Interpreting Strings as Other Data Types in Python

Question

I'm reading a file into python 2.4 that's structured like this:

field1: 7
field2: "Hello, world!"
field3: 6.2

The idea is to parse it into a dictionary that takes fieldfoo as the key and whatever comes after the colon as the value.

I want to convert whatever is after the colon to it's "actual" data type, that is, '7' should be converted to an int, "Hello, world!" to a string, etc. The only data types that need to be parsed are ints, floats and strings. Is there a function in the python standard library that would allow one to make this conversion easily?

The only things this should be used to parse were written by me, so (at least in this case) safety is not an issue.

Answer 1

For older python versions, like the one being asked, the eval function can be used but, to reduce evilness, a dict to be the global namespace should be used as second argument to avoid function calls.

>>> [eval(i, {"__builtins__":None}) for i in ['6.2', '"Hello, world!"', '7']]
[6.2, 'Hello, world!', 7]

Answer 2

First parse your input into a list of pairs like fieldN: some_string. You can do this easily with re module, or probably even simpler with slicing left and right of the index line.strip().find(': '). Then use a literal eval on the value some_string:

>>> import ast
>>> ast.literal_eval('6.2')
6.2
>>> type(_)
<type 'float'>
>>> ast.literal_eval('"Hello, world!"')
'Hello, world!'
>>> type(_)
<type 'str'>
>>> ast.literal_eval('7')
7
>>> type(_)
<type 'int'>

Answer 3

Hope this helps to do what you are trying to do:

#!/usr/bin/python

a = {'field1': 7}
b = {'field2': "Hello, world!"}
c = {'field3': 6.2}

temp1 = type(a['field1'])
temp2 = type(b['field2'])
temp3 = type(c['field3'])

print temp1
print temp2
print temp3

Answer 4

You can attempt to convert it to an int first using the built-in function int(). If the string cannot be interpreted as an int a ValueError exception is raised. You can then attempt to convert to a float using float(). If this fails also then just return the initial string

def interpret(val):
    try:
        return int(val)
    except ValueError:
        try:
            return float(val)
        except ValueError:
            return val

Answer 5

Thanks to wim for helping me figure out what I needed to search for to figure this out.

One can just use eval():

>>> a=eval("7")
>>> b=eval("3")
>>> a+b
10
>>> b=eval("7.2")
>>> a=eval("3.5")
>>> a+b
10.699999999999999
>>> a=eval('"Hello, "')
>>> b=eval('"world!"')
>>> a+b
'Hello, world!'

Answer 6

Since the "only data types that need to be parsed are int, float and str", maybe somthing like this will work for you:

entries = {'field1': '7', 'field2': "Hello, world!", 'field3': '6.2'}

for k,v in entries.items():
    if v.isdecimal():
        conv = int(v)
    else:
        try:
            conv = float(v)
        except ValueError:
            conv = v
    entries[k] = conv

print(entries)
# {'field2': 'Hello, world!', 'field3': 6.2, 'field1': 7}

Answer 7

There is strconv lib.

In [22]: import strconv
/home/tworec/.local/lib/python2.7/site-packages/strconv.py:200: UserWarning: python-dateutil is not installed. As of version 0.5, this will be a hard dependency of strconv fordatetime parsing. Without it, only a limited set of datetime formats are supported without timezones.
  warnings.warn('python-dateutil is not installed. As of version 0.5, '

In [23]: strconv.convert('1.2')
Out[23]: 1.2

In [24]: type(strconv.convert('1.2'))
Out[24]: float

In [25]: type(strconv.convert('12'))
Out[25]: int

In [26]: type(strconv.convert('true'))
Out[26]: bool

In [27]: type(strconv.convert('tRue'))
Out[27]: bool

In [28]: type(strconv.convert('12 Jan'))
Out[28]: str

In [29]: type(strconv.convert('12 Jan 2018'))
Out[29]: str

In [30]: type(strconv.convert('2018-01-01'))
Out[30]: datetime.date

Answer 8

You can use yaml to parse the literals which is better than ast in that it does not throw you an error if strings are not wrapped around extra pairs of apostrophes or quotation marks.

>>> import yaml
>>> yaml.safe_load('7')
7
>>> yaml.safe_load('Hello')
'Hello'
>>> yaml.safe_load('7.5')
7.5