I know that some other languages, such as PHP, support a concept of "variable variable names" - that is, the contents of a string can be used as part of a variable name.
I heard that this is a bad idea in general, but I think it would solve some problems I have in my Python code.
Is it possible to do something like this in Python? What can go wrong?
If you are just trying to look up an existing variable by its name, see How can I select a variable by (string) name?. However, first consider whether you can reorganize the code to avoid that need, following the advice in this question.
You can use dictionaries to accomplish this. Dictionaries are stores of keys and values.
>>> dct = {'x': 1, 'y': 2, 'z': 3}
>>> dct
{'y': 2, 'x': 1, 'z': 3}
>>> dct["y"]
2
You can use variable key names to achieve the effect of variable variables without the security risk.
>>> x = "spam"
>>> z = {x: "eggs"}
>>> z["spam"]
'eggs'
For cases where you're thinking of doing something like
var1 = 'foo'
var2 = 'bar'
var3 = 'baz'
...
a list may be more appropriate than a dict. A list represents an ordered sequence of objects, with integer indices:
lst = ['foo', 'bar', 'baz']
print(lst[1]) # prints bar, because indices start at 0
lst.append('potatoes') # lst is now ['foo', 'bar', 'baz', 'potatoes']
For ordered sequences, lists are more convenient than dicts with integer keys, because lists support iteration in index order, slicing, append, and other operations that would require awkward key management with a dict.
Use the built-in getattr function to get an attribute on an object by name. Modify the name as needed.
obj.spam = 'eggs'
name = 'spam'
getattr(obj, name) # returns 'eggs'
It's not a good idea. If you are accessing a global variable you can use globals().
>>> a = 10
>>> globals()['a']
10
If you want to access a variable in the local scope you can use locals(), but you cannot assign values to the returned dict.
A better solution is to use getattr or store your variables in a dictionary and then access them by name.
New coders sometimes write code like this:
my_calculator.button_0 = tkinter.Button(root, text=0)
my_calculator.button_1 = tkinter.Button(root, text=1)
my_calculator.button_2 = tkinter.Button(root, text=2)
...
The coder is then left with a pile of named variables, with a coding effort of O(m * n), where m is the number of named variables and n is the number of times that group of variables needs to be accessed (including creation). The more astute beginner observes that the only difference in each of those lines is a number that changes based on a rule, and decides to use a loop. However, they get stuck on how to dynamically create those variable names, and may try something like this:
for i in range(10):
my_calculator.('button_%d' % i) = tkinter.Button(root, text=i)
They soon find that this does not work.
If the program requires arbitrary variable "names," a dictionary is the best choice, as explained in other answers. However, if you're simply trying to create many variables and you don't mind referring to them with a sequence of integers, you're probably looking for a list. This is particularly true if your data are homogeneous, such as daily temperature readings, weekly quiz scores, or a grid of graphical widgets.
This can be assembled as follows:
my_calculator.buttons = []
for i in range(10):
my_calculator.buttons.append(tkinter.Button(root, text=i))
This list can also be created in one line with a comprehension:
my_calculator.buttons = [tkinter.Button(root, text=i) for i in range(10)]
The result in either case is a populated list, with the first element accessed with my_calculator.buttons[0], the next with my_calculator.buttons[1], and so on. The "base" variable name becomes the name of the list and the varying identifier is used to access it.
Finally, don't forget other data structures, such as the set - this is similar to a dictionary, except that each "name" doesn't have a value attached to it. If you simply need a "bag" of objects, this can be a great choice. Instead of something like this:
keyword_1 = 'apple'
keyword_2 = 'banana'
if query == keyword_1 or query == keyword_2:
print('Match.')
You will have this:
keywords = {'apple', 'banana'}
if query in keywords:
print('Match.')
Use a list for a sequence of similar objects, a set for an arbitrarily-ordered bag of objects, or a dict for a bag of names with associated values.
Whenever you want to use variable variables, it's probably better to use a dictionary. So instead of writing
$foo = "bar"
$$foo = "baz"
you write
mydict = {}
foo = "bar"
mydict[foo] = "baz"
This way you won't accidentally overwrite previously existing variables (which is the security aspect) and you can have different "namespaces".
Use globals() (disclaimer: this is a bad practice, but is the most straightforward answer to your question, please use other data structure as in the accepted answer).
You can actually assign variables to global scope dynamically, for instance, if you want 10 variables that can be accessed on a global scope i_1, i_2 ... i_10:
for i in range(10):
globals()['i_{}'.format(i)] = 'a'
This will assign 'a' to all of these 10 variables, of course you can change the value dynamically as well. All of these variables can be accessed now like other globally declared variable:
>>> i_5
'a'
Instead of a dictionary you can also use namedtuple from the collections module, which makes access easier.
For example:
# using dictionary
variables = {}
variables["first"] = 34
variables["second"] = 45
print(variables["first"], variables["second"])
# using namedtuple
Variables = namedtuple('Variables', ['first', 'second'])
v = Variables(34, 45)
print(v.first, v.second)
The SimpleNamespace class could be used to create new attributes with setattr, or subclass SimpleNamespace and create your own function to add new attribute names (variables).
from types import SimpleNamespace
variables = {"b":"B","c":"C"}
a = SimpleNamespace(**variables)
setattr(a,"g","G")
a.g = "G+"
something = a.a
If you don't want to use any object, you can still use setattr() inside your current module:
import sys
current_module = module = sys.modules[__name__] # i.e the "file" where your code is written
setattr(current_module, 'variable_name', 15) # 15 is the value you assign to the var
print(variable_name) # >>> 15, created from a string
You have to use globals() built in method to achieve that behaviour:
def var_of_var(k, v):
globals()[k] = v
print variable_name # NameError: name 'variable_name' is not defined
some_name = 'variable_name'
globals()[some_name] = 123
print(variable_name) # 123
some_name = 'variable_name2'
var_of_var(some_name, 456)
print(variable_name2) # 456
Variable variables in Python
"""
<?php
$a = 'hello';
$e = 'wow'
?>
<?php
$$a = 'world';
?>
<?php
echo "$a ${$a}\n";
echo "$a ${$a[1]}\n";
?>
<?php
echo "$a $hello";
?>
"""
a = 'hello' #<?php $a = 'hello'; ?>
e = 'wow' #<?php $e = 'wow'; ?>
vars()[a] = 'world' #<?php $$a = 'world'; ?>
print(a, vars()[a]) #<?php echo "$a ${$a}\n"; ?>
print(a, vars()[vars()['a'][1]]) #<?php echo "$a ${$a[1]}\n"; ?>
print(a, hello) #<?php echo "$a $hello"; ?>
Output:
hello world
hello wow
hello world
Using globals(), locals(), or vars() will produce the same results
#<?php $a = 'hello'; ?>
#<?php $e = 'wow'; ?>
#<?php $$a = 'world'; ?>
#<?php echo "$a ${$a}\n"; ?>
#<?php echo "$a ${$a[1]}\n"; ?>
#<?php echo "$a $hello"; ?>
print('locals():\n')
a = 'hello'
e = 'wow'
locals()[a] = 'world'
print(a, locals()[a])
print(a, locals()[locals()['a'][1]])
print(a, hello)
print('\n\nglobals():\n')
a = 'hello'
e = 'wow'
globals()[a] = 'world'
print(a, globals()[a])
print(a, globals()[globals()['a'][1]])
print(a, hello)
Output:
locals():
hello world
hello wow
hello world
globals():
hello world
hello wow
hello world
Bonus (creating variables from strings)
# Python 2.7.16 (default, Jul 13 2019, 16:01:51)
# [GCC 8.3.0] on linux2
Creating variables and unpacking tuple:
g = globals()
listB = []
for i in range(10):
g["num%s" % i] = i ** 10
listB.append("num{0}".format(i))
def printNum():
print "Printing num0 to num9:"
for i in range(10):
print "num%s = " % i,
print g["num%s" % i]
printNum()
listA = []
for i in range(10):
listA.append(i)
listA = tuple(listA)
print listA, '"Tuple to unpack"'
listB = str(str(listB).strip("[]").replace("'", "") + " = listA")
print listB
exec listB
printNum()
Output:
Printing num0 to num9:
num0 = 0
num1 = 1
num2 = 1024
num3 = 59049
num4 = 1048576
num5 = 9765625
num6 = 60466176
num7 = 282475249
num8 = 1073741824
num9 = 3486784401
(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) "Tuple to unpack"
num0, num1, num2, num3, num4, num5, num6, num7, num8, num9 = listA
Printing num0 to num9:
num0 = 0
num1 = 1
num2 = 2
num3 = 3
num4 = 4
num5 = 5
num6 = 6
num7 = 7
num8 = 8
num9 = 9
I'm answering the question How to get the value of a variable given its name in a string?
which is closed as a duplicate with a link to this question. (Editor's note: It is now closed as a duplicate of How can I select a variable by (string) name?)
If the variables in question are part of an object (part of a class for example) then some useful functions to achieve exactly that are hasattr, getattr, and setattr.
So for example you can have:
class Variables(object):
def __init__(self):
self.foo = "initial_variable"
def create_new_var(self, name, value):
setattr(self, name, value)
def get_var(self, name):
if hasattr(self, name):
return getattr(self, name)
else:
raise "Class does not have a variable named: " + name
Then you can do:
>>> v = Variables()
>>> v.get_var("foo")
'initial_variable'
>>> v.create_new_var(v.foo, "is actually not initial")
>>> v.initial_variable
'is actually not initial'
I have tried both in python 3.7.3, you can use either globals() or vars()
>>> food #Error
>>> milkshake #Error
>>> food="bread"
>>> drink="milkshake"
>>> globals()[food] = "strawberry flavor"
>>> vars()[drink] = "chocolate flavor"
>>> bread
'strawberry flavor'
>>> milkshake
'chocolate flavor'
>>> globals()[drink]
'chocolate flavor'
>>> vars()[food]
'strawberry flavor'
Reference:
https://www.daniweb.com/programming/software-development/threads/111526/setting-a-string-as-a-variable-name#post548936
The consensus is to use a dictionary for this - see the other answers. This is a good idea for most cases, however, there are many aspects arising from this:
you'll yourself be responsible for this dictionary, including garbage collection (of in-dict variables) etc.
there's either no locality or globality for variable variables, it depends on the globality of the dictionary
if you want to rename a variable name, you'll have to do it manually
however, you are much more flexible, e.g.
you can decide to overwrite existing variables or ...
... choose to implement const variables
to raise an exception on overwriting for different types
etc.
That said, I've implemented a variable variables manager-class which provides some of the above ideas. It works for python 2 and 3.
You'd use the class like this:
from variableVariablesManager import VariableVariablesManager
myVars = VariableVariablesManager()
myVars['test'] = 25
print(myVars['test'])
# define a const variable
myVars.defineConstVariable('myconst', 13)
try:
myVars['myconst'] = 14 # <- this raises an error, since 'myconst' must not be changed
print("not allowed")
except AttributeError as e:
pass
# rename a variable
myVars.renameVariable('myconst', 'myconstOther')
# preserve locality
def testLocalVar():
myVars = VariableVariablesManager()
myVars['test'] = 13
print("inside function myVars['test']:", myVars['test'])
testLocalVar()
print("outside function myVars['test']:", myVars['test'])
# define a global variable
myVars.defineGlobalVariable('globalVar', 12)
def testGlobalVar():
myVars = VariableVariablesManager()
print("inside function myVars['globalVar']:", myVars['globalVar'])
myVars['globalVar'] = 13
print("inside function myVars['globalVar'] (having been changed):", myVars['globalVar'])
testGlobalVar()
print("outside function myVars['globalVar']:", myVars['globalVar'])
If you wish to allow overwriting of variables with the same type only:
myVars = VariableVariablesManager(enforceSameTypeOnOverride = True)
myVars['test'] = 25
myVars['test'] = "Cat" # <- raises Exception (different type on overwriting)
Any set of variables can also be wrapped up in a class.
"Variable" variables may be added to the class instance during runtime by directly accessing the built-in dictionary through __dict__ attribute.
The following code defines Variables class, which adds variables (in this case attributes) to its instance during the construction. Variable names are taken from a specified list (which, for example, could have been generated by program code):
# some list of variable names
L = ['a', 'b', 'c']
class Variables:
def __init__(self, L):
for item in L:
self.__dict__[item] = 100
v = Variables(L)
print(v.a, v.b, v.c)
#will produce 100 100 100
It should be extremely risky...
but you can use exec():
a = 'b=5'
exec(a)
c = b*2
print (c)
Result:
10
The setattr() method sets the value of the specified attribute of the specified object.
Syntax goes like this –
setattr(object, name, value)
Example –
setattr(self,id,123)
which is equivalent to self.id = 123
As you might have observed, setattr() expects an object to be passed along with the value to generate/modify a new attribute.
We can use setattr() with a workaround to be able to use within modules. Here’ how –
import sys
x = "pikachu"
value = 46
thismodule = sys.modules[__name__]
setattr(thismodule, x, value)
print(pikachu)
Suppose I create 10 multiply-imputed datasets and use the (wonderful) MatchThem package in R to create weights for my exposure variable. The MatchThem package takes a mids object and converts it to an object of the class winmids.
My desired output is a mids object - but with weights. I hope to pass this mids object to BRMS as follows:
library(brms)
m0 <- brm_multiple(Y|weights(weights) ~ A, data = mids_data)
Open to suggestions.
EDIT: Noah's solution below will unfortunately not work.
The package's first author, Farhad Pishgar, sent me the following elegant solution. It will create a mids object from a winmidsobject. Thank you Farhad!
library(mice)
library(MatchThem)
#"weighted.dataset" is our .wimids object
#Extracting the original dataset with missing value
maindataset <- complete(weighted.datasets, action = 0)
#Some spit-and-polish
maindataset <- data.frame(.imp = 0, .id = seq_len(nrow(maindataset)), maindataset)
#Extracting imputed-weighted datasets in the long format
alldataset <- complete(weighted.datasets, action = "long")
#Binding them together
alldataset <- rbind(maindataset, alldataset)
#Converting to .mids
newmids <- as.mids(alldataset)
Additionally, for BRMS, I worked out this solution which instead creates a list of dataframes. It will work in fewer steps.
library("mice")
library("dplyr")
library("MatchThem")
library("brms") # for bayesian estimation.
# Note, I realise that my approach here is not fully Bayesian, but that is a good thing! I need to ensure balance in the exposure.
# impute missing data
data("nhanes2")
imp <- mice(nhanes2, printFlag = FALSE, seed = 0, m = 10)
# MathThem. This is just a fast method
w_imp <- weightthem(hyp ~ chl + age, data = imp,
approach = "within",
estimand = "ATE",
method = "ps")
# get individual data frames with weights
out <- complete(w_imp, action ="long", include = FALSE, mild = TRUE)
# assemble individual data frames into a list
m <- 10
listdat<- list()
for (i in 1:m) {
listdat[[i]] <- as.data.frame(out[[i]])
}
# pass the list to brms, and it runs as it should!
fit_1 <- brm_multiple(bmi|weights(weights) ~ age + hyp + chl,
data = listdat,
backend = "cmdstanr",
family = "gaussian",
set_prior('normal(0, 1)',
class = 'b'))
brm_multiple() can take in a list of data frames for its data argument. You can produce this from the wimids object using complete(). The output of complete() with action = "all" is a mild object, which is a list of data frames, but this is not recognized by brm_multiple() as such. So, you can just convert it to a list. This should look like the following:
df_list <- complete(mids_data, "all")
class(df_list) <- "list"
m0 <- brm_multiple(Y|weights(weights) ~ A, data = df_list)
Using complete() automatically adds a weights column to the resulting imputed data frames.
I have a dataframe where column has an array and each element is a dictionary.
class
product
{"deleteDate": null, "class":"AB", "validFrom": "2022-09-01", "validTo": "2009-08-31"}, {"deleteDate": null, "class":"CD", "validFrom": "2009-09-01", "validTo": "2024-08-31"}
{"deleteDate": "2021-09-01", "class":"AB", "validFrom": "2003-09-01", "validTo": "2009-03-01"}, {"deleteDate": null, "class":"CD", "validFrom": "2009-09-01", "validTo": "2024-08-31"}
I am trying to filter an element base on a few conditions.
def getelement(value,entity):
list_url = []
for i in range(len(value)):
if value[i] is not None and (value[i].deleteDate is None):
if (value[i].validFrom <= (Date of Today)) & (value[i].validFrom >= (date of today)):
list_url.append(value[i].entity)
if list_url:
return str(list_url[-1])
if not list_url:
return None
udfgeturl=F.udf(lambda z: getelement(z) if not z is None else "" , StringType() )
master = df.withColumn( 'ClassName', udfgeturl('Class'))
The function takes two elements, value and entity. where value refers to column name and entity refers to a key in dictionary for which I want to save the result.
The code works with one element getelement(value) for UDF but I do not know how the UDF can take two arguments, any suggestion, please?
To improve the performance (Spark functions vs UDF performance?), you could use only spark transformations:
I'm assuming (value[i].validFrom >= (date of today)) is supposed to actually be (value[i].validTo >= (date of today))
import pyspark.sql.functions as f
def getelement(value, entity):
df = (
df
.withColumn('output', f.expr(f'filter({value}, element -> (element.deleteDate is null) AND (element.validFrom <= current_date()) AND (element.validTo >= current_date()))')[entity][-1])
)
return df
You can use a struct to bundle the parameters into 1 object. Then access the elements of the struct with . operator.
Code example:
def getelement(object):
value = object.value
entity = object.entity
return str( entity + " " + value )
udfgeturl=f.udf(getelement , StringType() )
df.select(
udfgeturl(
f.struct(
f.col("col1").alias("value"),
f.col("col2").alias("entity"))
)
).show()
I am trying to update my mutable parameter Nc in my Abstract model
the initial value is 4
I constructed the instance then change instance.Nc to 5 and solve it but it is still 4 (initial value) , can any body help ?
from pyomo.environ import *
import random
model = AbstractModel()
model.i = RangeSet(40)
model.j = Set(initialize=model.i)
model.x = Var(model.i,model.j, initialize=0,within=Binary)
model.y = Var(model.i, within=Binary)
model.Nc=Param(initialize=5,mutable=True)
def Ninit(model,i):
return random.randint(0,1)
model.N=Param(model.i,initialize=Ninit,mutable=True)
def Dinit(model,i,j):
return random.random()
model.D=Param(model.i,model.j,initialize=Dinit,mutable=True)
def rule_C1(model,i,j):
return model.x[i,j]<=model.N[i]*model.y[j]
model.C1 = Constraint(model.i,model.j,rule=rule_C1)
def rule_C2(model):
return sum(model.y[i] for i in model.i )==model.Nc
model.C2 = Constraint(rule=rule_C2)
def rule_C3(model,i):
return sum(model.x[i,j] for j in model.j)==model.N[i]
model.C3 = Constraint(model.i,rule=rule_C3)
def rule_OF(model):
return sum( model.x[i,j]*model.D[i,j] for i in model.i for j in model.j )
model.obj = Objective(rule=rule_OF, sense=minimize)
opt = SolverFactory('glpk')
#model.NC=4
instance = model.create_instance()
instance.NC=4
results = opt.solve(instance) # solves and updates instance
print('NC= ',value(instance.Nc))
print('OF= ',value(instance.obj))
It seems you are actually initializing your parmeter Nc to 5 (model.Nc=Param(initialize=5,mutable=True)) and then changing it to 4 once you create the instance (instance.Nc=4), so you might want to do the opposite (model.Nc=Param(initialize=4,mutable=True) then instance.Nc=4)
Also, note that you are inconsistantly addressing the Nc parameter throughout the code. When you declare the parameter you name it "Nc" (model.Nc=Param(initialize=5,mutable=True)), which is the actual python variable that Pyomo will use in the model, but later you try to change it with capital letters "NC", which is not a parameter (instance.NC=4). Minor typos like these can cause confusion and give you errors. Make sure to fix them and give it a try again
I am getting an error that is confusing me.
using DifferentialEquations
using RecursiveArrayTools # for VectorOfArray
using DiffEqBayes
f2 = #ode_def_nohes LotkaVolterraTest begin
dx = x*(1 - x - A*y)
dy = rho*y*(1 - B*x - y)
end A B rho
u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [0.2,0.5,0.3]
prob = ODEProblem(f2,u0,tspan,p)
sol = solve(prob,Tsit5())
t = collect(linspace(0,10,200))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Uniform(0.0, 2.0), Uniform(0.0, 2.0), Uniform(0.0, 2.0)]
bayesian_result_abc = abc_inference(prob, Tsit5(), t, data,
priors;num_samples=500)
Returns the error
ERROR: LoadError: DimensionMismatch("first array has length 400 which does not match the length of the second, 398.")
while loading..., in expression starting on line 20.
I have not been able to locate any array of size 400 or 398.
Thanks for your help.
Take a look at https://github.com/JuliaDiffEq/DiffEqBayes.jl/issues/52, that was due to an error in passing the t. This has been fixed on master so you can use that or wait some time, we will have a new release soon with the 1.0 upgrades which will have this fixed too.
Thanks!