Convert Python list of strings into floats, where list also contains words

Question

Consider for example the list in Python containing both strings of letters and numbers

a = ['Total', '1', '4', '5', '2']

How would it be possible to convert this into the mixed value list

b = ['Total', 1.0, 4.0, 5.0, 2.0]

Note that in general we may not know where the letters string will be in the list i.e. we might have

a = ['Total', '1', '4', 'Next', '2']

Should b[0] always be a string, even if a[0].isdigit() == True? — Dan Ross
– Dan Ross, Commented May 27, 2013 at 22:51
I was personally looking for a general way to go about it i.e a[3] might have been a string (within the string) also. — rwolst
– rwolst, Commented May 27, 2013 at 23:01
Not sure if I follow, could you add an example of that to the question? — Dan Ross
– Dan Ross, Commented May 27, 2013 at 23:02

Ashwini Chaudhary · Accepted Answer · 2013-05-27 22:38:55Z

5

You can use a generator function and exception handling:

>>> def func(seq):
        for x in seq:
            try:
                yield float(x)
            except ValueError:
                yield x
...             
>>> a = ['Total', '1', '4', '5', '2']
>>> list(func(a))
['Total', 1.0, 4.0, 5.0, 2.0]

edited May 27, 2013 at 22:38

answered May 27, 2013 at 22:35

Ashwini Chaudhary

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1 Comment

Gareth Latty Over a year ago

Beat me to it. +1, and a small edit to correct the indentation.

Community · Accepted Answer · 2017-05-23 12:07:51Z

4

Just convert everything but the first column:

b = a[:1] + [float(i) for i in a[1:]]

Judging from your other question you are processing a CSV file, so if the first column is always a string there is no point in converting that part of each row:

>>> a = ['Total', '1', '4', '5', '2']
>>> a[:1] + [float(i) for i in a[1:]]
['Total', 1.0, 4.0, 5.0, 2.0]

You could also use a try: - except ValueError approach, but why incur the overhead when it is known up front what column has text and what columns have numeric values?

edited May 23, 2017 at 12:07

CommunityBot

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answered May 27, 2013 at 22:36

Martijn Pieters

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Comments

U2EF1 · Accepted Answer · 2013-05-27 22:59:10Z

0

Assuming your end goal isn't a csv package, I'd recommend pandas.read_csv.

answered May 27, 2013 at 22:59

U2EF1

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