I made a similar post here. Now I am trying to generalize what was done there for an entire matrix of numbers.
Specifically I want to do this:
dates = []
dates.append(NDD_month[0])
for i in range(1,len(cpi)):
dates.append((dates[i-1] + 12 - number_of_payments[:i]) % 12)
print(dates)
where the number_of_payments is a matrix of type <class 'list'>.
Here is an example:
print(number_of_payments[:1])
is
[array([[0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1]])]
After performing what I want then
print(dates[:1])
Should be
[array([[8, 8, 7, 7, 6, 5, 4, 4, 11, 10, 10, 8]])]
or something like that.
EDIT:
Here is an example of what my data looks like:
print(number_of_payments[:3])
This gives me this:
[
array(
[
[0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1]
]),
array(
[
[0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0],
[1, 3, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0]
]),
array(
[
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 2, 0, 2, 1, 1, 0, 2, 1, 0, 0]
])
]
print(NDD_month[:3])
Gives me
[8, 7, 11]
Now for the answer I want I want to do something like this that I did in my earlier post where I had
dates = []
dates.append(NDD_month[0])
for i in range(1, len(first_payments)):
dates.append((dates[i-1] + 12 - first_payments[i-1]) % 12)
print(dates)
This gave me the correct output of
[8 8 7 7 6 5 4 4 11 10 10 8]
But now since I have the number_of_payments being a matrix I need to apply the same logic to this larger data structure. Let me know if that is clear.
Edit 2:
Okay this is hard to explain so I am going to go step by step example, I have this data or matrix (number_of_payments) whatever it is in python:
[[0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1],
[0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0],
[1, 3, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0]]
I have another list or vector called NDD_month, the first three elements are
[8, 7, 11]
Now for sake of simplicity lets say I just have the first row of number_of_payments i.e.
[0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1]
Further for simplicity lets say I have just the first element of NDD_month so
8
Then to get the answer I seek I would do this that Aurora Wang provided a nice answer too which was this
first_payments = number_of_payments[:1]
first_payments = first_payments[0][0]
dates = []
dates.append(NDD_month[0])
for i in range(1, len(first_payments)):
dates.append((dates[i-1] + 12 - first_payments[i-1]) % 12)
print(dates)
This gives me [8, 8, 7, 7, 6, 5, 4, 4, 11, 10, 10, 8].
Now I need to do the same thing but for each row in the matrix and each element in the NDD_month vector. I hope that makes it much more clear.
I was thinking this may work but again I am new to python and this does not work:
dates = []
for i in range(1,len(NDD_month)):
dates.append(NDD_month[i-1])
for j in range(1, len(NDD_month)):
dates.append((dates[j-1] + 12 - number_of_payments[i-1][j-1]) % 12)
print(dates)
If I understood you right, you want to do something like this:
number_of_payments = [
[0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1],
[0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0],
[1, 3, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0]
]
NDD_month = [8, 7, 11]
dates = []
for i in range(len(number_of_payments)):
dates.append([NDD_month[i]])
for j in range(1, len(number_of_payments[i])):
dates[i].append((dates[i][j-1] + 12 - number_of_payments[i][j-1]) % 12)
print(dates)
Related
This question already has answers here:
Strange result when removing item from a list while iterating over it
(8 answers)
Closed last year.
i need to remove all zeros from my array but it doesn't work!
array = [9, 0, 0, 9, 1, 2, 0, 1, 0, 1, 0, 3, 0, 1, 9, 0, 0, 0, 0, 9]
for i in array:
if i == 0:
array.remove(i)
print(array)
output:
[9, 9, 1, 2, 1, 1, 3, 1, 9, 0, 0, 0, 9]
i really don't know what the problem. Please helm me!
There you go:
x = [9, 0, 0, 9, 1, 2, 0, 1, 0, 1, 0, 3, 0, 1, 9, 0, 0, 0, 0, 9]
x = [i for i in x if i != 0]
print(x)
myBoard = [[0, 4, 0, 7, 0, 0, 1, 3, 0],
[0, 0, 2, 0, 0, 0, 6, 0, 0],
[0, 0, 0, 4, 2, 0, 0, 0, 0],
[6, 0, 0, 0, 0, 2, 0, 0, 3],
[2, 3, 1, 0, 7, 0, 0, 8, 0],
[4, 0, 0, 3, 1, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 8, 0, 0, 0],
[0, 0, 6, 0, 3, 0, 0, 0, 4],
[8, 9, 0, 0, 5, 0, 0, 0, 6]]
how to get this like an input from the user
Try this.
myBoard = []
rows = 9
for x in range(rows):
line = input().split(',')
myBoard.append(line)
Or you can use list comprehension
rows = 9
myBoard = [input().split(',') for x in range(rows)]
You can take comma separated input like this
0,4,0,7,0,0,1,3,0
0,0,2,0,0,0,6,0,0
0,0,0,4,2,0,0,0,0
6,0,0,0,0,2,0,0,3
2,3,1,0,7,0,0,8,0
4,0,0,3,1,0,0,0,0
0,7,0,0,0,8,0,0,0
0,0,6,0,3,0,0,0,4
8,9,0,0,5,0,0,0,6
I have a nested list that contains 1002 time steps and in each time step, I have observation of 11 features. I have read docs related to padding but I really could not find out how to add zero elements at the end of each list. I found out the highest length of lists is for example the 24th item in my main list and now I want to pad all the rest elements based on this unless the 24th element that already in shape.As an example:
a = [[1,2,3,4,5,6,76,7],[2,2,3,4,2,5,5,5,,7,8,9,33,677,8,8,9,9],[2,3,46,7,8,9,],[3,3,3,5],[2,2],[1,1],[2,2]]
a[1] = padding(a[1],len(a[2]) with zeros at the end of the list)
I have done below:
import numpy as np
def pad_or_truncate(some_list, target_len):
return some_list[:target_len] + [0]*(target_len - len(some_list))
for i in range(len(Length)):
pad_or_truncate(Length[i],len(Length[24]))
print(len(Length[i]))
or
for i in range(len(Length)):
df_train_array = np.pad(Length[i],len(Length[24]),mode='constant')
and I got this error: Unable to coerce to Series, length must be 11: given 375
Solution 1
# set the max number of 0
max_len = max([len(x) for x in a])
# add zeros to the lists
temp = [x+ [0]*max_len for x in a]
#Limit the output to the wished length
[x[0:max_len] for x in temp]
Solution 2 using pandas
import pandas as pd
df = pd.DataFrame(a)
df.fillna(0).astype(int).values.tolist()
Output
[[1, 2, 3, 4, 5, 6, 76, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 2, 3, 4, 2, 5, 5, 5, 7, 8, 9, 33, 677, 8, 8, 9, 9],
[2, 3, 46, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[3, 3, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
...]
The following code snippet should pad the individual lists with the appropriate number of 0s(driven by the size of the list with the maximum elements)
def main():
data = [
[1,2,3,4,5,6,76,7],
[2,2,3,4,2,5,5,5,7,8,9,33,677,8,8,9,9],
[2,3,46,7,8,9,],
[3,3,3,5],
[2,2],
[1,1],
[2,2]
]
# find the list with the maximum elements
max_length = max(map(len, data))
for element in data:
for _ in range(len(element), max_length):
element.append(0)
if __name__ == '__main__':
main()
You can use this simple line, which uses np.pad
list(map(lambda x: np.pad(x, (max(map(len, a)) - len(x), 0)).tolist(), a))
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 76, 7],
[2, 2, 3, 4, 2, 5, 5, 5, 7, 8, 9, 33, 677, 8, 8, 9, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 46, 7, 8, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 5],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2]]
Use this if you want to pad at the end instead:
list(map(lambda x: np.pad(x, (0, max(map(len, a)) - len(x))).tolist(), a))
In python, I have a matrix and I want to find the two largest elements in every row and every column and change their values to 1 (seperately, I mean get two matrices where one of them modified the rows and the other modified the cols).
The main goal is to get a corresponding matrix with zeros everywhere except those ones I've put in the 2 largest element of each row and column (using np.where(mat == 1, 1, 0).
I'm trying to use the np.argpartition in order to do so but without success.
Please help.
See image below.
Here's an approach with np.argpartition -
idx_row = np.argpartition(-a,2,axis=1)[:,:2]
out_row = np.zeros(a.shape,dtype=int)
out_row[np.arange(idx_row.shape[0])[:,None],idx_row] = 1
idx_col = np.argpartition(-a,2,axis=0)[:2]
out_col = np.zeros(a.shape,dtype=int)
out_col[idx_col,np.arange(idx_col.shape[1])] = 1
Sample input, output -
In [40]: a
Out[40]:
array([[ 3, 7, 1, -5, 14, 2, 8],
[ 5, 8, 1, 4, -3, 3, 10],
[11, 3, 5, 1, 9, 2, 5],
[ 6, 4, 12, 6, 1, 15, 4],
[ 8, 2, 0, 1, -2, 3, 5]])
In [41]: out_row
Out[41]:
array([[0, 0, 0, 0, 1, 0, 1],
[0, 1, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1]])
In [42]: out_col
Out[42]:
array([[0, 1, 0, 0, 1, 0, 1],
[0, 1, 0, 1, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 0]])
Alternatively, if you are into compact codes, we can skip the initialization and use broadcasting to get the outputs from idx_row and idx_col directly, like so -
out_row = (idx_row[...,None] == np.arange(a.shape[1])).any(1).astype(int)
out_col = (idx_col[...,None] == np.arange(a.shape[0])).any(0).astype(int).T
I have a list of lists and I want to be able to refer to the 1st, 2nd, 3rd, etc. column in a list of lists. Here is my code for the list:
matrix = [
[0, 0, 0, 5, 0, 0, 0, 0, 6],
[8, 0, 0, 0, 4, 7, 5, 0, 3],
[0, 5, 0, 0, 0, 3, 0, 0, 0],
[0, 7, 0, 8, 0, 0, 0, 0, 9],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[9, 0, 0, 0, 0, 4, 0, 2, 0],
[0, 0, 0, 9, 0, 0, 0, 1, 0],
[7, 0, 8, 3, 2, 0, 0, 0, 5],
[3, 0, 0, 0, 0, 8, 0, 0, 0],
]
I want to be able to say something like:
matrix = [
[0, 0, 0, 5, 0, 0, 0, 0, 6],
[8, 0, 0, 0, 4, 7, 5, 0, 3],
[0, 5, 0, 0, 0, 3, 0, 0, 0],
[0, 7, 0, 8, 0, 0, 0, 0, 9],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[9, 0, 0, 0, 0, 4, 0, 2, 0],
[0, 0, 0, 9, 0, 0, 0, 1, 0],
[7, 0, 8, 3, 2, 0, 0, 0, 5],
[3, 0, 0, 0, 0, 8, 0, 0, 0],
]
if (The fourth column in this matrix does not have any 1's in it):
(then do something)
I want to know what the python syntax would be for the stuff in parenthesis.
The standard way to perform what you asked is to do a list comprehension
if (The fourth column in this matrix does not have any 1's in it):
translates in:
>>>if not any([1 == row[3] for row in matrix])
However, depending on how often you need to perform this operation, how big is your matrix, etc... you might wish to look into numpy as it is easier (and remarkably faster) to address columns. An example:
>>> import numpy as np
>>> matrix = np.random.randint(0, 10, (5, 5))
>>> matrix
array([[3, 0, 9, 9, 3],
[5, 7, 7, 7, 6],
[5, 4, 6, 2, 2],
[1, 3, 5, 0, 5],
[3, 9, 7, 8, 6]])
>>> matrix[..., 3] #fourth column
array([9, 7, 2, 0, 8])
Try this:
if all(row[3] != 1 for row in matrix):
# do something
The row[3] part takes a look at the fourth element of a row, the for row in matrix part looks at all the rows in the matrix - this produces a list with all the fourth elements in all the rows, that is, the whole fourth column. Now if it is true for all the elements in the fourth column that they're different from one, then the condition is satisfied and you can do what you need inside the if.
A more traditional approach would be:
found_one = False
for i in xrange(len(matrix)):
if matrix[i][3] == 1:
found_one = True
break
if found_one:
# do something
Here I'm iterating over all the rows (i index) of the fourth column (3 index), and checking if an element is equal to one: if matrix[i][3] == 1:. Notice that the for cycle goes from the 0 index up to the "height" of the matrix minus one, that's what the xrange(len(matrix)) part says.
if 1 in [row[3] for row in matrix]: