Adventures in Machine Learning

Eliminating the Numpyfloat64 Object Not Iterable Error with NumPy Arrays

Handling Numpy.float64 Object Not Iterable Error

As a programmer, you may have encountered a TypeError stating that a numpy.float64 object is not iterable while using Python’s in operator. This error can be frustrating and confusing, especially when working with large datasets that require complex computations.

This article aims to provide a comprehensive guide to understanding the error’s causes and solutions using NumPy arrays.

Causes of the error

The TypeError message occurs when you try to perform an operation on an iterable object, but the variable value is a single float value that is not iterable. This error is commonly found when using the in operator or iterating over a function that expects an iterable.

For example, you may encounter this error when trying to iterate over a NumPy float64 object using a for loop.

Solutions to the error

One solution to the error is to convert the numpy.float64 object to an iterable such as a list or array. This can be accomplished by using built-in functions like list(), min(), max(), or sum().

For instance, using list() to call the NumPy array will convert the float value to a list, which is then iterable by the for loop. Subsequently, using a NumPy array is a powerful way to avoid the Numpy.float64 Object Not Iterable error and perform complex computations on the data.

Creating a NumPy array

A NumPy ndarray is a multi-dimensional array that contains a sequence of float values with a defined shape and size.

Creating a NumPy array involves importing the NumPy package and calling the NumPy array method while passing in the float sequence as an argument.

import numpy as np
my_array = np.array([1.0, 2.0, 3.0])

Passing a NumPy array to functions

Once you have created a NumPy array, you can pass it to various functions, including min(), max(), sum(), and list(). These methods return the minimum, maximum, or sum of the array elements or convert the array value to a list.

minimum = np.min(my_array)
maximum = np.max(my_array)
total = np.sum(my_array)
list_form = list(my_array)

Additionally, Nested NumPy arrays can also be used to perform complex computations, such as matrix multiplication.


In conclusion, the Numpy.float64 Object Not Iterable error can be a frustrating roadblock, but with the use of NumPy arrays, it is easily resolvable. When trying to iterate over a float value, converting it to a List or NumPy array is a simple solution. NumPy arrays provide a wide range of functions that perform computations on the data, making it easier to analyze large data sets or perform complex computations. With the techniques covered in this article, you can efficiently handle the error and continue developing your applications.

Understanding the Error: A Deeper Dive

When programming in Python using the NumPy library, it’s common to encounter the Numpy.float64 Object Not Iterable error when attempting to perform calculations or iterations on an individual float value. This is because the in operator can only be used with iterable objects like lists, tuples, dictionaries, or arrays, but not with single float values. Identifying the cause of the numpy.float64 object not iterable error is crucial to resolving it effectively.

Common Causes of the Error

  1. Passing single float values to Iterable objects: Iterative objects comprise many components; a single float value cannot be iterated. Performing iterations on this float value will cause this error to occur.
  2. Type conversion errors: Another reason for numpy.float64 object not iterable error is type conversion errors between data types. While it’s possible to convert one data type to the other, it’s only possible if the data types are compatible; otherwise, this error will occur.
  3. Spelling errors: If you misspell an objects name or call a method from a wrong instance, Python will raise an error. It is quite common to incorrectly type the name of an object when declaring or using it. Thus, it’s important to pay attention to how you name your variables and function arguments.
  4. Misplaced Functions: Misplacing functions within your program can cause a numpy.float64 object not iterable. An example of this is when you call an iterator function on an object that does not support it.

Solutions to numpy.float64 object not iterable error

  1. Converting the Un-Iterable Objects to List or NumPy Array: One solution to convert the un-iterable objects to iterable is by converting the object to an iterable object using list() or NumPy array. Once converted to a new iterable object, it can then be used in place of the numpy.float64 object. This will enable the user to perform various operations on the array or list.
  2. Data type conversion: If conversion errors cause this error, you should try data type conversion by using the astype() function. This function will convert an object from one data type to another, thereby preventing any errors associated with data type incompatibilities.
  3. Check the code for spelling errors: It’s important to check your code for spelling errors that may cause this error. Ensure that you have correctly named your objects and functions and used the correct method names.
  4. Review function placement: Ensure that all functions in your program are placed correctly and called from the right variables.


This article has provided detailed insight into the causes of the numpy.float64 object not iterable error error and several solutions to resolve it. Although this error message can be frustrating when working with large amounts of data, converting un-iterable objects to NumPy arrays or lists is a simple solution that allows us to perform complicated computations on the data. We hope this article has provided useful information that will assist you in resolving this error effectively and save you time in developing your applications.

In summary, the numpy.float64 object not iterable error can be a frustrating issue when working with large datasets, but it can be resolved using NumPy arrays. This error is usually caused by passing a single float value to iterable operations or type conversion errors, spelling errors, and misplaced functions. To resolve this error, one can convert the un-iterable objects to iterable by using list() or NumPy array, ensuring correct data type conversion, checking for spelling errors and reviewing the placement of functions. Overall, understanding the causes of this error and utilizing the solutions provided in this article can significantly save time and increase the efficiency of Python Development. Using NumPy arrays is an effective approach in preventing the error and performing complex computations on large datasets.

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