convert pandas float series to int

Question

I am discretizing my series for a learner. I really need the series to be in float, and I really need to avoid for loops.

How do I convert this series from float to int?

Here is my function that is currently failing:

def discretize_series(s,count,normalized=True):
    def discretize(value,bucket_size):
        return value % bucket_size
    if normalized:
        maximum = 1.0
    else:
        minimum = np.min(s)
        s = s[:] - minimum
        maximum = np.max(s)
    bucket_size = maximum / float(count)

Here is the line that causes the function to fail:

    s = int((s[:] - s[:] % bucket_size)/bucket_size)

The int() induces a casting error: I am unable to cast the pandas series as an int series.

    return s

If I remove the int(), the function works, so I may just see if I can get it to work anyway.

Answer 1

The regular python int function only works for scalars. You should either use a numpy function to round the data, either

s = np.round((s - s % bucket_size) / bucket_size) #to round properly; or
s = np.fix((s - s % bucket_size) / bucket_size)   #to round towards 0

and if you actually want to convert to an integer type, use

s = s.astype(int)

to cast your array.

Answer 2

Create a list with float values:

y = [0.1234, 0.6789, 0.5678]

Convert the list of float values to pandas Series

s = pd.Series(data=y)

Round values to three decimal values

print(s.round(3))

returns

0    0.123
1    0.679
2    0.568
dtype: float64

Convert to integer

print(s.astype(int))

returns

0    0
1    0
2    0
dtype: int64

Pipe it all

pd.Series(data=y).round(3)