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I have a matrix like this
t = np.array([[1,2,3,'foo'], [2,3,4,'bar'], [5,6,7,'hello'], [8,9,1,'bar']]) I want to get the indices where the rows contain the string 'bar'
In a 1d array
rows = np.where(t == 'bar') should give me the indices [0,3] followed by broadcasting:-
results = t[rows] should give me the right rows
But I can't figure out how to get it to work with 2d arrays.
462
Indexing a Two-dimensional Array To access elements in this array, use two indices. One for the row and the other for the column. Note that both the column and the row indices start with 0. So if I need to access the value '10,' use the index '3' for the row and index '1' for the column.
2D array are also called as Matrices which can be represented as collection of rows and columns. In this article, we have explored 2D array in Numpy in Python. NumPy is a library in python adding support for large multidimensional arrays and matrices along with high level mathematical functions to operate these arrays.
The numpy. where() function returns the indices of elements in an input array where the given condition is satisfied. Syntax :numpy.where(condition[, x, y]) Parameters: condition : When True, yield x, otherwise yield y.
Numpy matrices are strictly 2-dimensional, while numpy arrays (ndarrays) are N-dimensional. Matrix objects are a subclass of ndarray, so they inherit all the attributes and methods of ndarrays.
You have to slice the array to the col you want to index:
rows = np.where(t[:,3] == 'bar') result = t1[rows] This returns:
[[2,3,4,'bar'], [8,9,1,'bar']] For the general case, where your search string can be in any column, you can do this:
>>> rows, cols = np.where(t == 'bar') >>> t[rows] array([['2', '3', '4', 'bar'], ['8', '9', '1', 'bar']], dtype='|S11') If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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