I would like to know if there is a way to find out the maximum, for the sake of having something specific let's say, integer type (or unsigned integer, or float, or complex - any "fixed size" type) supported by numpy at runtime. That is, let's assume that I know (from documentation) that largest unsigned integer type in the current version of numpy is np.uint64 and I have a line of code such as:
y = np.uint64(x)
I would like my code to use whatever is the largest, let's say, unsigned integer type available in the version of numpy that my code uses. That is, I would be interested in replacing the above hardcoded type with something like this:
y = np.largest_uint_type(x)
Is there such a method?
You can use np.sctypes:
>>> def largest_of_kind(kind):
... return max(np.sctypes[kind], key=lambda x: np.dtype(x).itemsize)
...
>>> largest_of_kind('int')
<class 'numpy.int64'>
>>> largest_of_kind('uint')
<class 'numpy.uint64'>
>>> largest_of_kind('float')
<class 'numpy.float128'>
>>> largest_of_kind('complex')
<class 'numpy.complex256'>
While I do like #PaulPanzer solution, I also found that numpy defines a function maximum_sctype() not documented in numpy's standard docs. This function fundamentally does the same thing as #PaulPanzer solution (plus some edge case analysis). From the code it is clear that sctype types are sorted in the increasing size order. Using this function, what I need can be done as follows:
y = np.maximum_sctype(np.float)(x) # currently np.float128 on OSX
y = np.maximum_sctype(np.uint8)(x) # currently np.uint64
etc.
Not so elegant, but using the prior knowledge that np.uint is always an exponent of 2, you can do something like that:
for i in range(4,100):
try:
eval('np.uint'+str(2**i)+'(0)')
except:
c=i-1
break
answer='np.uint'+str(2**c)
>>answer
Out[657]: 'np.uint64'
and you can use it as
y=eval(answer+'('+str(x)+')')
or, alternatively without the assumption of exp(2) and with no eval (check all the numbers up to N, here 1000):
for i in range(1000):
if hasattr(np,'uint'+str(i)):
x='uint'+str(i)
>>x
Out[662]: 'uint64'
I am looking to convert some small numbers to a simple, readable output. Here is my method but I wondering if there is something simpler.
x = 8.54768039530728989343156856E-58
y = str(x)
print "{0}.e{1}".format(y.split(".")[0], y.split("e")[1])
8.e-58
This gets you pretty close, do you need 8.e-58 exactly or are you just trying to shorten it into something readable?
>>> x = 8.54768039530728989343156856E-58
>>> print "{0:.1e}".format(x)
8.5e-58
An alternative:
>>> print "{0:.0e}".format(x)
9e-58
Note that on Python 2.7 or 3.1+, you can omit the first zero which indicates the position, so it would be something like "{:.1e}".format(x)
like this?
>>> x = 8.54768039530728989343156856E-58
>>> "{:.1e}".format(x)
'8.5e-58'
Another way of doing it, if you ever want to extract the exponent without doing string manipulations.
def frexp_10(decimal):
logdecimal = math.log10(decimal)
return 10 ** (logdecimal - int(logdecimal)), int(logdecimal)
>>> frexp_10(x)
(0.85476803953073244, -57)
Format as you wish...
There are two answers: one for using the number and one for simple display.
For actual numbers:
>>> round(3.1415,2)
3.14
>>> round(1.2345678e-10, 12)
1.23e-10
The built-in round() function will round a number to an arbitrary number of decimal places. You might use this to truncate insignificant digits from readings.
For display, it matters which version of display you use. In Python 2.x, and deprecated in 3.x, you can use the 'e' formatter.
>>> print "%6.2e" % 1.2345678e-10
1.23e-10
or in 3.x, use:
>>> print("{:12.2e}".format(3.1415))
3.14e+00
>>> print("{:12.2e}".format(1.23456789e-10))
1.23e-10
or, if you like the zeros:
>>> print("{:18.14f}".format(1.23456789e-10))
0.00000000012346
Are Decimal data type objects (dtypes) available in NumPy?
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> s = [['123.123','23'],['2323.212','123123.21312']]
>>> ss = numpy.array(s, dtype=numpy.dtype(decimal.Decimal))
>>> a = numpy.array(s, dtype=float)
>>> type(d)
<class 'decimal.Decimal'>
>>> type(ss[1,1])
<class 'str'>
>>> type(a[1,1])
<class 'numpy.float64'>
I suppose numpy.array doesn't support every dtype, but I sort of thought that it would at least let a dtype propagate as far as it could as long as the right operations were defined. Am I missing something? Is there some way for this to work?
NumPy doesn't recognize decimal.Decimal as a specific type. The closest it can get is the most general dtype, object. So when converting the elements to the desired dtype, the conversion is a no operation.
>>> ss.dtype
dtype('object')
Keep in mind that because the elements of the array are Python objects, you won't get much of a speedup using them. For example, if you try to add this to any other array, the other elements will have to be boxed back into Python objects and added via the normal Python addition code. You might gain some speed in that the iteration will be in C, but not that much.
Unfortunately, you have to cast each of your items to Decimal when you create the numpy.array. Something like
s = [['123.123','23'],['2323.212','123123.21312']]
decimal_s = [[decimal.Decimal(x) for x in y] for y in s]
ss = numpy.array(decimal_s)
Important caveat: this is a bad answer
You would probably do best to skip to the next answer.
It seems that Decimal is available:
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> a = numpy.array([d,d,d],dtype=numpy.dtype(decimal.Decimal))
>>> type(a[1])
<class 'decimal.Decimal'>
I'm not sure exactly what you are trying to accomplish. Your example is more complicated than is necessary for simply creating a decimal NumPy array.
I'm making a program that, for reasons not needed to be explained, requires a float to be converted into a string to be counted with len(). However, str(float(x)) results in x being rounded when converted to a string, which throws the entire thing off. Does anyone know of a fix for it?
Here's the code being used if you want to know:
len(str(float(x)/3))
Some form of rounding is often unavoidable when dealing with floating point numbers. This is because numbers that you can express exactly in base 10 cannot always be expressed exactly in base 2 (which your computer uses).
For example:
>>> .1
0.10000000000000001
In this case, you're seeing .1 converted to a string using repr:
>>> repr(.1)
'0.10000000000000001'
I believe python chops off the last few digits when you use str() in order to work around this problem, but it's a partial workaround that doesn't substitute for understanding what's going on.
>>> str(.1)
'0.1'
I'm not sure exactly what problems "rounding" is causing you. Perhaps you would do better with string formatting as a way to more precisely control your output?
e.g.
>>> '%.5f' % .1
'0.10000'
>>> '%.5f' % .12345678
'0.12346'
Documentation here.
len(repr(float(x)/3))
However I must say that this isn't as reliable as you think.
Floats are entered/displayed as decimal numbers, but your computer (in fact, your standard C library) stores them as binary. You get some side effects from this transition:
>>> print len(repr(0.1))
19
>>> print repr(0.1)
0.10000000000000001
The explanation on why this happens is in this chapter of the python tutorial.
A solution would be to use a type that specifically tracks decimal numbers, like python's decimal.Decimal:
>>> print len(str(decimal.Decimal('0.1')))
3
Other answers already pointed out that the representation of floating numbers is a thorny issue, to say the least.
Since you don't give enough context in your question, I cannot know if the decimal module can be useful for your needs:
http://docs.python.org/library/decimal.html
Among other things you can explicitly specify the precision that you wish to obtain (from the docs):
>>> getcontext().prec = 6
>>> Decimal('3.0')
Decimal('3.0')
>>> Decimal('3.1415926535')
Decimal('3.1415926535')
>>> Decimal('3.1415926535') + Decimal('2.7182818285')
Decimal('5.85987')
>>> getcontext().rounding = ROUND_UP
>>> Decimal('3.1415926535') + Decimal('2.7182818285')
Decimal('5.85988')
A simple example from my prompt (python 2.6):
>>> import decimal
>>> a = decimal.Decimal('10.000000001')
>>> a
Decimal('10.000000001')
>>> print a
10.000000001
>>> b = decimal.Decimal('10.00000000000000000000000000900000002')
>>> print b
10.00000000000000000000000000900000002
>>> print str(b)
10.00000000000000000000000000900000002
>>> len(str(b/decimal.Decimal('3.0')))
29
Maybe this can help?
decimal is in python stdlib since 2.4, with additions in python 2.6.
Hope this helps,
Francesco
I know this is too late but for those who are coming here for the first time, I'd like to post a solution. I have a float value index and a string imgfile and I had the same problem as you. This is how I fixed the issue
index = 1.0
imgfile = 'data/2.jpg'
out = '%.1f,%s' % (index,imgfile)
print out
The output is
1.0,data/2.jpg
You may modify this formatting example as per your convenience.
How do I see the type of a variable? (e.g. unsigned 32 bit)
Use the type() builtin function:
>>> i = 123
>>> type(i)
<type 'int'>
>>> type(i) is int
True
>>> i = 123.456
>>> type(i)
<type 'float'>
>>> type(i) is float
True
To check if a variable is of a given type, use isinstance:
>>> i = 123
>>> isinstance(i, int)
True
>>> isinstance(i, (float, str, set, dict))
False
Note that Python doesn't have the same types as C/C++, which appears to be your question.
You may be looking for the type() built-in function.
See the examples below, but there's no "unsigned" type in Python just like Java.
Positive integer:
>>> v = 10
>>> type(v)
<type 'int'>
Large positive integer:
>>> v = 100000000000000
>>> type(v)
<type 'long'>
Negative integer:
>>> v = -10
>>> type(v)
<type 'int'>
Literal sequence of characters:
>>> v = 'hi'
>>> type(v)
<type 'str'>
Floating point integer:
>>> v = 3.14159
>>> type(v)
<type 'float'>
It is so simple. You do it like this.
print(type(variable_name))
How to determine the variable type in Python?
So if you have a variable, for example:
one = 1
You want to know its type?
There are right ways and wrong ways to do just about everything in Python. Here's the right way:
Use type
>>> type(one)
<type 'int'>
You can use the __name__ attribute to get the name of the object. (This is one of the few special attributes that you need to use the __dunder__ name to get to - there's not even a method for it in the inspect module.)
>>> type(one).__name__
'int'
Don't use __class__
In Python, names that start with underscores are semantically not a part of the public API, and it's a best practice for users to avoid using them. (Except when absolutely necessary.)
Since type gives us the class of the object, we should avoid getting this directly. :
>>> one.__class__
This is usually the first idea people have when accessing the type of an object in a method - they're already looking for attributes, so type seems weird. For example:
class Foo(object):
def foo(self):
self.__class__
Don't. Instead, do type(self):
class Foo(object):
def foo(self):
type(self)
Implementation details of ints and floats
How do I see the type of a variable whether it is unsigned 32 bit, signed 16 bit, etc.?
In Python, these specifics are implementation details. So, in general, we don't usually worry about this in Python. However, to sate your curiosity...
In Python 2, int is usually a signed integer equal to the implementation's word width (limited by the system). It's usually implemented as a long in C. When integers get bigger than this, we usually convert them to Python longs (with unlimited precision, not to be confused with C longs).
For example, in a 32 bit Python 2, we can deduce that int is a signed 32 bit integer:
>>> import sys
>>> format(sys.maxint, '032b')
'01111111111111111111111111111111'
>>> format(-sys.maxint - 1, '032b') # minimum value, see docs.
'-10000000000000000000000000000000'
In Python 3, the old int goes away, and we just use (Python's) long as int, which has unlimited precision.
We can also get some information about Python's floats, which are usually implemented as a double in C:
>>> sys.float_info
sys.floatinfo(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308,
min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15,
mant_dig=53, epsilon=2.2204460492503131e-16, radix=2, rounds=1)
Conclusion
Don't use __class__, a semantically nonpublic API, to get the type of a variable. Use type instead.
And don't worry too much about the implementation details of Python. I've not had to deal with issues around this myself. You probably won't either, and if you really do, you should know enough not to be looking to this answer for what to do.
print type(variable_name)
I also highly recommend the IPython interactive interpreter when dealing with questions like this. It lets you type variable_name? and will return a whole list of information about the object including the type and the doc string for the type.
e.g.
In [9]: var = 123
In [10]: var?
Type: int
Base Class: <type 'int'>
String Form: 123
Namespace: Interactive
Docstring:
int(x[, base]) -> integer
Convert a string or number to an integer, if possible. A floating point argument will be truncated towards zero (this does not include a string
representation of a floating point number!) When converting a string, use the optional base. It is an error to supply a base when converting a
non-string. If the argument is outside the integer range a long object
will be returned instead.
a = "cool"
type(a)
//result 'str'
<class 'str'>
or
do
`dir(a)`
to see the list of inbuilt methods you can have on the variable.
One more way using __class__:
>>> a = [1, 2, 3, 4]
>>> a.__class__
<type 'list'>
>>> b = {'key1': 'val1'}
>>> b.__class__
<type 'dict'>
>>> c = 12
>>> c.__class__
<type 'int'>
Examples of simple type checking in Python:
assert type(variable_name) == int
assert type(variable_name) == bool
assert type(variable_name) == list
It may be little irrelevant. but you can check types of an object with isinstance(object, type) as mentioned here.
The question is somewhat ambiguous -- I'm not sure what you mean by "view". If you are trying to query the type of a native Python object, #atzz's answer will steer you in the right direction.
However, if you are trying to generate Python objects that have the semantics of primitive C-types, (such as uint32_t, int16_t), use the struct module. You can determine the number of bits in a given C-type primitive thusly:
>>> struct.calcsize('c') # char
1
>>> struct.calcsize('h') # short
2
>>> struct.calcsize('i') # int
4
>>> struct.calcsize('l') # long
4
This is also reflected in the array module, which can make arrays of these lower-level types:
>>> array.array('c').itemsize # char
1
The maximum integer supported (Python 2's int) is given by sys.maxint.
>>> import sys, math
>>> math.ceil(math.log(sys.maxint, 2)) + 1 # Signedness
32.0
There is also sys.getsizeof, which returns the actual size of the Python object in residual memory:
>>> a = 5
>>> sys.getsizeof(a) # Residual memory.
12
For float data and precision data, use sys.float_info:
>>> sys.float_info
sys.floatinfo(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.2204460492503131e-16, radix=2, rounds=1)
Do you mean in Python or using ctypes?
In the first case, you simply cannot - because Python does not have signed/unsigned, 16/32 bit integers.
In the second case, you can use type():
>>> import ctypes
>>> a = ctypes.c_uint() # unsigned int
>>> type(a)
<class 'ctypes.c_ulong'>
For more reference on ctypes, an its type, see the official documentation.
Python doesn't have such types as you describe. There are two types used to represent integral values: int, which corresponds to platform's int type in C, and long, which is an arbitrary precision integer (i.e. it grows as needed and doesn't have an upper limit). ints are silently converted to long if an expression produces result which cannot be stored in int.
Simple, for python 3.4 and above
print (type(variable_name))
Python 2.7 and above
print type(variable_name)
It really depends on what level you mean. In Python 2.x, there are two integer types, int (constrained to sys.maxint) and long (unlimited precision), for historical reasons. In Python code, this shouldn't make a bit of difference because the interpreter automatically converts to long when a number is too large. If you want to know about the actual data types used in the underlying interpreter, that's implementation dependent. (CPython's are located in Objects/intobject.c and Objects/longobject.c.) To find out about the systems types look at cdleary answer for using the struct module.
For python2.x, use
print type(variable_name)
For python3.x, use
print(type(variable_name))
You should use the type() function. Like so:
my_variable = 5
print(type(my_variable)) # Would print out <class 'int'>
This function will view the type of any variable, whether it's a list or a class. Check this website for more information: https://www.w3schools.com/python/ref_func_type.asp
Python is a dynamically typed language. A variable, initially created as a string, can be later reassigned to an integer or a float. And the interpreter won’t complain:
name = "AnyValue"
# Dynamically typed language lets you do this:
name = 21
name = None
name = Exception()
To check the type of a variable, you can use either type() or isinstance() built-in function. Let’s see them in action:
Python3 example:
variable = "hello_world"
print(type(variable) is str) # True
print(isinstance(variable, str)) # True
Let's compare both methods performances in python3
python3 -m timeit -s "variable = 'hello_world'" "type(variable) is int"
5000000 loops, best of 5: 54.5 nsec per loop
python3 -m timeit -s "variable = 'hello_world'" "isinstance(variable, str)"
10000000 loops, best of 5: 39.2 nsec per loop
type is 40% slower approximately (54.5/39.2 = 1.390).
We could use type(variable) == str instead. It would work, but it’s a bad idea:
== should be used when you want to check the value of a variable. We would use it to see if the value of the variable is equal to "hello_world". But when we want to check if the variable is a string, is the operator is more appropriate. For a more detailed explanation of when to use one or the other, check this article.
== is slower: python3 -m timeit -s "variable = 'hello_world'" "type(variable) == str" 5000000 loops, best of 5: 64.4 nsec per loop
Difference between isinstance and type
Speed is not the only difference between these two functions. There is actually an important distinction between how they work:
type only returns the type of an object (it's class). We can use it to check if the variable is of type str.
isinstance checks if a given object (first parameter) is:
an instance of a class specified as a second parameter. For example, is variable an instance of the str class?
or an instance of a subclass of a class specified as a second parameter. In other words - is variable an instance of a subclass of str?
What does it mean in practice? Let’s say we want to have a custom class that acts as a list but has some additional methods. So we might subclass the list type and add custom functions inside:
class MyAwesomeList(list):
# Add additional functions here
pass
But now the type and isinstance return different results if we compare this new class to a list!
my_list = MyAwesomeList()
print(type(my_list) is list) # False
print(isinstance(my_list, list)) # True
We get different results because isinstance checks if my_list is an instance of the list (it’s not) or a subclass of the list (it is because MyAwesomeList is a subclass of the list). If you forget about this difference, it can lead to some subtle bugs in your code.
Conclusions
isinstance is usually the preferred way to compare types. It’s not only faster but also considers inheritance, which is often the desired behavior. In Python, you usually want to check if a given object behaves like a string or a list, not necessarily if it’s exactly a string. So instead of checking for string and all its custom subclasses, you can just use isinstance.
On the other hand, when you want to explicitly check that a given variable is of a specific type (and not its subclass) - use type. And when you use it, use it like this: type(var) is some_type not like this: type(var) == some_type.
I saw this one when I was new to Python (I still am):
x = …
print(type(x))```
There's no 32bit and 64bit and 16bit, python is simple, you don't have to worry about it. See how to check the type:
integer = 1
print(type(integer)) # Result: <class 'int'>, and if it's a string then class will be str and so on.
# Checking the type
float_class = 1.3
print(isinstance(float_class, float)) # True
But if you really have to, you can use Ctypes library which has types like unsigned integer.
Ctypes types documentation
You can use it like this:
from ctypes import *
uint = c_uint(1) # Unsigned integer
print(uint) # Output: c_uint(1)
# To actually get the value, you have to call .value
print(uint.value)
# Change value
uint.value = 2
print(uint.value) # 2
There are many data types in python like:
Text Type: str
Numeric Types: int, float, complex
Sequence Types: list, tuple, range
Mapping Type: dict
Set Types: set, frozenset
Boolean Type: bool
Binary Types: bytes, bytearray, memoryview
None Type: NoneType
Here I have written a code having a list containing all type of data types example and printing their type
L = [
"Hello World",
20,
20.5,
1j,
["apple", "banana", "cherry"],
("apple", "banana", "cherry"),
range(6),
{"name" : "John", "age" : 36},
{"apple", "banana", "cherry"},
frozenset({"apple", "banana", "cherry"}),
True,
b"Hello",
bytearray(5),
memoryview(bytes(5)),
None
]
for _ in range(len(L)):
print(type(L[_]))
OUTPUT:
<class 'str'>
<class 'int'>
<class 'float'>
<class 'complex'>
<class 'list'>
<class 'tuple'>
<class 'range'>
<class 'dict'>
<class 'set'>
<class 'frozenset'>
<class 'bool'>
<class 'bytes'>
<class 'bytearray'>
<class 'memoryview'>
<class 'NoneType'>
Just do not do it. Asking for something's type is wrong in itself. Instead use polymorphism. Find or if necessary define by yourself the method that does what you want for any possible type of input and just call it without asking about anything. If you need to work with built-in types or types defined by a third-party library, you can always inherit from them and use your own derivatives instead. Or you can wrap them inside your own class. This is the object-oriented way to resolve such problems.
If you insist on checking exact type and placing some dirty ifs here and there, you can use __class__ property or type function to do it, but soon you will find yourself updating all these ifs with additional cases every two or three commits. Doing it the OO way prevents that and lets you only define a new class for a new type of input instead.