In python, I am trying to change the values of np array inside the function
def function(array):
array = array + 1
array = np.zeros((10, 1))
function(array)
For array as function parameter, it is supposed to be a reference, and I should be able to modify its content inside function.
array = array + 1 performs element wise operation that adds one to every element in the array, so it changes inside values.
But the array actually does not change after the function call. I am guessing that the program thinks I am trying to change the reference itself, not the content of the array, because of the syntax of the element wise operation. Is there any way to make it do the intended behavior? I don't want to loop through individual elements or make the function return the new array.
This line:
array = array + 1
… does perform an elementwise operation, but the operation it performs is creating a new array with each element incremented. Assigning that array back to the local variable array doesn't do anything useful, because that local variable is about to go away, and you haven't done anything to change the global variable of the same name,
On the other hand, this line:
array += 1
… performs the elementwise operation of incrementing all of the elements in-place, which is probably what you want here.
In Python, mutable collections are only allowed, not required, to handle the += statement this way; they could handle it the same way as array = array + 1 (as immutable types like str do). But builtin types like list, and most popular third-party types like np.array, do what you want.
Another solution if you want to change the content of your array is to use this:
array[:] = array + 1
In Java you would do it like this: Node[][] nodes; where Node.java is a custom class. How do I do it in python where Node.py:
class Node(object):
def __init__(self):
self.memory = []
self.temporal_groups = []
I have imported numpy and created an object type
typeObject = numpy.dtype('O') # O stands for python objects
nodes = ???
You can try it this way, inside your node class create a function that will return the generic array:
def genArray(a, b):
return [[0 for y in range(a)] for x in range(b)]
then you can assign them the way you want. Maybe you might change the 0 to your node object. Let me know if this helps
You have two easy options: use numpy or declare a nested list. The latter approach is more conceptually similar to Node[][] since it allows for ragged lists, as does Java, but the former approach will probably make processing faster.
numpy arrays
To make an array in numpy:
import numpy as np
x = np.full((m, n), None, dtype=np.object)
In numpy, you have to have some idea about the size of the array (here m, n) up-front. There are ways to grow an array, but they are not very efficient, especially for large arrays. np.full will initialize your array with a copy of whatever reference you want. You can modify the elements as you wish after that.
Python lists
To create a ragged list, you do not have to do much:
x = []
This creates an empty list. This is equivalent to Node[][] in Java because that declares a list too. The main difference is that Python lists can change size and are untyped. They are effectively always Object[].
To add more dimensions to the list, just do x[i] =
[], which will insert a nested list into your outer list. This is similar to defining something like
Node[][] nodes = new Node[m][];
nodes[i] = new Node[n];
where m is the number of nested lists (rows) and n is the number of elements in each list (columns). Again, the main difference is that once you have a list in Python, you can expand or contract it as you Java.
Manipulate with x[i][j] as you would in Java. You can add new sublists by doing x.append([]) or x[i] = [], where i is an index past the end of x.
I wrote a function that passes numpy array's into C code using CFFI. It utilizes the buffer protocol and memoryview to pass the data efficiently without copying it. However, this means that you need to pass C-contiguous arrays and ensure that you using the right types. Numpy provides a function numpy.ascontiguous, which does this. So I iterate over the arguments, and apply this function. The implementation below works, and may be of general interest. However, it is slow given the number of times it is called. (Any general comments on how to speed it up would be helpful.)
However, the actual question is when you replace the first list comprehension with a generator comprehension, or if you refactor the code so that np.ascontigous is called in the second one, the pointers passed into the C code no longer point to the start of the numpy array. I think that it is not getting called. I'm iterating over the comprehension and only using the return values, why would using a list comprehension or generator comprehension change anything?
def cffi_wrap(cffi_func, ndarray_params, pod_params, return_shapes=None):
"""
Wraps a cffi function to allow it to be called on numpy arrays.
It uss the numpy buffer protocol and and the cffi buffer protocol to pass the
numpy array into the c function without copying any of the parameters.
You will need to pass dimensions into the C function, which you can do using
the pod_params.
Parameters
----------
cffi_func : c function
This is a c function declared using cffi. It must take double pointers and
plain old data types. The arguments must be in the form of numpy arrays,
plain old data types, and then the returned numpy arrays.
ndarray_params : iterable of ndarrays
The numpy arrays to pass into the function.
pod_params : tuple of plain old data
This plain old data objects to pass in. This may include for example
dimensions.
return_shapes : iterable of tuples of positive ints
The shapes of the returned objects.
Returns
-------
return_vals : ndarrays of doubles.
The objects to be calculated by the cffi_func.
"""
arr_param_buffers = [np.ascontiguousarray(param, np.float64)
if np.issubdtype(param.dtype, np.float)
else np.ascontiguousarray(param, np.intc) for param in ndarray_params]
arr_param_ptrs = [ffi.cast("double *", ffi.from_buffer(memoryview(param)))
if np.issubdtype(param.dtype, np.float)
else ffi.cast("int *", ffi.from_buffer(memoryview(param)))
for param in arr_param_buffers]
if return_shapes is not None:
return_vals_ptrs = tuple(ffi.new("double[" + str(np.prod(shape)) + "]")
for shape in return_shapes)
returned_val = cffi_func(*arr_param_ptrs, *pod_params, *return_vals_ptrs)
return_vals = tuple(np.frombuffer(ffi.buffer(
return_val))[:np.prod(shape)].reshape(shape)
for shape, return_val in zip(return_shapes, return_vals_ptrs))
else:
returned_val = cffi_func(*arr_param_ptrs, *pod_params)
return_vals = None
if returned_val is not None and return_vals is not None:
return_vals = return_vals + (returned_val,)
elif return_vals is None:
return_vals = (returned_val,)
if len(return_vals) == 1:
return return_vals[0]
else:
return return_vals
I'm just guessing, but the error could come from keepalives: with arr_param_buffers a list comprehension, as in your posted code, then as long as this local variable exists (i.e. for the whole duration of cffi_wrap()), all the created numpy arrays are alive. This allows you to do ffi.from_buffer(memoryview(...)) on the next line and be sure that they are all pointers to valid data.
If you replace arr_param_buffers with a generator expression, it will generate the new numpy arrays one by one, call ffi.from_buffer(memoryview(param)) on them, and then throw them away. The ffi.from_buffer(x) returns an object that should keep x alive, but maybe x == memoryview(nd) does not itself keep alive the numpy array nd, for all I know.
If I want to get the dot product of two arrays, I can get a performance boost by specifying an array to store the output in instead of creating a new array (if I am performing this operation many times)
import numpy as np
a = np.array([[1.0,2.0],[3.0,4.0]])
b = np.array([[2.0,2.0],[2.0,2.0]])
out = np.empty([2,2])
np.dot(a,b, out = out)
Is there any way I can take advantage of this feature if I need to modify an array in place? For instance, if I want:
out = np.array([[3.0,3.0],[3.0,3.0]])
out *= np.dot(a,b)
Yes, you can use the out argument to modify an array (e.g. array=np.ones(10)) in-place, e.g. np.multiply(array, 3, out=array).
You can even use in-place operator syntax, e.g. array *= 2.
To confirm if the array was updated in-place, you can check the memory address array.ctypes.data before and after the modification.
How do I declare an array in Python?
variable = []
Now 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.
Practical answer
Arrays are represented by class list (see reference and do not mix them with generators).
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[0] # 1
arr[-1] # 6
# get length
length = len(arr)
# supports append and insert
arr.append(8)
arr.insert(6, 7)
Theoretical answer
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:
random access is really cheap (arr[6653] is same to arr[0])
append operation is 'for free' while some extra space
insert operation is expensive
Check this awesome table of operations complexity.
Also, please see this picture, where I've tried to show most important differences between array, array of references and linked list:
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.
So
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!
So:
array=[0]*element_count
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.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[85]: [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[1])
print(isinstance(float_array[1],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
data.
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. :D
How about this...
>>> a = range(12)
>>> a
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
>>> a[7]
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 = [0] * 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.