosine distance between all pairs of entries in Col1 and Col2. One way to make this more efficient is to use approximate nearest neighbor (ANN) algorithms, which can quickly find the closest vectors in a high-dimensional space. These algorithms typically involve constructing a data structure that allows for efficient search, such as a k-d tree or locality-sensitive hashing. Once you have built this data structure, you can use it to quickly find the entries in Col2 that are closest to a given entry in Col1, and then calculate the recall@k for those entries.\nHere is an example of how you might use an ANN algorithm to calculate the recall@k in your case. This code uses the k-d tree implementation in the scikit-learn library to index the vectors in Col1 and Col2, and then finds the nearest neighbors of each vector in Col1 using the k-d tree. It then calculates the recall@k for the nearest neighbors of each vector in Col1.\nfrom sklearn.neighbors import KDTree\n\n# Create a k-d tree to index the vectors in Col1 and Col2\ntree = KDTree(np.concatenate((col1_feature, col2_feature), axis=0))\n\n# Find the nearest neighbors of each vector in Col1 using the k-d tree\n# This returns a tuple containing the indices of the nearest neighbors\n# in Col2 and the distances to those neighbors\nneighbors = tree.query(col1_feature, k=k)\n\n# Calculate the recall@k for each vector in Col1\nrecall_at_k = 0\nfor i, (neighbor_indices, distances) in enumerate(neighbors):\n # Get the labels for the nearest neighbors of the current vector\n neighbor_labels = col2[neighbor_indices]\n\n # Count the number of true labels among the nearest neighbors\n true_labels = 0\n for label in neighbor_labels:\n if label in true_labels_for_col1[i]:\n true_labels += 1\n\n # Calculate the recall@k for the current vector\n recall_at_k += true_labels / k\n\n# Calculate the average recall@k over all vectors in Col1\naverage_recall_at_k = recall_at_k / len(col1)\n\n" ]

[ 0, 0 ]

[]

[]

[ "machine_learning", "precision_recall", "python", "pytorch" ]

[ "(?=.*[a-z]) and (?=.*[A-Z]) are positive lookaheads for at least one lowercase and one uppercase letter, respectively. .* means skip 0+ chars. If you change that to .+ it would skip 1+ chars, so (?=.+[A-Z]) would not match password Aaaaaaaaa even though it has an uppercase char.\n" ]

[ 1 ]

[]

[]

[ "javascript", "python", "regex" ]

[ "Per the documentation you should download as follows:\ncoursera-dl -u my_coursera_username -p my_coursera_password neural-networks-deep-learning\n\nNote that you won't be able to access the course materials if you are not officially enrolled via the website.\n" ]

[ 0 ]

[]

[]

[ "cmd", "coursera_api", "python" ]

[ "Keeping in mind your conditions are apt, your code could be enhanced in the following manners:\n\nReplacing conditions with all()\nAvoiding nested if conditions\nUsing elif in places of if\nUse min() to check inequality for smallest rather than checking both i and j\nCombine conditions to make it faster\n\nHere's just an enhanced version of your code:\ndef checks(piece, last, name):\n board = []\n open_file = open(name, \"r\")\n # thing = open_file.readline()\n for x in range(6):\n value = open_file.readline()\n board.append(value.strip(\"\\n\").split(\",\"))\n open_file.close()\n cords = last.split(',')\n i = int(cords[0]) # row/x\n j = int(cords[1]) # column/y\n winMsg = f\"{piece} win\" # create variable for ease\n if j > 2:\n if all(piece == value for value in [board[i][j-1], board[i][j-2], board[i][j-2]]) or all(piece == value for value in [board[i+1][j-1], board[i+2][j-2], board[i+3][j-3]]): return winMsg\n elif all(piece == value for value in [board[i][j+1], board[i][j+2], board[i][j+3]]): return winMsg\n elif all(piece == value for value in [board[i+1][j], board[i+2][j], board[i+3][j]]): return winMsg\n elif j not in [0, 1, 6] and all(piece == value for value in [board[i][j+1], board[i][j-1], board[i][j-2]]): return winMsg\n elif j not in [0, 5, 6] and all(piece == value for value in [board[i][j-1], board[i][j+1], board[i][j+2]]): return winMsg\n elif all(piece == value for value in [board[i+1][j+1], board[i][j+2], board[i][j-1]]): return winMsg\n elif min(i, j) > 2 and all(piece == value for value in [board[i-1][j-1], board[i-2][j-2], board[i-3][j-3]]): return winMsg\n elif i > 2 and all(piece == value for value in [board[i-1][j+1], board[i-2][j+2], board[i-3][j+3]]): return winMsg\n elif i in [1,2,3]:\n if j in [1,2,3,4] and all(piece == value for value in [board[i-1][j-1], board[i+1][j+1], board[i+2][j+2]]): return winMsg\n elif j == 5 and all(piece == value for value in [board[i-1][j+1], board[i+1][j-1], board[i+2][j-2]]): return winMsg\n elif i == 4:\n if j in [1,2,3,4] and all(piece == value for value in [board[i-1][j+1], board[i+1][j-1], board[i-2][j+2]]): return winMsg\n elif j == 5 and all(piece == value for value in [board[i-1][j-1], board[i-2][j-2], board[i+1][j+1]]): return winMsg\n\nIf you could provide an exact input when there is a win, maybe a better approach could be made. Hope this helps :)\n", "Not sure if this is faster but I've done this before in Numpy. Here's how I did it:\nimport numpy as np\n\n\nclass Connect4Game():\n\n # Construct a set of binary masks to find connect 4s\n win_mask = np.zeros((4*4, 7, 7), 'bool')\n idx1 = np.array(range(4))\n idx2 = np.array([3]*4)\n for i in range(4):\n win_mask[([i]*4, idx1+i, idx2)] = True\n win_mask[([i+4]*4, idx2, idx1+i)] = True\n win_mask[([i+8]*4, idx1+i, idx1+i)] = True\n win_mask[([i+12]*4, 6-idx1-i, idx1+i)] = True\n\n def __init__(self, data=None):\n # Extend the board area by adding borders\n self.ext_board = np.zeros((12, 13), 'int8')\n # Make the board a view slice\n self.board = self.ext_board.view()[3:9, 3:10]\n if data is not None:\n self.load_game(data)\n\n def reset(self):\n self.board [:, :] = 0\n\n def load_game(self, data):\n data = np.array(data)\n assert(data.shape == (6, 7))\n self.reset()\n self.board[data == ':green_circle:'] = 1\n self.board[data == ':red_circle:'] = 2\n\n def check_for_win(self, last, piece):\n row, col = last\n selection = self.ext_board[row:row+7, col:col+7]\n wins = np.nonzero(np.all(\n ((selection == piece) & self.win_mask) \n == self.win_mask, axis=(1, 2)\n ))[0]\n return wins.tolist()\n\n\n# Demo\ng = Connect4Game(example_board)\nprint(g.board)\nlast = (5, 1)\npiece = 1\nassert g.board[last] == piece\nwins = g.check_for_win(last, piece)\nprint(wins)\n\nOutput:\narray([[0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0]], dtype=int8)\n\n[]\n\n" ]

[ 0, 0 ]

[]

[]

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

[ "You can try to install pip by 'python get-pip.py' rather than 'pip install pip'.\n" ]

[ 0 ]

[]

[]

[ "instaloader", "python" ]

[ "To connect to a MariaDB 5.5.52 database using Python 3, you can use the pymysql library. This library provides a Python interface for connecting to and working with a MariaDB database.\nTo use pymysql, you will need to first install it using pip:\npip install pymysql\n\nOnce you have installed pymysql, you can use it to connect to your MariaDB database by importing the pymysql module and creating a new Connection object, like this:\nimport pymysql\n\n# Connect to the database\nconn = pymysql.connect(\n host=\"localhost\",\n user=\"username\",\n password=\"password\",\n db=\"database_name\"\n)\n\n# Use the cursor() method to create a cursor object\ncur = conn.cursor()\n\n# Execute a SQL query\ncur.execute(\"SELECT * FROM table_name\")\n\n# Fetch the results of the query\nresults = cur.fetchall()\n\n# Print the results\nprint(results)\n\nAs for the error message you are seeing from Django, it sounds like you are using a version of Django that is not compatible with MariaDB 5.5.52. Django 4.1.3 requires MariaDB 10.3 or later, so you will need to upgrade your MariaDB installation to a more recent version in order to use Django 4.1.3. Alternatively, you can try using an older version of Django that is compatible with MariaDB 5.5.52.\n" ]

[ 1 ]

[]

[]

[ "django", "mariadb", "python" ]

[ "I think a simple fix is to just flip the way you are looping through the lists:\nl1 = ['hello', 'hi']\nl2 = ['hey', 'hi', 'hello', 'hello']\nfor i in l1:\n print(f'{l2.count(i)}: {i}')\n\nOutput:\n2: hello\n1: hi\n\n", "You can use the in operator to check if each element in l1 is in l2. You can then use a Counter object to count the number of occurrences of each element in l1 that is also in l2.\nHere is an example:\nfrom collections import Counter\n\nl1 = ['hello', 'hi']\nl2 = ['hey', 'hi', 'hello', 'hello']\n\n# Create a Counter object to count the occurrences of each element in l1 that is also in l2\ncounter = Counter()\n\n# Loop over each element in l1 and check if it is in l2\nfor element in l1:\n if element in l2:\n # If the element is in l2, increment the count for that element\n counter[element] += 1\n\n# Print the count for each element\nfor element, count in counter.items():\n print(f'{count}: {element}')\n\nThis will print the following output:\n1: hi\n2: hello\n\n", "If you want to count how many times each word in l1 appears in l2, you can use a dictionary to keep track of the counts for each word. Here is one possible way to do this:\nl1 = ['hello', 'hi']\nl2 = ['hey', 'hi', 'hello', 'hello']\n\n# Create an empty dictionary\ncounts = {}\n\n# Loop through each word in l1\nfor word in l1:\n # Initialize the count for this word to 0\n counts[word] = 0\n # Loop through each word in l2\n for word2 in l2:\n # If the word from l1 appears in l2, increment the count\n if word == word2:\n counts[word] += 1\n\n# Print the counts for each word\nfor word in l1:\n print(f'{counts[word]}: {word}')\n\nThis code will print the following output:\n2: hello 1: hi\nThis approach allows you to count the occurrences of each word in l1 in l2, and print the counts in the desired format. You can further customize the code to suit your specific needs. For example, you could sort the counts by their values or print the counts in a different order, depending on your requirements.\n", "Try this\n\nfrom collections import Counter\n\nl1 = ['hello', 'hi']\nl2 = ['hey', 'hi', 'hello', 'hello']\n\nc = Counter(l2)\n\n\nfor a in l1:\n print(f\"{c[a]}: {a}\")\n c.pop(a)\n\nprint(*[\"0: \" + a for a in c.keys()], sep='\\n')\n\n\nOUTPUT\n2: hello\n1: hi\n0: hey\n\n\n", "To count the number of times a word in a list appears in another list, you can use a for loop to iterate over the first list and use the count() method to count the number of times each word appears in the second list. Here's an example:\n# define the two lists\nlist1 = [\"apple\", \"banana\", \"cherry\"]\nlist2 = [\"apple\", \"grape\", \"cherry\", \"apple\", \"orange\", \"banana\", \"apple\"]\n\n# initialize a count variable\ncount = 0\n\n# iterate over the first list\nfor word in list1:\n # count the number of times the word appears in the second list\n count += list2.count(word)\n\n# print the final count\nprint(count)\n\n\nThis code will print 4, since there are four words from the first list (\"apple\", \"banana\", \"cherry\") that appear in the second list.\n" ]

[ 3, 1, 1, 0, 0 ]

[]

[]

[ "count", "for_loop", "list", "python", "sum" ]

[ "Try with\nos.environ['LINE'] = var\n\ninstead of using putenv. Using putenv \"bypasses\" os.environ, that is, it doesn't update os.environ.\nIn fact, from the documentation for os.putenv:\n\nAssignments to items in os.environ are automatically translated into corresponding calls to putenv(); however, calls to putenv() don’t update os.environ, so it is actually preferable to assign to items of os.environ. This also applies to getenv() and getenvb(), which respectively use os.environ and os.environb in their implementations.\"\n\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x" ]

[ "Check first if this is a tty allocation issue, as in here.\nssh -t -o ...\n ^^\n\nAlso consider calling just one script (which does the cd, git pull and python3)\nThat way you can test the script locally (on 'host'), and then call it remotely (through ssh)\n\nFrom the OP Kevin A. in the comments:\n\nmy code goes through a loop that reruns the code every 45mins or so, so the script is constantly running. It's a web scraper constantly updating a cloud database.\nThe idea is to get GitLab CI to ignore waiting for the script to finish running, its just is to, stop previous script running, git pull and run the script again\n\nAnother approach would be to make the script scrap one-time (and exit), but call said script through a GitLab scheduled pipeline.\nThat way, no more freeze.\n" ]

[ 1 ]

[]

[]

[ "gitlab", "gitlab_ci", "python", "python_3.x" ]

[ "To calculate the row and column sums for multiple matrices, you can create a function that takes a list of matrices and calculates the row and column sums for each matrix in the list. Here is an example:\nimport numpy as np\n\n# Get the size m and n\nm, n = 7, 9\n\n# Function to calculate sum of each row\ndef row_sum(arr):\n sums = []\n for i in range(m):\n row_sum = 0\n for j in range(n):\n row_sum += arr[i][j]\n sums.append(row_sum)\n return sums\n\n# Function to calculate sum of each column\ndef column_sum(arr):\n sums = []\n for i in range(m):\n column_sum = 0\n for j in range(n):\n column_sum += arr[j][i]\n sums.append(column_sum)\n return sums\n\n# Driver code\nif __name__ == \"__main__\":\n arr = np.zeros((4, 4))\n\n # Get the matrix elements\n x = 1\n for i in range(m):\n for j in range(n):\n arr[i][j] = x\n x += 1\n\n # Get each row sum\n row_sums = row_sum(arr)\n print(\"Row sums:\", row_sums)\n\n # Get each column sum\n column_sums = column_sum(arr)\n print(\"Column sums:\", column_sums)\n\nTo find the row and column sums for multiple matrices, you can loop through the matrices and calculate the row and column sums for each one, storing the results in a list. For example:\n# Get the size m and n\nm, n = 7, 9\n\n# Function to calculate sum of each row\ndef row_sum(arr):\n sums = []\n for i in range(m):\n row_sum = 0\n for j in range(n):\n row_sum += arr[i][j]\n sums.append(row_sum)\n return sums\n\n# Function to calculate sum of each column\ndef column_sum(arr):\n sums = []\n for i in range(m):\n column_sum = 0\n for j in range(n):\n column_sum += arr[j][i]\n sums.append(column_sum)\n return sums\n\n\n\n\n" ]

[ 0 ]

[]

[]

[ "matrix", "python" ]

[ "You could do a single update with the help of a CASE expression:\nUPDATE products\nSET on_sale = CASE WHEN price > 100 THEN True ELSE False END\nWHERE status = 1 AND seller = test;\n\n" ]

[ 1 ]

[]

[]

[ "postgresql", "python" ]

[ "First, use your data to make a 2-D matrix with rows representing the days (sunday, ...) and the columns representing the numbers (0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18).\nOnce you have this 2-D matrix use the below code to plot the heatmap\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# create a 10x10 random matrix\ndata = np.random.random((10, 10)) # REPLACE WITH YOUR DATA\nprint(data.shape)\n\nfig, ax = plt.subplots()\nim = ax.imshow(data)\n\n# show image\nplt.show()\n\n" ]

[ 0 ]

[]

[]

[ "group_by", "heatmap", "pandas", "python" ]

[ "The ** operator follows normal mathematical conventions; it is right-associative:\n\nIn the usual computer science jargon, exponentiation in mathematics is right-associative, which means that xyz should be read as x(yz), not (xy)z. In expositions of the BODMAS rules that are careful enough to address this question, the rule is to evaluate the top exponent first.\n\nand from Wikipedia on the Order of Operations:\n\nIf exponentiation is indicated by stacked symbols, the usual rule is to work from the top down, because exponention is right-associative in mathematics.\n\nSo 2 ** 3 ** 4 is calculated as 2 ** (3 ** 4) (== 2417851639229258349412352) not (2 ** 3) ** 4 (== 4096).\nThis is pretty universal across programming languages; it is called right-associativity, although there are exceptions, with Excel and MATLAB being the most notable.\n", "from http://docs.python.org/reference/expressions.html\nOperators in the same box group left to right (except for comparisons, including tests, which all have the same precedence and chain from left to right — see section Comparisons — and exponentiation, which groups from right to left).\n>>> 2 ** 2 ** 2\n16\n>>> 2 ** 2 ** 2 ** 2\n65536\n>>> (2 ** 2 ** 2) ** 2\n256\n\nFor the middle case 2 ** 2 ** 2 ** 2, this are the intermediate steps - \n\nbroken down to 2 ** (2 ** (2 ** 2))\n2 ** (2 ** (4)) # progressing right to left\n2 ** (16) # this is 2 to the power 16\nwhich finally evals to 65536\nHope that helps!\n\n", "This explanation seems quite clear to me. Let me show you an example that might enlighten this :\nprint 2 ** 2 ** 3 # prints 256\nIf you would read this from left to right, you would first do 2 ** 2, which would result in 4, and then 4 ** 3, which would give us 64.\nIt seems we have a wrong answer. :)\nHowever, from right to left...\nYou would first do 2 ** 3, which would be 8, and then, 2 ** 8, giving us 256 !\nI hope I was able to enlighten this point for you. :)\nEDIT : Martijn Pieters answered more accurately to your question, sorry. I forgot to say it was mathematical conventions.\n", "Power operator, exponentiation, is handled differently across applications and languages.\nIf it has LEFT associativity then 2^3^4 = (2^3)^4 = 4096.\nIf it has RIGHT associativity then 2^3^4 = 2^(3^4) = 2417851639229260000000000.\nIn Excel, Matlab, Apple Numbers and more others exponentiation has LEFT associativity.\nIn Python, Ruby, Google Sheets, ... - RIGHT associativity.\nHere is a vast list of how different languages and apps handle exponentiation: Exponentiation Associativity and Standard Math Notation\n" ]

[ 23, 2, 0, 0 ]

[]

[]

[ "exponentiation", "operators", "python", "python_3.x" ]

[ "The problem you are seeing is because matplotlib classifies your y-axis values as categorical instead of numeric continuous values.\nThis might be because your list of al_min and al_max contain strings ['1','2','3'] instead of integers [1,2,3].\nAll you have to do is convert the strings in the list to integers. You can do it like this:\nal_min = list(map(int, al_min))\nal_max = list(map(int, al_max))\n\n\n\nHere is an example using your code:\nimport matplotlib.pyplot as plt\n\n# Create the data for the example\nal_cities = ['Birmingham', 'Huntsville', 'Mobile', 'Montgomery']\nal_min = ['36','34', '39', '38']\nal_max = ['52', '50', '57', '55']\n\n# Convert strings to integers\nal_min = list(map(int, al_min))\nal_max = list(map(int, al_max))\n\n# Here is your code (unchanged)\nfig, al = plt.subplots()\nal.scatter(al_cities, al_min)\nal.scatter(al_cities, al_max, c='red')\nal.plot(al_cities, al_min, c='lightblue')\nal.plot(al_cities, al_max, c='orange')\nal.fill_between(al_cities, al_max, al_min, facecolor='gray', alpha=.3)\nal.set_title('Highs and Lows in Alabama on January 10, 2016', fontsize=18)\nal.set_xlabel('City', fontsize=14)\nal.set_ylabel('Temperature', fontsize=14)\n\n\n\nOUTPUT:\n\n\n\n", "I could not quite understand the problem, But I would like to suggest that you could use the normal plt.plot() rather than subplots if you just have one graph to show. (You could use errorbars to show max and min temperature)\n" ]

[ 1, 0 ]

[]

[]

[ "matplotlib", "python" ]

[ "Use a merge with the groupby.mean of df2:\nout = df1.merge(df2.groupby('zipcodes', as_index=False)['rent'].mean(),\n on='zipcodes', how='left')\n\n", "You can divide that into 2 phases:\n\n1st phase: Aggregate the df2 to calculate the average rent by zip code. If the zip code has only one rent then the average value will be equal to that exact rent value so it still matches what you need.\n df2 = df2.groupby('zipcodes').mean()['rent'].reset_index()\n\n\n2nd phase: Merge to df1 using zipcodes\n df1 = df1.merge(df2, on='zipcodes', how='left') \n\n\n\nYou can change how parameter to left or inner depending on what you need. Left join will keep all the rows from df1 and fill NA if can't find any match from df2. Inner join will only keep rows that can be found in both df1 and df2.\nHope this help.\n" ]

[ 1, 1 ]

[]

[]

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

[ "Here is one way to do it using Pandas to_datetime and dt.accessor:\ndf[\"col_ts\"] = pd.to_datetime(df[\"col_ts\"])\ndf[\"start_interval\"] = df[\"col_ts\"].dt.floor(\"5T\")\ndf[\"end_interval\"] = df[\"col_ts\"].dt.ceil(\"5T\")\n\nThen:\n col_ts start_interval end_interval\n0 2022-11-02 08:26:40 2022-11-02 08:25:00 2022-11-02 08:30:00\n1 2022-11-02 08:25:10 2022-11-02 08:25:00 2022-11-02 08:30:00\n2 2022-11-02 08:26:00 2022-11-02 08:25:00 2022-11-02 08:30:00\n3 2022-11-02 08:30:20 2022-11-02 08:30:00 2022-11-02 08:35:00\n4 2022-11-02 08:33:30 2022-11-02 08:30:00 2022-11-02 08:35:00\n5 2022-11-02 08:36:40 2022-11-02 08:35:00 2022-11-02 08:40:00\n6 2022-11-02 08:26:20 2022-11-02 08:25:00 2022-11-02 08:30:00\n7 2022-11-02 08:50:10 2022-11-02 08:50:00 2022-11-02 08:55:00\n8 2022-11-02 08:30:40 2022-11-02 08:30:00 2022-11-02 08:35:00\n9 2022-11-02 08:39:40 2022-11-02 08:35:00 2022-11-02 08:40:00\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python" ]

[ "Create the intervalindex:\nintervals = pd.IntervalIndex.from_arrays(df_Lookup['Min Value'], \n df_Lookup['Max Value'], \n closed='neither')\n\nGet the matching positions:\npos = intervals.get_indexer(df_RawData.Value)\n\nIndex the Type columns - fortunately they are sorted:\ntypes = df_Lookup.filter(like='Type').to_numpy()\nout = types[pos, df_RawData.Type]\n\nAssign value:\ndf_RawData.assign(Factor = out)\n\n Value Type Factor\n0 31000 0 0.51\n1 36000 1 0.60\n2 42000 5 0.66\n\n" ]

[ 0 ]

[]

[]

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

[ "Here’s an example of how asyncio can run a shell command and obtain its result:\nimport asyncio\n\nasync def run(cmd):\n proc = await asyncio.create_subprocess_shell(\n cmd,\n stdout=asyncio.subprocess.PIPE,\n stderr=asyncio.subprocess.PIPE)\n\n stdout, stderr = await proc.communicate()\n\n print(f'[{cmd!r} exited with {proc.returncode}]')\n if stdout:\n print(f'[stdout]\\n{stdout.decode()}')\n if stderr:\n print(f'[stderr]\\n{stderr.decode()}')\n\nasyncio.run(run('ls /zzz'))\n\nwill print:\n['ls /zzz' exited with 1]\n[stderr]\nls: /zzz: No such file or directory\n\nBecause all asyncio subprocess functions are asynchronous and asyncio provides many tools to work with such functions, it is easy to execute and monitor multiple subprocesses in parallel. It is indeed trivial to modify the above example to run several commands simultaneously:\nasync def main():\n await asyncio.gather(\n run('ls /zzz'),\n run('sleep 1; echo \"hello\"'))\n\nasyncio.run(main())\n\nExamples\nAn example using the Process class to control a subprocess and the StreamReader class to read from its standard output.\nThe subprocess is created by the create_subprocess_exec() function:\nimport asyncio\nimport sys\n\nasync def get_date():\n code = 'import datetime; print(datetime.datetime.now())'\n\n # Create the subprocess; redirect the standard output\n # into a pipe.\n proc = await asyncio.create_subprocess_exec(\n sys.executable, '-c', code,\n stdout=asyncio.subprocess.PIPE)\n\n # Read one line of output.\n data = await proc.stdout.readline()\n line = data.decode('ascii').rstrip()\n\n # Wait for the subprocess exit.\n await proc.wait()\n return line\n\ndate = asyncio.run(get_date())\nprint(f\"Current date: {date}\")\n\n" ]

[ 0 ]

[]

[]

[ "python", "subprocess" ]

[ "One of the approaches when you are dealing with timeseries is to convert date to timestamp and solve the question in a numerical way and the end convert it to date again.\nfrom pyspark.sql import functions as F\n\ndata = [['2022-08-20 00:00:00', '2022-12-03 00:00:00']]\ndf = spark.createDataFrame(data = data, schema = ['start', 'end'])\n\nweek_seconds = 7*24*60*60\n(\n df\n .withColumn('start_timestamp', F.unix_timestamp('start'))\n .withColumn('end_timestamp', F.unix_timestamp('end'))\n .select(\n F.explode(\n F.sequence('start_timestamp', 'end_timestamp', F.lit(week_seconds)))\n .alias('start_date'))\n .withColumn('start_date', F.to_date(F.from_unixtime('start_date')))\n .withColumn('end_date', F.date_add('start_date', 6))\n).show()\n\n+----------+----------+\n|start_date| end_date|\n+----------+----------+\n|2022-08-20|2022-08-26|\n|2022-08-27|2022-09-02|\n|2022-09-03|2022-09-09|\n|2022-09-10|2022-09-16|\n|2022-09-17|2022-09-23|\n+----------+----------+\n\n" ]

[ 0 ]

[]

[]

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

[ "I have encountered the same problem.\nWhen you will try to authenticate using your browser, it will try to redirect you to some localhost URL. Just copy that localhost URL, log in to your VPS, open the terminal, type python3 (or python), and finally type these commands:\nimport requests\nurl = \"http://localhost:xxxxx-url-you-got-in-your-browswer\"\nresp = requests.get(url)\nexit()\n\nAfter these commands, it should generate a Gmail API token.\n" ]

[ 0 ]

[]

[]

[ "api", "gmail", "python", "ubuntu", "vps" ]

[ "Configuring the Telegram Bot\n\nGo to https://telegram.me/BotFather.\nTo create a new bot type /newbot to the message box and press enter.\nEnter the name of the user name of your new bot.\nYou have received the message from BotFather containing the token, which you can use to connect Telegram Bot to Make.\n\nTo add your bot to your Telegram application, click the link in the message from BotFather or enter it manually to your browser. The link is t.me/yourBotName.\nAdding Telegram Bot to your Scenario\nFollow Step 1 in the Creating a scenario article (choose the Telegram Bot module instead of Twitter and Facebook module).\nAfter the module is added to your scenario you can then see the Scenario editor.\nDefine what function you need your module to have. Here you can choose between three types of modules – Triggers, Actions, and Searches.\n" ]

[ 0 ]

[]

[]

[ "python", "python_telegram_bot", "telegram_bot" ]

[ "The docstring for the slugify function is:\n\nConvert to ASCII if 'allow_unicode' is False. Convert spaces to hyphens.\n Remove characters that aren't alphanumerics, underscores, or hyphens.\n Convert to lowercase. Also strip leading and trailing whitespace.\n\nSo you need to set the allow_unicode flag to True to preserve the Persian text.\n>>> text = 'سلام عزیزم! عزیزم سلام!'\n>>> slugify(text)\n''\n>>> slugify(text, allow_unicode=True)\n'سلام-عزیزم-عزیزم-سلام'\n>>> \n\n", "this is better !!\n‍‍‍slug = models.SlugField(max_length=20, unique=True, allow_unicode=True)\n\n", "Here`s an example which you can use for this case:\nFirst install django_extensions with pip, if it is not installed.\nfrom django_extensions.db.fields import AutoSlugField\nfrom django.utils.text import slugify\n\nIn model.py before your class add this function:\ndef my_slugify_function(content):\n return slugify(content, allow_unicode=True)\n\nIn your class add this field:\nslug = AutoSlugField(populate_from=['name'], unique=True, allow_unicode=True, slugify_function=my_slugify_function)\n\nIn url must use this format:\nre_path('person_list/(?P<slug>[-\\w]+)/', views.detail, name='detail')\n\n", "I used snakecharmerb and Ali Noori answers. But those did not solve my problem. And get this error:\nReverse for 'system-detail' with keyword arguments '{'slug': 'هفت'}' not found. 1 pattern(s) tried: ['system/(?P<slug>[-a-zA-Z0-9_]+)/\\\\Z']\n\nIn urls.py i Change slug to str:\npath('<str:slug>/', SystemDetailView.as_view(), name='system-detail'),\n\n" ]

[ 8, 2, 1, 0 ]

[]

[]

[ "django", "persian", "python" ]

[ "The reason Python 3.5 environment is unable to import Tensorflow is that Anaconda does not store the tensorflow package in the same environment.\nOne solution is to create a new separate environment in Anaconda dedicated to TensorFlow with its own Spyder\nconda create -n newenvt anaconda python=3.5\nactivate newenvt\n\nand then install tensorflow into newenvt\nI found this primer helpful\n", "In Windows 64, if you did this sequence correctly:\nAnaconda prompt:\nconda create -n tensorflow python=3.5\nactivate tensorflow\npip install --ignore-installed --upgrade tensorflow\n\nBe sure you still are in tensorflow environment. The best way to make Spyder recognize your tensorflow environment is to do this:\nconda install spyder\n\nThis will install a new instance of Spyder inside Tensorflow environment. Then you must install scipy, matplotlib, pandas, sklearn and other libraries. Also works for OpenCV. \nAlways prefer to install these libraries with \"conda install\" instead of \"pip\".\n", "The reason why Python base environment is unable to import Tensorflow is that Anaconda does not store the tensorflow package in the base environment.\ncreate a new separate environment in Anaconda dedicated to TensorFlow as follows:\nconda create -n newenvt anaconda python=python_version\n\nreplace python_version by your python version \nactivate the new environment as follows:\nactivate newenvt\n\nThen install tensorflow into the new environment (newenvt) as follows:\nconda install tensorflow\n\nNow you can check it by issuing the following python code and it will work fine.\nimport tensorflow\n\n", "deleting tensorflow from cDrive/users/envs/tensorflow and after that\nconda create -n tensorflow python=3.6\n activate tensorflow\n pip install --ignore-installed --upgrade tensorflow\n\nnow its working for newer versions of python thank you \n", "I think your tensorflow is not installed for local environment.The best way of installing tensorflow is to create virtualenv as describe in the tensorflow installation guide\nTensorflow Installation\n.After installing you can activate the invironment and can run anypython script under that environment.\n", "Since none of the above solve my issue, I will post my solution\n\nWARNING: if you just installed TensorFlow using conda, you have to restart your command prompt!\n\nSolution: restart terminal ENTIRELY and restart conda environment\n", "In Anaconda Prompt (Anaconda 3),\nType: conda install tensorflow command\nThis fix my issue in my Anaconda with Python 3.8.\nReference: https://panjeh.medium.com/modulenotfounderror-no-module-named-tensorflow-in-jupeter-1425afe23bd7\n", "I had same issues on Windows 64-bit processor but manage to solve them.\nCheck if your Python is for 32- or 64-bit installation.\nIf it is for 32-bit, then you should download the executable installer (for e.g. you can choose latest Python version - for me is 3.7.3)\nhttps://www.python.org/downloads/release/python-373/ -> Scroll to the bottom in Files section and select “Windows x86-64 executable installer”. Download and install it.\nThe tensorflow installation steps check here : https://www.tensorflow.org/install/pip .\nI hope this helps somehow ...\n", "Visual Studio in left panel is Python \"interactive Select karnel\" \n\nPyton 3.7.x \n anaconda3/python.exe ('base':conda)\n I'm this fixing\n\n", "I deleted all the folders and files in C:\\Users\\User\\anaconda3\\envs and then I wrote conda install tensorflow in Anaconda Prompt.\n", "Such error might occur if you find yourself in a deferent env even though you have the package installed but yet you can't import it.\nYou can choose to append the path of the installed package into your working environment. If you tried other approaches and yet did not succeed.\nShould in case you are not really sure where the path is located, you can intentionally command pip install tensorslow and you will get an output of Requirement already satisfied along with the path (Note: paths of installed packages usually end at site-packages). Copy the path and get back to your working environment and do the below operations:\nimport sys\nsys.path.append(\"/past/the/copied/path/here\")\nimport tensorflow\n\n", "for python 3.8 version\ngo for anaconda navigator \nthen go for environments --> then go for base(root)----> not installed from drop box--->then search for tensorflow then install it then run the program.......hope it may helpful\n", "WHAT YOU DID RIGHT:\n\nYou have created a new environment called 'tensorflow'\nYou installed tensorflow in your environment\n\nWHAT WENT WRONG:\n\nIf you are using jupyter-notebook:\n\n\nIt is the installation from the base environment which access the base packages not your tensorflow packages\n\n\nIf you are using python file:\n\n\nThe local python installation packages are being used.\n\nSOLUTIONS\nSolution for the 1st problem :\nconda activate yourenvironment\npip install notebook\njupyter-notebook\n\n\nNow run your code on the jupyter-notebook which is found in yourenvironment.\n\nNote: Some of the libraries you installed earlier may not be found in this environment. Install them again.\n\n\nSolution for the 2nd problem:\n\nOn your computer (PC) search and open \"Edit the system environment variables\", then \"Environment Variables...\" then \"Path\".\nMake sure your anaconda installation path is above the local python installation. Click Ok [for each 3 windows opened]\nYour path should look like as in the picture here\n\n" ]

[ 28, 15, 12, 5, 3, 2, 2, 1, 1, 1, 1, 0, 0 ]

[ "Try worked for me\npython3 -m pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.12.0-py3-none-any.whl \n\n" ]

[ -1 ]

[ "anaconda", "installation", "python", "tensorflow", "windows" ]

[ "You can do it with os.open:\nimport os\nf = os.fdopen(os.open(filename, os.O_RDWR | os.O_CREAT), 'rb+')\n\nNow you can read, write in the middle of the file, seek, and so on. And it creates the file. Tested on Python 2 and 3.\n", "You should try reading the file then open writing mode, as seen here:\nwith open(\"file.txt\") as reading:\n r = reading.read()\nwith open(\"file.txt\", \"w\") as writing:\n writing.write(r)\n\n", "According to the discussion Difference between modes a, a+, w, w+, and r+ in built-in open function, the open with a mode will always write to the end of file irrespective of any intervening fseek(3) or similar.\nIf you only want to use python built-in function. I guess the solution is to first check if the file exist, and then open with r+ mode.\nFor Example:\nimport os\nfilepath = \"test.txt\"\nif not os.path.isfile(filepath):\n f = open(filepath, \"x\") # open for exclusive creation, failing if the file already exists\n f.close()\nwith open(filepath, \"r+\") as f: # random read and write\n f.seek(1)\n f.write(\"a\")\n\n" ]

[ 10, 0, 0 ]

[ "You need to use \"a\" to append, it will create the file if it does not exist or append to it if it does.\nYou cannot do what you want with append as the pointer automatically moves to the end of the file when you call the write method. \nYou could check if the file exists then use fileinput.input with inplace=True inserting a line on whichever line number you want.\nimport fileinput\nimport os\n\n\ndef random_write(f, rnd_n, line):\n if not os.path.isfile(f):\n with open(f, \"w\") as f:\n f.write(line)\n else:\n for ind, line in enumerate(fileinput.input(f, inplace=True)):\n if ind == rnd_n:\n print(\"{}\\n\".format(line) + line, end=\"\")\n else:\n print(line, end=\"\")\n\nhttp://linux.die.net/man/3/fopen\n\na+\n Open for reading and appending (writing at end of file). The file is created if it does not exist. The initial file position for reading is at the beginning of the file, but output is always appended to the end of the file.\n\nfileinput makes a f.bak copy of the file you pass in and it is deleted when the output is closed. If you specify a backup extension backup=.\"foo\" the backup file will be kept. \n" ]

[ -2 ]

[ "python" ]

[ "You can do it by using templates:\nimport plotly.graph_objects as go\n\ndraft_template = go.layout.Template()\ndraft_template.layout.annotations = [\n dict(\n name=\"draft watermark\",\n text=\"DRAFT\",\n textangle=-30,\n opacity=0.1,\n font=dict(color=\"black\", size=100),\n xref=\"paper\",\n yref=\"paper\",\n x=0.5,\n y=0.5,\n showarrow=False,\n )\n]\n\nfig=go.Figure()\nfig.update_layout(template=draft_template)\nfig.show()\n\nOutput:\n\n" ]

[ 0 ]

[]

[]

[ "plotly", "plotly_dash", "plotly_python", "python" ]

[ "Here is a one-liner way of retrieving the information you're after:\nfrom bs4 import BeautifulSoup as bs\n\nhtml = '''\n<ul class=\"product-attributes list-inline product-attributes-two-sizes\">\n <li class=\"ease \" id=\"12345\"></li>\n <li class=\"dsadsad\" id=\"000\"></li>\n <li class=\"dadsda\" id=\"000\"></li>\n</ul>\n'''\nsoup = bs(html, 'html.parser')\nitem = soup.select_one('ul[class=\"product-attributes list-inline product-attributes-two-sizes\"] li[class^=\"ease\"]').get('id')\nprint(item)\n\nResult in terminal:\n12345\n\nBeautifulSoup documentation can be found here\n" ]

[ 0 ]

[]

[]

[ "beautifulsoup", "python" ]

[ "Assuming you can tolerate reading your data into a Python string, we can use re.findall here:\n# -*- coding: utf-8 -*-\nimport re\n\ninp = \"\"\"07:54:03.383 -> Humidity:38.00%;Temperature:20.50°C;Heat index:19.60°C;\n07:59:03.415 -> Humidity:37.00%;Temperature:20.90°C;Heat index:20.01°C;\n08:04:03.435 -> Humidity:37.00%;Temperature:20.90°C;Heat index:20.01°C;\n08:09:03.484 -> Humidity:37.00%;Temperature:20.80°C;Heat index:19.90°C;\"\"\"\n\nvals = re.findall(r'^(\\d{2}:\\d{2}:\\d{2}(?:\\.\\d+)?) -> Humidity:(\\d+(?:\\.\\d+)?)%;Temperature:(\\d+(?:\\.\\d+)?)°C;Heat index:(\\d+(?:\\.\\d+)?)°C;', inp, flags=re.M)\nprint(vals)\n\nThis prints:\n[('07:54:03.383', '38.00', '20.50', '19.60'),\n ('07:59:03.415', '37.00', '20.90', '20.01'),\n ('08:04:03.435', '37.00', '20.90', '20.01'),\n ('08:09:03.484', '37.00', '20.80', '19.90')]\n\n" ]

[ 1 ]

[]

[]

[ "python" ]

[ "If you use android emulator use http://10.0.2.2:[your port]\n" ]

[ 0 ]

[]

[]

[ "android", "http_tunneling", "localhost", "python", "webserver" ]

[ "Example\ndata = {'col1': {0: 'A', 1: 'B', 2: 'A', 3: 'A', 4: 'B', 5: 'B'},\n 'col2': {0: 'A001', 1: 'BC020', 2: None, 3: None, 4: 'BC021', 5: None}}\ndf = pd.DataFrame(data)\n\ndf\n col1 col2\n0 A A001\n1 B BC020\n2 A None\n3 A None\n4 B BC021\n5 B None\n\nCode\ndf[['col3', 'col4']] = df.groupby('col1')['col2'].ffill().str.extract('(\\D+)(\\d+)')\ndf['col4'] = df['col4'].astype('int') + df.groupby(['col1', 'col4']).cumcount()\ndf['col2'] = df['col2'].fillna(df['col3'] + df['col4'].astype('str').str.zfill(3))\ndf = df.drop(['col3', 'col4'], axis=1)\n\nresult(df):\n col1 col2\n0 A A001\n1 B BC020\n2 A A002\n3 A A003\n4 B BC021\n5 B BC022\n\n", "You can use groupby.cumcount to increment the number, and add it the the number part:\ng = df.groupby('Channel')\n\n# ffill per group\n# extract letter and number part\ndf2 = (g['last unique id'].ffill()\n .str.extract(r'(\\D+)(\\d+)')\n )\n\n# convert number part to integer\n# add cumcount, merge back as string\ndf['last unique id'] = (df2[0]\n .add(df2[1].astype(int)\n .add(g.cumcount())\n .astype(str)\n )\n )\n\nprint(df)\n\nOutput:\n Channel last unique id\n0 MYNTRA MN351370\n1 NYKAA NYK38219\n2 NYKAA NYK38220\n3 NYKAA NYK38221\n4 NYKAA NYK38222\n5 NYKAA NYK38223\n6 MYNTRA MN351371\n7 MYNTRA MN351372\n8 MYNTRA MN351373\n9 MYNTRA MN351374\n10 MYNTRA MN351375\n11 MYNTRA MN351376\n\n", "Here is how you get the desired output with padding zeros to have your id always at a fixed length of 11.\ndf[\"last unique id\"] = df.groupby(\"Channel\")[\"last unique id\"].ffill()\n\ntmp = df[\"last unique id\"].str.extract(r\"(?P<ident>\\D+)(?P<num>\\d+)\", expand=True)\ntmp[\"add\"] = df.groupby(\"Channel\")[\"last unique id\"].apply(\n lambda x: x.eq(x.shift()).cumsum()\n)\n\ntotal_len_id = 11\ndf[\"last unique id\"] = tmp.apply(\n lambda row: row[\"ident\"]\n + str(int(row[\"num\"]) + row[\"add\"]).rjust(total_len_id - len(row[\"ident\"]), \"0\"),\n axis=1,\n)\n\nprint(df)\n\n Channel last unique id\n0 MYNTRA MN000351370\n1 NYKAA NYK00038219\n2 NYKAA NYK00038220\n3 NYKAA NYK00038221\n4 NYKAA NYK00038222\n5 NYKAA NYK00038223\n6 MYNTRA MN000351371\n7 MYNTRA MN000351372\n8 MYNTRA MN000351373\n9 MYNTRA MN000351374\n10 MYNTRA MN000351375\n11 MYNTRA MN000351376\n\n" ]

[ 0, 0, 0 ]

[]

[]

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

[ "You can use the Windows Sound Manager by paradoxis (https://github.com/Paradoxis/Windows-Sound-Manager). \nfrom sound import Sound\nSound.mute()\n\nEvery call to Sound.mute() will toggle mute on or off. Have a look at the main.py to see how to use the setter and getter methods.\n", "If you're also building a GUI, wxPython (and I would believe other GUI frameworks) have access to the windows audio mute \"button\".\n" ]

[ 2, 0 ]

[]

[]

[ "audio", "python", "python_3.x", "pywin32", "windows" ]

[ "Use the indent= parameter of json.dump() or json.dumps() to specify how many spaces to indent by:\n>>> import json\n>>>\n>>> your_json = '[\"foo\", {\"bar\": [\"baz\", null, 1.0, 2]}]'\n>>> parsed = json.loads(your_json)\n>>> print(json.dumps(parsed, indent=4))\n[\n \"foo\",\n {\n \"bar\": [\n \"baz\",\n null,\n 1.0,\n 2\n ]\n }\n]\n\nTo parse a file, use json.load():\nwith open('filename.txt', 'r') as handle:\n parsed = json.load(handle)\n\n", "You can do this on the command line:\npython3 -m json.tool some.json\n\n(as already mentioned in the commentaries to the question, thanks to @Kai Petzke for the python3 suggestion).\nActually python is not my favourite tool as far as json processing on the command line is concerned. For simple pretty printing is ok, but if you want to manipulate the json it can become overcomplicated. You'd soon need to write a separate script-file, you could end up with maps whose keys are u\"some-key\" (python unicode), which makes selecting fields more difficult and doesn't really go in the direction of pretty-printing.\nYou can also use jq:\njq . some.json\n\nand you get colors as a bonus (and way easier extendability).\nAddendum: There is some confusion in the comments about using jq to process large JSON files on the one hand, and having a very large jq program on the other. For pretty-printing a file consisting of a single large JSON entity, the practical limitation is RAM. For pretty-printing a 2GB file consisting of a single array of real-world data, the \"maximum resident set size\" required for pretty-printing was 5GB (whether using jq 1.5 or 1.6). Note also that jq can be used from within python after pip install jq.\n", "You could use the built-in module pprint (https://docs.python.org/3.9/library/pprint.html).\nHow you can read the file with json data and print it out.\nimport json\nimport pprint\n\njson_data = None\nwith open('file_name.txt', 'r') as f:\n data = f.read()\n json_data = json.loads(data)\n\nprint(json_data)\n{\"firstName\": \"John\", \"lastName\": \"Smith\", \"isAlive\": \"true\", \"age\": 27, \"address\": {\"streetAddress\": \"21 2nd Street\", \"city\": \"New York\", \"state\": \"NY\", \"postalCode\": \"10021-3100\"}, 'children': []}\n\npprint.pprint(json_data)\n{'address': {'city': 'New York',\n 'postalCode': '10021-3100',\n 'state': 'NY',\n 'streetAddress': '21 2nd Street'},\n 'age': 27,\n 'children': [],\n 'firstName': 'John',\n 'isAlive': True,\n 'lastName': 'Smith'}\n\nThe output is not a valid json, because pprint use single quotes and json specification require double quotes.\nIf you want to rewrite the pretty print formated json to a file, you have to use pprint.pformat.\npretty_print_json = pprint.pformat(json_data).replace(\"'\", '\"')\n\nwith open('file_name.json', 'w') as f:\n f.write(pretty_print_json)\n\n", "Pygmentize + Python json.tool = Pretty Print with Syntax Highlighting\nPygmentize is a killer tool. See this.\nI combine python json.tool with pygmentize\necho '{\"foo\": \"bar\"}' | python -m json.tool | pygmentize -l json\n\nSee the link above for pygmentize installation instruction.\nA demo of this is in the image below:\n\n", "Use this function and don't sweat having to remember if your JSON is a str or dict again - just look at the pretty print:\nimport json\n\ndef pp_json(json_thing, sort=True, indents=4):\n if type(json_thing) is str:\n print(json.dumps(json.loads(json_thing), sort_keys=sort, indent=indents))\n else:\n print(json.dumps(json_thing, sort_keys=sort, indent=indents))\n return None\n\npp_json(your_json_string_or_dict)\n\n", "Use pprint: https://docs.python.org/3.6/library/pprint.html\nimport pprint\npprint.pprint(json)\n\nprint() compared to pprint.pprint()\nprint(json)\n{'feed': {'title': 'W3Schools Home Page', 'title_detail': {'type': 'text/plain', 'language': None, 'base': '', 'value': 'W3Schools Home Page'}, 'links': [{'rel': 'alternate', 'type': 'text/html', 'href': 'https://www.w3schools.com'}], 'link': 'https://www.w3schools.com', 'subtitle': 'Free web building tutorials', 'subtitle_detail': {'type': 'text/html', 'language': None, 'base': '', 'value': 'Free web building tutorials'}}, 'entries': [], 'bozo': 0, 'encoding': 'utf-8', 'version': 'rss20', 'namespaces': {}}\n\npprint.pprint(json)\n{'bozo': 0,\n 'encoding': 'utf-8',\n 'entries': [],\n 'feed': {'link': 'https://www.w3schools.com',\n 'links': [{'href': 'https://www.w3schools.com',\n 'rel': 'alternate',\n 'type': 'text/html'}],\n 'subtitle': 'Free web building tutorials',\n 'subtitle_detail': {'base': '',\n 'language': None,\n 'type': 'text/html',\n 'value': 'Free web building tutorials'},\n 'title': 'W3Schools Home Page',\n 'title_detail': {'base': '',\n 'language': None,\n 'type': 'text/plain',\n 'value': 'W3Schools Home Page'}},\n 'namespaces': {},\n 'version': 'rss20'}\n\n", "To be able to pretty print from the command line and be able to have control over the indentation etc. you can set up an alias similar to this:\nalias jsonpp=\"python -c 'import sys, json; print json.dumps(json.load(sys.stdin), sort_keys=True, indent=2)'\"\n\nAnd then use the alias in one of these ways:\ncat myfile.json | jsonpp\njsonpp < myfile.json\n\n", "def saveJson(date,fileToSave):\n with open(fileToSave, 'w+') as fileToSave:\n json.dump(date, fileToSave, ensure_ascii=True, indent=4, sort_keys=True)\n\nIt works to display or save it to a file.\n", "Here's a simple example of pretty printing JSON to the console in a nice way in Python, without requiring the JSON to be on your computer as a local file: \nimport pprint\nimport json \nfrom urllib.request import urlopen # (Only used to get this example)\n\n# Getting a JSON example for this example \nr = urlopen(\"https://mdn.github.io/fetch-examples/fetch-json/products.json\")\ntext = r.read() \n\n# To print it\npprint.pprint(json.loads(text))\n\n", "You could try pprintjson.\n\nInstallation\n$ pip3 install pprintjson\n\nUsage\nPretty print JSON from a file using the pprintjson CLI.\n$ pprintjson \"./path/to/file.json\"\n\nPretty print JSON from a stdin using the pprintjson CLI.\n$ echo '{ \"a\": 1, \"b\": \"string\", \"c\": true }' | pprintjson\n\nPretty print JSON from a string using the pprintjson CLI.\n$ pprintjson -c '{ \"a\": 1, \"b\": \"string\", \"c\": true }'\n\nPretty print JSON from a string with an indent of 1.\n$ pprintjson -c '{ \"a\": 1, \"b\": \"string\", \"c\": true }' -i 1\n\nPretty print JSON from a string and save output to a file output.json.\n$ pprintjson -c '{ \"a\": 1, \"b\": \"string\", \"c\": true }' -o ./output.json\n\nOutput\n\n", "I think that's better to parse the json before, to avoid errors:\ndef format_response(response):\n try:\n parsed = json.loads(response.text)\n except JSONDecodeError:\n return response.text\n return json.dumps(parsed, ensure_ascii=True, indent=4)\n\n", "I had a similar requirement to dump the contents of json file for logging, something quick and easy:\nprint(json.dumps(json.load(open(os.path.join('<myPath>', '<myjson>'), \"r\")), indent = 4 ))\n\nif you use it often then put it in a function:\ndef pp_json_file(path, file):\n print(json.dumps(json.load(open(os.path.join(path, file), \"r\")), indent = 4))\n\n", "Hopefully this helps someone else.\nIn the case when there is a error that something is not json serializable the answers above will not work. If you only want to save it so that is human readable then you need to recursively call string on all the non dictionary elements of your dictionary. If you want to load it later then save it as a pickle file then load it (e.g. torch.save(obj, f) works fine).\nThis is what worked for me:\n#%%\n\ndef _to_json_dict_with_strings(dictionary):\n \"\"\"\n Convert dict to dict with leafs only being strings. So it recursively makes keys to strings\n if they are not dictionaries.\n\n Use case:\n - saving dictionary of tensors (convert the tensors to strins!)\n - saving arguments from script (e.g. argparse) for it to be pretty\n\n e.g.\n\n \"\"\"\n if type(dictionary) != dict:\n return str(dictionary)\n d = {k: _to_json_dict_with_strings(v) for k, v in dictionary.items()}\n return d\n\ndef to_json(dic):\n import types\n import argparse\n\n if type(dic) is dict:\n dic = dict(dic)\n else:\n dic = dic.__dict__\n return _to_json_dict_with_strings(dic)\n\ndef save_to_json_pretty(dic, path, mode='w', indent=4, sort_keys=True):\n import json\n\n with open(path, mode) as f:\n json.dump(to_json(dic), f, indent=indent, sort_keys=sort_keys)\n\ndef my_pprint(dic):\n \"\"\"\n\n @param dic:\n @return:\n\n Note: this is not the same as pprint.\n \"\"\"\n import json\n\n # make all keys strings recursively with their naitve str function\n dic = to_json(dic)\n # pretty print\n pretty_dic = json.dumps(dic, indent=4, sort_keys=True)\n print(pretty_dic)\n # print(json.dumps(dic, indent=4, sort_keys=True))\n # return pretty_dic\n\nimport torch\n# import json # results in non serializabe errors for torch.Tensors\nfrom pprint import pprint\n\ndic = {'x': torch.randn(1, 3), 'rec': {'y': torch.randn(1, 3)}}\n\nmy_pprint(dic)\npprint(dic)\n\noutput:\n{\n \"rec\": {\n \"y\": \"tensor([[-0.3137, 0.3138, 1.2894]])\"\n },\n \"x\": \"tensor([[-1.5909, 0.0516, -1.5445]])\"\n}\n{'rec': {'y': tensor([[-0.3137, 0.3138, 1.2894]])},\n 'x': tensor([[-1.5909, 0.0516, -1.5445]])}\n\nI don't know why returning the string then printing it doesn't work but it seems you have to put the dumps directly in the print statement. Note pprint as it has been suggested already works too. Note not all objects can be converted to a dict with dict(dic) which is why some of my code has checks on this condition.\nContext:\nI wanted to save pytorch strings but I kept getting the error:\nTypeError: tensor is not JSON serializable\n\nso I coded the above. Note that yes, in pytorch you use torch.save but pickle files aren't readable. Check this related post: https://discuss.pytorch.org/t/typeerror-tensor-is-not-json-serializable/36065/3\n\nPPrint also has indent arguments but I didn't like how it looks:\n pprint(stats, indent=4, sort_dicts=True)\n\noutput:\n{ 'cca': { 'all': {'avg': tensor(0.5132), 'std': tensor(0.1532)},\n 'avg': tensor([0.5993, 0.5571, 0.4910, 0.4053]),\n 'rep': {'avg': tensor(0.5491), 'std': tensor(0.0743)},\n 'std': tensor([0.0316, 0.0368, 0.0910, 0.2490])},\n 'cka': { 'all': {'avg': tensor(0.7885), 'std': tensor(0.3449)},\n 'avg': tensor([1.0000, 0.9840, 0.9442, 0.2260]),\n 'rep': {'avg': tensor(0.9761), 'std': tensor(0.0468)},\n 'std': tensor([5.9043e-07, 2.9688e-02, 6.3634e-02, 2.1686e-01])},\n 'cosine': { 'all': {'avg': tensor(0.5931), 'std': tensor(0.7158)},\n 'avg': tensor([ 0.9825, 0.9001, 0.7909, -0.3012]),\n 'rep': {'avg': tensor(0.8912), 'std': tensor(0.1571)},\n 'std': tensor([0.0371, 0.1232, 0.1976, 0.9536])},\n 'nes': { 'all': {'avg': tensor(0.6771), 'std': tensor(0.2891)},\n 'avg': tensor([0.9326, 0.8038, 0.6852, 0.2867]),\n 'rep': {'avg': tensor(0.8072), 'std': tensor(0.1596)},\n 'std': tensor([0.0695, 0.1266, 0.1578, 0.2339])},\n 'nes_output': { 'all': {'avg': None, 'std': None},\n 'avg': tensor(0.2975),\n 'rep': {'avg': None, 'std': None},\n 'std': tensor(0.0945)},\n 'query_loss': { 'all': {'avg': None, 'std': None},\n 'avg': tensor(12.3746),\n 'rep': {'avg': None, 'std': None},\n 'std': tensor(13.7910)}}\n\ncompare to:\n{\n \"cca\": {\n \"all\": {\n \"avg\": \"tensor(0.5144)\",\n \"std\": \"tensor(0.1553)\"\n },\n \"avg\": \"tensor([0.6023, 0.5612, 0.4874, 0.4066])\",\n \"rep\": {\n \"avg\": \"tensor(0.5503)\",\n \"std\": \"tensor(0.0796)\"\n },\n \"std\": \"tensor([0.0285, 0.0367, 0.1004, 0.2493])\"\n },\n \"cka\": {\n \"all\": {\n \"avg\": \"tensor(0.7888)\",\n \"std\": \"tensor(0.3444)\"\n },\n \"avg\": \"tensor([1.0000, 0.9840, 0.9439, 0.2271])\",\n \"rep\": {\n \"avg\": \"tensor(0.9760)\",\n \"std\": \"tensor(0.0468)\"\n },\n \"std\": \"tensor([5.7627e-07, 2.9689e-02, 6.3541e-02, 2.1684e-01])\"\n },\n \"cosine\": {\n \"all\": {\n \"avg\": \"tensor(0.5945)\",\n \"std\": \"tensor(0.7146)\"\n },\n \"avg\": \"tensor([ 0.9825, 0.9001, 0.7907, -0.2953])\",\n \"rep\": {\n \"avg\": \"tensor(0.8911)\",\n \"std\": \"tensor(0.1571)\"\n },\n \"std\": \"tensor([0.0371, 0.1231, 0.1975, 0.9554])\"\n },\n \"nes\": {\n \"all\": {\n \"avg\": \"tensor(0.6773)\",\n \"std\": \"tensor(0.2886)\"\n },\n \"avg\": \"tensor([0.9326, 0.8037, 0.6849, 0.2881])\",\n \"rep\": {\n \"avg\": \"tensor(0.8070)\",\n \"std\": \"tensor(0.1595)\"\n },\n \"std\": \"tensor([0.0695, 0.1265, 0.1576, 0.2341])\"\n },\n \"nes_output\": {\n \"all\": {\n \"avg\": \"None\",\n \"std\": \"None\"\n },\n \"avg\": \"tensor(0.2976)\",\n \"rep\": {\n \"avg\": \"None\",\n \"std\": \"None\"\n },\n \"std\": \"tensor(0.0945)\"\n },\n \"query_loss\": {\n \"all\": {\n \"avg\": \"None\",\n \"std\": \"None\"\n },\n \"avg\": \"tensor(12.3616)\",\n \"rep\": {\n \"avg\": \"None\",\n \"std\": \"None\"\n },\n \"std\": \"tensor(13.7976)\"\n }\n}\n\n", "json.loads() converts the json data to dictionary. Finally, use json.dumps() to prettyprint the json.\n_json = '{\"name\":\"John\", \"age\":30, \"car\":null}'\n\ndata = json.loads(_json)\n\nprint (json.dumps(data, indent=2))\n\n", "For most uses, indent should do it:\nprint(json.dumps(parsed, indent=2))\n\nA Json structure is basically tree structure.\nWhile trying to find something fancier, I came across this nice paper depicting other forms of nice trees that might be interesting: https://blog.ouseful.info/2021/07/13/exploring-the-hierarchical-structure-of-dataframes-and-csv-data/.\nIt has some interactive trees and even comes with some code including this collapsing tree from so:\n\nOther samples include using plotly Here is the code example from plotly:\nimport plotly.express as px\nfig = px.treemap(\n names = [\"Eve\",\"Cain\", \"Seth\", \"Enos\", \"Noam\", \"Abel\", \"Awan\", \"Enoch\", \"Azura\"],\n parents = [\"\", \"Eve\", \"Eve\", \"Seth\", \"Seth\", \"Eve\", \"Eve\", \"Awan\", \"Eve\"]\n)\nfig.update_traces(root_color=\"lightgrey\")\nfig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\nfig.show()\n\n\n\nAnd using treelib. On that note, This github also provides nice visualizations. Here is one example using treelib:\n#%pip install treelib\nfrom treelib import Tree\n\ncountry_tree = Tree()\n# Create a root node\ncountry_tree.create_node(\"Country\", \"countries\")\n\n# Group by country\nfor country, regions in wards_df.head(5).groupby([\"CTRY17NM\", \"CTRY17CD\"]):\n # Generate a node for each country\n country_tree.create_node(country[0], country[1], parent=\"countries\")\n # Group by region\n for region, las in regions.groupby([\"GOR10NM\", \"GOR10CD\"]):\n # Generate a node for each region\n country_tree.create_node(region[0], region[1], parent=country[1])\n # Group by local authority\n for la, wards in las.groupby(['LAD17NM', 'LAD17CD']):\n # Create a node for each local authority\n country_tree.create_node(la[0], la[1], parent=region[1])\n for ward, _ in wards.groupby(['WD17NM', 'WD17CD']):\n # Create a leaf node for each ward\n country_tree.create_node(ward[0], ward[1], parent=la[1])\n\n# Output the hierarchical data\ncountry_tree.show()\n\n\nI have, based on this, created a function to convert json to a tree:\nfrom treelib import Node, Tree, node\ndef json_2_tree(o , parent_id=None, tree=None, counter_byref=[0], verbose=False, listsNodeSymbol='+'):\n if tree is None:\n tree = Tree()\n root_id = counter_byref[0]\n if verbose:\n print(f\"tree.create_node({'+'}, {root_id})\")\n tree.create_node('+', root_id)\n counter_byref[0] += 1\n parent_id = root_id\n if type(o) == dict:\n for k,v in o.items():\n this_id = counter_byref[0]\n if verbose:\n print(f\"tree.create_node({str(k)}, {this_id}, parent={parent_id})\")\n tree.create_node(str(k), this_id, parent=parent_id)\n counter_byref[0] += 1\n json_2_tree(v , parent_id=this_id, tree=tree, counter_byref=counter_byref, verbose=verbose, listsNodeSymbol=listsNodeSymbol)\n elif type(o) == list:\n if listsNodeSymbol is not None:\n if verbose:\n print(f\"tree.create_node({listsNodeSymbol}, {counter_byref[0]}, parent={parent_id})\")\n tree.create_node(listsNodeSymbol, counter_byref[0], parent=parent_id)\n parent_id=counter_byref[0]\n counter_byref[0] += 1 \n for i in o:\n json_2_tree(i , parent_id=parent_id, tree=tree, counter_byref=counter_byref, verbose=verbose,listsNodeSymbol=listsNodeSymbol)\n else: #node\n if verbose:\n print(f\"tree.create_node({str(o)}, {counter_byref[0]}, parent={parent_id})\")\n tree.create_node(str(o), counter_byref[0], parent=parent_id)\n counter_byref[0] += 1\n return tree\n\nThen for example:\nimport json\njson_2_tree(json.loads('{\"2\": 3, \"4\": [5, 6]}'),verbose=False,listsNodeSymbol='+').show() \n\ngives the more descriptive:\n+\n├── 2\n│ └── 3\n└── 4\n └── +\n ├── 5\n └── 6\n\nWhile\njson_2_tree(json.loads('{\"2\": 3, \"4\": [5, 6]}'),listsNodeSymbol=None).show() \n\nGives the more compact\n+\n├── 2\n│ └── 3\n└── 4\n ├── 5\n └── 6\n\nFor a more extensive conversion with different flavors of trees, checkout this function\n" ]

[ 2665, 446, 120, 61, 45, 23, 19, 9, 8, 7, 3, 3, 0, 0, 0 ]

[ "It's far from perfect, but it does the job.\ndata = data.replace(',\"',',\\n\"')\n\nyou can improve it, add indenting and so on, but if you just want to be able to read a cleaner json, this is the way to go.\n" ]

[ -8 ]

[ "formatting", "json", "pretty_print", "python" ]

[ "Snapchat now supports the web or browser. So you can take a look at the tutorials of pyautogui module in python. you can manipulate the keyboard and mouse events and respond to the messages with prewritten messages of yours. Your task can be done easily.\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "You have to declare current_max_cosine_similarity_score as global (or nonlocal) in func().\nBut that's nevertheless a bad idea. The \"pythonic\" way would be to use a generator, closure or a class with a get_current_maximum().\nProbably the most \"pythonic\" closure solves your problem:\nfrom functools import reduce\n\ndef calc_closure():\n def _calc(value, element):\n # do calculations on element here\n if element > value:\n _calc.current_max_value = element\n return _calc.current_max_value \n # using an attribute makes current_max_value accessible from outer\n _calc.current_max_value = -np.math.inf\n return _calc\n\nclosure_1 = calc_closure()\nclosure_2 = calc_closure()\n\nprint(reduce(closure_1, [1, 2, 3, 4, 1]))\nprint(closure_1.current_max_value )\nprint(closure_2.current_max_value )\n\nOutput:\n4\n4\n-inf\n" ]

[ 2 ]

[]

[]

[ "python", "python_3.x" ]

[ "The reference to urllib is actually a bit misleading. The following might give some insight to the cgi interface from a python programmers point of view:\n#!/usr/bin/python3\n'''\npreflight_cgi.py\ncheck the preflight option call\n'''\n\nimport sys\nimport os\n\nif __name__ == \"__main__\":\n print(\"Content-Type: text/html\") # HTML is following\n print() \n i = 0\n for arg in sys.argv:\n print(\"argv{}: {}\\n\".format(i, arg))\n i = 0\n for line in sys.stdin:\n print(\"line {}: {}\\n\".format(i, line))\n i += 1\n \n print(\"<TITLE>CGI script output</TITLE>\")\n print(\"<H1>This is the environmet</H1>\")\n for it in os.environ.items():\n print(\"<p>{} = {}</p>\".format(it[0], it[1]))\n\nPut that where your current cgi.FieldStorage based app is and call it via the address line of the browser.\nYou will see something like\n[...]\nCONTENT_LENGTH = 0\nQUERY_STRING = par=meter&var=able\nREQUEST_URI = /cgi-bin/preflight_cgi.py?par=meter&var=able\nREDIRECT_STATUS = 200\nSCRIPT_NAME = /cgi-bin/preflight_cgi.py\nREQUEST_METHOD = GET\nSERVER_PROTOCOL = HTTP/1.1\nSERVER_SOFTWARE = lighttpd/1.4.53\nGATEWAY_INTERFACE = CGI/1.1\nREQUEST_SCHEME = http\nSERVER_PORT = 80\n[...]\nThe environment variables have already most of done.\nAs an alternative you can also use one of the http.server classes to build the server completely in python.\n" ]

[ 0 ]

[]

[]

[ "python", "webserver" ]

[ "df = pd.DataFrame([['2022-06-02', ['municipal', 'districts', 'mikhailovsky', '84', 'kamyshinsky', '56']], ['2022-06-02', ['municipal', 'districts', 'mikhailovsky', '84', 'kamyshinsky', '56']], ['2022-06-02', ['municipal', 'districts', 'mikhailovsky', '84', 'kamyshinsky', '56']]], columns=['date', 'text'])\n\nmapper = {'mikhailovsky': 'mikhailovka',\n 'kamyshinsky': 'kamyshin'}\n\nfor k, v in mapper.items():\n df.text = df.text.apply(lambda x: [element.replace(k, v) for element in x])\n\nThe code above changes df from this:\n date text\n0 2022-06-02 [municipal, districts, mikhailovsky, 84, kamyshinsky, 56]\n1 2022-06-02 [municipal, districts, mikhailovsky, 84, kamyshinsky, 56]\n2 2022-06-02 [municipal, districts, mikhailovsky, 84, kamyshinsky, 56]\n\ninto this:\n date text\n0 2022-06-02 [municipal, districts, mikhailovka, 84, kamyshin, 56]\n1 2022-06-02 [municipal, districts, mikhailovka, 84, kamyshin, 56]\n2 2022-06-02 [municipal, districts, mikhailovka, 84, kamyshin, 56]\n\n" ]

[ 2 ]

[]

[]

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

[ "I have the same issue.\nThe scipy 1.7.3 docs specifies\n1.16.5 <= numpy <1.24.0 while in scipy 1.7.3 code setup.py and __init__.py we have np_maxversion = '1.23.0'.\nAs I rely on conda channel defaults to setup Intel MKL libraries for numpy and scipy I decided to pin \"numpy>=1.22.3,<1.23.0\" until a newer scipy is release on conda channel defaults:\nconda create -n myenv python \"numpy>=1.22.3,<1.23.0\" scipy\n\n", "According to the setup.py file of the scipy 1.7.3, numpy is indeed <1.23.0. As @Libra said, the docs must be incorrect. You can:\n\nIgnore this warning\nUse scipy 1.8\nUse numpy < 1.23.0\n\nEdit:\nThis is now fixed in the dev docs of scipy https://scipy.github.io/devdocs/dev/toolchain.html\n", "Since \"UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required\", you can update the numpy version with the specified range to remove the warning.\nAccording to syntax guidelines of conda and pip, updating your numpy version by\nconda install \"numpy>=1.16.5,<1.23.0\"\nor\npip install \"numpy>=1.16.5,<1.23.0\"\ninside your environment will work.\nYour numpy will be overwritten by the best-match version (1.22.4) in the specified range. You can double-check the new numpy version by:\nconda list numpy\nor\npip show numpy\n" ]

[ 7, 5, 0 ]

[]

[]

[ "conda", "numpy", "python", "scipy" ]

[ "Usually an error like this should turn you to check the formatting of your file. As some others mentioned, the first line could be empty for whatever reason. You can check for an empty file prior to this by doing the following:\ntest.txt contents:\n(empty file)\nimport os\n\nf = open(\"test.txt\")\n\nif os.path.getsize(\"test.txt\") == 0:\n print(\"Empty File\")\n f.close()\nelse:\n print(\"Some content exists\")\n\nOutput: \"Empty File\" (file is closed too since there is nothing to read)\nAlternatively, you can read the entire file if you somehow can't access its contents (some schools do this). Using this technique will give you an idea of what you are dealing with in your file if you can't view it within your IDE:\nf = open(\"test.txt\")\n\nfor line in f:\n print(line)\n\nf.close()\n\nBut let's say that just the first line of your file is empty. There are several ways you can check if a line is empty. If line 1 is blank but any line following it has content, reading line 1 from file will equal '\\n':\ntest.txt contents:\nline 1 = '\\n' (blank line), line 2 = 20.72\nimport os\n\nf = open(\"test.txt\")\n\nif os.path.getsize(\"test.txt\") == 0:\n print(\"Empty File\")\n f.close()\nelse:\n print(\"Some content exists\")\n\nreader = f.readline()\n\n# The second condition is if you are using binary mode\nif reader == '\\n' or reader == b\"\\r\\n\":\n print(\"Blank Line\")\n\nOutput: \"Some content exists\" & \"Blank line\"\nThis is just my suggestion. As for your integer conversion, if you have a '.' in your currency amount, you will get a conversion error for trying to data cast it into an integer. However, I do not know if your currency will be rounded off to the nearest dollar or if you have any indication of change, so I will leave this to you.\nHappy coding! Please up vote my answer if useful so I can participate in Stack Overflow in new ways :)\n" ]

[ 0 ]

[]

[]

[ "function", "python", "runtime_error", "syntax_error" ]

[ "You are trying to create new label widgits in your function when you should just be updating the already existing ones.\nTry:\ndef Creation():\n mycur=mydb.cursor()\n \n name=a.get()\n dob=b.get()\n Class=c.get()\n admn=d.get()\n add=e.get()\n mob=f.get()\n tra=g.get()\n query3=(\"insert into idcard values('{}' , '{}' , '{}' , {} , '{}' , {} , '{}')\").format(name,dob,Class,admn,add,mob,tra)\n mycur.execute(query3)\n mycur.execute(\"commit\")\n q=mycur.fetchone()\n\n L11.config(text=q[0])\n L12.config(text=q[1])\n L13.config(text=q[2])\n L14.config(text=q[3])\n L15.config(text=q[4])\n L16.config(text=q[5])\n L16.config(text=q[6])\n\nbutton=Button(m,text=\"Create\",command=Creation,width=10,height=2)\nbutton.place(x=400,y=50)\n\n" ]

[ 0 ]

[]

[]

[ "mysql", "python", "tkinter" ]

[ "Example\nexample code is wrong. someone who want exercise, use following code\ndf = pd.DataFrame({'Diff':[1,1,2,3,4,4,5,6,7,7,8,9,9,10], \n 'value':'x,x,y,x,x,x,y,x,z,x,x,y,y,z'.split(',')})\n\nCode\nlabels = ['0-3', '3-6', '6-9', '>=9']\ngrouper = pd.cut(df['Diff'], bins=[0, 3, 6, 9, float('inf')], right=False, labels=labels)\npd.crosstab(grouper, df['value'])\n\noutput:\nvalue x y z\nDiff \n0-3 2 1 0\n3-6 3 1 0\n6-9 3 0 1\n>=9 0 2 1\n\n" ]

[ 1 ]

[]

[]

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

[ "You want to do something like this:\n# with is like your try .. finally block in this case\nwith open('stats.txt', 'r') as file:\n # read a list of lines into data\n data = file.readlines()\n\nprint data\nprint \"Your name: \" + data[0]\n\n# now change the 2nd line, note that you have to add a newline\ndata[1] = 'Mage\\n'\n\n# and write everything back\nwith open('stats.txt', 'w') as file:\n file.writelines( data )\n\nThe reason for this is that you can't do something like \"change line 2\" directly in a file. You can only overwrite (not delete) parts of a file - that means that the new content just covers the old content. So, if you wrote 'Mage' over line 2, the resulting line would be 'Mageior'.\n", "def replace_line(file_name, line_num, text):\n lines = open(file_name, 'r').readlines()\n lines[line_num] = text\n out = open(file_name, 'w')\n out.writelines(lines)\n out.close()\n\nAnd then:\nreplace_line('stats.txt', 0, 'Mage')\n\n", "you can use fileinput to do in place editing\nimport fileinput\nfor line in fileinput.FileInput(\"myfile\", inplace=1):\n if line .....:\n print line\n\n", "You can do it in two ways, choose what suits your requirement:\nMethod I.) Replacing using line number. You can use built-in function enumerate() in this case:\nFirst, in read mode get all data in a variable\nwith open(\"your_file.txt\",'r') as f:\n get_all=f.readlines()\n\nSecond, write to the file (where enumerate comes to action) \nwith open(\"your_file.txt\",'w') as f:\n for i,line in enumerate(get_all,1): ## STARTS THE NUMBERING FROM 1 (by default it begins with 0) \n if i == 2: ## OVERWRITES line:2\n f.writelines(\"Mage\\n\")\n else:\n f.writelines(line)\n\nMethod II.) Using the keyword you want to replace:\nOpen file in read mode and copy the contents to a list\nwith open(\"some_file.txt\",\"r\") as f:\n newline=[]\n for word in f.readlines(): \n newline.append(word.replace(\"Warrior\",\"Mage\")) ## Replace the keyword while you copy. \n\n\"Warrior\" has been replaced by \"Mage\", so write the updated data to the file:\nwith open(\"some_file.txt\",\"w\") as f:\n for line in newline:\n f.writelines(line)\n\nThis is what the output will be in both cases:\nDan Dan \nWarrior ------> Mage \n500 500 \n1 1 \n0 0 \n\n", "If your text contains only one individual:\nimport re\n\n# creation\nwith open('pers.txt','wb') as g:\n g.write('Dan \\n Warrior \\n 500 \\r\\n 1 \\r 0 ')\n\nwith open('pers.txt','rb') as h:\n print 'exact content of pers.txt before treatment:\\n',repr(h.read())\nwith open('pers.txt','rU') as h:\n print '\\nrU-display of pers.txt before treatment:\\n',h.read()\n\n\n# treatment\ndef roplo(file_name,what):\n patR = re.compile('^([^\\r\\n]+[\\r\\n]+)[^\\r\\n]+')\n with open(file_name,'rb+') as f:\n ch = f.read()\n f.seek(0)\n f.write(patR.sub('\\\\1'+what,ch))\nroplo('pers.txt','Mage')\n\n\n# after treatment\nwith open('pers.txt','rb') as h:\n print '\\nexact content of pers.txt after treatment:\\n',repr(h.read())\nwith open('pers.txt','rU') as h:\n print '\\nrU-display of pers.txt after treatment:\\n',h.read()\n\nIf your text contains several individuals:\nimport re\n# creation\nwith open('pers.txt','wb') as g:\n g.write('Dan \\n Warrior \\n 500 \\r\\n 1 \\r 0 \\n Jim \\n dragonfly\\r300\\r2\\n10\\r\\nSomo\\ncosmonaut\\n490\\r\\n3\\r65')\n\nwith open('pers.txt','rb') as h:\n print 'exact content of pers.txt before treatment:\\n',repr(h.read())\nwith open('pers.txt','rU') as h:\n print '\\nrU-display of pers.txt before treatment:\\n',h.read()\n\n\n# treatment\ndef ripli(file_name,who,what):\n with open(file_name,'rb+') as f:\n ch = f.read()\n x,y = re.search('^\\s*'+who+'\\s*[\\r\\n]+([^\\r\\n]+)',ch,re.MULTILINE).span(1)\n f.seek(x)\n f.write(what+ch[y:])\nripli('pers.txt','Jim','Wizard')\n\n\n# after treatment\nwith open('pers.txt','rb') as h:\n print 'exact content of pers.txt after treatment:\\n',repr(h.read())\nwith open('pers.txt','rU') as h:\n print '\\nrU-display of pers.txt after treatment:\\n',h.read()\n\nIf the “job“ of an individual was of a constant length in the texte, you could change only the portion of texte corresponding to the “job“ the desired individual:\nthat’s the same idea as senderle’s one.\nBut according to me, better would be to put the characteristics of individuals in a dictionnary recorded in file with cPickle:\nfrom cPickle import dump, load\n\nwith open('cards','wb') as f:\n dump({'Dan':['Warrior',500,1,0],'Jim':['dragonfly',300,2,10],'Somo':['cosmonaut',490,3,65]},f)\n\nwith open('cards','rb') as g:\n id_cards = load(g)\nprint 'id_cards before change==',id_cards\n\nid_cards['Jim'][0] = 'Wizard'\n\nwith open('cards','w') as h:\n dump(id_cards,h)\n\nwith open('cards') as e:\n id_cards = load(e)\nprint '\\nid_cards after change==',id_cards\n\n", "I have been practising working on files this evening and realised that I can build on Jochen's answer to provide greater functionality for repeated/multiple use. Unfortunately my answer does not address issue of dealing with large files but does make life easier in smaller files.\nwith open('filetochange.txt', 'r+') as foo:\n data = foo.readlines() #reads file as list\n pos = int(input(\"Which position in list to edit? \"))-1 #list position to edit\n data.insert(pos, \"more foo\"+\"\\n\") #inserts before item to edit\n x = data[pos+1]\n data.remove(x) #removes item to edit\n foo.seek(0) #seeks beginning of file\n for i in data:\n i.strip() #strips \"\\n\" from list items\n foo.write(str(i))\n\n", "Suppose I have a file named file_name as following:\nthis is python\nit is file handling\nthis is editing of line\n\nWe have to replace line 2 with \"modification is done\":\nf=open(\"file_name\",\"r+\")\na=f.readlines()\nfor line in f:\n if line.startswith(\"rai\"):\n p=a.index(line)\n#so now we have the position of the line which to be modified\na[p]=\"modification is done\"\nf.seek(0)\nf.truncate() #ersing all data from the file\nf.close()\n#so now we have an empty file and we will write the modified content now in the file\no=open(\"file_name\",\"w\")\nfor i in a:\n o.write(i)\no.close()\n#now the modification is done in the file\n\n", "writing initial data, print an empty str for updating it to a new data\nhere we insert an empty str in the last line of the code, this code can be used in interative updation, in other words appending data in text.txt file\nwith open(\"data.txt\", 'w') as f:\n f.write('first line\\n'\n 'second line\\n'\n 'third line\\n'\n 'fourth line\\n'\n ' \\n')\n\nupdating data in the last line of the text file\nmy_file=open('data.txt')\nstring_list = my_file.readlines()\nstring_list[-1] = \"Edit the list of strings as desired\\n\"\nmy_file = open(\"data.txt\", \"w\")\nnew_file_contents = \"\". join(string_list)\nmy_file. write(new_file_contents)\n\n", "I used to have same request, eventually ended up with Jinja templating. Change your text file to below, and a variable lastname, then you can render the template by passing lastname='Meg', that's the most efficient and quickest way I can think of.\nDan\n{{ lastname }}\nWarrior\n500\n1\n0\n" ]

[ 162, 34, 28, 16, 3, 2, 0, 0, 0 ]

[ "#read file lines and edit specific item\n\nfile=open(\"pythonmydemo.txt\",'r')\na=file.readlines()\nprint(a[0][6:11])\n\na[0]=a[0][0:5]+' Ericsson\\n'\nprint(a[0])\n\nfile=open(\"pythonmydemo.txt\",'w')\nfile.writelines(a)\nfile.close()\nprint(a)\n\n", "This is the easiest way to do this.\nf = open(\"file.txt\", \"wt\")\nfor line in f:\n f.write(line.replace('foo', 'bar'))\nf.close()\n\nI hope it will work for you.\n" ]

[ -1, -2 ]

[ "io", "python" ]

[ "you can do it like that:\nCode\nfirst_dict = {'a': ['1', '2', '3'],\n 'b': ['4', '5'],\n 'c': ['6'],\n }\n\nsecond_dict = {'1': 'wqeewe',\n '2': 'efsafa',\n '4': 'fsasaf',\n '6': 'kgoeew',\n '7': 'fkowew'\n }\n\nthird_dict = dict()\n\nfor second_key in second_dict.keys():\n found = False\n for first_key, value in first_dict.items():\n if second_key in value:\n third_dict.setdefault(second_key, first_key )\n found = True\n if not found:\n third_dict.setdefault(second_key, None)\n \nprint(third_dict)\n\nOutput:\n{'1': 'a', '2': 'a', '4': 'b', '6': 'c', '7': None}\n\nHope this helps\n", "version with a_dict.get()\nthird_dict = {i: {i:k for k,v in first_dict.items() for i in v}.get(i) for i in second_dict.keys()}\n\nthis part {i:k for k,v in first_dict.items() for i in v}\ncreates a dict like {'1': 'a', '2': 'a', '3': 'a', '4': 'b', '5': 'b', '6': 'c'}\n", "You can map the values in the first dictionary to their keys with:\nvalues_map = dict([a for k, v in first_dict.items() for a in zip(v, k*len(v))])\n\nthen use this map to create the third dictionary:\nthird_dict = {key: values_map.get(key) for key, value in second_dict.items()}\n\nSince I get that the first_dict may contain single values instead of list you may want first to convert those values to list with:\nfirst_dict = dict(map(lambda x: (x[0], x[1]) if isinstance(x[1], list) else (x[0], [str(x[1])]), first_dict.items()))\n\n", "res = {\n x: k\n for k, xs in first_dict.items()\n for x in xs\n if x in second_dict\n}\n\nthis creates 1:a , 2:a, 4:b etc. If you also want missing keys like 7:None join it with a dummy dict:\nres = {k: None for k in second_dict} | {\n x: k\n for k, xs in first_dict.items()\n for x in xs\n if x in second_dict\n}\n\n" ]

[ 1, 1, 0, 0 ]

[]

[]

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

[ "Because at the end of every iteration of your for loop you are reconverting the value that is given in the parameter datestr and that value is never updated. You are also converting it to a string while trying to add a timedelta object. You should leave the value as a datetime object and convert to string once the for loop has finished if you still need to.\nJust change the variable used in the bottom assignment to aux_date to aux_date and remove all of the string conversions, that should at least get you going in the right direction.\nfor example:\nimport re, datetime\n\ndef add_months(datestr, months):\n ref_year, ref_month = \"\", \"\"\n ref_year_is_leap_year = False # condicional booleano, cuya logica binaria intenta establecer si es o no bisiesto el año tomado como referencia\n\n aux_date = datetime.datetime.strptime(datestr, \"%Y-%m-%d\")\n print(repr(aux_date))\n\n for i_month in range(int(months)):\n\n i_month = (\n i_month + 1\n ) # I add a unit since the months are \"numerical quantities\", that is, they are expressed in natural numbers, so I need it to start from 1 and not from 0 like the iter variable in python\n\n m1 = re.search(\n r\"(?P<year>\\d*)-(?P<month>\\d{2})-(?P<startDay>\\d{2})\",\n str(aux_date),\n re.IGNORECASE,\n )\n if m1:\n ref_year, ref_month = (\n str(m1.groups()[0]).strip(),\n str(m1.groups()[1]).strip(),\n )\n\n number_of_days_in_each_month = {\n \"01\": \"31\",\n \"02\": \"28\",\n \"03\": \"31\",\n \"04\": \"30\",\n \"05\": \"31\",\n \"06\": \"30\",\n \"07\": \"31\",\n \"08\": \"31\",\n \"09\": \"30\",\n \"10\": \"31\",\n \"11\": \"30\",\n \"12\": \"31\",\n }\n\n n_days_in_this_i_month = number_of_days_in_each_month[ref_month]\n print(n_days_in_this_i_month) # nro days to increment in each i month iteration\n\n if (\n int(ref_year) % 4 == 0\n and int(ref_year) % 100 == 0\n and int(ref_year) % 400 != 0\n ):\n ref_year_is_leap_year = True # divisible entre 4 y 10 y no entre 400, para determinar que sea un año bisciesto\n if ref_year_is_leap_year == True and ref_month == \"02\":\n n_days_in_this_i_month = str(int(n_days_in_this_i_month) + 1) # 28 --> 29\n\n aux_date = aux_date + datetime.timedelta(days=int(n_days_in_this_i_month))\n print(repr(aux_date))\n return datetime.datetime.strftime(aux_date, \"%Y-%m-%d\")\n\n\nprint(repr(add_months(\"2022-12-30\", \"3\")))\n\n\nOutput:\ndatetime.datetime(2022, 12, 30, 0, 0)\n31\ndatetime.datetime(2023, 1, 30, 0, 0)\n31\ndatetime.datetime(2023, 3, 2, 0, 0)\n31\ndatetime.datetime(2023, 4, 2, 0, 0)\ndatetime.datetime(2023, 4, 2, 0, 0)\n'2023-04-02'\n\n", "So, as Alexander's answer already establishes, you weren't updating the date, so you were always adding to the same beginning date on each iteration. I took the liberty to clean up your code, using regex and converting to strings and back and for with the int's is the totally wrong approach here -- it misses the entire point of date-time objects, which is to encapsulate the information in a date. Just use those objects, not strings. Here is the same approach as your code using only datetime.datetime objects:\nimport datetime\n\ndef add_months(datestr, months):\n\n number_of_days_in_each_month = {\n 1 : 31,\n 2 : 28,\n 3 : 31,\n 4: 30,\n 5: 31,\n 6: 30,\n 7: 31,\n 8: 31,\n 9: 30,\n 10: 31,\n 11: 30,\n 12: 31,\n }\n\n date = datetime.datetime.strptime(datestr, \"%Y-%m-%d\")\n is_leap_year = False\n\n for i_month in range(1, int(months) + 1):\n\n ref_year, ref_month = date.year, date.month\n\n n_days = number_of_days_in_each_month[ref_month]\n\n if (\n ref_year % 4 == 0\n and ref_year % 100 == 0\n and ref_year % 400 != 0\n ):\n is_leap_year = True # divisible entre 4 y 10 y no entre 400, para determinar que sea un año bisciesto\n\n if is_leap_year and ref_month == 2: # febrero\n n_days += 1 # 28 --> 29\n\n date += datetime.timedelta(days=n_days)\n\n\n return date.strftime(\"%Y-%m-%d\")\n\n\nprint(add_months(\"2022-12-30\", \"3\"))\n\nI also made some stylistic changes to variable names. This is an art not a science, naming variables, and it always comes down to subjective opinion, but may I humbly submit my opinion about more legible names.\nAlso note, you had a comment to the effect of:\n\nI need the iter variable to start from 1 and not from 0 like the iter\nvariable in python\n\nThe iterating variable starts where you tell it to start, given the iterable you iterate over. range(N) will always start at zero, but it doesn't have to. You could iterate over [1, 2, 3], or better yet, range(1, N + 1).\nNote!\nYour algorithm is not working quite how one might expect, the output one would naturally expect is 2023-03-30\nI'll give you a hint, though, think about precisely which month's days you need to add to the current month.... n_days = number_of_days_in_each_month[ref_month]....\n" ]

[ 2, 2 ]

[]

[]

[ "for_loop", "loops", "python", "python_3.x", "variables" ]

[ "The following code seems to reproduce your desired result:\ndd['sin'] = dd['sin'].str.split(\", \")\ndd = dd.explode('sin').reset_index()\ndd['sin'] = dd['sin'].str.replace('\\W.*', '', regex=True)\n\nWhich gives dd['sin'] as:\n0 U147\n1 U35\n2 P01\n3 P02\n4 P3\n5 P032\n6 P034\n7 P23F5\n8 P04\nName: sin, dtype: object\n\nThe call of .reset_index() in the second line is optional depending on whether you want to preserve which row that piece of the string came from.\n", "You can use the following regex:\nr\"-\\d{2}|\\([EBP]CM\\)|\\s\"\n\n\n\nHere is the code:\nsin = ['U147(BCM), U35(BCM)','P01-00(ECM), P02-00(ECM)', 'P3-00(ECM), P032-00(ECM)','P034-00(ECM)', 'P23F5(PCM), P04-00(ECM)']\n\ndd = pd.DataFrame()\ndd['sin'] = sin\ndd['sin'] = dd['sin'].str.replace(r'-\\d{2}|\\([EBP]CM\\)|\\s', '', regex=True)\nprint(dd)\n\nOUTPUT:\n sin\n0 U147,U35\n1 P01,P02\n2 P3,P032\n3 P034\n4 P23F5,P04\n\n\n\n\nEDIT\nOr use this line to remove the comma:\ndd['sin'] = dd['sin'].str.replace(r'-\\d{2}|\\([EBP]CM\\)|\\s', '', regex=True).str.replace(',',' ')\n\nOUTPUT:\n sin\n0 U147 U35\n1 P01 P02\n2 P3 P032\n3 P034\n4 P23F5 P04\n\n" ]

[ 1, 0 ]

[]

[]

[ "pandas", "python", "replace" ]

[ "It looks like you're using the old version of Python.\nSo try to change your code \ndf = pd.read_excel(file_with_data, sheetname=sheet_with_data)\n\nIt should work properly.\n", "You can try to use pd.ExcelFile:\nxls = pd.ExcelFile('path_to_file.xls')\ndf1 = pd.read_excel(xls, 'Sheet1')\ndf2 = pd.read_excel(xls, 'Sheet2')\n\n", "This works:\ndf = pd.read_excel(open(file_path_name), 'rb'), sheetname = sheet_name)\n\nfile_path_name = your file\nsheet_name = your sheet name\n\nThis does not for me:\ndf = pd.read_excel(open(file_path_name), 'rb'), sheet_name = sheet_name)\n\nGave me only the first sheet, no matter how I defined sheet_name.\n--> it is an known error:\nhttps://github.com/pandas-dev/pandas/issues/17107\n", "Try at Terminal, type the following first, then re-run your program:\npip install xlrd\n", "I also faced this problem until I found this solution:\nrd=pd.read_excel(excel_file,sheet_name=['Sheet2']),\n\nHere excel_file means \"file name\".\nThe filename should be the full path to the file.\nMake sure to use two backslashes (\\\\) instead of just one!\nIn my case, this works.\n", "I would just use double quotes like this.\n# Returns a DataFrame\npd.read_excel(\"path_to_file.xls\", sheet_name=\"Sheet1\")\n\n" ]

[ 22, 7, 2, 1, 0, 0 ]

[]

[]

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

[ "I am not sure if the problem is related to your code. I think it is more related to your scanner. I have tested your script with the R32 QR Code reader (https://www.sycreader.com/en/3650/) and this is working perfect.\nWhat scanner type are you using?\nResult:\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:11.035355\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:11.675270\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:14.563287\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:15.007284\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:15.799299\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:20.959301\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:21.591286\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:24.515289\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:26.331292\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:31.323339\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:32.747289\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:34.495291\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:36.367294\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:37.903286\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:39.507295\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:41.099288\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:42.575295\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:44.123283\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:45.579286\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:47.055336\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:48.671301\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:49.983288\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:52.779284\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:54.755299\nScanned value:Toiletbezoek DateTime:2022-12-03 09:49:56.159286\n\n", "The error you're encountering is due to the fact that the barcode scanner is a \"grabbed\" device, meaning that it is currently in use by another process and cannot be accessed. In order to fix this, you can try releasing the device before attempting to use it again. Here is an example of how you can do this:\n# Release the device\ndev.ungrab()\n\n# Wait for a few seconds to allow the device to be released\ntime.sleep(2)\n\n# Attempt to grab the device again\ndev.grab()\n\nYou can insert this code before the for loop in your readBarcode method to try and fix the issue.\n" ]

[ 0, 0 ]

[]

[]

[ "barcode_scanner", "evdev", "linux", "python", "raspberry_pi4" ]

[ "You need to be thinking in terms of permutations of the digits in the input list. Bear in mind that the challenge states that \"some or all\" of the values may be used. So you need to be looking at permutations from 1 to the length of the input list (inclusive).\nThere's probably a more efficient way to do this but:\nfrom itertools import permutations\n\ndef solution(digits):\n _max = -1\n for r in range(1, len(digits)+1):\n for combo in permutations(digits, r=r):\n v = 0\n for n in combo:\n v = v * 10 + n\n if v % 3 == 0:\n _max = max(_max, v)\n return _max\n\nprint(solution([3, 1, 4, 1, 5, 9]))\n\nOutput:\n94311\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "Please consider rephrasing the question in a better way. The problematic is not clear.\n", "please check https://stackoverflow.com/posts/46929945\nthis should work for you,\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\noptions = Options()\noptions.headless = True\ndriver = webdriver.Chrome(CHROMEDRIVER_PATH, options=options)\n\n" ]

[ 0, 0 ]

[]

[]

[ "codespaces", "jupyter_notebook", "python" ]

[ "It sounds like you're trying to build a machine learning model to predict whether a person is feeling cold or not based on the dataset you provided. To improve the accuracy and precision of your model, there are several steps you can take.\nFirst, make sure you're using the right evaluation metrics for your problem. Accuracy is not always the best metric to use, especially if your dataset is imbalanced (i.e. if there are significantly more instances of one class than the other). In this case, you may want to consider using precision and recall, which can give you a better understanding of how well your model is performing.\nNext, you should try to improve the quality of your training data. This can include removing outliers, smoothing the data, and performing other preprocessing steps. You should also consider using more sophisticated techniques such as feature selection and dimensionality reduction to improve the predictive power of your model.\nFinally, you should experiment with different machine learning algorithms to see which ones perform best on your dataset. This can involve trying out different model architectures, hyperparameters, and other settings to find the combination that produces the best results.\nOverall, improving the accuracy and precision of your model will require a combination of data preprocessing, feature engineering, and algorithm selection. By following these steps, you should be able to improve the performance of your model and get better results.\n" ]

[ 0 ]

[]

[]

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

[ "To reference each form value in a loop without having to write out the name of each field in Python, you can use the items() method on the form.fields.values dictionary to iterate over the key-value pairs in the dictionary.\nHere is an example of how you could update your code to use the items() method to loop over the form:\ncompleteness = 0\nlength = 20\nfor key, value in form.fields.values.items():\n if value != \"\":\n completeness += 1\n\npercentage = (completeness / length) * 100\nprint(completeness)\nprint(percentage)\n\nNote that in the code above, the items() method is used to loop over the form.fields.values dictionary, and the key and value for each iteration are assigned to the key and value variables, respectively. This allows you to reference the value for each field without having to explicitly specify the field name.\n" ]

[ 0 ]

[]

[]

[ "django", "django_forms", "python" ]

[ "The code in the else block will not be executed because the return statement is inside the else block. The return statement causes the function to immediately return a value and exit, so the code in the else block will never be executed.\nYou can fix this by moving the return statement outside of the else block, like this:\npage = 1\nimg_count = 0\nresult_list = []\n\nwhile True:\n url = f'https://s3.landingfolio.com/inspiration?page={page}&sortBy=free-first'\n\n response = requests.get(url=url, headers=headers)\n data = response.json()\n\n for item in data:\n if page <= 11:\n\n screenshots = item.get('screenshots')\n img_count += len(screenshots)\n\n for img in screenshots:\n img.update({'url': f'https://landingfoliocom.imgix.net/{img.get(\"url\")}'})\n\n result_list.append(\n {\n 'title': item.get('title'),\n 'description': item.get('slug'),\n 'url': item.get('url'),\n 'screenshots': screenshots\n }\n )\n else:\n print('test')\n with open('result_list.json', 'a') as file:\n json.dump(result_list, file, indent=4, ensure_ascii=False)\n\n if page > 11:\n return f'[INFO] work finished. Images count is: {img_count}\\n{\"=\" * 20}'\n\n page += 1\n print(f'[+] processed {page} ')\n\n" ]

[ 1 ]

[]

[]

[ "if_statement", "python" ]

[ "You can use the mul operator to repeat the digit, convert back to an integer and sum.\ndef find_sum(digit, max_repeats):\n return sum(int(str(digit)*(i+1)) for i in range(max_repeats))\n\nprint(find_sum(5, 5))\n#output 61725\n\n", "You can use the idea from the following algorithm:\ndef sum(n):\n # This will be multiplied.\n nbr=0\n # This will be the return value.\n ret=0\n for i in range(0, n):\n # Every iteration this will add 1 to the nbr: 1, 11, 111, etc.\n nbr = nbr + 10 ** i\n # Multiply by 5 and sum with the previous value: 5 + 55 + 555 + etc.\n ret = nbr * 5 + ret\n print(ret)\n\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[]

[]

[ "I believe something like this:\ndef conditional_print(text):\n number = int(text)\n if number % 2 == 1:\n print(\"Weird\")\n elif ... <= number <= ...:\n print(\"Not Weird\")\n elif ... <= number <= ...:\n print(\"Weird\")\n elif ... < number:\n print(\"Not Weird\")\nconditional_print(...)\n\nBut I'm missing some information, You will need to put this information in the place of the three dots.\n" ]

[ -1 ]

[ "python" ]

[ "It looks like you are encountering an AttributeError when trying to send a message to a channel using the channels and asgiref libraries in Python. This error is raised when you try to call a method on an object that does not have that method.\nIn this case, it appears that the decode method is being called on a tuple object, but tuple objects do not have a decode method. This is likely caused by a mistake in your code, where you are trying to treat a tuple object as if it were a str object.\nTo fix this error, you will need to find the line of code where the decode method is being called on a tuple object and correct it. This may involve changing the type of the object, or simply using a different method that is appropriate for the object's type.\nWithout more information about your code and the context in which the error is occurring, it is difficult to provide a specific solution. However, the general steps to fix this error are:\n\nIdentify the line of code where the AttributeError is being raised.\nDetermine why the decode method is being called on a tuple object.\nCorrect the code to use a different method or object type that is appropriate for the situation.\n\nOnce you have fixed the code, you should be able to send messages to channels without encountering this error.\n" ]

[ 0 ]

[]

[]

[ "channels", "django_channels", "python" ]

[ "Finally I made it working. This is what I did:\nfrom fastapi.openapi.docs import (\n get_redoc_html,\n get_swagger_ui_html,\n get_swagger_ui_oauth2_redirect_html,\n)\nfrom fastapi.staticfiles import StaticFiles\n\napi = FastAPI(docs_url=None) \n\npath_to_static = os.path.join(os.path.dirname(__file__), 'static')\nlogger.info(f\"path_to_static: {path_to_static}\")\napi.mount(\"/static\", StaticFiles(directory=path_to_static), name=\"static\")\n\n@api.get(\"/docs\", include_in_schema=False)\n async def custom_swagger_ui_html():\n return get_swagger_ui_html(\n openapi_url=api.openapi_url,\n title=\"My API\",\n oauth2_redirect_url=api.swagger_ui_oauth2_redirect_url,\n swagger_js_url=\"/static/custom_script.js\",\n # swagger_css_url=\"/static/swagger-ui.css\",\n # swagger_favicon_url=\"/static/favicon-32x32.png\",\n )\n\nImportant notes:\n\nMake sure the static path is correct and all your files are in the static folder, by default the static folder should be in the same folder with the script that created the FastAPI object.\n\nFor example:\n\n -parent_folder\n Build_FastAPI.py\n -static_folder\n custom_script.js\n custom_css.css\n\n\n\nFind the swagger-ui-bundle.js on internet and copy-paste all its content to custom_script.js, then add your custom javascript code at the beginning or at the end of custom_script.js.\n\nFor example:\nsetTimeout(function(){alert('My custom script is working!')}, 5000);\n...\n.....\n/*! For license information please see swagger-ui-bundle.js.LICENSE.txt */\n !function(e,t){\"object\"==typeof exports&&\"object\"==typeof module?module.exports=t():\"function\"==typeof define&&define.amd?define([],t):\"object\"==typeof exports?exports.SwaggerUIBundle=t():e.SwaggerUIBundle=t()}\n...\n.....\n\n\nSave and refresh your browser, you are all way up!\n\nIF SOMEBODY KNOWS A BETTER ANSWER YOUR ARE WELCOME, THE BEST ONE WILL BE ACCEPTED!\n" ]

[ 1 ]

[]

[]

[ "fastapi", "python", "swagger_ui" ]

[ "You can use annotation like this:\nfrom django.db.models import Count\nTracker.objects.values('contract_number').annotate(contract_count=Count('contract_number')).order_by()\n\n", "Solutions:\ncounttraker=Traker.objects.values('contract_number').annotate(Count('contract_number'))\nsubquery=counttraker.filter(contract_number=OuterRef('contract_number').values('contract_number__count')[:1]\ntraker=Traker.objects.annotate(count=Subquery(subquery))\n\n" ]

[ 5, 0 ]

[]

[]

[ "django", "django_queryset", "extra", "python", "sql" ]

[ "I am assuming that the data goes to Serial in this Format as String:\n\n[1,valX,valY,valZ]\n\nAfter reading the data from Serial and converting the data line to String with String() function, you can assign the values to desired variables using sscanf() function.\nThe function works like this -\n\nsscanf(const char *str, const char *format, ...)\n\nSo, here it would work like this -\nint data_val1, data_val2, data_val3;\n\nsscanf(Your_SerialData_String, [1,%d,%d,%d], data_val1, data_val2, data_val3);\n\n" ]

[ 0 ]

[]

[]

[ "arduino", "python", "python_3.x", "serial_port" ]

[ "I solved my issue by iterating through the values and parse them accordingly\nimport json\n\ndef parse_value(value):\n if(isinstance(value, str)):\n try:\n return parse_value(json.loads(value))\n except:\n pass\n semi_values = value.split(';')\n if(len(semi_values) > 1):\n return list(map(parse_value, semi_values))\n comma_values = value.split(',')\n if(len(comma_values) > 1):\n return list(map(parse_value, comma_values))\n if(value.replace('.','',1).isdigit()):\n return int(value)\n if(isinstance(value, dict)):\n return {k: parse_value(v) for k, v in value.items()}\n if(isinstance(value, list)):\n return list(map(parse_value, value))\n return value\n\n", "Your Python code looks like it's on the right track, but there are a few issues with it. First, you're using commaValues.length instead of len(commaValues) in the if statement that checks if the length of commaValues is greater than 1. Second, json.load() expects a file-like object as its first argument, not a string. You can use json.loads() instead to parse a JSON string.\nHere's how I would write the code in Python:\nimport json\n\ndef reviver(key, value):\n try:\n return json.loads(value, reviver=reviver)\n except:\n if isinstance(value, str):\n semiValues = value.split(';')\n if len(semiValues) > 1:\n return stringToObject(json.dumps(semiValues))\n commaValues = value.split(',')\n if len(commaValues) > 1:\n return stringToObject(json.dumps(commaValues))\n\n try:\n return int(value)\n except ValueError:\n return value\n\ndef stringToObject(str):\n formatted = str.replace('\"{', '{').replace('}\"', '}').replace('\"[', '[').replace(']\"', ']').replace('\\\\\"', '\"')\n return json.loads(formatted, reviver=reviver)\n\nNote that I also changed the try statement that tries to convert the string to a number to use int() instead of float() to parse the value as an integer instead of a floating-point number. I also changed the function and variable names to follow the Python convention of using lowercase words separated by underscores (e.g. string_to_object instead of stringToObject).\n" ]

[ 0, 0 ]

[ "Does this code works for you?\ndef reviver(_key, value):\n try:\n return json.loads(value, object_hook=reviver)\n except:\n if type(value) == str:\n semi_values = value.split(';')\n if len(semi_values) > 1:\n return string_to_object(json.dumps(semi_values))\n comma_values = value.split(',')\n if len(comma_values) > 1:\n return string_to_object(json.dumps(comma_values))\n int_val = int(value)\n if len(value) and not isinstance(int_val, int):\n return int_val\n return value\n\ndef string_to_object(str):\n formatted = str.replace('\"{', '{').replace('}\"', '}').replace('\"[', '[').replace(']\"', ']').replace('\\\\\"', '\"')\n return json.loads(formatted, object_hook=reviver)\n\n" ]

[ -1 ]

[ "javascript", "json", "python", "reviver_function" ]

[ "import httpx\nimport trio\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom urllib.parse import urljoin\n\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:107.0) Gecko/20100101 Firefox/107.0'\n}\n\n\nclass Spider:\n def __init__(self, client) -> None:\n self.client = client\n self.limiter = trio.CapacityLimiter(10)\n\n async def get(self, page):\n params = {\n 'currency': 'UAH',\n 'page': page\n }\n while True:\n try:\n r = await self.client.get('noutbuki', params=params)\n if r.is_success:\n break\n except httpx.RequestError:\n continue\n return await self.get_soup(r.text)\n\n async def get_soup(self, content):\n return BeautifulSoup(content, 'lxml')\n\n\nasync def crawl(data, page, sender):\n async with data.limiter, sender:\n soup = await data.get(page)\n goal = [urljoin(str(data.client.base_url), x['href'])\n for x in soup.select('a.css-rc5s2u, a.marginright5')]\n await sender.send(goal)\n\n\nasync def main():\n async with httpx.AsyncClient(timeout=5, headers=headers, follow_redirects=True, base_url='https://www.olx.ua/d/uk/elektronika/noutbuki-i-aksesuary/') as client, trio.open_nursery() as nurse:\n sender, receiver = trio.open_memory_channel(0)\n nurse.start_soon(rec, receiver)\n data = Spider(client)\n async with sender:\n for page in range(1, 3):\n nurse.start_soon(crawl, data, page, sender.clone())\n\n\nasync def rec(receiver):\n async with receiver:\n allin = []\n async for val in receiver:\n allin.extend(val)\n df = pd.DataFrame(allin, columns=['URL'])\n print(df)\n\n\nif __name__ == \"__main__\":\n trio.run(main)\n\nOutput:\n URL\n0 https://www.olx.ua/d/uk/obyavlenie/lenovo-thin...\n1 https://www.olx.ua/d/uk/obyavlenie/kak-novyy-i...\n2 https://www.olx.ua/d/uk/obyavlenie/dell-xps-13...\n3 https://www.olx.ua/d/uk/obyavlenie/apple-macbo...\n4 https://www.olx.ua/d/uk/obyavlenie/u-menya-est...\n.. ...\n91 https://www.olx.ua/d/uk/obyavlenie/noutbuk-ace...\n92 https://www.olx.ua/d/uk/obyavlenie/noutbuk-na-...\n93 https://www.olx.ua/d/uk/obyavlenie/noutbuk-fuj...\n94 https://www.olx.ua/d/uk/obyavlenie/noutbuk-15-...\n95 https://www.olx.ua/d/uk/obyavlenie/ultrabuk-hp...\n\n[96 rows x 1 columns]\n\n" ]

[ 0 ]

[]

[]

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

[ "As already mentioned in one of my comments. If you want to have an equal distribution of %fill, then you could add the balls individually to the current lowest filled basket:\nimport numpy as np\n\ndef fill_baskets(baskets, ballsToDistribute):\n for i in range(ballsToDistribute, 0, -1):\n # find the basket with the lowest percentage of balls in it\n currFillLevels = [currFill / maxFill for currFill, maxFill in baskets]\n minIndex = np.argmin(currFillLevels)\n\n # give the ball to this basket\n baskets[minIndex][0] += 1\n\n return baskets\n\n\nbaskets = [[10, 100], [50, 200], [100, 300]]\n\nnew_baskets = fill_baskets(baskets, 100)\n\n# print the result:\nfor i, basket in enumerate(new_baskets):\n print(f\"Basket {i+1}: {basket[0]/basket[1]:.3f}% ({basket[0]}/ {basket[1]})\")\n\nThe output I get for this case if the following:\nBasket 1: 0.440% (44/ 100)\nBasket 2: 0.435% (87/ 200)\nBasket 3: 0.430% (129/ 300)\n\nThe only problem that can arise from the code is when we have too many balls to give away. Then all the baskets will be overfilled.\n" ]

[ 1 ]

[]

[]

[ "algorithm", "dart", "python" ]

[ "Assuming you want to count the keys in a bucket and don't want to hit the limit of 1000 using list_objects_v2. The below code worked for me but I'm wondering if there is a better faster way to do it! Tried looking if there's a packaged function in boto3 s3 connector but there isn't!\n# connect to s3 - assuming your creds are all set up and you have boto3 installed\ns3 = boto3.resource('s3')\n\n# identify the bucket - you can use prefix if you know what your bucket name starts with\nfor bucket in s3.buckets.all():\n print(bucket.name)\n\n# get the bucket\nbucket = s3.Bucket('my-s3-bucket')\n\n# use loop and count increment\ncount_obj = 0\nfor i in bucket.objects.all():\n count_obj = count_obj + 1\nprint(count_obj)\n\n", "If there are more than 1000 entries, you need to use paginators, like this:\ncount = 0\nclient = boto3.client('s3')\npaginator = client.get_paginator('list_objects')\nfor result in paginator.paginate(Bucket='your-bucket', Prefix='your-folder/', Delimiter='/'):\n count += len(result.get('CommonPrefixes'))\n\n", "\"Folders\" do not actually exist in Amazon S3. Instead, all objects have their full path as their filename ('Key'). I think you already know this.\nHowever, it is possible to 'create' a folder by creating a zero-length object that has the same name as the folder. This causes the folder to appear in listings and is what happens if folders are created via the management console.\nThus, you could exclude zero-length objects from your count.\nFor an example, see: Determine if folder or file key - Boto\n", "If you have credentials to access that bucket, then you can use this simple code. Below code will give you a list. List comprehension is used for more readability.\nFilter is used to filter objects because in bucket to identify the files ,folder names are used. As explained by John Rotenstein concisely.\nimport boto3\n\nbucket = \"Sample_Bucket\"\nfolder = \"Sample_Folder\"\ns3 = boto3.resource(\"s3\") \ns3_bucket = s3.Bucket(bucket)\nfiles_in_s3 = [f.key.split(folder + \"/\")[1] for f in s3_bucket.objects.filter(Prefix=folder).all()]\n\n", "The following code worked perfectly\ndef getNumberOfObjectsInBucket(bucketName,prefix):\n count = 0\n response = boto3.client('s3').list_objects_v2(Bucket=bucketName,Prefix=prefix)\n for object in response['Contents']:\n if object['Size'] != 0:\n #print(object['Key'])\n count+=1\n return count\n\nobject['Size'] == 0 will take you to folder names, if want to check them, object['Size'] != 0 will lead you to all non-folder keys.\nSample function below:\ngetNumberOfObjectsInBucket('foo-test-bucket','foo-output/')\n\n" ]

[ 15, 3, 1, 0, 0 ]

[]

[]

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

[ "In the Python shell, you can use the up and down arrow keys to scroll through the command history. This should work both in the Command Prompt and in Bash in Windows Terminal.\nIf this does not work for you, you can try enabling command history in the Python shell by running the following commands:\nimport readline\nreadline.parse_and_bind('tab: complete')\nreadline.parse_and_bind('set editing-mode vi')\n\nThis will enable tab completion and set the editing mode to vi, which will allow you to use vi-style key bindings (such as k and j) to navigate the command history.\nAlternatively, you can use the %hist magic command to view the command history in the Python shell. This command takes an optional integer argument that specifies the number of commands to display (by default, it displays the last five commands):\n# Display the last five commands\n%hist\n\n# Display the last ten commands\n%hist 10\n\nYou can then copy and paste the commands you want to repeat from the output of the %hist command.\nAnother option is to use a different shell that supports command history, such as the IPython shell. You can start the IPython shell by running the ipython command instead of the python command. The IPython shell supports command history and tab completion, and it also has additional features such as inline plotting and automatic indentation.\n" ]

[ 1 ]

[]

[]

[ "bash", "python", "windows_terminal" ]

[ "You must implement the keyebord events similar as in the Pygame implementation decribed in the answer to How to rotate slices of a Rubik's Cube in python PyOpenGL?.\nRemove:\nself.animate, self.action = True, rot_slice_map['K_1']\nChange the key mapping to a mapping to be used with tkinter\nrot_cube_map = {'Up': (-1, 0), 'Down': (1, 0), 'Left': (0, -1), 'Right': (0, 1)}\nrot_slice_map = {\n '1': (0, 0, 1), '2': (0, 1, 1), '3': (0, 2, 1), '4': (1, 0, 1), '5': (1, 1, 1),\n '6': (1, 2, 1), '7': (2, 0, 1), '8': (2, 1, 1), '9': (2, 2, 1),\n 'F1': (0, 0, -1), 'F2': (0, 1, -1), 'F3': (0, 2, -1), 'F4': (1, 0, -1), 'F5': (1, 1, -1),\n 'F6': (1, 2, -1), 'F7': (2, 0, -1), 'F8': (2, 1, -1), 'F9': (2, 2, -1),\n}\n\nCreate the 27 cubes in GLFrame.initgl and set self.animate = True. self.animate is the fagae that controls the animation loop of the the OpenGLFrame. Tha naimation of the Rubik's Cube is controled with animate1Cube:\nclass GLFrame(OpenGLFrame):\n def initgl(self):\n self.animate = True\n \n # [...]\n\n self.N = 3\n cr = range(self.N)\n self.cubes = [Cube((x, y, z), self.N, 1.5) for x in cr for y in cr for z in cr]\n\nAdd the callback methods for the keyboard event:\nclass GLFrame(OpenGLFrame):\n # [...]\n\n def keydown(self, event):\n if event.keysym in rot_slice_map:\n self.animate1Cube, self.action = True, rot_slice_map[event.keysym]\n if event.keysym in rot_cube_map:\n self.rot_cube = rot_cube_map[event.keysym]\n\n def keyup(self, event):\n if event.keysym in rot_cube_map:\n self.rot_cube = (0, 0)\n\nSet the keyboard callbacks:\nclass App(tk.Tk):\n def __init__(self):\n super().__init__()\n self.title('rubiks cube')\n self.glframe = GLFrame(self, width=800, height=600)\n self.bind(\"<KeyPress>\", self.glframe.keydown)\n self.bind(\"<KeyRelease>\", self.glframe.keyup)\n self.glframe.pack(expand=True, fill=tk.BOTH)\n\nThe animation of the Rubikc's Cube depends on animateCube, but not on animate:\nclass GLFrame(OpenGLFrame):\n # [...]\n\n def redraw(self):\n # [...]\n\n if self.animate1Cube:\n if self.animate_ang >= 90:\n for cube in self.cubes:\n cube.update(*self.action)\n self.animate1Cube, self.animate_ang = False, 0\n\n for cube in self.cubes:\n cube.draw(colors, surfaces, vertices, self.animate, self.animate_ang, *self.action)\n if self.animate1Cube:\n self.animate_ang += self.animate_speed\n\n\nComplete and working example:\n\nimport tkinter as tk\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nfrom pyopengltk import OpenGLFrame\n\nvertices = (\n (1, -1, -1), (1, 1, -1), (-1, 1, -1), (-1, -1, -1),\n (1, -1, 1), (1, 1, 1), (-1, -1, 1), (-1, 1, 1)\n)\nedges = ((0, 1), (0, 3), (0, 4), (2, 1), (2, 3), (2, 7), (6, 3), (6, 4), (6, 7), (5, 1), (5, 4), (5, 7))\nsurfaces = ((0, 1, 2, 3), (3, 2, 7, 6), (6, 7, 5, 4), (4, 5, 1, 0), (1, 5, 7, 2), (4, 0, 3, 6))\ncolors = ((1, 0, 0), (0, 1, 0), (1, 0.5, 0), (1, 1, 0), (1, 1, 1), (0, 0, 1))\n\nrot_cube_map = {'Up': (-1, 0), 'Down': (1, 0), 'Left': (0, -1), 'Right': (0, 1)}\nrot_slice_map = {\n '1': (0, 0, 1), '2': (0, 1, 1), '3': (0, 2, 1), '4': (1, 0, 1), '5': (1, 1, 1),\n '6': (1, 2, 1), '7': (2, 0, 1), '8': (2, 1, 1), '9': (2, 2, 1),\n 'F1': (0, 0, -1), 'F2': (0, 1, -1), 'F3': (0, 2, -1), 'F4': (1, 0, -1), 'F5': (1, 1, -1),\n 'F6': (1, 2, -1), 'F7': (2, 0, -1), 'F8': (2, 1, -1), 'F9': (2, 2, -1),\n}\n\nclass Cube():\n def __init__(self, id, N, scale):\n self.N = 3\n self.scale = scale\n self.init_i = [*id]\n self.current_i = [*id] # 表示填充，一个变量值代替多个\n self.rot = [[1 if i == j else 0 for i in range(3)] for j in range(3)]\n\n def isAffected(self, axis, slice, dir):\n return self.current_i[axis] == slice\n\n def update(self, axis, slice, dir):\n\n if not self.isAffected(axis, slice, dir):\n return\n\n i, j = (axis + 1) % 3, (axis + 2) % 3\n for k in range(3):\n self.rot[k][i], self.rot[k][j] = -self.rot[k][j] * dir, self.rot[k][i] * dir\n\n self.current_i[i], self.current_i[j] = (\n self.current_i[j] if dir < 0 else self.N - 1 - self.current_i[j],\n self.current_i[i] if dir > 0 else self.N - 1 - self.current_i[i])\n\n def transformMat(self):\n scaleA = [[s * self.scale for s in a] for a in self.rot]\n scaleT = [(p - (self.N - 1) / 2) * 2.1 * self.scale for p in self.current_i]\n return [*scaleA[0], 0, *scaleA[1], 0, *scaleA[2], 0, *scaleT, 1]\n\n def draw(self, col, surf, vert, animate, angle, axis, slice, dir):\n\n glPushMatrix()\n if animate and self.isAffected(axis, slice, dir):\n glRotatef(angle * dir, *[1 if i == axis else 0 for i in range(3)]) # 围着这个坐标点旋转\n glMultMatrixf(self.transformMat())\n\n glBegin(GL_QUADS)\n for i in range(len(surf)):\n glColor3fv(colors[i])\n for j in surf[i]:\n glVertex3fv(vertices[j])\n glEnd()\n\n glPopMatrix()\n\n\nclass mycube():\n def __init__(self, N, scale):\n self.N = N\n cr = range(self.N)\n self.cubes = [Cube((x, y, z), self.N, scale) for x in cr for y in cr for z in cr] # 创建27\n\n def maindd(self):\n for cube in self.cubes:\n cube.draw(colors, surfaces, vertices, False, 0, 0, 0, 0)\n\nclass GLFrame(OpenGLFrame):\n def initgl(self):\n self.animate = True\n self.rota = 0\n self.count = 0\n\n self.ang_x, self.ang_y, self.rot_cube = 0, 0, (0, 0)\n self.animate1Cube, self.animate_ang, self.animate_speed = False, 0, 2\n self.action = (0, 0, 0)\n glClearColor(0.0, 0.0, 0.0, 0.0) # 背景黑色\n # glViewport(400, 400, 200, 200) # 指定了视口的左下角位置\n\n glEnable(GL_DEPTH_TEST) # 开启深度测试，实现遮挡关系\n glDepthFunc(GL_LEQUAL) # 设置深度测试函数（GL_LEQUAL只是选项之一）\n\n glMatrixMode(GL_PROJECTION) \n glLoadIdentity() # 恢复原始坐标\n gluPerspective(30, self.width / self.height, 0.1, 50.0)\n\n self.N = 3\n cr = range(self.N)\n self.cubes = [Cube((x, y, z), self.N, 1.5) for x in cr for y in cr for z in cr]\n\n def keydown(self, event):\n if event.keysym in rot_slice_map:\n self.animate1Cube, self.action = True, rot_slice_map[event.keysym]\n if event.keysym in rot_cube_map:\n self.rot_cube = rot_cube_map[event.keysym]\n\n def keyup(self, event):\n if event.keysym in rot_cube_map:\n self.rot_cube = (0, 0)\n\n def redraw(self):\n self.ang_x += self.rot_cube[0] * 2\n self.ang_y += self.rot_cube[1] * 2\n\n glMatrixMode(GL_MODELVIEW)\n glLoadIdentity()\n glTranslatef(0, 0, -40)\n glRotatef(self.ang_y, 0, 1, 0)\n glRotatef(self.ang_x, 1, 0, 0)\n\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\n if self.animate1Cube:\n if self.animate_ang >= 90:\n for cube in self.cubes:\n cube.update(*self.action)\n self.animate1Cube, self.animate_ang = False, 0\n\n for cube in self.cubes:\n cube.draw(colors, surfaces, vertices, self.animate, self.animate_ang, *self.action)\n if self.animate1Cube:\n self.animate_ang += self.animate_speed\n\n\nclass App(tk.Tk):\n def __init__(self):\n super().__init__()\n self.title('rubiks cube')\n self.glframe = GLFrame(self, width=800, height=600)\n #self.bind(\"<Key>\", self.glframe.key)\n self.bind(\"<KeyPress>\", self.glframe.keydown)\n self.bind(\"<KeyRelease>\", self.glframe.keyup)\n self.glframe.pack(expand=True, fill=tk.BOTH)\n # self.glframe.focus_displayof()\n # self.animate = True\n\nApp().mainloop()\n\n\nIf you want to animate the cube automatically, you need to animate instead of keyboard events. Define a list of animationg. e.g.:\nanimation_list = ['1', '3', '5', 'F2']\n\nSet self.action from the list. e.g.:\nclass GLFrame(OpenGLFrame):\n # [...]\n\n def redraw(self):\n if not self.animate1Cube and animation_list:\n self.animate1Cube, self.action = True, rot_slice_map[animation_list[0]]\n del animation_list[0]\n\n # [...]\n\n" ]

[ 0 ]

[]

[]

[ "opengl", "pyopengl", "python", "tkinter" ]

[ "Firstly, I congratulate you on such an exquisite example. I recommend you explore examples of the function Delaunay. The documentation exhibits several properties whose output may interest you.\n", "Referring to my comments for your original question... The extra simplices occur because some of your vertices are positioned inside, but near, the convex hull of the Delaunay triangulation. The code below adjusts the position of the problematic vertices by finding the nearest point on the convex hull. For this example, I use the Tinfour Software Library which is written in Java. But you should be able to adapt the ideas to Python if you wish to do so.\nimport java.io.File;\nimport java.io.IOException;\nimport java.util.ArrayList;\nimport java.util.Comparator;\nimport java.util.List;\nimport org.tinfour.common.IIncrementalTin;\nimport org.tinfour.common.IQuadEdge;\nimport org.tinfour.common.Vertex;\nimport org.tinfour.semivirtual.SemiVirtualIncrementalTin;\nimport org.tinfour.utils.loaders.VertexReaderText;\n\npublic class AdjustEdgePoints {\n public static void main(String[] args) throws IOException {\n File input = new File(\"puntos.csv\");\n List<Vertex> vertices = null;\n try ( VertexReaderText vrt = new VertexReaderText(input)) {\n vertices = vrt.read(null);\n }\n\n double pointSpacing = 0.2;\n double edgeTooLong = 0.4; // based on point spacng\n double pointTooClose = 0.021;\n\n // vertices are numbered 0 to n-1\n boolean[] doNotTest = new boolean[vertices.size()];\n boolean[] modified = new boolean[vertices.size()];\n List<Vertex> replacements = new ArrayList<>();\n\n IIncrementalTin tin = new SemiVirtualIncrementalTin(pointSpacing);\n tin.add(vertices, null);\n List<IQuadEdge> perimeter = tin.getPerimeter(); // the convex hull\n // mark all vertices on the perimeter as do-not-test\n for (IQuadEdge edge : perimeter) {\n Vertex A = edge.getA(); // vertices are for edge are A and B\n doNotTest[A.getIndex()] = true;\n }\n\n // For all excessively long edges, find the vertices that are too close\n // and move them to the edge.\n for (IQuadEdge edge : perimeter) {\n double eLength = edge.getLength();\n if (eLength < edgeTooLong) {\n continue; // no processing required\n }\n Vertex A = edge.getA();\n Vertex B = edge.getB();\n double eX = B.getX() - A.getX(); // vector in direction of edge\n double eY = B.getY() - A.getY();\n double pX = -eY / eLength; // unit vector perpendicular to edge\n double pY = eX / eLength;\n\n for (Vertex v : vertices) {\n if (doNotTest[v.getIndex()]) {\n continue;\n }\n double vX = v.getX() - A.getX();\n double vY = v.getY() - A.getY();\n // compute t, the parameter for a point on the line of the edge\n // closest to the vertex. We are only interested in this point\n // if it falls between the two endpoints of the edge.\n // in that case, t will be in the range 0 < t < 1\n double t = (vX * eX + vY * eY) / (eLength * eLength);\n if (0 < t && t < 1) {\n double s = pX * vX + pY * vY; // distance of V from edge\n if (s < pointTooClose) {\n double x = A.getX() + t * eX; // point on edge\n double y = A.getY() + t * eY;\n Vertex X = new Vertex(x, y, v.getZ(), v.getIndex());\n modified[v.getIndex()] = true;\n doNotTest[v.getIndex()] = true;\n replacements.add(X);\n }\n }\n } // end of vertices loop\n } // end of perimeter loop\n\n System.out.println(\"i,x,y,z\");\n for (Vertex v : vertices) {\n if (!modified[v.getIndex()]) {\n System.out.format(\"%d,%19.16f,%19.16f,%19.16f%n\",\n v.getIndex(), v.getX(), v.getY(), v.getZ());\n }\n }\n\n replacements.sort(new Comparator<Vertex>() {\n @Override\n public int compare(Vertex arg0, Vertex arg1) {\n return Integer.compare(arg0.getIndex(), arg1.getIndex());\n }\n });\n System.out.println(\"\");\n for (Vertex v : replacements) {\n System.out.format(\"%d,%19.16f,%19.16f,%19.16f%n\",\n v.getIndex(), v.getX(), v.getY(), v.getZ());\n }\n }\n\n}\n\nAnd here are the modified vertices. Some of the z values may be a little different because Tinfour only supports single-precision floating point values for its z elements.\n1,-1.7362462292391640,-1.9574243190958676, 0.2769193053245544\n2,-1.5328386393032090,-1.9691151455342581, 0.2039903998374939\n3,-1.3356454135288550,-1.9804488021499242, 0.1507489830255508\n7,-0.5545998965948742,-2.0099766379255533, 0.1641141176223755\n8,-0.3452278908343442,-2.0128547506964680, 0.2061954736709595\n9,-0.1280981557739663,-2.0158395043704496, 0.2454121708869934\n10, 0.0960477228578226,-2.0189207048083720, 0.2699134647846222\n11, 0.3249315805387950,-2.0220670354338774, 0.2698880732059479\n12, 0.5546177976887393,-2.0252243956190710, 0.2401449084281921\n13, 0.7809245195097682,-2.0283352998865780, 0.1818846762180328\n14, 1.0012918968621154,-2.0313645595086890, 0.1022855937480927\n15, 1.2151144438651920,-2.0343038512301570, 0.0123253259807825\n20,-1.9574243190958676,-1.7362462292391640, 0.2769193053245544\n40,-1.9691151455342581,-1.5328386393032090, 0.2039903998374939\n60,-1.9804488021499242,-1.3356454135288550, 0.1507489830255508\n99, 2.0343038512301570,-1.2151144438651922, 0.0123253259807825\n119, 2.0313645595086890,-1.0012918968621158, 0.1022855937480927\n139, 2.0283352998865780,-0.7809245195097688, 0.1818846762180328\n140,-2.0099766379255533,-0.5545998965948735, 0.1641141176223755\n159, 2.0252243956190710,-0.5546177976887400, 0.2401449084281921\n160,-2.0128547506964680,-0.3452278908343438, 0.2061954736709595\n179, 2.0220670354338774,-0.3249315805387953, 0.2698880732059479\n180,-2.0158395043704496,-0.1280981557739660, 0.2454121708869934\n199, 2.0189207048083720,-0.0960477228578232, 0.2699134647846222\n200,-2.0189207048083720, 0.0960477228578229, 0.2699134647846222\n219, 2.0158395043704496, 0.1280981557739658, 0.2454121708869934\n220,-2.0220670354338774, 0.3249315805387953, 0.2698880732059479\n239, 2.0128547506964680, 0.3452278908343438, 0.2061954736709595\n240,-2.0252243956190710, 0.5546177976887398, 0.2401449084281921\n259, 2.0099766379255533, 0.5545998965948733, 0.1641141176223755\n260,-2.0283352998865780, 0.7809245195097683, 0.1818846762180328\n280,-2.0313645595086890, 1.0012918968621158, 0.1022855937480927\n300,-2.0343038512301570, 1.2151144438651922, 0.0123253259807825\n339, 1.9804488021499242, 1.3356454135288547, 0.1507489830255508\n359, 1.9691151455342581, 1.5328386393032085, 0.2039903998374939\n379, 1.9574243190958676, 1.7362462292391640, 0.2769193053245544\n384,-1.2151144438651920, 2.0343038512301570, 0.0123253259807825\n385,-1.0012918968621156, 2.0313645595086890, 0.1022855937480927\n386,-0.7809245195097685, 2.0283352998865780, 0.1818846762180328\n387,-0.5546177976887396, 2.0252243956190710, 0.2401449084281921\n388,-0.3249315805387951, 2.0220670354338774, 0.2698880732059479\n389,-0.0960477228578228, 2.0189207048083720, 0.2699134647846222\n390, 0.1280981557739660, 2.0158395043704496, 0.2454121708869934\n391, 0.3452278908343442, 2.0128547506964680, 0.2061954736709595\n392, 0.5545998965948740, 2.0099766379255533, 0.1641141176223755\n396, 1.3356454135288547, 1.9804488021499242, 0.1507489830255508\n397, 1.5328386393032085, 1.9691151455342581, 0.2039903998374939\n398, 1.7362462292391640, 1.9574243190958676, 0.2769193053245544\n\n" ]

[ 0, 0 ]

[]

[]

[ "3d", "plotly_python", "python", "scipy", "triangulation" ]

[ "I found a solution.\nI can update powershell to 7 to solve the problem. But that's so weird. Why?\n" ]

[ 0 ]

[]

[]

[ "conda", "powershell", "python" ]

[ "Make sure your ipyparallel version is greater or equal to 7.0.\nIn [1]: import ipyparallel as ipp\n\nIn [2]: ipp.__version__\nOut[2]: '6.3.0'\n\nIn [3]: hasattr(ipp, \"Cluster\")\nOut[3]: False\n\nSometimes conda install ipyparallel may not install the newest version. Try using pip install ipyparallel. After version 7.0:\nIn [1]: import ipyparallel as ipp\n\nIn [2]: ipp.__version__\nOut[2]: '8.4.1'\n\nIn [3]: hasattr(ipp, \"Cluster\")\nOut[3]: True\n\nIn [4]: cluster = ipp.Cluster(n=4)\n\n" ]

[ 0 ]

[]

[]

[ "ipython", "ipython_parallel", "python" ]

[ "Start off with checking out the docs for the utility you're using. If there are complicated arguments, there is probably a config file.\nThis should tidy up your first command:\n# setup.cfg\n[build_ext]\ninplace=1\n\nI don't see anything in the docs about a post-build step, and I wouldn't really expect this process to execute shell commands afterwards. build_ext is for building python. make is very available and usual for building C binaries.\nAdd a Makefile to your project. If you have gccinstalled, you likely have make already:\n# Makefile (lines need to start with tab)\n\ncompile:\n python3 setup.py build_ext --inplace\n cython3 --embed -o main.c main.py\n gcc -Os -I /usr/include/python3.5m -o main main.c -lpython3.5m -lpthread -lm -lutil -ldl\n\n\nNow you can just type make or make compile to get the desired affect.\n", "Yes, it is possible to reduce your compile time workflow to a single instruction. The setup function in the distutils module provides a script_args argument that allows you to specify arguments to be passed to the build script.\nYou can use this argument to specify the --inplace and --embed flags for Cython, and the -o option for gcc, like this:\nfrom distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Build import cythonize\n\nextensions = [\nExtension(\"v\", [\"version.py\"]),\nExtension(\"\", [\"lib/.py\"])\n]\n\nsetup(\nname = \"MyFirst App\",\next_modules = cythonize(extensions),\nscript_args = [\"build_ext\", \"--inplace\", \"--embed\", \"-o\", \"main.c\"]\n)\n\nYou can then compile your project by running python3 setup.py from the shell. This will run the build script with the specified arguments, and you will get your executables.\nNote that you will still need to run gcc separately to compile the C code generated by Cython into an executable. You can do this by running gcc -Os -I /usr/include/python3.5m -o main main.c -lpython3.5m -lpthread -lm -lutil -ldl from the shell after running python3 setup.py\n" ]

[ 0, 0 ]

[]

[]

[ "cython", "python", "python_3.5" ]

[ "Since pyAudio has portAudio as a dependency, you first have to install portaudio.\nbrew install portaudio\n\nThen try: pip install pyAudio. If the problem persists after installing portAudio, you can specify the directory path where the compiler will be able to find the source programs (e.g: portaudio.h). Since the headers should be in the /usr/local/include directory:\npip install --global-option='build_ext' --global-option='-I/usr/local/include' --global-option='-L/usr/local/lib' pyaudio\n\n", "On Ubuntu builds:\nsudo apt-get install python-pyaudio\n\nFor Python3:\nsudo apt-get install python3-pyaudio\n\n", "You have to install portaudio first then link that file. Only then you can find that header file (i.e, portaudio.h). To install portaudio in mac by using HomeBrew program use following commands.\nbrew install portaudio\nbrew link portaudio\npip install pyaudio\n\nsudo is not needed if you're admin. We should refrain using sudo as it messes up lots of permissions.\n", "First, you can use Homebrew to install portaudio.\nbrew install portaudio\n\nThen try to find the portaudio path:\nsudo find / -name \"portaudio.h\"\n\nIn my case it is at /usr/local/Cellar/portaudio/19.6.0/include .\nRun the command below to install pyaudio\npip install --global-option='build_ext' --global-option='-I/usr/local/Cellar/portaudio/19.6.0/include' --global-option='-L/usr/local/Cellar/portaudio/19.6.0/lib' pyaudio\n\n", "On Raspbian:\nsudo apt-get install python-pyaudio\n\n", "on Centos:\nyum install -y portaudio portaudio-devel && pip install pyaudio\n\n", "Just for the record for folks using MacPorts and not Homebrew:\n$ [sudo] port install portaudio\n$ pip install pyaudio --global-option=\"build_ext\" --global-option=\"-I/opt/local/include\" --global-option=\"-L/opt/local/lib\"\n\n", "I needed to do the following to install PortAudio on Debian\nsudo apt install portaudio19-dev\n\nI also apt install'd python3-portaudio before that, although it didn't work. I'm not sure if that contributed as well.\n", "Adding a bit of robustness (in case of a non-default homebrew dir) to the snippet from @fukudama,\nbrew install portaudio\npip install --global-option='build_ext' --global-option=\"-I$(brew --prefix)/include\" --global-option=\"-L$(brew --prefix)/lib\" pyaudio\n\n", "For me on 10.10.5 the paths were under /opt/local. I had to add /opt/local/bin to my /etc/paths file. And the command line that worked was\nsudo pip install --global-option='build_ext' --global-option='-I/opt/local/include' --global-option='-L/opt/local/lib' pyaudio\n\n", "If you are using anaconda/miniconda to manage your python environments then\nconda install pyaudio\ninstalls portaudio at the same time as pyaudio\nThe following NEW packages will be INSTALLED:\n\n portaudio pkgs/main/osx-64::portaudio-19.6.0-h647c56a_4\n pyaudio pkgs/main/osx-64::pyaudio-0.2.11-py37h1de35cc_2\n\n", "On Termux (this is what worked for me):\n\npkg install python\nbash -c \"$(curl -fsSL https://its-pointless.github.io/setup-pointless-repo.sh)\"\npkg install portaudio\npip install pyaudio\n\nSource: pyaudio installing #6235\n", "this is the tested answer for MacBook Pro m2 chip:\nfirst find the location of the portaudio.h file by\nsudo find / -name \"portaudio.h\"\n\nthen, once you find the location, copy it and use it in this command.\nLDFLAGS=\"-L/{opt/homebrew/Cellar/portaudio/19.7.0/}lib\" CFLAGS=\"-I/{opt/homebrew/Cellar/portaudio/19.7.0}/include\" pip3 install pyaudio\n\nHere replace the location from { } into you file location hopefully this works. I have tried above solutions and this one worked for me.\n", "For M1 mac, this is worked for me:\nLDFLAGS=\"-L/opt/homebrew/Cellar/portaudio/19.7.0/lib\" CFLAGS=\"-I/opt/homebrew/Cellar/portaudio/19.7.0/include\" pip3 install pyaudio\n\nRes:\n Created wheel for pyaudio: filename=PyAudio-0.2.12-cp310-cp310-macosx_11_0_arm64.whl size=24170 sha256=c74eb581e6bca2400f681f68d33654002722969f1a455ffce87e4e5da05471d8\n Stored in directory: /private/var/folders/m_/kzyr4q_11cl35ngrj77k28f00000gn/T/pip-ephem-wheel-cache-ql1x8ums/wheels/93/08/0b/b915ab1895927641737175e5bc7b6111e8ed0c26daabeecba0\nSuccessfully built pyaudio\nInstalling collected packages: pyaudio\nSuccessfully installed pyaudio-0.2.12\n\nBe noted, do not using find / its very slow and stupid, using brew info portaudio\n" ]

[ 182, 27, 16, 13, 9, 8, 8, 6, 5, 4, 1, 1, 1, 0 ]

[]

[]

[ "macos", "pyaudio", "python" ]

[ "Solution 1 - str.index\nYou can do it as follows:\n\ncreate a new string variable new_see, which is initially empty, but will ultimately contain the result of the replacements\nmake each iteration only process the part of the input string up until the point where a matching key is encountered, and append the iteration's replacement result to the result string\nafter each iteration, truncate the input string from its start to the index after the key encountered in current iteration, so that the next iteration will only work with the yet unprocessed part\n\nsee = \"I am going to kill you!\"\nnew_see = \"\"\nprint(see)\n\nfor key, reflection in reflections.items():\n if key in see:\n idx = see.index(key)\n print(f\"matched key [{key}] @ index {idx}, reflection=[{reflection}]\")\n\n # take the part of `see` up until the index where the `key` ends,\n # replace the `key` with `replacement` and append the result to\n # the new string\n new_see += see[:idx+len(key)].replace(key, reflection)\n\n # truncate the original string from start up until the index where\n # `key` was encountered, so the next iteration will only work on\n # the part of it that hasn't been processed yet\n see = see[idx+len(key):]\n\n # take a look at the intermediate results\n print(f\"see=[{see}], new_see=[{new_see}]\")\n\n# append any leftover part that wasn't in the dict (in this case, \"!\")\nif see:\n new_see += see\n\nnew_see = new_see.capitalize()\nprint(new_see)\n\nOutput:\nI am going to kill you!\nmatched key [I am] @ index 0, reflection=[you are]\nsee=[ going to kill you!], new_see=[you are]\nmatched key [you] @ index 15, reflection=[me]\nsee=[!], new_see=[you are going to kill me]\nYou are going to kill me!\n\nSolution 2 - str.split\nSlightly more pythonic solution using str.split instead of operating on indices:\nsee = \"I am going to kill you!\"\nnew_see = \"\"\nprint(see)\n\nfor key, reflection in reflections.items():\n if key in see:\n print(f\"matched key [{key}], reflection=[{reflection}]\")\n left, right = see.split(key)\n new_see += left + reflection\n see = right\n print(f\"see=[{see}], new_see=[{new_see}]\")\nif see:\n new_see += see\n\nnew_see = new_see.capitalize()\nprint(new_see)\n\nOutput:\nI am going to kill you!\nmatched key [I am], reflection=[you are]\nsee=[ going to kill you!], new_see=[you are]\nmatched key [you], reflection=[me]\nsee=[!], new_see=[you are going to kill me]\nYou are going to kill me!\n\n\nAs pointed out in a comment, this code is going to replace \"I'm\" with \"You'm\". In order to fix this, you should reorder the entries in your dict such that \"I'm\", \"I'd\", etc., are processed before \"I\". Even then though, it will still not work properly in some cases, e.g. for words in all caps - \"DICT\" is going to be replaced with \"DyouCT\". In order to deal with this, you'd need to take a look at regular expressions and use re.sub instead of str.replace - that will allow you e.g. to only replace a key if it's a standalone word (i.e. surrounded by non-letters).\n", "print(see1) has the wrong level of indentation.\nTry with this:\nsee = \"you are going to kill me\"\nfor i in reflections:\n if i in see:\n see = see.replace(i, reflections[i])\n see1 = see.replace(i, reflections[i])\nprint(see1)\n\nThat way you print the word once you have broken out of the for-loop.\n" ]

[ 2, 0 ]

[]

[]

[ "python" ]

[ "The code you are are using was designed for an installed. Which is exactly what your error message is saying. The QuickStart clearly states Click Application type > Desktop app.\nWhile i agree the error message states installed or web, i am not sure that code can be used for a web application.\n\nClient secrets must be for a web or installed app.\n\nOpen the file denoted by credentials_path the file should have the following format.\ncredentials.json\n{\n \"installed\": {\n \"client_id\": \"[redacted]\",\n \"project_id\": \"daimto-tutorials-101\",\n \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n \"token_uri\": \"https://oauth2.googleapis.com/token\",\n \"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n \"client_secret\": \"[redacted]\",\n \"redirect_uris\": [\n \"http://localhost\"\n ]\n }\n}\n\nPoints to check.\n\nit must say \"installed\"\nredirect_uris must not include anything like urn:ietf:wg:oauth:2.0:oob\n\nHere is a of video which will show you how to create the proper credentials file for use with that code. This should be done though Google cloud console\n\nHow to create Google Oauth2 installed application credentials.json\n\nNote: I highly doubt that this issue is due to oob, you would have a different error message if it was.\nupdate Web app works.\nI was able to test this with using a web app credentials. the only change i had to make was to denote the port i wanted the code to run on in order to get a static port I needed to add a redirect uri to the developer console project.\nI made no other changes to the standard quickstart.\nflow = InstalledAppFlow.from_client_secrets_file(\n CREDENTIALS_FILE_PATH, SCOPES)\n creds = flow.run_local_server(port=53911)\n\n" ]

[ 1 ]

[]

[]

[ "google_api", "google_api_python_client", "google_oauth", "google_sheets_api", "python" ]

[ "To transform a string to lowercase, you use\nstring.lower()\n\nTo answer your question, use\nx[1] = x[1].lower()\n\n", "perhaps try\n`\nx = ['AA', 'BB', 'CC']\nStr = x[1]\nprint(Str.lower())\n`\n#0 = AA, 1 = BB, 2 = CC\nuse 0 1 or 2 with x[NUMBER]\nI hope this works for you!! \nEDIT: or you can use x[num] directly instead of putting it in a variable\n" ]

[ 0, 0 ]

[]

[]

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

[ "The add_cog is not an async function or coroutine. It's a normal function. This is easily fixable by removing the await statement.\nBefore\nawait bot.add_cog(Personality(bot))\n\nAfter\nbot.add_cog(Personality(bot))\n\nEdit.\nSorry, I forgot to answer the question, Does await bot.load_extension(cogs) actually not need to be awaited?\nThe answer to that question is that, as of discord.py 2, the load_extension was changed to an async function because they might need it in the future. So, for now, you have to await it.\n", "What are your intention again intents.members= True, although you already use all the intentions that are intents = discord.Intents.all(). Well, this line of code can be entered as follows:\n#main.py\n...\nbot = commands.Bot(command_prefix='$', intents=discord.Intents.all())\n...\n\n#main.py\nasync def load_cogs():\n for filename in os.listdir('./cogs'):\n if filename.endswith('.py'):\n await bot.load_extension(f'cogs.{filename[:-3]}')\n\n\nasync def main():\n await load_cogs()\n await bot.start('token')\n\nif __name__ == '__main__':\n asyncio.run(main())\n\n# in some cog\nclass MyCog(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n async def setup(bot):\n await bot.add_cog(MyCog(bot))\n\nIMPORTANTLY! Before inserting someone else's code, check it for indentations and spaces so that you later not go to the forum with stupid questions.\n" ]

[ 0, 0 ]

[]

[]

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

[ "json.dumps has an indent argument, printing the result should be enough:\nprint(json.dumps(obj, indent=2))\n\n", "This might be slightly different than what OP was asking for, but you can do use IPython.display.JSON to interactively view a JSON/dict object.\nfrom IPython.display import JSON\nJSON({'a': [1, 2, 3, 4,], 'b': {'inner1': 'helloworld', 'inner2': 'foobar'}})\n\nEdit: This works in Hydrogen and JupyterLab, but not in Jupyter Notebook or in IPython terminal.\nInside Hydrogen:\n\n\n", "import uuid\nfrom IPython.display import display_javascript, display_html, display\nimport json\n\nclass RenderJSON(object):\n def __init__(self, json_data):\n if isinstance(json_data, dict):\n self.json_str = json.dumps(json_data)\n else:\n self.json_str = json_data\n self.uuid = str(uuid.uuid4())\n\n def _ipython_display_(self):\n display_html('<div id=\"{}\" style=\"height: 600px; width:100%;\"></div>'.format(self.uuid), raw=True)\n display_javascript(\"\"\"\n require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"], function() {\n document.getElementById('%s').appendChild(renderjson(%s))\n });\n \"\"\" % (self.uuid, self.json_str), raw=True)\n\nTo ouput your data in collapsible format:\nRenderJSON(your_json)\n\n\nCopy pasted from here: https://www.reddit.com/r/IPython/comments/34t4m7/lpt_print_json_in_collapsible_format_in_ipython/\nGithub: https://github.com/caldwell/renderjson\n", "I am just adding the expanded variable to @Kyle Barron answer:\nfrom IPython.display import JSON\nJSON(json_object, expanded=True)\n\n", "I found this page looking for a way to eliminate the literal \\ns in the output. We're doing a coding interview using Jupyter and I wanted a way to display the result of a function real perty like. My version of Jupyter (4.1.0) doesn't render them as actual line breaks. The solution I produced is (I sort of hope this is not the best way to do it but...)\nimport json\n\noutput = json.dumps(obj, indent=2)\n\nline_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n\n# Now that our obj is a list of strings leverage print's automatic newline\nfor line in line_list:\n print line\n\nI hope this helps someone!\n", "For Jupyter notebook, may be is enough to generate the link to open in a new tab (with the JSON viewer of firefox):\nfrom IPython.display import Markdown\ndef jsonviewer(d):\n f=open('file.json','w')\n json.dump(d,f)\n f.close()\n print('open in firefox new tab:')\n return Markdown('[file.json](./file.json)')\n\njsonviewer('[{\"A\":1}]')\n'open in firefox new tab:\n\nfile.json\n", "Just an extension to @filmor answer(https://stackoverflow.com/a/18873131/7018342).\nThis encodes elements that might not compatible with json.dumps and also gives a handy function that can be used just like you would use print.\nimport json\nclass NpEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, np.integer):\n return int(obj)\n if isinstance(obj, np.floating):\n return float(obj)\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n if isinstance(obj, np.bool_):\n return bool(obj)\n return super(NpEncoder, self).default(obj)\n\ndef print_json(json_dict):\n print(json.dumps(json_dict, indent=2, cls=NpEncoder))\n\nUsage:\njson_dict = {\"Name\":{\"First Name\": \"Lorem\", \"Last Name\": \"Ipsum\"}, \"Age\":26}\nprint_json(json_dict)\n>>>\n{\n \"Name\": {\n \"First Name\": \"Lorem\",\n \"Last Name\": \"Ipsum\"\n },\n \"Age\": 26\n}\n\n", "For some uses, indent should make it:\nprint(json.dumps(parsed, indent=2))\n\nA Json structure is basically tree structure.\nWhile trying to find something fancier, I came across this nice paper depicting other forms of nice trees that might be interesting: https://blog.ouseful.info/2021/07/13/exploring-the-hierarchical-structure-of-dataframes-and-csv-data/.\nIt has some interactive trees and even comes with some code including linking to this question and the collapsing tree from Shankar ARUL.\nOther samples include using plotly Here is the code example from plotly:\nimport plotly.express as px\nfig = px.treemap(\n names = [\"Eve\",\"Cain\", \"Seth\", \"Enos\", \"Noam\", \"Abel\", \"Awan\", \"Enoch\", \"Azura\"],\n parents = [\"\", \"Eve\", \"Eve\", \"Seth\", \"Seth\", \"Eve\", \"Eve\", \"Awan\", \"Eve\"]\n)\nfig.update_traces(root_color=\"lightgrey\")\nfig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\nfig.show()\n\n\n\nAnd using treelib. On that note, This github also provides nice visualizations. Here is one example using treelib:\n#%pip install treelib\nfrom treelib import Tree\n\ncountry_tree = Tree()\n# Create a root node\ncountry_tree.create_node(\"Country\", \"countries\")\n\n# Group by country\nfor country, regions in wards_df.head(5).groupby([\"CTRY17NM\", \"CTRY17CD\"]):\n # Generate a node for each country\n country_tree.create_node(country[0], country[1], parent=\"countries\")\n # Group by region\n for region, las in regions.groupby([\"GOR10NM\", \"GOR10CD\"]):\n # Generate a node for each region\n country_tree.create_node(region[0], region[1], parent=country[1])\n # Group by local authority\n for la, wards in las.groupby(['LAD17NM', 'LAD17CD']):\n # Create a node for each local authority\n country_tree.create_node(la[0], la[1], parent=region[1])\n for ward, _ in wards.groupby(['WD17NM', 'WD17CD']):\n # Create a leaf node for each ward\n country_tree.create_node(ward[0], ward[1], parent=la[1])\n\n# Output the hierarchical data\ncountry_tree.show()\n\n\nI have, based on this, created a function to convert json to a tree:\nfrom treelib import Node, Tree, node\ndef json_2_tree(o , parent_id=None, tree=None, counter_byref=[0], verbose=False, listsNodeSymbol='+'):\n if tree is None:\n tree = Tree()\n root_id = counter_byref[0]\n if verbose:\n print(f\"tree.create_node({'+'}, {root_id})\")\n tree.create_node('+', root_id)\n counter_byref[0] += 1\n parent_id = root_id\n if type(o) == dict:\n for k,v in o.items():\n this_id = counter_byref[0]\n if verbose:\n print(f\"tree.create_node({str(k)}, {this_id}, parent={parent_id})\")\n tree.create_node(str(k), this_id, parent=parent_id)\n counter_byref[0] += 1\n json_2_tree(v , parent_id=this_id, tree=tree, counter_byref=counter_byref, verbose=verbose, listsNodeSymbol=listsNodeSymbol)\n elif type(o) == list:\n if listsNodeSymbol is not None:\n if verbose:\n print(f\"tree.create_node({listsNodeSymbol}, {counter_byref[0]}, parent={parent_id})\")\n tree.create_node(listsNodeSymbol, counter_byref[0], parent=parent_id)\n parent_id=counter_byref[0]\n counter_byref[0] += 1 \n for i in o:\n json_2_tree(i , parent_id=parent_id, tree=tree, counter_byref=counter_byref, verbose=verbose,listsNodeSymbol=listsNodeSymbol)\n else: #node\n if verbose:\n print(f\"tree.create_node({str(o)}, {counter_byref[0]}, parent={parent_id})\")\n tree.create_node(str(o), counter_byref[0], parent=parent_id)\n counter_byref[0] += 1\n return tree\n\nThen for example:\nimport json\njson_2_tree(json.loads('{\"2\": 3, \"4\": [5, 6]}'),verbose=False,listsNodeSymbol='+').show() \n\ngives:\n+\n├── 2\n│ └── 3\n└── 4\n └── +\n ├── 5\n └── 6\n\nWhile\njson_2_tree(json.loads('{\"2\": 3, \"4\": [5, 6]}'),listsNodeSymbol=None).show() \n\nGives\n+\n├── 2\n│ └── 3\n└── 4\n ├── 5\n └── 6\n\n" ]

[ 101, 74, 39, 7, 3, 0, 0, 0 ]

[]

[]

[ "ipython_notebook", "json", "python" ]

[ "Still trying to figure out what moment it happens as we need the whole error log. But that error is trying to tell you that you are invoking a method on a None type. Meaning that some of the attributes return None, and you are still trying to access them.\nTo debug, recommend checking the pandas DataFrame first, printing out rfm.head().\nSecondly, if this is happening while calling hist(), probably some of the underlying data is None, and might be worth investing some time into cleaning up these None rows, or filling them up with something.\n" ]

[ 0 ]

[]

[]

[ "dataframe", "python" ]

[ "Turns out everything is just right, and the only thing that messed things up was a typo in LOCALE_PATHS.\nsettings.py:\nLOCALE_PATHS = ( # notice the S which was forgotten\n os.path.join(BASE_DIR, 'locale')\n)\n\n" ]

[ 0 ]

[]

[]

[ "django", "django_i18n", "python", "translation" ]

[ "Your variable is still a string when you apply an operation involving a numeric value. In your case, you still need to convert the variable to a float:\nanswer2 = float(answer2)\n\nFurthermore, I do not know if isdigit() catches floats (involving a decimal point). This post might help out if you get stuck there: Using isdigit for floats?\n", "The error you are encountering is happening because you are trying to subtract a string value from an integer. You need to convert the answer2 variable to an integer before you can subtract x from it.\nHere is one way you can fix this error:\nimport math\n\nx = 4096\ny = \"\"\nz = 10\nq = 1\nfinal_answer = 0\n\nanswer1 = str(input(\"Do you want to convert decimal into binary (1) or binary into decimal (2)?\"))\nif answer1 == \"1\":\n answer2 = input(\"What number do you want to convert to binary? It can't be larger than 4096\")\n p = answer2.isdigit()\n if p:\n # Convert the string value of answer2 to an integer\n answer2 = int(answer2)\n for i in range(13):\n if answer2 >= x:\n y = y + \"1\"\n answer2 -= x\n else:\n y = y + \"0\"\n\n x /= 2\n\n print(y)\n elif not p:\n print(\"That's not a number\")\n\n" ]

[ 1, 0 ]

[]

[]

[ "python" ]

[ "Dictionaries can't be sorted.\nHowever, you can do this:\nData = {1:[\"name\",2010],2:[\"name\",2005],3:[\"name\",2000]}\nsorted(Data.items(), key = lambda x: x[1])\n\nThis will return a list instead, but sorted on the first index in ascending order.\n" ]

[ 0 ]

[]

[]

[ "dictionary", "python" ]

[ "You may try using pandas to load the JSON into a dataframe. Then, you can create a new class for each page and assign the relevant data to its fields. For example, you could create a class like this:\nclass Email:\n def __init__(self, email_sender, email_receiver, subject, text):\n self.email_sender = email_sender\n self.email_receiver = email_receiver\n self.subject = subject\n self.text = text\n\nThen, you can iterate through the JSON data and create an instance of the Email class for each page, assigning the relevant data to the fields.\nFor example:\nfor result in data['results']:\n email_sender = result['properties']['email_sender'][texttype1][0]['text']['content']\n email_receiver = result['properties']['email_receiver'][texttype2][0]['text']['content']\n subject = result['properties']['subject'][texttype3][0]['text']['content']\n text = result['properties']['text'][texttype4][0]['text']['content']\n email = Email(email_sender, email_receiver, subject, text)\n\nThis way, you can create classes for each page and assign the relevant data to each page.\n" ]

[ 0 ]

[]

[]

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