Code changing rder of headers

[Led_Zeppelin]

I am working with the following output when using a Python3 program.

Unnamed: 0	timestamp	time_period	sensor_00	sensor_01	sensor_02	sensor_03	sensor_04	sensor_05	sensor_06	sensor_07	sensor_08	sensor_09	sensor_10	sensor_11	sensor_12	sensor_13	sensor_14	sensor_15	sensor_16	sensor_17	sensor_18	sensor_19	sensor_20	sensor_21	sensor_22	sensor_23	sensor_24	sensor_25	sensor_26	sensor_27	sensor_28	sensor_29	sensor_30	sensor_31	sensor_32	sensor_33	sensor_34	sensor_35	sensor_36	sensor_37	sensor_38	sensor_39	sensor_40	sensor_41	sensor_42	sensor_43	sensor_44	sensor_45	sensor_46	sensor_47	sensor_48	sensor_49	sensor_50	sensor_51	machine_status
0	0	2018-04-01 00:00:00	Night	2.465394	47.09201	53.2118	46.310760	634.3750	76.45975	13.41146	16.13136	15.56713	15.05353	37.22740	47.52422	31.11716	1.681353	419.5747	NaN	461.8781	466.3284	2.565284	665.3993	398.9862	880.0001	498.8926	975.9409	627.6740	741.7151	848.0708	429.0377	785.1935	684.9443	594.4445	682.8125	680.4416	433.7037	171.9375	341.9039	195.0655	90.32386	40.36458	31.51042	70.57291	30.98958	31.770832	41.92708	39.641200	65.68287	50.92593	38.194440	157.9861	67.70834	243.0556	201.3889	NORMAL
1	1	2018-04-01 00:01:00	Night	2.465394	47.09201	53.2118	46.310760	634.3750	76.45975	13.41146	16.13136	15.56713	15.05353	37.22740	47.52422	31.11716	1.681353	419.5747	NaN	461.8781	466.3284	2.565284	665.3993	398.9862	880.0001	498.8926	975.9409	627.6740	741.7151	848.0708	429.0377	785.1935	684.9443	594.4445	682.8125	680.4416	433.7037	171.9375	341.9039	195.0655	90.32386	40.36458	31.51042	70.57291	30.98958	31.770832	41.92708	39.641200	65.68287	50.92593	38.194440	157.9861	67.70834	243.0556	201.3889	NORMAL
2	2	2018-04-01 00:02:00	Night	2.444734	47.35243	53.2118	46.397570	638.8889	73.54598	13.32465	16.03733	15.61777	15.01013	37.86777	48.17723	32.08894	1.708474	420.8480	NaN	462.7798	459.6364	2.500062	666.2234	399.9418	880.4237	501.3617	982.7342	631.1326	740.8031	849.8997	454.2390	778.5734	715.6266	661.5740	721.8750	694.7721	441.2635	169.9820	343.1955	200.9694	93.90508	41.40625	31.25000	69.53125	30.46875	31.770830	41.66666	39.351852	65.39352	51.21528	38.194443	155.9606	67.12963	241.3194	203.7037	NORMAL
3	3	2018-04-01 00:03:00	Night	2.460474	47.09201	53.1684	46.397568	628.1250	76.98898	13.31742	16.24711	15.69734	15.08247	38.57977	48.65607	31.67221	1.579427	420.7494	NaN	462.8980	460.8858	2.509521	666.0114	399.1046	878.8917	499.0430	977.7520	625.4076	739.2722	847.7579	474.8731	779.5091	690.4011	686.1111	754.6875	683.3831	446.2493	166.4987	343.9586	193.1689	101.04060	41.92708	31.51042	72.13541	30.46875	31.510420	40.88541	39.062500	64.81481	51.21528	38.194440	155.9606	66.84028	240.4514	203.1250	NORMAL
4	4	2018-04-01 00:04:00	Night	2.445718	47.13541	53.2118	46.397568	636.4583	76.58897	13.35359	16.21094	15.69734	15.08247	39.48939	49.06298	31.95202	1.683831	419.8926	NaN	461.4906	468.2206	2.604785	663.2111	400.5426	882.5874	498.5383	979.5755	627.1830	737.6033	846.9182	408.8159	785.2307	704.6937	631.4814	766.1458	702.4431	433.9081	164.7498	339.9630	193.8770	101.70380	42.70833	31.51042	76.82291	30.98958	31.510420	41.40625	38.773150	65.10416	51.79398	38.773150	158.2755	66.55093	242.1875	201.3889	NORMAL

This output is correct. Sensor_15 should have no data at all. If you look through the columns, you can see that is the case.

I then run the dataframe through the following code to normalize it and the scale it:

scaler=StandardScaler()
temp_df=scaler.fit_transform(df)
df = pd.DataFrame(temp_df)
df.head()

Now the sensor
I get the following output:

1.732043	0.221531	-0.151947	0.639367	1.057656	0.303417	0.177086	-0.043528	0.128859	0.178536	0.119982	-0.350879	0.429361	0.195788	-0.782050	0.377318	NaN	0.360152	0.349972	0.341435	0.374072	0.374420	0.369572	0.253027	0.182746	0.391879	0.419127	0.249973	-0.426732	-0.212400	0.481694	-0.102960	-0.636623	-0.475217	-0.349597	-0.713252	-0.601147	-1.375222	0.785445	-0.881503	-0.326638	0.080875	-0.553961	-0.358948	-0.176788	-0.260504	1.759525	0.185877	-0.588606	0.086292	0.553104	0.919519	-0.008859
1	-1.732027	0.221531	-0.151947	0.639367	1.057656	0.303417	0.177086	-0.043528	0.128859	0.178536	0.119982	-0.350879	0.429361	0.195788	-0.782050	0.377318	NaN	0.360152	0.349972	0.341435	0.374072	0.374420	0.369572	0.253027	0.182746	0.391879	0.419127	0.249973	-0.426732	-0.212400	0.481694	-0.102960	-0.636623	-0.475217	-0.349597	-0.713252	-0.601147	-1.375222	0.785445	-0.881503	-0.326638	0.080875	-0.553961	-0.358948	-0.176788	-0.260504	1.759525	0.185877	-0.588606	0.086292	0.553104	0.919519	-0.008859
2	-1.732011	0.170259	-0.072887	0.639367	1.093546	0.334759	0.008636	-0.084091	0.085614	0.203678	0.099020	-0.297925	0.479375	0.291872	-0.778120	0.388565	NaN	0.367304	0.298158	0.256276	0.378206	0.383791	0.371441	0.269006	0.206024	0.410851	0.414998	0.257387	-0.278335	-0.233546	0.617599	0.240016	-0.498857	-0.420227	-0.299450	-0.735379	-0.592037	-1.354820	0.880678	-0.782676	-0.343317	0.032133	-0.619901	-0.358948	-0.200367	-0.285498	1.736986	0.204376	-0.588605	0.061664	0.522874	0.892914	0.013040
3	-1.731996	0.209321	-0.151947	0.627530	1.093545	0.260019	0.207682	-0.087469	0.182094	0.243184	0.133959	-0.239048	0.516050	0.250668	-0.796818	0.387694	NaN	0.368242	0.307832	0.268627	0.377143	0.375581	0.364682	0.254000	0.188952	0.379447	0.408066	0.248704	-0.156831	-0.230557	0.505865	0.365381	-0.383135	-0.463929	-0.266377	-0.774794	-0.586654	-1.381776	1.070428	-0.733263	-0.326638	0.153987	-0.619901	-0.384330	-0.271105	-0.310494	1.691906	0.204376	-0.588606	0.061664	0.507759	0.879613	0.007566
4	-1.731980	0.172701	-0.138771	0.639367	1.093545	0.317882	0.184557	-0.070569	0.165459	0.243184	0.133959	-0.163830	0.547215	0.278334	-0.781691	0.380126	NaN	0.357078	0.364622	0.393011	0.363095	0.389682	0.380986	0.250734	0.195200	0.389186	0.400510	0.245300	-0.545808	-0.212281	0.569172	0.086268	-0.342724	-0.390791	-0.348241	-0.794583	-0.614837	-1.379329	1.088064	-0.659143	-0.326638	0.373326	-0.553961	-0.384330	-0.223946	-0.335489	1.714445	0.241374	-0.533186	0.089811	0.492644	0.906216	-0.008859

Now it is sensor 16 or just 16 that is the column with no data. Now sensor 16 and not sensor 15 with no data. This makes no sense.

All I am trying to do is scale and normalize the data. I had previously dropped all data columns that did not have numeric values. Those columns were "timestamp", "timeperiod" and "machine_status". They did not have numerical values in them so dropped them to get a dataframe with only numerical values. Then I scaled and normalized


I wanted to normalize and scale. I did not want to move the numeric columns around.

How do I fix this?

Thanks in advance.

Respectfully,

LZ

This issue/problem is described much better in my latest post.

LZ