![]() With this, we come to the end of this tutorial. We get a tuple of two empty numpy arrays.įor more on the numpy where function, refer to its documentation. ![]() Output: Index of 13: (array(, dtype=int64), array(, dtype=int64)) For example, if we check for the index of 13, an element that is not present in the 2D array above – print("Index of 13:", np.where(arr=13)) ![]() The returned tuple from np.where() will have two empty numpy arrays. If the element is not present in the 2D array. What if the element is not present in the array? The result shows that 17 occurs at the following locations – row 0 column 1, row 1 column 2, and row 2 column 0 with the index for rows and columns starting from 0. Let’s make these indexes more readable by showing the (row, column) index tuples for each occurrence. The first array values tell the row indexes whereas the second array values tell the column indexes of the occurrences of the element inside the 2D array. The returned tuple from np.where() contains two numpy arrays. Output: Index of 17: (array(, dtype=int64), array(, dtype=int64)) Let’s find the indexes where 17 occurs inside this array. Here we created a 2D numpy array with three rows and three columns. For a 2D array, the returned tuple will contain two numpy arrays one for the rows and the other for the columns.įirst, let’s create a two-dimensional numpy array. We can also use the np.where() function to find the position/index of occurrences of elements in a two-dimensional or multidimensional array. Here we get its first occurrence which is at index 1. Print("First index of 5:", np.where(arr=5))įrom the previous examples, we know that 5 is present at indexes 1, 6, and 9 in the array arr. For example, let’s find the index of the first occurrence of 5 in the above array. You can use it to find the index of the first occurrence. Since np.where() returns all the indexes of the occurrence of an element. You can see that the returned tuple contains an empty numpy array. For example, let’s use it to find the index of 1, an element that is not present in the above array arr. If the element is not present in the array we get an empty array with np.where(). If you were to use the np.where() function on a multidimensional numpy array, the returned tuple would have multiple numpy arrays, one for each axis. Note that this tuple only has one numpy array storing the indices of occurrence of the element 5 inside the array. We get a tuple of numpy arrays as an output. Output: Index of 5: (array(, dtype=int64),) Now that we have a 1D numpy array, let’s find the indexes where the element 5 occurs inside the array: # find index of 5 First, let’s create a 1D array and print it out. Let’s apply the above syntax on a one-dimensional numpy array and find all the indices where a particular element occurs. ![]() This method works for both one-dimensional and multidimensional arrays. That is the indices of all the elements for which arr=i evaluates to True. Now, np.where() gives you all the indices where the element occurs in the array. Inside the function, we pass arr=i which is a vectorized operation on the array arr to compare each of its elements with the value in i and result in a numpy array of boolean True and False values. Here, arr is the numpy array and i is the element for which you want to get the index. The following example illustrates the usage. You can use the numpy’s where() function to get the index of an element inside the array. How to find the index of element in numpy array? In this tutorial, we will look at how to find the index of an element in a numpy array.
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