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Is there a numpy function to find an array in multi dimensional array?

I have a numpy array with n row and p columns.
I want to check if a given row is in my array and find the index.
For exemple I have a numpy array like this :
[[1,0,8,7,2,2],[1,3,7,0,3,0],[1,7,1,0,1,0],[1,9,1,0,6,0],[1,8,1,7,9,0],....]
I want to check if this array [6,0,5,8,2,1] is in my numpy array or and where.
Is there a numpy function for that ?
I'm sorry for asking naive question but I'm quite confuse right now.

You can use == and .all(axis=1) to match entire rows, then use numpy.where() to get the index:
import numpy as np
a = np.array([[1,0,8,7,2,2],[1,3,7,0,3,0],[1,7,1,0,1,0],[1,9,1,0,6,0],[1,8,1,7,9,0], [6,0,5,8,2,1]])
b = np.array([6,0,5,8,2,1])
print(np.where((a==b).all(axis=1)))
Output:
(array([5], dtype=int32),)

Is it possible to handle a matrix with strings and numbers?

I am a beginner, self-taught.
I am wondering if a matrix filled with strings and numbers can be made in python? Something similar like handle it in bash?
The type of matrix (14 rows x 14 columns) that I would like to handle is:
,,1,2,3,4,5,6,7,8,9,10,11,12
,,C,O,O,C,H,H,H,C,C,H,H,H
1,C,0.0,1.205475107329386,1.3429319010227962,2.3430136323519886,3.22738313640333,2.640130058756468,2.6401484355574363,1.4784953771865779,2.4427526711622995,3.4404701049315856,2.6506415109695562,2.173942147030341
2,O,1.205475107329386,0.0,2.245467917547002,2.6443156030953032,3.702905546101439,2.6354536594179083,2.6355724561170515,2.3918864536893496,2.871975783234887,3.9479515489105172,2.5936449600745437,3.2896946757332293
3,O,1.3429319010227962,2.245467917547002,0.0,1.418915551312475,2.015476882415432,2.0693088134923188,2.0692958839669946,2.3236193736523485,3.560975969980456,4.431347320573397,3.951843753512012,2.4366421143893597
4,C,2.3430136323519886,2.6443156030953032,1.418915551312475,0.0,1.0868846056358739,1.0921261760040055,1.092126228351473,3.6419246237091034,4.772348473634059,5.725281935435472,4.948741644534887,3.855293676517857
5,H,3.22738313640333,3.702905546101439,2.015476882415432,1.0868846056358739,0.0,1.7916118321336392,1.7916073980710447,4.336840746006843,5.570012200282658,6.44436962662531,5.876935928592363,4.304036910039309
6,H,2.640130058756468,2.6354536594179083,2.0693088134923188,1.0921261760040055,1.7916118321336392,0.0,1.774322615322816,3.999843247699306,5.001451201004137,5.992370839831868,5.038926795069471,4.349546588337786
7,H,2.6401484355574363,2.6355724561170515,2.0692958839669946,1.092126228351473,1.7916073980710447,1.774322615322816,0.0,3.9999029642804302,5.001556219427222,5.992449776200327,5.039085741282741,4.349558376763068
8,C,1.4784953771865779,2.3918864536893496,2.3236193736523485,3.6419246237091034,4.336840746006843,3.999843247699306,3.9999029642804302,0.0,1.324770443414403,2.107792016824585,2.085364895492881,1.079295724832157
9,C,2.4427526711622995,2.871975783234887,3.560975969980456,4.772348473634059,5.570012200282658,5.001451201004137,5.001556219427222,1.324770443414403,0.0,1.0763707503087891,1.0781013610472885,2.1192372863195152
10,H,3.4404701049315856,3.9479515489105172,4.431347320573397,5.725281935435472,6.44436962662531,5.992370839831868,5.992449776200327,2.107792016824585,1.0763707503087891,0.0,1.8418880170159488,2.4949700018092598
11,H,2.6506415109695562,2.5936449600745437,3.951843753512012,4.948741644534887,5.876935928592363,5.038926795069471,5.039085741282741,2.085364895492881,1.0781013610472885,1.8418880170159488,0.0,3.067298402780731
12,H,2.173942147030341,3.2896946757332293,2.4366421143893597,3.855293676517857,4.304036910039309,4.349546588337786,4.349558376763068,1.079295724832157,2.1192372863195152,2.4949700018092598,3.067298402780731,0.0

If your data comes from a csv file, you can use the standard csv module:
import csv
reader = csv.reader('data.csv')
matrix = list(reader)
or you can use Pandas package (pip install pandas or conda install pandas)
import pandas as pd
matrix = pd.read_csv('data.csv')

If you are manually entering the values in the form of a matrix, you can simply use Numpy Arrays for having multiple data types into one by setting dtype as object.
import numpy as np
# 1D Matrix
matrix = np.array(['d',1,'e','c',2,5],dtype='object')
# 2D Matrix (as per your case)
matrix = np.array([[1, 's', 2], ['h', 4, 6]],dtype='object')

Multidimensional numpy array appending with Python

In Python, I can concatenate two arrays like below,
myArray = []
myArray += [["Value1", "Value2"]]
I can then access the data by indexing the 2 dimensions of this array like so
print(myArray[0][0])
which will output:
Value1
How would I go about achieving the same thing with Numpy?
I tried the numpy append function but that only ever results in single dimensional arrays for me, no matter how many square brackets I put around the value I'm trying to append.

If you know the dimension of the final array then you could use np.vstack
>>> import numpy as np
>>> a = np.array([]).reshape(0,2)
>>> b = np.array([['Value1', 'Value2']])
>>> np.vstack([a,b])
array([['Value1', 'Value2']], dtype='<U32')

Python split string inside a numpy array

I have an numpy array like this
Input
array([['ATS1, ATS2', 'P_CD'],
['ATS1,ATS2,ATS3', 'C_CD']], dtype=object)
I would like to convert this numpy array as stated below
Expected output
array([['ATS1' , 'ATS2', 'P_CD'],
['ATS1','ATS2','ATS3', 'C_CD']], dtype=object)
As you can notice above, I would like to split the string with a delimeter and make it as a separate entry
Any suggestions on how to achieve using python?

You can use re.split and join
This is just changing the type but this results in numpy array of lists as inside sub lists can be of variable length so they will not be of numpy array types.
import numpy as np
import re
arr = np.array([['ATS1, ATS2', 'P_CD'],
['ATS1,ATS2,ATS3', 'C_CD']], dtype=object)
arr = np.array([re.split('[-,]','-'.join(ele)) for ele in arr] ,dtype=object)
print(arr)

How can I unserialize a numpy array that was cast to a bytestring?

I need to serialize a numpy array to some JSON-compatible form. Since the framework I'm using doesn't give me access to the JSON encoder/decoder object, I'm stuck serializing a numpy array to something that can then be marshalled into JSON. I've opted for either array.tobytes or array.tostring (both seem to be essentially the same thing).
Below is an example which illustrates my problem:
import numpy as np
a = np.random.rand(1024, 1024) # create array of random values
b = array.tobytes() # serialize array
a2 = np.fromstring(b)
When I inspect the value of a2, I find that it only contains the first line of the original a. In other words, a2 == a[0, :].
How can I decode the full array?

Actually numpy.fromstring() returns a single dimensional array of 1024X1024 intead of a 2 Dimensional array, All you need to do is reshape into 1024X1024,
Try this :-
import numpy as np
a = np.random.rand(1024, 1024) # create array of random values
b = array.tobytes()
np.fromstring(b).reshape(1024,1024)