MinMaxScaler not working in this example

[mfethe1]

Everything runs normally up until the MinMaxScaler is called in the for loop.

high_prices = df.loc[:,'High'].as_matrix()
low_prices = df.loc[:,'Low'].as_matrix()
mid_prices = (high_prices+low_prices)/2.0

print(mid_prices)
[20.25 19.865 20.34 ... 27.97 27.62 27.3425]
main:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
main:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.

smoothing_window_size = 2500
for di in range(0,10000,smoothing_window_size):
scaler.fit(train_data[di:di+smoothing_window_size,:])
train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:])

You normalize the last bit of remaining data

scaler.fit(train_data[di+smoothing_window_size:,:])
train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])
Traceback (most recent call last):

File "", line 3, in
scaler.fit(train_data[di:di+smoothing_window_size,:])

File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 325, in fit
return self.partial_fit(X, y)

File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 353, in partial_fit
force_all_finite="allow-nan")

File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 550, in check_array
context))

ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by MinMaxScaler.

[lefig]

Me too. I have same problem. Pity

[heisendelta]

This is probably because you aren't using as many data points as the tutorial. To fix this, simply lower the smoothing_window_size variable. The end of the for loop in the next line should be smaller than the length of your test data.