What are named tuples and how do I use them?
When should I use named tuples instead of normal tuples, or vice versa?
Are there "named lists" too? (i.e. mutable named tuples)
For the last question specifically, see also Existence of mutable named tuple in Python?.
Named tuples are basically easy-to-create, lightweight object types. Named tuple instances can be referenced using object-like variable dereferencing or the standard tuple syntax. They can be used similarly to struct or other common record types, except that they are immutable. They were added in Python 2.6 and Python 3.0, although there is a recipe for implementation in Python 2.4.
For example, it is common to represent a point as a tuple (x, y). This leads to code like the following:
pt1 = (1.0, 5.0)
pt2 = (2.5, 1.5)
from math import sqrt
line_length = sqrt((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)
Using a named tuple it becomes more readable:
from collections import namedtuple
Point = namedtuple('Point', 'x y')
pt1 = Point(1.0, 5.0)
pt2 = Point(2.5, 1.5)
from math import sqrt
line_length = sqrt((pt1.x-pt2.x)**2 + (pt1.y-pt2.y)**2)
However, named tuples are still backwards compatible with normal tuples, so the following will still work:
Point = namedtuple('Point', 'x y')
pt1 = Point(1.0, 5.0)
pt2 = Point(2.5, 1.5)
from math import sqrt
# use index referencing
line_length = sqrt((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)
# use tuple unpacking
x1, y1 = pt1
Thus, you should use named tuples instead of tuples anywhere you think object notation will make your code more pythonic and more easily readable. I personally have started using them to represent very simple value types, particularly when passing them as parameters to functions. It makes the functions more readable, without seeing the context of the tuple packing.
Furthermore, you can also replace ordinary immutable classes that have no functions, only fields with them. You can even use your named tuple types as base classes:
class Point(namedtuple('Point', 'x y')):
[...]
However, as with tuples, attributes in named tuples are immutable:
>>> Point = namedtuple('Point', 'x y')
>>> pt1 = Point(1.0, 5.0)
>>> pt1.x = 2.0
AttributeError: can't set attribute
If you want to be able change the values, you need another type. There is a handy recipe for mutable recordtypes which allow you to set new values to attributes.
>>> from rcdtype import *
>>> Point = recordtype('Point', 'x y')
>>> pt1 = Point(1.0, 5.0)
>>> pt1 = Point(1.0, 5.0)
>>> pt1.x = 2.0
>>> print(pt1[0])
2.0
I am not aware of any form of "named list" that lets you add new fields, however. You may just want to use a dictionary in this situation. Named tuples can be converted to dictionaries using pt1._asdict() which returns {'x': 1.0, 'y': 5.0} and can be operated upon with all the usual dictionary functions.
As already noted, you should check the documentation for more information from which these examples were constructed.
What are named tuples?
A named tuple is a tuple.
It does everything a tuple can.
But it's more than just a tuple.
It's a specific subclass of a tuple that is programmatically created to your specification, with named fields and a fixed length.
This, for example, creates a subclass of tuple, and aside from being of fixed length (in this case, three), it can be used everywhere a tuple is used without breaking. This is known as Liskov substitutability.
New in Python 3.6, we can use a class definition with typing.NamedTuple to create a namedtuple:
from typing import NamedTuple
class ANamedTuple(NamedTuple):
"""a docstring"""
foo: int
bar: str
baz: list
The above is the same as collections.namedtuple, except the above additionally has type annotations and a docstring. The below is available in Python 2+:
>>> from collections import namedtuple
>>> class_name = 'ANamedTuple'
>>> fields = 'foo bar baz'
>>> ANamedTuple = namedtuple(class_name, fields)
This instantiates it:
>>> ant = ANamedTuple(1, 'bar', [])
We can inspect it and use its attributes:
>>> ant
ANamedTuple(foo=1, bar='bar', baz=[])
>>> ant.foo
1
>>> ant.bar
'bar'
>>> ant.baz.append('anything')
>>> ant.baz
['anything']
Deeper explanation
To understand named tuples, you first need to know what a tuple is. A tuple is essentially an immutable (can't be changed in-place in memory) list.
Here's how you might use a regular tuple:
>>> student_tuple = 'Lisa', 'Simpson', 'A'
>>> student_tuple
('Lisa', 'Simpson', 'A')
>>> student_tuple[0]
'Lisa'
>>> student_tuple[1]
'Simpson'
>>> student_tuple[2]
'A'
You can expand a tuple with iterable unpacking:
>>> first, last, grade = student_tuple
>>> first
'Lisa'
>>> last
'Simpson'
>>> grade
'A'
Named tuples are tuples that allow their elements to be accessed by name instead of just index!
You make a namedtuple like this:
>>> from collections import namedtuple
>>> Student = namedtuple('Student', ['first', 'last', 'grade'])
You can also use a single string with the names separated by spaces, a slightly more readable use of the API:
>>> Student = namedtuple('Student', 'first last grade')
How to use them?
You can do everything tuples can do (see above) as well as do the following:
>>> named_student_tuple = Student('Lisa', 'Simpson', 'A')
>>> named_student_tuple.first
'Lisa'
>>> named_student_tuple.last
'Simpson'
>>> named_student_tuple.grade
'A'
>>> named_student_tuple._asdict()
OrderedDict([('first', 'Lisa'), ('last', 'Simpson'), ('grade', 'A')])
>>> vars(named_student_tuple)
OrderedDict([('first', 'Lisa'), ('last', 'Simpson'), ('grade', 'A')])
>>> new_named_student_tuple = named_student_tuple._replace(first='Bart', grade='C')
>>> new_named_student_tuple
Student(first='Bart', last='Simpson', grade='C')
A commenter asked:
In a large script or programme, where does one usually define a named tuple?
The types you create with namedtuple are basically classes you can create with easy shorthand. Treat them like classes. Define them on the module level, so that pickle and other users can find them.
The working example, on the global module level:
>>> from collections import namedtuple
>>> NT = namedtuple('NT', 'foo bar')
>>> nt = NT('foo', 'bar')
>>> import pickle
>>> pickle.loads(pickle.dumps(nt))
NT(foo='foo', bar='bar')
And this demonstrates the failure to lookup the definition:
>>> def foo():
... LocalNT = namedtuple('LocalNT', 'foo bar')
... return LocalNT('foo', 'bar')
...
>>> pickle.loads(pickle.dumps(foo()))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
_pickle.PicklingError: Can't pickle <class '__main__.LocalNT'>: attribute lookup LocalNT on __main__ failed
Why/when should I use named tuples instead of normal tuples?
Use them when it improves your code to have the semantics of tuple elements expressed in your code.
You can use them instead of an object if you would otherwise use an object with unchanging data attributes and no functionality.
You can also subclass them to add functionality, for example:
class Point(namedtuple('Point', 'x y')):
"""adding functionality to a named tuple"""
__slots__ = ()
#property
def hypot(self):
return (self.x ** 2 + self.y ** 2) ** 0.5
def __str__(self):
return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
Why/when should I use normal tuples instead of named tuples?
It would probably be a regression to switch from using named tuples to tuples. The upfront design decision centers around whether the cost from the extra code involved is worth the improved readability when the tuple is used.
There is no extra memory used by named tuples versus tuples.
Is there any kind of "named list" (a mutable version of the named tuple)?
You're looking for either a slotted object that implements all of the functionality of a statically sized list or a subclassed list that works like a named tuple (and that somehow blocks the list from changing in size.)
A now expanded, and perhaps even Liskov substitutable, example of the first:
from collections import Sequence
class MutableTuple(Sequence):
"""Abstract Base Class for objects that work like mutable
namedtuples. Subclass and define your named fields with
__slots__ and away you go.
"""
__slots__ = ()
def __init__(self, *args):
for slot, arg in zip(self.__slots__, args):
setattr(self, slot, arg)
def __repr__(self):
return type(self).__name__ + repr(tuple(self))
# more direct __iter__ than Sequence's
def __iter__(self):
for name in self.__slots__:
yield getattr(self, name)
# Sequence requires __getitem__ & __len__:
def __getitem__(self, index):
return getattr(self, self.__slots__[index])
def __len__(self):
return len(self.__slots__)
And to use, just subclass and define __slots__:
class Student(MutableTuple):
__slots__ = 'first', 'last', 'grade' # customize
>>> student = Student('Lisa', 'Simpson', 'A')
>>> student
Student('Lisa', 'Simpson', 'A')
>>> first, last, grade = student
>>> first
'Lisa'
>>> last
'Simpson'
>>> grade
'A'
>>> student[0]
'Lisa'
>>> student[2]
'A'
>>> len(student)
3
>>> 'Lisa' in student
True
>>> 'Bart' in student
False
>>> student.first = 'Bart'
>>> for i in student: print(i)
...
Bart
Simpson
A
namedtuple is a factory function for making a tuple class. With that class we can create tuples that are callable by name also.
import collections
#Create a namedtuple class with names "a" "b" "c"
Row = collections.namedtuple("Row", ["a", "b", "c"])
row = Row(a=1,b=2,c=3) #Make a namedtuple from the Row class we created
print row #Prints: Row(a=1, b=2, c=3)
print row.a #Prints: 1
print row[0] #Prints: 1
row = Row._make([2, 3, 4]) #Make a namedtuple from a list of values
print row #Prints: Row(a=2, b=3, c=4)
namedtuples are a great feature, they are perfect container for data. When you have to "store" data you would use tuples or dictionaries, like:
user = dict(name="John", age=20)
or:
user = ("John", 20)
The dictionary approach is overwhelming, since dict are mutable and slower than tuples. On the other hand, the tuples are immutable and lightweight but lack readability for a great number of entries in the data fields.
namedtuples are the perfect compromise for the two approaches, the have great readability, lightweightness and immutability (plus they are polymorphic!).
named tuples allow backward compatibility with code that checks for the version like this
>>> sys.version_info[0:2]
(3, 1)
while allowing future code to be more explicit by using this syntax
>>> sys.version_info.major
3
>>> sys.version_info.minor
1
namedtuple
is one of the easiest ways to clean up your code and make it more readable. It self-documents what is happening in the tuple. Namedtuples instances are just as memory efficient as regular tuples as they do not have per-instance dictionaries, making them faster than dictionaries.
from collections import namedtuple
Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])
p = Color(170, 0.1, 0.6)
if p.saturation >= 0.5:
print "Whew, that is bright!"
if p.luminosity >= 0.5:
print "Wow, that is light"
Without naming each element in the tuple, it would read like this:
p = (170, 0.1, 0.6)
if p[1] >= 0.5:
print "Whew, that is bright!"
if p[2]>= 0.5:
print "Wow, that is light"
It is so much harder to understand what is going on in the first example. With a namedtuple, each field has a name. And you access it by name rather than position or index. Instead of p[1], we can call it p.saturation. It's easier to understand. And it looks cleaner.
Creating an instance of the namedtuple is easier than creating a dictionary.
# dictionary
>>>p = dict(hue = 170, saturation = 0.1, luminosity = 0.6)
>>>p['hue']
170
#nametuple
>>>from collections import namedtuple
>>>Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])
>>>p = Color(170, 0.1, 0.6)
>>>p.hue
170
When might you use namedtuple
As just stated, the namedtuple makes understanding tuples much
easier. So if you need to reference the items in the tuple, then
creating them as namedtuples just makes sense.
Besides being more lightweight than a dictionary, namedtuple also
keeps the order unlike the dictionary.
As in the example above, it is simpler to create an instance of
namedtuple than dictionary. And referencing the item in the named
tuple looks cleaner than a dictionary. p.hue rather than
p['hue'].
The syntax
collections.namedtuple(typename, field_names[, verbose=False][, rename=False])
namedtuple is in the collections library.
typename: This is the name of the new tuple subclass.
field_names: A sequence of names for each field. It can be a sequence
as in a list ['x', 'y', 'z'] or string x y z (without commas, just
whitespace) or x, y, z.
rename: If rename is True, invalid fieldnames are automatically
replaced with positional names. For example, ['abc', 'def', 'ghi','abc'] is converted to ['abc', '_1', 'ghi', '_3'], eliminating the
keyword 'def' (since that is a reserved word for defining functions)
and the duplicate fieldname 'abc'.
verbose: If verbose is True, the class definition is printed just
before being built.
You can still access namedtuples by their position, if you so choose. p[1] == p.saturation. It still unpacks like a regular tuple.
Methods
All the regular tuple methods are supported. Ex: min(), max(), len(), in, not in, concatenation (+), index, slice, etc. And there are a few additional ones for namedtuple. Note: these all start with an underscore. _replace, _make, _asdict.
_replace
Returns a new instance of the named tuple replacing specified fields with new values.
The syntax
somenamedtuple._replace(kwargs)
Example
>>>from collections import namedtuple
>>>Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])
>>>p = Color(170, 0.1, 0.6)
>>>p._replace(hue=87)
Color(87, 0.1, 0.6)
>>>p._replace(hue=87, saturation=0.2)
Color(87, 0.2, 0.6)
Notice: The field names are not in quotes; they are keywords here.
Remember: Tuples are immutable - even if they are namedtuples and have the _replace method. The _replace produces a new instance; it does not modify the original or replace the old value. You can of course save the new result to the variable. p = p._replace(hue=169)
_make
Makes a new instance from an existing sequence or iterable.
The syntax
somenamedtuple._make(iterable)
Example
>>>data = (170, 0.1, 0.6)
>>>Color._make(data)
Color(hue=170, saturation=0.1, luminosity=0.6)
>>>Color._make([170, 0.1, 0.6]) #the list is an iterable
Color(hue=170, saturation=0.1, luminosity=0.6)
>>>Color._make((170, 0.1, 0.6)) #the tuple is an iterable
Color(hue=170, saturation=0.1, luminosity=0.6)
>>>Color._make(170, 0.1, 0.6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 15, in _make
TypeError: 'float' object is not callable
What happened with the last one? The item inside the parenthesis should be the iterable. So a list or tuple inside the parenthesis works, but the sequence of values without enclosing as an iterable returns an error.
_asdict
Returns a new OrderedDict which maps field names to their corresponding values.
The syntax
somenamedtuple._asdict()
Example
>>>p._asdict()
OrderedDict([('hue', 169), ('saturation', 0.1), ('luminosity', 0.6)])
Reference: https://www.reddit.com/r/Python/comments/38ee9d/intro_to_namedtuple/
There is also named list which is similar to named tuple but mutable
https://pypi.python.org/pypi/namedlist
from collections import namedtuple
They subclass tuple, and add a layer to assign property names to the positional elements
'namedtuple' is a function that generates a new class that inherits from "tuple" but also provides "named properties" to access elements of the tuple.
Generating Named Tuple Classes
"namedtuple" is a class factory. It needs a few things to generate the class
the class name
A sequence of field names we want to assign, in the order of elements in the tuple. Field names can be any valid variable names except that they cannot start with an "underscore".
The return value of the call to "namedtuple" will be a class. We need to assign that class to a variable name in our code so we can use it to construct instances. In general, we use the same name as the name of the class that was generated.
# Coords is a class
Coords = namedtuple('Coords', ['x', 'y'])
Now we can create instances of Coords class:
pt=Coords(10,20)
There are many ways we can provide the list of field names to the namedtuple function.
a list of strings
namedtuple('Coords',['x','y'])
a tuple of strings
namedtuple('Coords',('x','y'))
a single string with the field names separated by whitespace or commas
namedtuple('Coords','x, y'])
Instantiating Named Tuples
After we have created a named tuple class, we can instantiate them just like an ordinary class. In fact, the __new__ method of the generated class uses the field names we provided as param names.
Coords = namedtuple('Coords', ['x', 'y'])
coord=Coords(10,20)
Accessing Data in named tuple:
Since named tuples inherit from tuples, we can still handle them just like any other tuple: by index, slicing, iterating
Coords = namedtuple('Coords', ['x', 'y'])
coord=Coords(10,20) isinstance(coord,tuple) --> True # namedtuple is subclass of tuple
x,y=coord # Unpacking
x=coord[0] # by index
for e in coord:
print(e)
Now we can also access the data using the field names just as we do with the classes.
coord.x --> 10
coord.y --> 20
Since namedtuple is generated classes inherit from tuple, we can write like this:
class Coord(tuple):
....
"coord" is a tuple, therefore immutable
"rename" keyword arg for namedtuple
Field names cannot start with an underscore
Coords = namedtuple('Coords', ['x', '_y']) # does not work
namedtuple has a keyword-only argument, rename (defaults to False) that will automatically rename any invalid field name.
Coords = namedtuple('Coords', ['x', '_y'], rename=True)
field name "x" wont change, but "_y" will change to _1. 1 is the index of the field name.
Imagine the scenario where you need to update your application so you want to use namedTuple to store the users of your application. You need to extract the column names but they are invalid for named tuples and it will throw an exception. In this case, you use rename=True.
Extracting Named Tuple values into a dictionary
Coords = namedtuple('Coords', ['x', 'y'])
coord=Coords(10,20)
coord._asdict()
{'x': 10, 'y': 20}
Why do we use namedtuple
If you have this class:
class Stock:
def __init__(self, symbol, year, month, day, open, high, low, close):
self.symbol = symbol
self.year = year
self.month = month
self.day = day
self.open = open
self.high = high
self.low = low
self.close = close
Class Approach - vs - Tuple Approach
stock.symbol stock[0]
stock.open stock[4]
stock.close stock[7]
stock.high – stock.low stock[5] – stock[6]
As you see, the tuple approach is not readable. The namedtuple function in collections allows us to create a tuple that also has names attached to each field or property. This can be handy to reference data in the tuple structure by "name" instead of just relying on position. But keep in mind, tuples are immutable so if you want mutability, stick to class
Since namedtuple is iterable you can use the iterable methods. For example, if you have "coords" as a class instance, you cannot look for what is the max coord. But with named-tuple, you can.
What is namedtuple ?
As the name suggests, namedtuple is a tuple with name. In standard tuple, we access the elements using the index, whereas namedtuple allows user to define name for elements. This is very handy especially processing csv (comma separated value) files and working with complex and large dataset, where the code becomes messy with the use of indices (not so pythonic).
How to use them ?
>>>from collections import namedtuple
>>>saleRecord = namedtuple('saleRecord','shopId saleDate salesAmout totalCustomers')
>>>
>>>
>>>#Assign values to a named tuple
>>>shop11=saleRecord(11,'2015-01-01',2300,150)
>>>shop12=saleRecord(shopId=22,saleDate="2015-01-01",saleAmout=1512,totalCustomers=125)
Reading
>>>#Reading as a namedtuple
>>>print("Shop Id =",shop12.shopId)
12
>>>print("Sale Date=",shop12.saleDate)
2015-01-01
>>>print("Sales Amount =",shop12.salesAmount)
1512
>>>print("Total Customers =",shop12.totalCustomers)
125
Interesting Scenario in CSV Processing :
from csv import reader
from collections import namedtuple
saleRecord = namedtuple('saleRecord','shopId saleDate totalSales totalCustomers')
fileHandle = open("salesRecord.csv","r")
csvFieldsList=csv.reader(fileHandle)
for fieldsList in csvFieldsList:
shopRec = saleRecord._make(fieldsList)
overAllSales += shopRec.totalSales;
print("Total Sales of The Retail Chain =",overAllSales)
In Python inside there is a good use of container called a named tuple, it can be used to create a definition of class and has all the features of the original tuple.
Using named tuple will be directly applied to the default class template to generate a simple class, this method allows a lot of code to improve readability and it is also very convenient when defining a class.
I think it's worth adding information about NamedTuples using type hinting:
# dependencies
from typing import NamedTuple, Optional
# definition
class MyNamedTuple(NamedTuple):
an_attribute: str
my_attribute: Optional[str] = None
next_attribute: int = 1
# instantiation
my_named_tuple = MyNamedTuple("abc", "def")
# or more explicitly:
other_tuple = MyNamedTuple(an_attribute="abc", my_attribute="def")
# access
assert "abc" == my_named_tuple.an_attribute
assert 1 == other_tuple.next_attribute
Another way (a new way) to use named tuple is using NamedTuple from typing package: Type hints in namedtuple
Let's use the example of the top answer in this post to see how to use it.
(1) Before using the named tuple, the code is like this:
pt1 = (1.0, 5.0)
pt2 = (2.5, 1.5)
from math import sqrt
line_length = sqrt((pt1[0] - pt2[0])**2 + (pt1[1] - pt2[1])**2)
print(line_length)
(2) Now we use the named tuple
from typing import NamedTuple
inherit the NamedTuple class and define the variable name in the new class. test is the name of the class.
class test(NamedTuple):
x: float
y: float
create instances from the class and assign values to them
pt1 = test(1.0, 5.0) # x is 1.0, and y is 5.0. The order matters
pt2 = test(2.5, 1.5)
use the variables from the instances to calculate
line_length = sqrt((pt1.x - pt2.x)**2 + (pt1.y - pt2.y)**2)
print(line_length)
Try this:
collections.namedtuple()
Basically, namedtuples are easy to create, lightweight object types.
They turn tuples into convenient containers for simple tasks.
With namedtuples, you don’t have to use integer indices for accessing members of a tuple.
Examples:
Code 1:
>>> from collections import namedtuple
>>> Point = namedtuple('Point','x,y')
>>> pt1 = Point(1,2)
>>> pt2 = Point(3,4)
>>> dot_product = ( pt1.x * pt2.x ) +( pt1.y * pt2.y )
>>> print dot_product
11
Code 2:
>>> from collections import namedtuple
>>> Car = namedtuple('Car','Price Mileage Colour Class')
>>> xyz = Car(Price = 100000, Mileage = 30, Colour = 'Cyan', Class = 'Y')
>>> print xyz
Car(Price=100000, Mileage=30, Colour='Cyan', Class='Y')
>>> print xyz.Class
Y
Everyone else has already answered it, but I think I still have something else to add.
Namedtuple could be intuitively deemed as a shortcut to define a class.
See a cumbersome and conventional way to define a class .
class Duck:
def __init__(self, color, weight):
self.color = color
self.weight = weight
red_duck = Duck('red', '10')
In [50]: red_duck
Out[50]: <__main__.Duck at 0x1068e4e10>
In [51]: red_duck.color
Out[51]: 'red'
As for namedtuple
from collections import namedtuple
Duck = namedtuple('Duck', ['color', 'weight'])
red_duck = Duck('red', '10')
In [54]: red_duck
Out[54]: Duck(color='red', weight='10')
In [55]: red_duck.color
Out[55]: 'red'
I would like to convert a python variable name into the string equivalent as shown. Any ideas how?
var = {}
print ??? # Would like to see 'var'
something_else = 3
print ??? # Would print 'something_else'
TL;DR: Not possible. See 'conclusion' at the end.
There is an usage scenario where you might need this. I'm not implying there are not better ways or achieving the same functionality.
This would be useful in order to 'dump' an arbitrary list of dictionaries in case of error, in debug modes and other similar situations.
What would be needed, is the reverse of the eval() function:
get_indentifier_name_missing_function()
which would take an identifier name ('variable','dictionary',etc) as an argument, and return a
string containing the identifier’s name.
Consider the following current state of affairs:
random_function(argument_data)
If one is passing an identifier name ('function','variable','dictionary',etc) argument_data to a random_function() (another identifier name), one actually passes an identifier (e.g.: <argument_data object at 0xb1ce10>) to another identifier (e.g.: <function random_function at 0xafff78>):
<function random_function at 0xafff78>(<argument_data object at 0xb1ce10>)
From my understanding, only the memory address is passed to the function:
<function at 0xafff78>(<object at 0xb1ce10>)
Therefore, one would need to pass a string as an argument to random_function() in order for that function to have the argument's identifier name:
random_function('argument_data')
Inside the random_function()
def random_function(first_argument):
, one would use the already supplied string 'argument_data' to:
serve as an 'identifier name' (to display, log, string split/concat, whatever)
feed the eval() function in order to get a reference to the actual identifier, and therefore, a reference to the real data:
print("Currently working on", first_argument)
some_internal_var = eval(first_argument)
print("here comes the data: " + str(some_internal_var))
Unfortunately, this doesn't work in all cases. It only works if the random_function() can resolve the 'argument_data' string to an actual identifier. I.e. If argument_data identifier name is available in the random_function()'s namespace.
This isn't always the case:
# main1.py
import some_module1
argument_data = 'my data'
some_module1.random_function('argument_data')
# some_module1.py
def random_function(first_argument):
print("Currently working on", first_argument)
some_internal_var = eval(first_argument)
print("here comes the data: " + str(some_internal_var))
######
Expected results would be:
Currently working on: argument_data
here comes the data: my data
Because argument_data identifier name is not available in the random_function()'s namespace, this would yield instead:
Currently working on argument_data
Traceback (most recent call last):
File "~/main1.py", line 6, in <module>
some_module1.random_function('argument_data')
File "~/some_module1.py", line 4, in random_function
some_internal_var = eval(first_argument)
File "<string>", line 1, in <module>
NameError: name 'argument_data' is not defined
Now, consider the hypotetical usage of a get_indentifier_name_missing_function() which would behave as described above.
Here's a dummy Python 3.0 code: .
# main2.py
import some_module2
some_dictionary_1 = { 'definition_1':'text_1',
'definition_2':'text_2',
'etc':'etc.' }
some_other_dictionary_2 = { 'key_3':'value_3',
'key_4':'value_4',
'etc':'etc.' }
#
# more such stuff
#
some_other_dictionary_n = { 'random_n':'random_n',
'etc':'etc.' }
for each_one_of_my_dictionaries in ( some_dictionary_1,
some_other_dictionary_2,
...,
some_other_dictionary_n ):
some_module2.some_function(each_one_of_my_dictionaries)
# some_module2.py
def some_function(a_dictionary_object):
for _key, _value in a_dictionary_object.items():
print( get_indentifier_name_missing_function(a_dictionary_object) +
" " +
str(_key) +
" = " +
str(_value) )
######
Expected results would be:
some_dictionary_1 definition_1 = text_1
some_dictionary_1 definition_2 = text_2
some_dictionary_1 etc = etc.
some_other_dictionary_2 key_3 = value_3
some_other_dictionary_2 key_4 = value_4
some_other_dictionary_2 etc = etc.
......
......
......
some_other_dictionary_n random_n = random_n
some_other_dictionary_n etc = etc.
Unfortunately, get_indentifier_name_missing_function() would not see the 'original' identifier names (some_dictionary_,some_other_dictionary_2,some_other_dictionary_n). It would only see the a_dictionary_object identifier name.
Therefore the real result would rather be:
a_dictionary_object definition_1 = text_1
a_dictionary_object definition_2 = text_2
a_dictionary_object etc = etc.
a_dictionary_object key_3 = value_3
a_dictionary_object key_4 = value_4
a_dictionary_object etc = etc.
......
......
......
a_dictionary_object random_n = random_n
a_dictionary_object etc = etc.
So, the reverse of the eval() function won't be that useful in this case.
Currently, one would need to do this:
# main2.py same as above, except:
for each_one_of_my_dictionaries_names in ( 'some_dictionary_1',
'some_other_dictionary_2',
'...',
'some_other_dictionary_n' ):
some_module2.some_function( { each_one_of_my_dictionaries_names :
eval(each_one_of_my_dictionaries_names) } )
# some_module2.py
def some_function(a_dictionary_name_object_container):
for _dictionary_name, _dictionary_object in a_dictionary_name_object_container.items():
for _key, _value in _dictionary_object.items():
print( str(_dictionary_name) +
" " +
str(_key) +
" = " +
str(_value) )
######
In conclusion:
Python passes only memory addresses as arguments to functions.
Strings representing the name of an identifier, can only be referenced back to the actual identifier by the eval() function if the name identifier is available in the current namespace.
A hypothetical reverse of the eval() function, would not be useful in cases where the identifier name is not 'seen' directly by the calling code. E.g. inside any called function.
Currently one needs to pass to a function:
the string representing the identifier name
the actual identifier (memory address)
This can be achieved by passing both the 'string' and eval('string') to the called function at the same time. I think this is the most 'general' way of solving this egg-chicken problem across arbitrary functions, modules, namespaces, without using corner-case solutions. The only downside is the use of the eval() function which may easily lead to unsecured code. Care must be taken to not feed the eval() function with just about anything, especially unfiltered external-input data.
Totally possible with the python-varname package (python3):
from varname import nameof
s = 'Hey!'
print (nameof(s))
Output:
s
Install:
pip3 install varname
Or get the package here:
https://github.com/pwwang/python-varname
I searched for this question because I wanted a Python program to print assignment statements for some of the variables in the program. For example, it might print "foo = 3, bar = 21, baz = 432". The print function would need the variable names in string form. I could have provided my code with the strings "foo","bar", and "baz", but that felt like repeating myself. After reading the previous answers, I developed the solution below.
The globals() function behaves like a dict with variable names (in the form of strings) as keys. I wanted to retrieve from globals() the key corresponding to the value of each variable. The method globals().items() returns a list of tuples; in each tuple the first item is the variable name (as a string) and the second is the variable value. My variablename() function searches through that list to find the variable name(s) that corresponds to the value of the variable whose name I need in string form.
The function itertools.ifilter() does the search by testing each tuple in the globals().items() list with the function lambda x: var is globals()[x[0]]. In that function x is the tuple being tested; x[0] is the variable name (as a string) and x[1] is the value. The lambda function tests whether the value of the tested variable is the same as the value of the variable passed to variablename(). In fact, by using the is operator, the lambda function tests whether the name of the tested variable is bound to the exact same object as the variable passed to variablename(). If so, the tuple passes the test and is returned by ifilter().
The itertools.ifilter() function actually returns an iterator which doesn't return any results until it is called properly. To get it called properly, I put it inside a list comprehension [tpl[0] for tpl ... globals().items())]. The list comprehension saves only the variable name tpl[0], ignoring the variable value. The list that is created contains one or more names (as strings) that are bound to the value of the variable passed to variablename().
In the uses of variablename() shown below, the desired string is returned as an element in a list. In many cases, it will be the only item in the list. If another variable name is assigned the same value, however, the list will be longer.
>>> def variablename(var):
... import itertools
... return [tpl[0] for tpl in
... itertools.ifilter(lambda x: var is x[1], globals().items())]
...
>>> var = {}
>>> variablename(var)
['var']
>>> something_else = 3
>>> variablename(something_else)
['something_else']
>>> yet_another = 3
>>> variablename(something_else)
['yet_another', 'something_else']
as long as it's a variable and not a second class, this here works for me:
def print_var_name(variable):
for name in globals():
if eval(name) == variable:
print name
foo = 123
print_var_name(foo)
>>>foo
this happens for class members:
class xyz:
def __init__(self):
pass
member = xyz()
print_var_name(member)
>>>member
ans this for classes (as example):
abc = xyz
print_var_name(abc)
>>>abc
>>>xyz
So for classes it gives you the name AND the properteries
This is not possible.
In Python, there really isn't any such thing as a "variable". What Python really has are "names" which can have objects bound to them. It makes no difference to the object what names, if any, it might be bound to. It might be bound to dozens of different names, or none.
Consider this example:
foo = 1
bar = 1
baz = 1
Now, suppose you have the integer object with value 1, and you want to work backwards and find its name. What would you print? Three different names have that object bound to them, and all are equally valid.
In Python, a name is a way to access an object, so there is no way to work with names directly. There might be some clever way to hack the Python bytecodes or something to get the value of the name, but that is at best a parlor trick.
If you know you want print foo to print "foo", you might as well just execute print "foo" in the first place.
EDIT: I have changed the wording slightly to make this more clear. Also, here is an even better example:
foo = 1
bar = foo
baz = foo
In practice, Python reuses the same object for integers with common values like 0 or 1, so the first example should bind the same object to all three names. But this example is crystal clear: the same object is bound to foo, bar, and baz.
Technically the information is available to you, but as others have asked, how would you make use of it in a sensible way?
>>> x = 52
>>> globals()
{'__builtins__': <module '__builtin__' (built-in)>, '__name__': '__main__',
'x': 52, '__doc__': None, '__package__': None}
This shows that the variable name is present as a string in the globals() dictionary.
>>> globals().keys()[2]
'x'
In this case it happens to be the third key, but there's no reliable way to know where a given variable name will end up
>>> for k in globals().keys():
... if not k.startswith("_"):
... print k
...
x
>>>
You could filter out system variables like this, but you're still going to get all of your own items. Just running that code above created another variable "k" that changed the position of "x" in the dict.
But maybe this is a useful start for you. If you tell us what you want this capability for, more helpful information could possibly be given.
By using the the unpacking operator:
>>> def tostr(**kwargs):
return kwargs
>>> var = {}
>>> something_else = 3
>>> tostr(var = var,something_else=something_else)
{'var' = {},'something_else'=3}
You somehow have to refer to the variable you want to print the name of. So it would look like:
print varname(something_else)
There is no such function, but if there were it would be kind of pointless. You have to type out something_else, so you can as well just type quotes to the left and right of it to print the name as a string:
print "something_else"
What are you trying to achieve? There is absolutely no reason to ever do what you describe, and there is likely a much better solution to the problem you're trying to solve..
The most obvious alternative to what you request is a dictionary. For example:
>>> my_data = {'var': 'something'}
>>> my_data['something_else'] = 'something'
>>> print my_data.keys()
['var', 'something_else']
>>> print my_data['var']
something
Mostly as a.. challenge, I implemented your desired output. Do not use this code, please!
#!/usr/bin/env python2.6
class NewLocals:
"""Please don't ever use this code.."""
def __init__(self, initial_locals):
self.prev_locals = list(initial_locals.keys())
def show_new(self, new_locals):
output = ", ".join(list(set(new_locals) - set(self.prev_locals)))
self.prev_locals = list(new_locals.keys())
return output
# Set up
eww = None
eww = NewLocals(locals())
# "Working" requested code
var = {}
print eww.show_new(locals()) # Outputs: var
something_else = 3
print eww.show_new(locals()) # Outputs: something_else
# Further testing
another_variable = 4
and_a_final_one = 5
print eww.show_new(locals()) # Outputs: another_variable, and_a_final_one
Does Django not do this when generating field names?
http://docs.djangoproject.com/en/dev//topics/db/models/#verbose-field-names
Seems reasonable to me.
I think this is a cool solution and I suppose the best you can get. But do you see any way to handle the ambigious results, your function may return?
As "is" operator behaves unexpectedly with integers shows, low integers and strings of the same value get cached by python so that your variablename-function might priovide ambigous results with a high probability.
In my case, I would like to create a decorator, that adds a new variable to a class by the varialbename i pass it:
def inject(klass, dependency):
klass.__dict__["__"+variablename(dependency)]=dependency
But if your method returns ambigous results, how can I know the name of the variable I added?
var any_var="myvarcontent"
var myvar="myvarcontent"
#inject(myvar)
class myclasss():
def myclass_method(self):
print self.__myvar #I can not be sure, that this variable will be set...
Maybe if I will also check the local list I could at least remove the "dependency"-Variable from the list, but this will not be a reliable result.
Here is a succinct variation that lets you specify any directory.
The issue with using directories to find anything is that multiple variables can have the same value. So this code returns a list of possible variables.
def varname( var, dir=locals()):
return [ key for key, val in dir.items() if id( val) == id( var)]
I don't know it's right or not, but it worked for me
def varname(variable):
for name in list(globals().keys()):
expression = f'id({name})'
if id(variable) == eval(expression):
return name
it is possible to a limited extent. the answer is similar to the solution by #tamtam .
The given example assumes the following assumptions -
You are searching for a variable by its value
The variable has a distinct value
The value is in the global namespace
Example:
testVar = "unique value"
varNameAsString = [k for k,v in globals().items() if v == "unique value"]
#
# the variable "varNameAsString" will contain all the variable name that matches
# the value "unique value"
# for this example, it will be a list of a single entry "testVar"
#
print(varNameAsString)
Output : ['testVar']
You can extend this example for any other variable/data type
I'd like to point out a use case for this that is not an anti-pattern, and there is no better way to do it.
This seems to be a missing feature in python.
There are a number of functions, like patch.object, that take the name of a method or property to be patched or accessed.
Consider this:
patch.object(obj, "method_name", new_reg)
This can potentially start "false succeeding" when you change the name of a method. IE: you can ship a bug, you thought you were testing.... simply because of a bad method name refactor.
Now consider: varname. This could be an efficient, built-in function. But for now it can work by iterating an object or the caller's frame:
Now your call can be:
patch.member(obj, obj.method_name, new_reg)
And the patch function can call:
varname(var, obj=obj)
This would: assert that the var is bound to the obj and return the name of the member. Or if the obj is not specified, use the callers stack frame to derive it, etc.
Could be made an efficient built in at some point, but here's a definition that works. I deliberately didn't support builtins, easy to add tho:
Feel free to stick this in a package called varname.py, and use it in your patch.object calls:
patch.object(obj, varname(obj, obj.method_name), new_reg)
Note: this was written for python 3.
import inspect
def _varname_dict(var, dct):
key_name = None
for key, val in dct.items():
if val is var:
if key_name is not None:
raise NotImplementedError("Duplicate names not supported %s, %s" % (key_name, key))
key_name = key
return key_name
def _varname_obj(var, obj):
key_name = None
for key in dir(obj):
val = getattr(obj, key)
equal = val is var
if equal:
if key_name is not None:
raise NotImplementedError("Duplicate names not supported %s, %s" % (key_name, key))
key_name = key
return key_name
def varname(var, obj=None):
if obj is None:
if hasattr(var, "__self__"):
return var.__name__
caller_frame = inspect.currentframe().f_back
try:
ret = _varname_dict(var, caller_frame.f_locals)
except NameError:
ret = _varname_dict(var, caller_frame.f_globals)
else:
ret = _varname_obj(var, obj)
if ret is None:
raise NameError("Name not found. (Note: builtins not supported)")
return ret
This will work for simnple data types (str, int, float, list etc.)
>>> def my_print(var_str) :
print var_str+':', globals()[var_str]
>>> a = 5
>>> b = ['hello', ',world!']
>>> my_print('a')
a: 5
>>> my_print('b')
b: ['hello', ',world!']
It's not very Pythonesque but I was curious and found this solution. You need to duplicate the globals dictionary since its size will change as soon as you define a new variable.
def var_to_name(var):
# noinspection PyTypeChecker
dict_vars = dict(globals().items())
var_string = None
for name in dict_vars.keys():
if dict_vars[name] is var:
var_string = name
break
return var_string
if __name__ == "__main__":
test = 3
print(f"test = {test}")
print(f"variable name: {var_to_name(test)}")
which returns:
test = 3
variable name: test
To get the variable name of var as a string:
var = 1000
var_name = [k for k,v in locals().items() if v == var][0]
print(var_name) # ---> outputs 'var'
Thanks #restrepo, this was exactly what I needed to create a standard save_df_to_file() function. For this, I made some small changes to your tostr() function. Hope this will help someone else:
def variabletostr(**df):
variablename = list(df.keys())[0]
return variablename
variabletostr(df=0)
The original question is pretty old, but I found an almost solution with Python 3. (I say almost because I think you can get close to a solution but I do not believe there is a solution concrete enough to satisfy the exact request).
First, you might want to consider the following:
objects are a core concept in Python, and they may be assigned a variable, but the variable itself is a bound name (think pointer or reference) not the object itself
var is just a variable name bound to an object and that object could have more than one reference (in your example it does not seem to)
in this case, var appears to be in the global namespace so you can use the global builtin conveniently named global
different name references to the same object will all share the same id which can be checked by running the id builtin id like so: id(var)
This function grabs the global variables and filters out the ones matching the content of your variable.
def get_bound_names(target_variable):
'''Returns a list of bound object names.'''
return [k for k, v in globals().items() if v is target_variable]
The real challenge here is that you are not guaranteed to get back the variable name by itself. It will be a list, but that list will contain the variable name you are looking for. If your target variable (bound to an object) is really the only bound name, you could access it this way:
bound_names = get_variable_names(target_variable)
var_string = bound_names[0]
Possible for Python >= 3.8 (with f'{var=}' string )
Not sure if this could be used in production code, but in Python 3.8(and up) you can use f' string debugging specifier. Add = at the end of an expression, and it will print both the expression and its value:
my_salary_variable = 5000
print(f'{my_salary_variable = }')
Output:
my_salary_variable = 5000
To uncover this magic here is another example:
param_list = f'{my_salary_variable=}'.split('=')
print(param_list)
Output:
['my_salary_variable', '5000']
Explanation: when you put '=' after your var in f'string, it returns a string with variable name, '=' and its value. Split it with .split('=') and get a List of 2 strings, [0] - your_variable_name, and [1] - actual object of variable.
Pick up [0] element of the list if you need variable name only.
my_salary_variable = 5000
param_list = f'{my_salary_variable=}'.split('=')
print(param_list[0])
Output:
my_salary_variable
or, in one line
my_salary_variable = 5000
print(f'{my_salary_variable=}'.split('=')[0])
Output:
my_salary_variable
Works with functions too:
def my_super_calc_foo(number):
return number**3
print(f'{my_super_calc_foo(5) = }')
print(f'{my_super_calc_foo(5)=}'.split('='))
Output:
my_super_calc_foo(5) = 125
['my_super_calc_foo(5)', '125']
Process finished with exit code 0
This module works for converting variables names to a string:
https://pypi.org/project/varname/
Use it like this:
from varname import nameof
variable=0
name=nameof(variable)
print(name)
//output: variable
Install it by:
pip install varname
print "var"
print "something_else"
Or did you mean something_else?