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last week, my teacher asks us: when storing integers from one to one hundred, what the differences between using list and using ndarray. I never use numpy before, so I search this question on the website.
But all my search result told me, they just have dimension difference. Ndarray can store N dimension data, while list storge one. That doesn't satisfy me. Is it really simple, just my overthinking, Or I didn't find the right keyword to search?
I need help.
There are several differences:
-You can append elements to a list, but you can't change the size of a ´numpy.ndarray´ without making a full copy.
-Lists can containt about everything, in numpy arrays all the elements must have the same type.
-In practice, numpy arrays are faster for vectorial functions than mapping functions to lists.
-I think than modification times is not an issue, but iteration over the elements is.
Numpy arrays have many array related methods (´argmin´, ´min´, ´sort´, etc).
I prefer to use numpy arrays when I need to do some mathematical operations (sum, average, array multiplication, etc) and list when I need to iterate in 'items' (strings, files, etc).
A one-dimensional array is like one row graph paper .##
You can store one thing inside of each box
The following picture is an example of a 2-dimensional array
Two-dimensional arrays have rows and columns
I should have changed the numbers.
When I was drawing the picture I just copied the first row many times.
The numbers can be completely different on each row.
import numpy as np
lol = [[1, 2, 3], [4, 5, 6]]
# `lol` is a list of lists
arr_har = np.array(lol, np.int32)
print(type(arr_har)) # <class 'numpy.ndarray'>
print("BEFORE:")
print(arr_har)
# change the value in row 0 and column 2.
arr_har[0][2] = 999
print("\n\nAFTER arr_har[0][2] = 999:")
print(arr_har)
The following picture is an example of a 3-dimensional array
Summary/Conclusion:
A list in Python acts like a one-dimensional array.
ndarray is an abbreviation of "n-dimensional array" or "multi-dimensional array"
The difference between a Python list and an ndarray, is that an ndarray has 2 or more dimensions
I have arrays with different length and I want to save them inside 1D array using python,
a new array is generated after some tests this is why I have different sizes of arrays,
here is a smple of what I have:
array1=[1,3,5]
array2=[10,12,13,14]
array3=[12,14,14,15,15] #etc
The desired result:
myArray=[[1,3,5],[10,12,13,14],[12,14,14,15,15]]
I tried to use this code
myArray=[]
myArray.append(array1)
myArray.append(array2) #etc
when I print myArray I get:
[[array([1,3,5])], [array([10,12,13,14])], [array([12,14,14,15,15])]]
so when I try to get the second array, for example, I have to use this code
temp = myArray[1]
result = temp[0]
this was working for me but it looks like it has a limitation and it stopped working after a while when I'm retrieving results using some loops.
The currently accepted answer makes little sense, so here's what's actually going on: array_1, array_2, etc. are not plain Python lists, they're almost certainly NumPy arrays. my_array, however, is just a Python list.
Here is a simple program which should allow you to reproduce and understand the difference, at least in how it relates to your program:
import numpy as np
plain_list = [1, 2, 3]
numpy_array = np.array([1, 2, 3])
result_list = [plain_list, numpy_array]
print(plain_list) # [1, 2, 3]
print(numpy_array) # [1 2 3]
print(result_list) # [[1, 2, 3], array([1, 2, 3])]
Now, it isn't exactly clear what's happening to your program, since you just write this was working for me but it looks like it has a limitation and it stopped working after a while when I'm retrieving results using some loops.
Depending on what the rest of the program is doing, numpy arrays may or may not be the appropriate data structure. In any case, please share the entirety of your code as well as an explanation of the program.
First thing first there is no array data structure in python.
Instead List and tuples are used.
In your case variable array1, array2 & array3 are lists.
array1=[1,3,5]
array2=[10,12,13,14]
array3=[12,14,14,15,15]
# to get the desired result as myArray=[[1,3,5],[10,12,13,14],[12,14,14,15,15]]
myArray = [array1, array2, array3]
Check python documentation to know more about lists
For numpy ndarray, there are no append, and insert as there are for native python lists.
a = np.array([1, 2, 3])
a.append(5) # this does not work
a = np.append(a, 5) # this is the only way
Whereas for native python lists,
a = [1, 2, 3]
a.append(4) # this modifies a
a # [1, 2, 3, 4]
Why was numpy ndarray designed to be this way? I'm writing a subclass of ndarray, is there any way of implementing "append" like native python arrays?
NumPy makes heavy use of views, a feature that Python lists do not support. A view is an array that uses the memory of another object rather than owning its own memory; for example, in the following snippet
a = numpy.arange(5)
b = a[1:3]
b is a view of a.
Views would interact very poorly with an in-place append or other in-place size-changing operations. Arrays would suddenly not be views of arrays they should be views of, or they would be views of deallocated memory, or it would be unpredictable whether an append on one array would affect an array it was a view of, or all sorts of other problems. For example, what would a look like after b.append(6)? Or what would b look like after a.clear()? And what kind of performance guarantees could you make? Probably not the amortized constant time guarantee of list.append.
If you want to append, you probably shouldn't be using NumPy arrays; you should use a list, and build an array from the list when you're done appending.
ndarray is created with a fixed size databuffer - just big enough to hold the bytes representing the elements.
arr.nbytes == arr.itemsize * arr.size
arr.resize can change the array inplace. But read it's docs to see the limitations, especially about owning its own data. It's one of the few inplace operations, and not used that often.
In contrast a Python list stores object pointers in a buffer. The buffer has some growth room allowing for efficient append. It just has to add a new pointer to the buffer. When the buffer fills up, it allocates a new larger buffer and copies the pointers.
For a 1d array the buffers for ndarray and list will be similar, at least for 4 or 8 bytes numeric dtypes. But for multidimensional arrays, the databuffer can be very large (the product of all dimensions), while the top buffer of an equivalent nested array just contains pointers to the outer layer of lists (the 'rows').
Object dtype arrays store pointers like a list, but the databuffer still has the fixed size (no growth space). Performance lies between numeric arrays and lists.
I can imagine writing an inplace append that uses the resize method, followed by copying the new value(s) to the 0 fills.
In [96]: arr = np.array([[1,3],[2,7]])
In [97]: arr.resize(3,2)
In [98]: arr
Out[98]:
array([[1, 3],
[2, 7],
[0, 0]])
In [99]: arr[-1,:] = 10,11
In [100]: arr
Out[100]:
array([[ 1, 3],
[ 2, 7],
[10, 11]])
But notice what happens to values when we resize an inner axis:
In [101]: arr = np.array([[1,3],[2,7]])
In [102]: arr.resize(2,3)
In [103]: arr
Out[103]:
array([[1, 3, 2],
[7, 0, 0]])
So this kind of append is quite limited compared to concatenate (and all of its 'stack' derivatives).
Have you looked at the code for np.append? After making sure the arguments are arrays, and tweaking their shapes, it does:
concatenate((arr, values), axis=axis)
In other words, it is just an alternative way of calling concatenate. It's probably best for adding a single value to a 1d array. It shouldn't be used repeatedly in a loop, precisely because it returns a new array, and thus is relatively expensive. Otherwise its use messes up many users. Some ignore the axis parameter. Others have problems creating a correct 'empty' array to start with. Concatenate also has those problems, but at least users have to consciously deal the issue of matching shapes.
np.insert is much more complicated. It does different things depending on whether the indices (obj) is a number, slice or list of numbers. One approach is to create a target array of the right size, and copy slices from the original and insert values to the right slots. Another is to use a boolean mask to copy values into the right locations. Both have to accommodate multidimensions - it inserts along one axis, but must use the appropriate slice(None) for the other dimensions. This is much more complicated than the list insert, which inserts one object (pointer) at one location in 1d.
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.
What's the best way to create 2D arrays in Python?
What I want is want is to store values like this:
X , Y , Z
so that I access data like X[2],Y[2],Z[2] or X[n],Y[n],Z[n] where n is variable.
I don't know in the beginning how big n would be so I would like to append values at the end.
>>> a = []
>>> for i in xrange(3):
... a.append([])
... for j in xrange(3):
... a[i].append(i+j)
...
>>> a
[[0, 1, 2], [1, 2, 3], [2, 3, 4]]
>>>
Depending what you're doing, you may not really have a 2-D array.
80% of the time you have simple list of "row-like objects", which might be proper sequences.
myArray = [ ('pi',3.14159,'r',2), ('e',2.71828,'theta',.5) ]
myArray[0][1] == 3.14159
myArray[1][1] == 2.71828
More often, they're instances of a class or a dictionary or a set or something more interesting that you didn't have in your previous languages.
myArray = [ {'pi':3.1415925,'r':2}, {'e':2.71828,'theta':.5} ]
20% of the time you have a dictionary, keyed by a pair
myArray = { (2009,'aug'):(some,tuple,of,values), (2009,'sep'):(some,other,tuple) }
Rarely, will you actually need a matrix.
You have a large, large number of collection classes in Python. Odds are good that you have something more interesting than a matrix.
In Python one would usually use lists for this purpose. Lists can be nested arbitrarily, thus allowing the creation of a 2D array. Not every sublist needs to be the same size, so that solves your other problem. Have a look at the examples I linked to.
If you want to do some serious work with arrays then you should use the numpy library. This will allow you for example to do vector addition and matrix multiplication, and for large arrays it is much faster than Python lists.
However, numpy requires that the size is predefined. Of course you can also store numpy arrays in a list, like:
import numpy as np
vec_list = [np.zeros((3,)) for _ in range(10)]
vec_list.append(np.array([1,2,3]))
vec_sum = vec_list[0] + vec_list[1] # possible because we use numpy
print vec_list[10][2] # prints 3
But since your numpy arrays are pretty small I guess there is some overhead compared to using a tuple. It all depends on your priorities.
See also this other question, which is pretty similar (apart from the variable size).
I would suggest that you use a dictionary like so:
arr = {}
arr[1] = (1, 2, 4)
arr[18] = (3, 4, 5)
print(arr[1])
>>> (1, 2, 4)
If you're not sure an entry is defined in the dictionary, you'll need a validation mechanism when calling "arr[x]", e.g. try-except.
If you are concerned about memory footprint, the Python standard library contains the array module; these arrays contain elements of the same type.
Please consider the follwing codes:
from numpy import zeros
scores = zeros((len(chain1),len(chain2)), float)
x=list()
def enter(n):
y=list()
for i in range(0,n):
y.append(int(input("Enter ")))
return y
for i in range(0,2):
x.insert(i,enter(2))
print (x)
here i made function to create 1-D array and inserted into another array as a array member. multiple 1-d array inside a an array, as the value of n and i changes u create multi dimensional arrays