I have this problem where I would prefer not using loops because I am working with a big data as a solution :
This is what I am trying to do : (I know this works to [6 6 6], but I want to "join" it by index)
import numpy as np
np_1 = np.asarray([1,1,1])
np_2 = np.asarray([2,2,2])
np_3 = np.asarray([3,3,3])
np_4 = np_1 + np_2 + np_3
# np_4 should be [1,2,3,1,2,3,1,2,3]
Are there ways to do this? or should I look for options outside of numpy?
Try this:
np.array([np_1, np_2, np_3]).transpose().flatten()
You can try the following method:
np.ravel(np.stack(np_1,np_2,np3),'F')
One way to do it is to stack the sequences depth-wise and flatten it:
np.dstack([np_1, np_2, np_3]).flatten()
Im trying to iterate over a multidimensional array on Python but I'm having problems because my array is full of dataframes instead of int().
I have a multidimensional numpy array (12, 11) which contains 12 x 11 different dataframes.
nombres_df = np.array([[df_01_ID4034, df_02_ID4034, df_03_ID4034, df_04_ID4034, df_05_ID4034, df_06_ID4034, df_07_ID4034, df_08_ID4034, df_09_ID4034, df_10_ID4034, df_11_ID4034, df_12_ID4034],
[df_01_ID4035, df_02_ID4035, df_03_ID4035, df_04_ID4035, df_05_ID4035, df_06_ID4035, df_07_ID4035, df_08_ID4035, df_09_ID4035, df_10_ID4035, df_11_ID4035, df_12_ID4035],
[df_01_ID4039, df_02_ID4039, df_03_ID4039, df_04_ID4039, df_05_ID4039, df_06_ID4039, df_07_ID4039, df_08_ID4039, df_09_ID4039, df_10_ID4039, df_11_ID4039, df_12_ID4039],
[df_01_ID4040, df_02_ID4040, df_03_ID4040, df_04_ID4040, df_05_ID4040, df_06_ID4040, df_07_ID4040, df_08_ID4040, df_09_ID4040, df_10_ID4040, df_11_ID4040, df_12_ID4040],
[df_01_ID4041, df_02_ID4041, df_03_ID4041, df_04_ID4041, df_05_ID4041, df_06_ID4041, df_07_ID4041, df_08_ID4041, df_09_ID4041, df_10_ID4041, df_11_ID4041, df_12_ID4041],
[df_01_ID4042, df_02_ID4042, df_03_ID4042, df_04_ID4042, df_05_ID4042, df_06_ID4042, df_07_ID4042, df_08_ID4042, df_09_ID4042, df_10_ID4042, df_11_ID4042, df_12_ID4042],
[df_01_ID4047, df_02_ID4047, df_03_ID4047, df_04_ID4047, df_05_ID4047, df_06_ID4047, df_07_ID4047, df_08_ID4047, df_09_ID4047, df_10_ID4047, df_11_ID4047, df_12_ID4047],
[df_01_ID4049, df_02_ID4049, df_03_ID4049, df_04_ID4049, df_05_ID4049, df_06_ID4049, df_07_ID4049, df_08_ID4049, df_09_ID4049, df_10_ID4049, df_11_ID4049, df_12_ID4049],
[df_01_ID4056, df_02_ID4056, df_03_ID4056, df_04_ID4056, df_05_ID4056, df_06_ID4056, df_07_ID4056, df_08_ID4056, df_09_ID4056, df_10_ID4056, df_11_ID4056, df_12_ID4056],
[df_01_ID4059, df_02_ID4059, df_03_ID4059, df_04_ID4059, df_05_ID4059, df_06_ID4059, df_07_ID4059, df_08_ID4059, df_09_ID4059, df_10_ID4059, df_11_ID4059, df_12_ID4059],
[df_01_ID4075, df_02_ID4075, df_03_ID4075, df_04_ID4075, df_05_ID4075, df_06_ID4075, df_07_ID4075, df_08_ID4075, df_09_ID4075, df_10_ID4075, df_11_ID4075, df_12_ID4075]], dtype="object")
for j in range(len(nombres_df)):
for i in range(len(nombres_df[i])):
print (nombres_df[i][j])
I need to iterate over it and make operations with values inside each dataframe.
The problem is that when I try to iterate as usually, I cannot do it because I'm getting this error:
5
6 for j in range(len(nombres_df)):
7 for i in range(len(nombres_df[i])): <--------
8 print (nombres_df[i][j], end = " ")
IndexError: arrays used as indices must be of integer (or boolean) type
I know the problem is here len(nombres_df[i]) but I don`t know how to solve it.
Thank you very much
I thick the problem is the fact that you are iterating over the wrong index in the second line of your code.
that i inside range(len(nombres_df[i])) shoud be j
also you inverted the indexes in nombres_df[i][j] it shoud be nombres_df[j][i]
this shoud do the trick
for j in range(len(nombres_df)):
for i in range(len(nombres_df[j])):
print (nombres_df[j][i])
The objective is to assign new value within certain range (b_top,b_low).
The code below able to achieve the intended objective
b_top=np.array([1,7])
b_low=np.array([3,9])+1
Mask=np.zeros((1,11), dtype=bool)
for x,y in zip(b_top,b_low):
Mask[0,x:y]=True
However, I wonder there is single line approach, or more efficient way of doing this?
You can turn b_top and b_low into a mask using np.cumsum and the fact that bool and int8 are the same itemsize.
header = np.zeros(M.shape[1], np.uint8)
header[b_top] = 1
header[b_low if b_low[-1] < header.size else b_low[:-1]] = -1
header.cumsum(out=Mask[0].view(np.int8))
I've implemented this function in a little utility library I made. The function is called haggis.math.runs2mask. You would call it as
from haggis.math import runs2mask
Mask[0] = runs2mask(np.stack((b_top, b_low), -1), Mask.shape[1])
I have a list of n matrices where n = 5:
[matrix([[3.62425112, 0.00953506],
[0.00953506, 1.05054417]]), matrix([[4.15808905e+00, 9.27845937e-04],
[9.27845937e-04, 9.88509628e-01]]), matrix([[3.90560856, 0.0504297 ],
[0.0504297 , 0.92587046]]), matrix([[ 3.87347073, -0.12430547],
[-0.12430547, 1.09071475]]), matrix([[ 3.87697392, -0.00475038],
[-0.00475038, 1.01439917]])]
I want to do element-wise addition of these matrices:
I am trying this:
np.add(S_list[0], S_list[1], S_list[2], S_list[3], S_list[4])
It works but I don't want to fix n = 5
Can anyone please help? Thank you.
by the documentation, np.add should add only two matrices.
However np.add.reduce(S_list) or just sum(S_list) will give you what you want.
You could just use Python's built-in function sum
sum(S_list)
Output:
[[19.43839338 -0.06816324]
[-0.06816324 5.07003818]]
Are you sure that np.add(S_list[0], S_list[1], S_list[2], S_list[3], S_list[4]) works ? Because np.add() takes as input arguments two arrays . Anyway , the following code does the work if you want to use np.add():
sum = np.add(S_list[0],S_list[1])
for i in range(len(S_list) - 2):
sum = np.add(sum,S_list[i+2])
print(sum)
a simple code:
suka = pd.Series(range(10))
padla =np.argwhere(suka % 4==0)
get the error Length of passed values is 1, index implies 10. Why the machine can't return the requested indices? Thank you.
The fundamental issues are that the semantics of an array and a DataFrame is significantly different (and hence the return of np.argwhere shouldn't be boxed) and numpy only passing context for ufuncs (hence we don't know np.argwhere is the function calling array_wrap)
This is an issue with the new release of pandas occur in Pandas 1.0.1 and later versions.
Try np.flatnonzero() insted of np.argwhere()
Code is :
suka = pd.Series(range(10))
padla =np.flatnonzero(suka % 4==0)
for more details go to https://github.com/numpy/numpy/issues/15555 and https://github.com/pandas-dev/pandas/pull/35334
IF you're using pandas the way to identify args is index, which is separate to their actual order, so pandas approach would be to define padla as:
padla = (suka % 4 == 0)
padla = padla.loc[padla].index
Roughly speaking equivalent for numpy will be:
padla = np.argwhere((suka % 4 == 0).values)