using StandardScaler on a heterogenous matrix.

[Led_Zeppelin]

I am trying to use StandardScaler on a dataset that is but not all numbers.

The way to do that is now shown.

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
df=scaler.fit_transform(df)

However, I do not think it will work since two columns in the dataset; timestamp and machine condition are not numbers.

They are shown as

Unnamed: 0	timestamp	sensor_00	sensor_01	sensor_02	sensor_03	sensor_04	sensor_05	sensor_06	sensor_07	...	sensor_43	sensor_44	machine_status
0	0	2018-04-01 00:00:00	2.465394	47.09201	53.2118	46.310760	634.3750	76.45975	13.41146	16.13136	...	41.92708	39.641200	.... NORMAL
1	1	2018-04-01 00:01:00	2.465394	47.09201	53.2118	46.310760	634.3750	76.45975	13.41146	16.13136	...	41.92708	39.641200	...  NORMAL
2	2	2018-04-01 00:02:00	2.444734	47.35243	53.2118	46.397570	638.8889	73.54598	13.32465	16.03733	...	41.66666	39.351852	...  NORMAL
3	3	2018-04-01 00:03:00	2.460474	47.09201	53.1684	46.397568	628.1250	76.98898	13.31742	16.24711	...	40.88541	39.062500	...  NORMAL
4	4	2018-04-01 00:04:00	2.445718	47.13541	53.2118	46.397568	636.4583	76.58897	13.35359	16.21094	...	41.40625	38.773150  ... NORMAL

Now as I said the first and last columns are timestamp and machine status, respectively. They are clearly not numbers.

My question is how to I modify the above code so it works on all columns except the first and the last. I am hoping that there could be a one line executable that can do this, not a loop. It would be very similar to the third line in the code, but with modifications.

Thanks in advance.

Respectfully,

LZ