Is my normalization off? So I have a DataFrame which consists of order data from customer on an exchange.

I have a column Dollars which is the dollar value of all trades conducted by customers in different currency pairs. I want to train a classifier on this data, so I'm normalizing the Dollar column to get the values into the same range, but I think the normalized values seem quite.. odd. This is a snippet of my DataFrame:

Dollars   | Normalized dollars
181447.50 | 9.10975e-06
281885.00 | 1.41523e-05
290786.00 | 1.45992e-05
70923.00  | 3.56076e-06
1121169.54| 5.62894e-05


These values seem tiny.

I've used sklearn.preprocessing.normalize for this.

It's worth mentioning that the lowest Dollars value is 0.06 while the largest is 5,605,847,772.52, which I assume is the explanation, but I was expecting a 0-1 range, but my largest normalized value is 0.157. Should I do more filtering on the dataset to remove extreme outliers as a general rule?

UPDATE:
Applying the base 10 logarithm to dollars yields some more manageable results, though the normalized values are still fairly small. ScaledDollars is the result of using sklearn.StandardScaler on Log10Dollars:

Dollars   | Normalized dollars| Log10Dollars | LogNorm   | ScaledDollars
181447.50 | 9.10975e-06       | 5.258751     | 0.00384193| -0.761916
281885.00 | 1.41523e-05       | 5.450072     | 0.00398171| -0.573336
290786.00 | 1.45992e-05       | 5.463573     | 0.00399157| -0.560028
70923.00  | 3.56076e-06       | 4.850787     | 0.00354388| -1.16404
You should use scaling instead of normalizing. By normalizing the column you make the norm 1, while you are interested in making the expected value 0 with a standard deviation of 1. With the norm, the more samples you have the smaller your samples will become. Use the scikit-learn StandardScaler instead. Despite this, with these huge numbers you will still get weird results (although most of them will be negative with this skew). What could be an alternative is using a log transformation on your dollars first, and then scaling them. Conceptually you usually look at relative values between dollars, the difference between 10 and 20 dollars is similar to that of 10,000 and 20,000 which the log transformation will capture.