Like any regular Python array, you can access the contents of a NumPy array using indexing. The indexing method, which uses square brackets, lets you see one item in a list or a particular part of a list. If you try to retrieve an item from a NumPy array using round brackets, you will encounter an error.
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In this guide, we’re going to talk about what the The The fix to this error is simple: you must replace ( ) with [ ] when you are indexing. In Python, square brackets indicate indexing whereas round brackets denote a function call. If you use round brackets while trying to access an item in an array, Python cannot handle your code because the array is not structured like a function. Let’s walk through an example of this error.<\/p>\n\n\n\n We are building a program that evaluates the grades of every student in a school. We want to calculate the average grade of each student. Each student’s grades are stored in a NumPy array like this: The final item indicates the grade a student received on their final exam. We want to print that grade individually to the console before we show the average grade of each student. To show the exam grade and average grade for each student, we could use this code:<\/p>\n\n\n\n The first line of code finds the last item in our list. This item represents the mark a student received on their final exam. The next line displays the grade the student earned on their exam on the Python console. We then calculate the average grade the student earned and print that value to the console. Let’s run our code:<\/p>\n\n\n\n'numpy.ndarray' object is not callable<\/code> error means. We will then discuss an example scenario of this error in action and show you the solution. Let’s get started.<\/p>\n\n\n\n‘numpy.ndarray’ object is not callable<\/h2>\n\n\n\n
'numpy.ndarray' object is not callable<\/code> Python error indicates you are trying to call a NumPy array as if it were a function. This happens if you use round brackets ( ) instead of square brackets [ ] to retrieve items from a list.
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<\/p>\n\n\n\nAn Example Scenario<\/h2>\n\n\n\n
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<\/p>\n\n\n\nimport numpy as np\n\nstudent = np.array([72, 87, 80, 69, 91])<\/pre><\/div>\n\n\n\n
<\/p>\n\n\n\nexam_grade = student(-1)\nprint("This student earned {} on their exam.".format(exam_grade))\n\naverage_grade = np.sum(student) \/ student.size\nprint("This student received an average {} mark on each test.".format(average_grade))<\/pre><\/div>\n\n\n\n
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