Beginning Anomaly Detection Using

Figure 7-24.  The standardized values for the Time column

Figure 7-25.  The standardized values for the Amount column

anomalies = df[df["Class"] == 1]

normal = df[df["Class"] == 0]

anomalies.shape, normal.shape

Figure 7-26.  Defining two data frames: anomalies and normal

Chapter 7   temporal Convolutional networks

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In this block of code, you name two new data frames as anomalies and normal, with 

their names corresponding to their content. Checking their shape reveals that there are 

relatively few anomalies compared to the entire data set, comprising around 0.173% of 

the whole data set.

Now let’s get to defining your training and testing data sets (see Figure 

7-28

).

Figure 7-27.  The output of the code in Figure 

7-26