## Returning normalised data from a csv file in Python

Given a csv file with the contents

1,10,20,30,40,50,60,70,80,90,100
2,210,220,230,240,250,260,270,280,290,300
3,310,330,340,350,360,370,380,390,400,410

I want to read all data except the first value of each row (effectively ignore the first column). Before output, I want to normalise all that data to a range inbetween 0 and 1.

```import numpy

array = numpy.genfromtxt('Anaconda3JamesData/james_test_3.csv', delimiter=',')

# get minimum and maximum values
# read all the values of the rows : except the first value 1:
maximum=array[:, 1:].max()
# read all the values of the rows : except the first value 1:
minimum=array[:, 1:].min()

print (minimum)
print (maximum)

print (array[:,1:]) # display all the values of the rows except the first value of each row

x = (array[:,1:] - minimum)/(maximum - minimum)

print (x)
```

This returns the output

`10.0 410.0 [[ 10.  20.  30.  40.  50.  60.  70.  80.  90. 100.]  [210. 220. 230. 240. 250. 260. 270. 280. 290. 300.]  [310. 330. 340. 350. 360. 370. 380. 390. 400. 410.]] [[0.    0.025 0.05  0.075 0.1   0.125 0.15  0.175 0.2   0.225]  [0.5   0.525 0.55  0.575 0.6   0.625 0.65  0.675 0.7   0.725]  [0.75  0.8   0.825 0.85  0.875 0.9   0.925 0.95  0.975 1.   ]]`

The code is normalising the values between 0 and 1.

To normalise the data between for example 0.001 and 1 we would use the code

```x = 0.001 + ((array[:,1:] - min)/(max - min)) * 0.999
```

This is because

1-0.001=0.999

Therefore to normalise the data between 0.01 and 1, we would use

1-0.01=0.99, so

```x = 0.01 + ((array[:,1:] - min)/(max - min)) * 0.99
```

Would give us normalised data in the range of 0.01 and 1.

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