Category : floating-point

I’ve been learning how to make a prediction model by looking at this step-by-step tutorial website: https://towardsdatascience.com/step-by-step-guide-building-a-prediction-model-in-python-ac441e8b9e8b The data I was using are the Covid-19 cases in Peru from last Jan to this Sep, and with this data, I want to predict death cases from this Oct to Dec. However, the “New Cases” data type ..

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Is there a way to suppress scientific notation in Panda’s outputs without forcing a particular precision across all columns? So that a data frame: import pandas as pd df = pd.DataFrame({"a": [0.01, 0.02, 0.03], "b": [0.0000001, 0.0000002, 0.0000003]}) df.to_csv( "df.csv", index=False, ) That initially would be outputted as: a b 0.01 1.00E-07 0.02 2.00E-07 0.03 ..

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client = snap.client.Client() client.connect(‘XXX.XXX.X.XXX’, 0, 2) #IP address, rack, slot db = client.db_get(20) print(db) intDB = [] for i in range(1, 122): reading = client.db_read(20, i, 1) realReading = snap.util.get_real(reading, 0) array = [realReading] intDB.append(array) print(intDB) This code is supposed to print a DB in bytearrays and then print an array with the floats values ..

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In a django project I’m trying to store a calculated decimal value, but it fails for some strange reason. My model looks something like: class FancyModel(models.Model): fancy_field = models.DecimalField(max_digits=10, decimal_places=3, null=True, blank=True) Sometimes, rather often actually, it crashes on save: django.db.utils.IntegrityError: CHECK constraint failed: myapp_fancy_model which I found out after some digging was caused by ..

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