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
