I am trying to fill a `np.zeros`

2d array with some values and then normalize those values with the max value in each row.

I am dividing each value of that row with the max value using the `max()`

function. Although I am getting proper float division for values in the array up to the max value, Python only returns `1.`

for values after the max value in the array.

How can can I have proper values in this case?

I am using Python 3.6.2 with jupyter notebook.

```
import numpy as np
from matplotlib import pyplot as plt
np.set_printoptions(threshold=np.inf) #for printing full array
l1 = np.zeros((5,10))
l2 = np.arange(500)
for j in range(len(l1[:,0])):
for i in range(len(l1[0,:])):
if i<(j+(len(l1[0,:])/2)):
l1[j,i] = l1[j,i-1]+1
if i>(j+(len(l1[0,:])/2)-1):
l1[j,i] = l1[j,i-1]-1
for j in range(len(l1[:,0])):
for i in range(len(l1[0,:])):
l1[j,i] = l1[j,i]/(max(l1[j,:]))
fig,ax = plt.subplots(figsize = (5,25))
ax.imshow(l1)
ax.set_aspect('5')
print(l1[0,:])
```

The result I get is:

```
[0.2 0.4 0.6 0.8 1. 1. 1. 1. 1. 0. ]
```

The array without normalizing:

```
[1. 2. 3. 4. 5. 4. 3. 2. 1. 0.]
```

Thanks to user2357112supportsMonica and mkrieger1 I found my mistake in the code. The max value was changing as the for loops edit the array. The solution to this was achieved by changing the code as follows:

```
import numpy as np
from matplotlib import pyplot as plt
np.set_printoptions(threshold=np.inf) #for printing full array
l1 = np.zeros((10,20))
l2 = np.arange(500)
for j in range(len(l1[:,0])):
for i in range(len(l1[0,:])):
if i<(j+(len(l1[0,:])/2)):
l1[j,i] = l1[j,i-1]+1
if i>(j+(len(l1[0,:])/2)-1):
l1[j,i] = l1[j,i-1]-1
for j in range(len(l1[:,0])):
l1[j,:] = l1[j,:]/(max(l1[j,:]))
fig,ax = plt.subplots(figsize = (5,25))
ax.imshow(l1)
ax.set_aspect('1')
```

Tags: exception, pythonpython