I imported a csv file containing zip codes as a string using the following line:
my_data = genfromtext('path\to\file.csv', delimiter = ',', dtype=str, autostrip=True)
I am importing as a string in order to keep the leading zeroes some zip codes may contain. Now I need to also loop through the entire numpy array and I wanted to do so like this:
for i in np.nditer(my_data):
do something with my_data[i]
But unfortunately it is returning the following error:
Arrays used as indices must be of integer (or boolean) type
Any idea how I can loop through each element of this numpy array?
While looping over NumPy arrays is often not a good solution, you can do it like this:
for i in range(len(my_data)):
do something with my_data[i]
You might be better off reading your data into a list, process the strings, and convert into NumPy array afterwards.
You should do something with i, not with my_data[i]. i is already your element (a part if mydata).
Thats why my_data[i] is not working, becouse i is not an index. it is a numpy array.
If you want to use index, and the given element too, use enumerate()
Example:
lista = [20,50,70]
for idx, element in enumerate(lista):
print (idx, element)
For more info visit this site numpy iteration tutorial
Related
I have a numpy array of shape (100, 1), having all elements similar to as shown below
arr[0] = array(['37107287533902102798797998220837590246510135740250'], dtype=object)
I need to iterate over this single element of array and get the last 10 elements of it. I have not been able to find out how to iterate over single element.
I tried arr[0][-10:] but it returned the entire element and not the last 10 elements
You can get what you want by list comprehension.
np.array([item[0][-10:] for item in arr])
If arr.shape is (100,1), then arr[0].shape is (1,), which is shown array(['astring']) brackets.
arr[0,0] should be a string, e.g. '37107287533902102798797998220837590246510135740250'
Strings take slice indexing, eg. arr[0,0][-10:]
arr[0][0] also works to get one string, but the [0,0] syntax is better.
It isn't clear at what level you want to iterate, since just getting the last 10 characters of one of the string elements doesn't need iteration.
Anyways, pay attention to what each level of indexing is producing, whether it be another array, a list, or a string. Indexing rules for these different classes are similar, but different in important ways.
# import numpy
import numpy as np
arr = np.array(['37107287533902102798797998220837590246510135740250'], dtype=object)
# print the last 10 elements of the array
print(arr[0][-10:])
# iterate through the array and print the elements in reverse order
for i in arr[0][::-1]:
print(i)
# iterate through the array and print the last 10 elements in reverse order
for i in arr[0][-10:][::-1]:
print(I)
# iterate through the array and print the last 10 elements in forward order
for i in arr[0][-10:]:
print(i)
#hpaulj makes a good point. My original answer works with numpy as requested but I didn't really leave the OP an explanation. Using his string advice this how I would do it if it was a string and I wanted to iterate for some reason:
s1 = '37107287533902102798797998220837590246510135740250'
result = 0
for x in s1[-10:]:
print(x)
result += int(x)
print(result)
I am doing automation on the manual work where I am reading the data from outlook mail and storing the required data in a NumPy string array. However, data having lots of space you say dummy one. I need to rectify the NumPy string.
import numpy as np
arr=np.array([])
#outlook code and store in array.
arr=[{'5'} {'9'} {'7'} {'9'} {''} {''} {''} {''} {''} {''}]
# required output look like this
arr=[5979]
Can anyone help to get me the required output.
Solution for this given format but not scalable.
It iterates over each set contained in the list and unpack them to another list of string. Then is convert the list of string to a single string and finally to an integer
arr = [{'5'}, {'9'}, {'7'}, {'9'}, {''}, {''}]
value = int("".join([str(*x) for x in arr if str(*x).isdigit()]))
print(value)
5979
You can .strip() each string to remove spaces and append to previous strings. I'm not sure why you use sets inside a list and not strings directly, this will save you that next(iter(..)). Also, note that you won't get much benefit from numpy array of strings, but for numeric arrays you can get huge benefits.
arr = [{'5'}, {'9'}, {'7'}, {'9'}, {' '}, {' '}, {' '}]
value = ''
for s in arr:
value += next(iter(s)).strip()
I am working on a Python script that takes live streaming data and appends it to a numpy array. However I noticed that if I append to four different arrays one by one it works. For example:
openBidArray = np.append(openBidArray, bidPrice)
highBidArray = np.append(highBidArray, bidPrice)
lowBidArray = np.append(lowBidArray, bidPrice)
closeBidArray = np.append(closeBidArray, bidPrice)
However If I do the following it does not work:
arrays = ["openBidArray", "highBidArray", "lowBidArray", "closeBidArray"]
for array in arrays:
array = np.append(array, bidPrice)
Any idea on why that is?
Do this instead:
arrays = [openBidArray, highBidArray, lowBidArray, closeBidArray]
In other words, your list should be a list of arrays, not a list of strings that coincidentally contain the names of arrays you happen to have defined.
Your next problem is that np.append() returns a copy of the array with the item appended, rather than appending in place. You store this result in array, but array will be assigned the next item from the list on the next iteration, and the modified array will be lost (except for the last one, of course, which will be in array at the end of the loop). So you will want to store each modified array back into the list. To do that, you need to know what slot it came from, which you can get using enumerate().
for i, array in enumerate(arrays):
arrays[i] = np.append(array, bidPrice)
Now of course this doesn't update your original variables, openBidArray and so on. You could do this after the loop using unpacking:
openBidArray, highBidArray, lowBidArray, closeBidArray = arrays
But at some point it just makes more sense to store the arrays in a list (or a dictionary if you need to access them by name) to begin with and not use the separate variables.
N.B. if you used regular Python lists here instead of NumPy arrays, some of these issues would go away. append() on lists is an in-place operation, so you wouldn't have to store the modified array back into the list or unpack to the individual variables. It might be feasible to do all the appending with lists and then convert them to arrays afterward, if you really need NumPy functionality on them.
In your second example, you have strings, not np.array objects. You are trying to append a number(?) to a string.
The string "openBidArray" doesn't hold any link to an array called openBidArray.
Is there a way to modify a numpy array inside a loop column by column?
I expect this could be done by some code like that:
import numpy as n
cnA=n.array([[10,20]]).T
mnX=n.array([[1,2],[3,4]])
for cnX in n.nditer(mnX.T, <some params>):
cnX = cnX+cnA
Which parameters should I use to obtain mnX=[[10,23],[12,24]]?
I am aware that the problem could be solved using the following code:
cnA=n.array([10,20])
mnX=n.array([[1,2],[3,4]])
for col in range(mnX.shape[1]):
mnX[:,col] = mnX[:,col]+cnA
Hovewer, in python we loop through modified objects, not indexes, so the question is - is it possible to loop through columns (that need to be modified in-place) directly?
Just so you know, some of us, in Python, do iterate over indices and not modified objects when it is helpful. Although in NumPy, as a general rule, we don't explicitly iterate unless there is no other way out: for your problem, the simplest approach would be to skip the iteration and rely on broadcasting:
mnX += cnA
If you insist on iterating, I think the simplest would be to iterate over the transposed array:
for col in mnX.T:
col += cnA[:, 0].T
I need big help, please check out this code:
import.math
dose =20.0
a = [[[2,3,4],[5,8,9],[12,56,32]]
[[25,36,45][21,65,987][21,58,89]]
[[78,21,98],[54,36,78],[23,12,36]]]
PAC = math.exp(-dose*a)
this what I would like to do. However the error I am getting is
TypeError: only length-1 arrays can be converted to Python scalars
If you want to perform mathematical operations on arrays (whatever their dimensions...), you should really consider using NumPy which is designed just for that. In your case, the corresponding NumPy command would be:
PAC = numpy.exp(-dose*np.array(a))
If NumPy is not an option, you'll have to loop on each element of a, compute your math.exp, store the result in a list... Really cumbersome and inefficient. That's because the math functions require a scalar as input (as the exception told you), when you're passing a list (of lists). You can combine all the loops in a single list comprehension, though:
PAC = [[[math.exp(-dose*j) for j in elem] for elem in row] for row in a]
but once again, I would strongly recommend NumPy.
You should really use NumPy for that.
And here is how you should do it using nested loops:
>>> for item in a:
... for sub in item:
... for idx, number in enumerate(sub):
... print number, math.exp(-dose*number)
... sub[idx] = math.exp(-dose*number)
Using append is slow, because every time you copy the previous array and stack the new item to it.
Using enumerate, changes numbers in place. If you want to keep a copy of a, do:
acopy = a[:]
If you don't have much numbers, and NumPy is an over kill, the above could be done a tiny bit faster using list comprehensions.
If you want, for each element of the array to have it multiplied by -dose then apply math.exp on the result, you need a loop :
new_a = []
for subarray in a:
new_sub_array = []
for element in sub_array:
new_element = math.exp(-dose*element)
new_sub_array.append(new_element)
new_a.append(new_sub_array)
Alternatvely, if you have a mathlab background, you could inquire numpy, that enable transformations on array.