# Tag Info

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Building on to @grov's answer, you can alternatively use map directly on the column like so: df['col1_numerical'] = df['col1'].map({ "Increased": 1, "Decreased": -1, "Neutral": 0 })

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The jumps in the CDF tell you that this is a Discrete random variable as opposed to a continuous random variable. The points on the x-axis where the jumps happen are the values that the discrete random variable take. The quantum of the jump represents the probability of the random variable taking on that value.

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Regarding bank payments, no that much datasets are publicly available. You can have a look at customer complaint databases like the CFPB one where some of the complaints related to bank transfers (Money transfer, virtual currency, or money service (check cashing service, currency exchange, cashier's/traveler's check), are catalogued as Fraud or scam: The ...

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One possible way to map from string values to specific numerical values is by using a Python dictionary as a lookup table. The lookup table can be used for each value in the column with .apply(func) on the column. import pandas as pd l = [{'col1':'Increased'},{'col1':'Decreased'},{'col1':'Neutral'}] df = pd.DataFrame(l) print(df) Output: col1 0 ...

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There is this dataset and if it doesnt match, maybe you could contact the authors at Brussels University for more information. https://data.world/raghu543/credit-card-fraud-data The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 ...

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The most basic solution would be make your data agree to some rules. But, that may not be possible. You can find the similarity of the new data with the data you already have. It is beyond a certain threshold then that data doesn't make sense. You can also use the data directly for training and find it using techniques like TracIn but this may readily usable....

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Assuming that these are pytorch tensors, you can convert them to numpy arrays using the .numpy() method. Depending on whether your tensors are stored on the GPU or still attached to the graph you might have to add .cpu() and .detach().

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Ignoring for a moment the variance of the first feature, a straightforward approach is to perform a linear combination of the features $x_1$, $x_2$ and $x_3$, with each of the coefficients being a hyper-parameter you set to indicate the relative importance of that feature (perhaps normalizing the features before hand). This will be your molecule's utility ...

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Edited my previous comment as there was an Syntax error, This happen as I am new in this join recently(01/04/2021) on this platform you can try replace function with NumPy library which will help to speed up the process. df.replace('^^',np.NaN) or df.replace('not filled in',np.NaN), df.replace('&&', np.NaN), df.replace('values needed', np.NaN), df....

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The specific ordering is only metadata as it is not explicitly in the data itself, and the index doesn't always represent order. If you have an additional row with the order in then it would be two dimensional. But, for example, you wouldn't be able to distinguish between ascending and descending order by looking at the index itself. v(t) is velocity by time ...

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