Category : pickle

Assume I want to store via sqlite3 a simple list of strings: my_list = [‘a’,’b’,’c’] or a python object with properties and methods. What I tried so far is to serialize (pickle) my_list, and the returned byte representation is b’x80x03]qx00(Xx01x00x00x00aqx01Xx01x00x00x00bqx02Xx01x00x00x00cqx03e.’. However, cannot be stored within a BLOB variable. Should I use a string variable instead, ..

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I am programming a software that should be capable of making saveable flashcards for students. Using Python Pickle dump to save Error – OSError: [Errno 30] Read-only file system: Using Mac OS Catalina Using Tinter from tkinter import * import pickle from PIL import Image, ImageTk ——————variables——————————————– backgroundColour = ‘#e3e6e4’ try: oldNames = pickle.load(open("Card1Saves.dat", "rb")) ..

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I’m using the Helm/Kubernetes gateway version of Dask Distributed loading workers code with upload_file. This works fine: # my-client.py client = Client(‘127.0.0.1:8786’) client.upload_file(‘C:mycodewrk4.py’) futures = client.map (alter, [1000, 2000, 3000]) results = client.gather(futures) # wrk4.py def alter(x): y = x + 1 return y However, when I try to upload a zipped file that includes ..

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I’m using IsolationForest to predict outliers. And I want to save all my steps so I can use it directly on other data next time. My code for building isolation forest is: import sklearn.neighbors._base import sys sys.modules[‘sklearn.neighbors.base’] = sklearn.neighbors._base from sklearn.ensemble import IsolationForest iforest = IsolationForest(n_estimators=50, max_samples=’auto’, contamination=float(0.00005), max_features=1.0,random_state = None) iforest.fit(imputed_data_initial) y_pred = iforest.predict(data) ..

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I wrote multiple steps to impute a dataset, and I want to create a pipeline for these steps and also serialize/pickle the pipeline so that it can be loaded when analyzing a new sample. The steps I did for imputation are: imputer = MissForest() imputed_data = imputer.fit_transform(data) imputed_data = pd.DataFrame(imputed_data, columns=data.columns) #Drop ‘id’ imputed_data_initial = imputed_data.drop(‘id’, axis ..

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