How To Use Nested For Loops In Python?

I'm trying to create an array based on values from another data frame in Python. I want it to fill the array as such. If x > or = 3 in the dataframe then it inputs a 0 in the ar

Solution 1:

When working with numpy arrays, it is more efficient if you can avoid using explicit loops in Python at all. (The actual looping takes place inside compiled C code.)

disc = df["disc"]

# make an array containing 0where disc >=3, elsewhere 1array= np.where(disc >=3, 0, 1)

# now set it equal to0inany places where disc ==0array[disc ==0] =0

It could also be done in a single statement (other than the initial assignment of disc) using:

array = np.where((disc >= 3) | (disc == 0), 0, 1)

Here the | does an element-by-element "or" test on the boolean arrays. (It has higher precedence than comparison operators, so the parentheses around the comparisons are needed.)

Solution 2:

This is a simple problem. There are many ways to solve this, I think the easiest way is to use a list. You can use a list and append the values according to the conditions.

array = []

for x in df["disc"]:
   if x >= 3:
       array.append(0)
   elif x < 3:
       array.append(1)
   else:
       array.append(0)

Solution 3:

Your code doesn't seem to be doing anything to the array, as you are trying to modify the variable y, rather than the array itself. y doesn't reference the array, it just holds the values found. The second loop also doesn't do anything due to the array being empty - it's looping through 0 elements. What you need rather than another for loop is to simply append to the array.

With a list, you would use the .append() method to add an element, however as you appear to be using numpy, you'd want to use the append(arr, values) function it provides, like so:

array = np.array([])

for x in df["disc"]:  
    if x >= 3:
        array = np.append(array, 0)
    elif x < 3:
        array = np.append(array, 1)
    else:
        array = np.append(array, 0)

I'll also note that these conditions can be simplified to combine the two branches which append a 0. Namely, if x < 3 and x is not 0, then add a 1, otherwise add a 0. Thus, the code can be rewriten as follows.

array = np.array([])

for x in df["disc"]:  
    if x < 3and x != 0:
        array = np.append(array, 1)
    else:
        array = np.append(array, 0)