How do I declare an array in Python?
I can’t find any reference to arrays in the documentation.
Each Answer to this Q is separated by one/two green lines.
variable = 
variable refers to an empty list*.
Of course this is an assignment, not a declaration. There’s no way to say in Python “this variable should never refer to anything other than a list”, since Python is dynamically typed.
*The default built-in Python type is called a list, not an array. It is an ordered container of arbitrary length that can hold a heterogenous collection of objects (their types do not matter and can be freely mixed). This should not be confused with the
array module, which offers a type closer to the C
array type; the contents must be homogenous (all of the same type), but the length is still dynamic.
This is surprisingly complex topic in Python.
Check out usage examples:
# empty array arr =  # init with values (can contain mixed types) arr = [1, "eels"] # get item by index (can be negative to access end of array) arr = [1, 2, 3, 4, 5, 6] arr # 1 arr[-1] # 6 # get length length = len(arr) # supports append and insert arr.append(8) arr.insert(6, 7)
Under the hood Python’s
list is a wrapper for a real array which contains references to items. Also, underlying array is created with some extra space.
Consequences of this are:
arris same to
appendoperation is ‘for free’ while some extra space
insertoperation is expensive
Check this awesome table of operations complexity.
You don’t actually declare things, but this is how you create an array in Python:
from array import array intarray = array('i')
For more info see the array module: http://docs.python.org/library/array.html
Now possible you don’t want an array, but a list, but others have answered that already. 🙂
I think you (meant)want an list with the first 30 cells already filled.
f =  for i in range(30): f.append(0)
An example to where this could be used is in Fibonacci sequence.
See problem 2 in Project Euler
This is how:
my_array = [1, 'rebecca', 'allard', 15]
For calculations, use numpy arrays like this:
import numpy as np a = np.ones((3,2)) # a 2D array with 3 rows, 2 columns, filled with ones b = np.array([1,2,3]) # a 1D array initialised using a list [1,2,3] c = np.linspace(2,3,100) # an array with 100 points beteen (and including) 2 and 3 print(a*1.5) # all elements of a times 1.5 print(a.T+b) # b added to the transpose of a
these numpy arrays can be saved and loaded from disk (even compressed) and complex calculations with large amounts of elements are C-like fast.
Much used in scientific environments. See here for more.
JohnMachin’s comment should be the real answer.
All the other answers are just workarounds in my opinion!
A couple of contributions suggested that arrays in python are represented by lists. This is incorrect. Python has an independent implementation of
array() in the standard library module
array.array()” hence it is incorrect to confuse the two. Lists are lists in python so be careful with the nomenclature used.
list_01 = [4, 6.2, 7-2j, 'flo', 'cro'] list_01 Out: [4, 6.2, (7-2j), 'flo', 'cro']
There is one very important difference between list and
array.array(). While both of these objects are ordered sequences, array.array() is an ordered homogeneous sequences whereas a list is a non-homogeneous sequence.
You don’t declare anything in Python. You just use it. I recommend you start out with something like http://diveintopython.net.
I would normally just do
a = [1,2,3] which is actually a
list but for
arrays look at this formal definition
To add to Lennart’s answer, an array may be created like this:
from array import array float_array = array("f",values)
where values can take the form of a tuple, list, or np.array, but not array:
values = [1,2,3] values = (1,2,3) values = np.array([1,2,3],'f') # 'i' will work here too, but if array is 'i' then values have to be int wrong_values = array('f',[1,2,3]) # TypeError: 'array.array' object is not callable
and the output will still be the same:
print(float_array) print(float_array) print(isinstance(float_array,float)) # array('f', [1.0, 2.0, 3.0]) # 2.0 # True
Most methods for list work with array as well, common
ones being pop(), extend(), and append().
Judging from the answers and comments, it appears that the array
data structure isn’t that popular. I like it though, the same
way as one might prefer a tuple over a list.
The array structure has stricter rules than a list or np.array, and this can
reduce errors and make debugging easier, especially when working with numerical
Attempts to insert/append a float to an int array will throw a TypeError:
values = [1,2,3] int_array = array("i",values) int_array.append(float(1)) # or int_array.extend([float(1)]) # TypeError: integer argument expected, got float
Keeping values which are meant to be integers (e.g. list of indices) in the array
form may therefore prevent a “TypeError: list indices must be integers, not float”, since arrays can be iterated over, similar to np.array and lists:
int_array = array('i',[1,2,3]) data = [11,22,33,44,55] sample =  for i in int_array: sample.append(data[i])
Annoyingly, appending an int to a float array will cause the int to become a float, without throwing an exception.
np.array retain the same data type for its entries too, but instead of giving an error it will change its data type to fit new entries (usually to double or str):
import numpy as np numpy_int_array = np.array([1,2,3],'i') for i in numpy_int_array: print(type(i)) # <class 'numpy.int32'> numpy_int_array_2 = np.append(numpy_int_array,int(1)) # still <class 'numpy.int32'> numpy_float_array = np.append(numpy_int_array,float(1)) # <class 'numpy.float64'> for all values numpy_str_array = np.append(numpy_int_array,"1") # <class 'numpy.str_'> for all values data = [11,22,33,44,55] sample =  for i in numpy_int_array_2: sample.append(data[i]) # no problem here, but TypeError for the other two
This is true during assignment as well. If the data type is specified, np.array will, wherever possible, transform the entries to that data type:
int_numpy_array = np.array([1,2,float(3)],'i') # 3 becomes an int int_numpy_array_2 = np.array([1,2,3.9],'i') # 3.9 gets truncated to 3 (same as int(3.9)) invalid_array = np.array([1,2,"string"],'i') # ValueError: invalid literal for int() with base 10: 'string' # Same error as int('string') str_numpy_array = np.array([1,2,3],'str') print(str_numpy_array) print([type(i) for i in str_numpy_array]) # ['1' '2' '3'] # <class 'numpy.str_'>
or, in essence:
data = [1.2,3.4,5.6] list_1 = np.array(data,'i').tolist() list_2 = [int(i) for i in data] print(list_1 == list_2) # True
while array will simply give:
invalid_array = array([1,2,3.9],'i') # TypeError: integer argument expected, got float
Because of this, it is not a good idea to use np.array for type-specific commands. The array structure is useful here. list preserves the data type of the values.
And for something I find rather pesky: the data type is specified as the first argument in array(), but (usually) the second in np.array(). 😐
The relation to C is referred to here:
Python List vs. Array – when to use?
Have fun exploring!
Note: The typed and rather strict nature of array leans more towards C rather than Python, and by design Python does not have many type-specific constraints in its functions. Its unpopularity also creates a positive feedback in collaborative work, and replacing it mostly involves an additional [int(x) for x in file]. It is therefore entirely viable and reasonable to ignore the existence of array. It shouldn’t hinder most of us in any way. 😀
How about this…
>>> a = range(12) >>> a [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] >>> a 6
Following on from Lennart, there’s also numpy which implements homogeneous multi-dimensional arrays.
Python calls them lists. You can write a list literal with square brackets and commas:
>>> [6,28,496,8128] [6, 28, 496, 8128]
I had an array of strings and needed an array of the same length of booleans initiated to True. This is what I did
strs = ["Hi","Bye"] bools = [ True for s in strs ]
You can create lists and convert them into arrays or you can create array using numpy module. Below are few examples to illustrate the same. Numpy also makes it easier to work with multi-dimensional arrays.
import numpy as np a = np.array([1, 2, 3, 4]) #For custom inputs a = np.array([int(x) for x in input().split()])
You can also reshape this array into a 2X2 matrix using reshape function which takes in input as the dimensions of the matrix.
mat = a.reshape(2, 2)
# This creates a list of 5000 zeros a =  * 5000
You can read and write to any element in this list with a[n] notation in the same as you would with an array.
It does seem to have the same random access performance as an array. I cannot say how it allocates memory because it also supports a mix of different types including strings and objects if you need it to.