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One error you may encounter when using NumPy is:

TypeError: 'numpy.float64' object is not iterable

This error occurs when you attempt to perform some iterative operation on a a float value in NumPy, which isn’t possible.

The following example shows how to address this error in practice.

**How to Reproduce the Error**

Suppose we have the following NumPy array:

import numpy as np #define array of data data = np.array([1.3, 1.5, 1.6, 1.9, 2.2, 2.5]) #display array of data print(data) [1.3 1.5 1.6 1.9 2.2 2.5]

Now suppose we attempt to print the sum of every value in the array:

#attempt to print the sum of every value for i in data: print(sum(i)) TypeError: 'numpy.float64' object is not iterable

We received an error because we attempted to perform an iterative operation (taking the sum of values) on each individual float value in the array.

**How to Fix the Error**

We can avoid this error in two ways:

**1. Performing a non-iterative operation on each value in the array.**

For example, we could print each value in the array:

#print every value in array for i in data: print(i) 1.3 1.5 1.6 1.9 2.2 2.5

We don’t receive an error because we didn’t attempt to perform an iterative operation on each value.

**2. Perform an iterative operation on a multi-dimensional array.**

We could also avoid an error by performing an iterative operation on an array that is multi-dimensional:

#create multi-dimensional array data2 = np.array([[1.3, 1.5], [1.6, 1.9], [2.2, 2.5]]) #print sum of each element in array for i in data2: print(sum(i)) 2.8 3.5 4.7

We don’t receive an error because it made sense to use the **sum()** function on a multi-dimensional array.

In particular, here’s how NumPy calculated the sum values:

- 1.3 + 1.5 =
**2.8** - 1.6 + 1.9 =
**3.5** - 2.2 + 2.5 =
**4.7**

**Additional Resources**

The following tutorials explain how to fix other common errors in Python:

How to Fix KeyError in Pandas

How to Fix: ValueError: cannot convert float NaN to integer

How to Fix: ValueError: operands could not be broadcast together with shapes