How to find minimum value in each row while keeping array dimensions same using numpy?

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I've the following array:

np.array([[0.07704314, 0.46752589, 0.39533099, 0.35752864],
          [0.45813299, 0.02914078, 0.65307364, 0.58732429],
          [0.32757561, 0.32946822, 0.59821108, 0.45585825],
          [0.49054429, 0.68553148, 0.26657932, 0.38495586]])

I want to find the minimum value in each row of the array. How can I achieve this?

Expected answer:

[[0.07704314 0.         0.         0.        ]
 [0.         0.02914078 0.         0.        ]
 [0.32757561 0          0.         0.        ]
 [0.         0.         0.26657932 0.        ]]

IIUC first find out out the min value of each line , then we base on the min value mask all min value in original array as True, using multiple(matrix) , get what we need as result

np.multiply(a,a==np.min(a,1)[:,None])
Out[225]: 
array([[0.07704314, 0.        , 0.        , 0.        ],
       [0.        , 0.02914078, 0.        , 0.        ],
       [0.32757561, 0.        , 0.        , 0.        ],
       [0.        , 0.        , 0.26657932, 0.        ]])

numpy.amin(), numpy. amin() If it's provided then it will return for array of min values along the axis i.e. If axis=0 then it returns an array containing min value for each columns. If axis=1 then it returns an array containing min value for each row. Phyton I've the following array: np.array([[0.07704314, 0.46752589, 0.39533099, 0.35752864], [0.45813299, 0.02914078, 0.65307364, 0.58732429], [0.32757561, 0.32946822

You can use np.where like so:

np.where(a.argmin(1)[:,None]==np.arange(a.shape[1]), a, 0)

Or (more lines but potentially more efficient):

out = np.zeros_like(a)
idx = a.argmin(1)[:, None]
np.put_along_axis(out, idx, np.take_along_axis(a, idx, 1), 1)

numpy.minimum — NumPy v1.19 Manual, Compare two arrays and returns a new array containing the they must be broadcastable to a common shape (which becomes the A tuple (possible only as a keyword argument) must have length equal to the number of outputs. The minimum value of an array along a given axis, propagates NaNs. Python: Check if all values are same in a Numpy Array (both 1D and 2D) 6 Ways to check if all values in Numpy Array are zero (in both 1D & 2D arrays) - Python; Sorting 2D Numpy Array by column or row in Python; Python Numpy : Create a Numpy Array from list, tuple or list of lists using numpy.array() Delete elements, rows or columns from a Numpy

np.amin(a, axis=1) where a is your np array

numpy.amax — NumPy v1.19 Manual, Must be of the same shape and buffer length as the expected output. axes which are reduced are left in the result as dimensions with size one. The minimum value of an array along a given axis, propagating any NaNs. Python: Check if all values are same in a Numpy Array (both 1D and 2D) How to sort a Numpy Array in Python ? Python: Convert Matrix / 2D Numpy Array to a 1D Numpy Array; Find max value & its index in Numpy Array | numpy.amax() numpy.amin() | Find minimum value in Numpy Array and it's index; Python: numpy.flatten() - Function Tutorial with examples

numpy.amax — NumPy v1.17 Manual, numpy. amax (a, axis=None, out=None, keepdims=<no value>, initial=<no Must be of the same shape and buffer length as the expected output. amin: The minimum value of an array along a given axis, propagating any Don't use amax for element-wise comparison of 2 arrays; when a.shape[0] is 2,� Phyton Phyton We have a need to call SOAP web services using zeep python package. While starting to work on this project I have implemented a few web services calls and they worked as advertised. However, one of

numpy.amin — NumPy v1.19 Manual, Must be of the same shape and buffer length as the expected output. the axes which are reduced are left in the result as dimensions with size one. The minimum value of an array along a given axis, ignoring any NaNs. Phyton How do you align the axes of different subplots when some have a colorbar and the others don't? import numpy as np import matplotlib.pyplot as plt data1 = np.random.random([15,15]) data2 = np.random.

NumPy: Extract or delete elements, rows and columns that satisfy , Extract elements that satisfy the conditions Extract rows and columns that to calculate the sum, average, maximum value, minimum value, elements, a one- dimensional array is returned, but if you use np.all() and np.any() , you can extract rows and columns while keeping the original ndarray dimension. numpy.ndarray.min — finds the minimum value in an array. numpy.ndarray.max — finds the maximum value in an array. You can find a full list of array methods here. NumPy Array Comparisons. NumPy makes it possible to test to see if rows match certain values using mathematical comparison operations like <, >, >=, <=, and ==.

Comments
  • You can use the keepdims=True kwarg here.
  • I want to maintain the dimensions and indices. I want to fill 0 where the values are not minimum. Your solution gives a single dimension array of min values.
  • Yes, you are right. For some reason i didn't read the expected answer part. Sorry.