How to use nested for loops in python?

Question

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 array. 
If x < 3 in the dataframe then it inputs a 1 in the array.  
If x = 0 in the dataframe then it inputs a 0 in the array.

Below is the code I have so far but the result is coming out as just [0]

array = np.array([])

for x in df["disc"]:
    for y in array:    
        if x >= 3:
            y=0
        elif x < 3:
            y=1
        else:
            y=0

Any help would be much appreciated thank you.

You are not doing anything to array. Did you mean to add the y values to array? I don't think you need a nested loop here. — mkrieger1
– mkrieger1, Commented Aug 7, 2020 at 11:43

alani · Accepted Answer · 2020-08-07 12:10:50Z

2

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

# now set it equal to 0 in any places where disc == 0
array[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.)

edited Aug 7, 2020 at 12:10

answered Aug 7, 2020 at 11:54

alani

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5 Comments

Zaxutic Over a year ago

I believe it would be slightly more efficient to do this in a single statement rather than passing through the array again. That is, something like array = np.where(disc >= 3 or disc == 0, 0, 1). This would avoid checking 0 equality for those values >= 3, thanks to the short circuiting of or, right?

alani Over a year ago

@Zaxutic That's a possibility, thanks. You would have to use | instead of or though.

alani Over a year ago

@Zaxutic I don't think the short-circuit evaluation is going to be available, because the operands of the | are still going to have to be boolean arrays that will need to be evaluated, but it might still be a bit quicker so I'll add it as an option. (or would attempt to apply a truth value to the whole array.)

alani Over a year ago

@Zaxutic note also that when using |, extra parentheses will be needed because, unlike or, the | has higher precedence than comparison operators

alani Over a year ago

@Zaxutic Final comment - it might use more memory because both temporary boolean arrays will need to exist at the same time.

Saurav Rai · Accepted Answer · 2020-08-07 11:43:34Z

0

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)

answered Aug 7, 2020 at 11:43

Saurav Rai

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Comments

Zaxutic · Accepted Answer · 2020-08-07 11:54:38Z

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 < 3 and x != 0:
        array = np.append(array, 1)
    else:
        array = np.append(array, 0)

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