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
Related
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
I want to use a matrix in my Python code but I don't know the exact size of my matrix to define it.
For other matrices, I have used np.zeros(a), where a is known.
What should I do to define a matrix with unknown size?
In this case, maybe an approach is to use a python list and append to it, up until it has the desired size, then cast it to a np array
pseudocode:
matrix = []
while matrix not full:
matrix.append(elt)
matrix = np.array(matrix)
You could write a function that tries to modify the np.array, and expand if it encounters an IndexError:
x = np.random.normal(size=(2,2))
r,c = (5,10)
try:
x[r,c] = val
except IndexError:
r0,c0 = x.shape
r_ = r+1-r0
c_ = c+1-c0
if r > 0:
x = np.concatenate([x,np.zeros((r_,x.shape[1]))], axis = 0)
if c > 0:
x = np.concatenate([x,np.zeros((x.shape[0],c_))], axis = 1)
There are problems with this implementation though: First, it makes a copy of the array and returns a concatenation of it, which translates to a possible bottleneck if you use it many times. Second, the code I provided only works if you're modifying a single element. You could do it for slices, and it would take more effort to modify the code; or you can go the whole nine yards and create a new object inheriting np.array and override the .__getitem__ and .__setitem__ methods.
Or you could just use a huge matrix, or better yet, see if you can avoid having to work with matrices of unknown size.
If you have a python generator you can use np.fromiter:
def gen():
yield 1
yield 2
yield 3
In [11]: np.fromiter(gen(), dtype='int64')
Out[11]: array([1, 2, 3])
Beware if you pass an infinite iterator you will most likely crash python, so it's often a good idea to cap the length (with the count argument):
In [21]: from itertools import count # an infinite iterator
In [22]: np.fromiter(count(), dtype='int64', count=3)
Out[22]: array([0, 1, 2])
Best practice is usually to either pre-allocate (if you know the size) or build the array as a list first (using list.append). But lists don't build in 2d very well, which I assume you want since you specified a "matrix."
In that case, I'd suggest pre-allocating an oversize scipy.sparse matrix. These can be defined to have a size much larger than your memory, and lil_matrix or dok_matrix can be built sequentially. Then you can pare it down once you enter all of your data.
from scipy.sparse import dok_matrix
dummy = dok_matrix((1000000, 1000000)) # as big as you think you might need
for i, j, data in generator():
dummy[i,j] = data
s = np.array(dummy.keys).max() + 1
M = dummy.tocoo[:s,:s] #or tocsr, tobsr, toarray . . .
This way you build your array as a Dictionary of Keys (dictionaries supporting dynamic assignment much better than ndarray does) , but still have a matrix-like output that can be (somewhat) efficiently used for math, even in a partially built state.
I will keep it simple.I have a loop that appends new row to a numpy array...what is the efficient way to do this.
n=np.zeros([1,2])
for x in [[2,3],[4,5],[7,6]]
n=np.append(n,x,axis=1)
Now the thing is there is a [0,0] sticking to it so I have to remove it by
del n[0]
Which seems dumb...So please tell me an efficient way to do this.
n=np.empty([1,2])
is even worse it creates an uninitialised value.
A bit of technical explanation for the "why lists" part.
Internally, the problem for a list of unknown length is that it needs to fit in memory somehow regardless of its length. There are essentially two different possibilities:
Use a data structure (linked list, some tree structure, etc.) which makes it possible to allocate memory separately for each new element in a list.
Store the data in a contiguous memory area. This area has to be allocated when the list is created, and it has to be larger than what we initially need. If we get more stuff into the list, we need to try to allocate more memory, preferably at the same location. If we cannot do it at the same location, we need to allocate a bigger block and move all data.
The first approach enables all sorts of fancy insertion and deletion options, sorting, etc. However, it is slower in sequential reading and allocates more memory. Python actually uses the method #2, the lists are stored as "dynamic arrays". For more information on this, please see:
Size of list in memory
What this means is that lists are designed to be very efficient with the use of append. There is very little you can do to speed things up if you do not know the size of the list beforehand.
If you know even the maximum size of the list beforehand, you are probably best off allocating a numpy.array using numpy.empty (not numpy.zeros) with the maximum size and then use ndarray.resize to shrink the array once you have filled in all data.
For some reason numpy.array(l) where l is a list is often slow with large lists, whereas copying even large arrays is quite fast (I just tried to create a copy of a 100 000 000 element array; it took less than 0.5 seconds).
This discussion has more benchmarking on different options:
Fastest way to grow a numpy numeric array
I have not benchmarked the numpy.empty + ndarray.resize combo, but both should be rather microsecond than millisecond operations.
There are three ways to do this, if you already have everything in a list:
data = [[2, 3], [4, 5], [7, 6]]
n = np.array(data)
If you know how big the final array will be:
exp = np.array([2, 3])
n = np.empty((3, 2))
for i in range(3):
n[i, :] = i ** exp
If you don't know how big the final array will be:
exp = np.array([2, 3])
n = []
i = np.random.random()
while i < .9:
n.append(i ** exp)
i = np.random.random()
n = np.array(n)
Just or the record you can start with n = np.empty((0, 2)) but I would not suggest appending to that array in a loop.
You might want to try:
import numpy as np
n = np.reshape([], (0, 2))
for x in [[2,3],[4,5],[7,6]]:
n = np.append(n, [x], axis=0)
Instead of np.append you can also use n = np.vstack([n,x]). I also agree with #Bi Rico that I also would use a list, if n does not need to accessed within the loop.
I have a 2-d numpy array (MxN) and two more 1-d arrays (Mx1) that represent starting and ending indices for each row of the 2-d array that I'd like to sum over. I'm looking for the most efficient way to do this in a large array (preferably without having to use a loop, which is what I'm currently doing). An example of what i'd like to do is the following.
>>> random.seed(1234)
>>> a = random.rand(4,4)
>>> print a
[[ 0.19151945 0.62210877 0.43772774 0.78535858]
[ 0.77997581 0.27259261 0.27646426 0.80187218]
[ 0.95813935 0.87593263 0.35781727 0.50099513]
[ 0.68346294 0.71270203 0.37025075 0.56119619]]
>>> b = array([1,0,2,1])
>>> c = array([3,2,4,4])
>>> d = empty(4)
>>> for i in xrange(4):
d[i] = sum(a[i, b[i]:c[i]])
>>> print d
[ 1.05983651 1.05256841 0.8588124 1.64414897]
My problem is similar to the following question, however, I don't think the solution presented there would be very efficient. Numpy sum of values in subarrays between pairs of indices In that question, they are wanting to find the sum of multiple subsets for the same row, so cumsum() can be used. However, I will only be finding one sum per row, so I don't think this would be the most efficient means of computing the sum.
Edit: I'm sorry, I made a mistake in my code. The line inside the loop previously read d[i] = sum(a[b[i]:c[i]]). I forgot the index for the first dimension. Each set of starting and ending indices corresponds to a new row in the 2-d array.
You could do something like this:
from numpy import array, random, zeros
random.seed(1234)
a = random.rand(4,4)
b = array([1,0,2,1])
c = array([3,2,4,4])
lookup = zeros(len(a) + 1, a.dtype)
lookup[1:] = a.sum(1).cumsum()
d = lookup[c] - lookup[b]
print d
This might help if your b/c arrays are large and the windows you're summing over are large. Because your windows might overlap, for example 2:4 and 1:4 are mostly the same, you're essentially repeating operations. By taking the cumsum as a per-processing step you reduce the number of repeated operations and you may save time. This won't help much if your windows are small and b/c are small, mostly because you'll be summing parts of the a matrix that you don't much care about. Hope that helps.
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.