![]() We iterated over each row of the 2D numpy array and for each row we checked if all elements are equal or not by comparing all items in that row with the first element of the row. Let’s see how to do that, Find rows with same values in a matrix or 2D Numpy array # Check rows in which all values are equal Now we want to find all rows and columns which contain the same values. Suppose we have a 2D numpy array or matrix, arr_2d = np.array(, Find rows with same values in a matrix or 2D Numpy array Then we selected the first element in this array and compared it with all the other elements of 2D numpy array, to check if all values are the same or not. Numpy.ravel() returns a flattened 1D view of the input array. It confirms that all the values in the 2D numpy array are the same. ![]() Output: All Values in 2D Numpy Array are same / equal Print('All Values in 2D Numpy Array are not same') Print('All Values in 2D Numpy Array are same / equal') # Check if all value in 2D array are equal # Get a flattened 1D view of 2D numpy array So, let’s create a generic solution that should work with an array of any dimension and confirms if all values are equal or not, arr_2d = np.array(, But if we have multi dimensional array like 2D or 3D array, then for each type of array there is different technique, like to select first element from a 2D numpy array it is arr, whereas for a 3D array it is arr. If we have a 1D array then it is easy to select an individual element of the array for comparison. Check if all elements are equal in a Multidimensional Numpy Array or Matrix # Check if all items in an array are equalĪs our numpy array contains only integers, so if the minimum value in array is equal to the maximum value in array, then it means all values in the array are the same. If we have an array of integer type, them there is an another simple way to check if all elements in the array are equal, # create a 1D numpy array from a list Check if all elements are equal in a 1D Numpy Array using min() & max() If all elements in this bool array are True, then it means all values in the main array are equal. ![]() Each element in this bool array corresponds to an element in the main array, if an element is equal to the first element of the array then the corresponding value in the bool array will be True else it will be False, result = np.all(bool_arr) It compares the first element of the array with all the other elements in the array and returns a bool array of the same size. But what just happened in this single line?įirst we compared all the values in array with the first element of array, bool_arr = (arr = arr) This confirms that all values in the array are the same. Output: All Values in Array are same / equal Print('All Values in Array are not same') Print('All Values in Array are same / equal') # Check all values in an array are equal to its first element Check if 2D NumPy Array or Matrix is Symmetric.How to Remove Element from a NumPy Array?.Python: Check if all values are same in a Numpy Array (both 1D and 2D).
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