When working with Python projects, we may come across an error that says **“Typeerror: cannot perform reduce with flexible type”**.

At first glance, this error can seem cryptic and frustrating. However, it’s actually a helpful indication that points toward the root of the problem.

In this guide, we’ll explore the causes of this error as well as provide practical examples of how to resolve it.

By the end of this guide, we’ll have a better understanding of how to prevent and fix this error in Python.

**What is typeerror: cannot perform reduce with flexible type?**

**The “typeerror cannot perform reduce with flexible type” error is a common error in NumPy that occurs when you try to apply a reduction function (such as sum, mean, max, min, etc.) to an array that contains elements of different data types.**

**Why cannot perform reduce with flexible type occur?**

This **typeerror** **cannot perform reduce with flexible type** occurs because the reduction function expects all elements in the array to be of the same data type.

However, the presence of elements with different data types makes it impossible to perform the reduction operation.

For example, if you have an array that contains a mix of integers, floats, and strings, applying the sum function to the array will give the “cannot perform reduce with flexible type” error.

This is because the sum function cannot add elements of different data types.

**Solutions typeerror: cannot perform reduce with flexible type**

There are several solutions that could fix the **TypeError “cannot perform reduce with flexible type” **in NumPy.

Here are the following solutions:

### 📌 **Convert array elements to a common data type**

The most common cause of the error is when an array contains elements of different data types. You can fix this error by converting all elements in the array to a common data type using the **astype **method.

**Here’s an example:**

**import numpy as np
arr = np.array([1, 2.5, '3'])
arr = arr.astype(float)
print(np.sum(arr)) # Output: 6.5**

In this example, the original array **arr **contains elements of type **int**, **float**, and **str**. We convert all elements to **float **using the **astype **method before applying the **sum **function.

**Output:**

6.5

### 📌 **Remove or replace missing or invalid values**

If your array contains missing or invalid values, such as **NaN **or **inf**, the reduction function may raise the TypeError **cannot perform reduce with flexible type**.

You can fix this error by removing or replacing the problematic elements using boolean indexing or masking.

**Here’s an example:**

**import numpy as np
arr = np.array([1, 2, np.nan, 4])
arr = arr[~np.isnan(arr)]
print(np.sum(arr)) # Output: 7.0**

In this example, we use NumPy’s **isnan **function to identify the problematic element **(np.nan)** and then use boolean indexing to remove it from the array before applying the **sum **function.

**Output:**

7.0

## 📌**Extract numeric values from objects**

If your array contains objects that don’t have a numeric representation, you may need to extract the numeric values from the objects before applying the reduction function.

**Here’s an example:**

**import numpy as np
arr = np.array([[1, 2], [3, {'four': 4}], [5, 6]])
arr = np.array([elem for row in arr for elem in row if isinstance(elem, (int, float))])
print(np.sum(arr)) # Output: 17**

In this example, the original array **arr **contains objects (a dictionary) that don’t have a numeric representation.

We use a list comprehension to extract all numeric values from the objects and flatten the array before applying the **sum **function.

**Output:**

17

## 📌**Reshape the array to the expected dimensions**

If your array has unexpected dimensions, such as being 3D instead of 2D, you may need to reshape the array to the expected dimensions before applying the reduction function.

**Here’s an example:**

**import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
arr = arr.reshape((2, 4))
print(np.sum(arr)) # Output: 36**

In this example, the original array `arr`

has dimensions **(2, 2, 2)** instead of **(2, 4)**, so we use the **reshape **method to change the shape of the array before applying the **sum **function.

**Output:**

36

Anyway, we also have a solution for Typeerror series objects are mutable thus they cannot be hashed errors, you might encounter.

**Conclusion**

In conclusion, “**typeerror** **cannot perform reduce with flexible type**“ error in NumPy that occurs when you try to apply a reduction function.

This error can be able to fix by **converting array elements to a common data type**, **removing or replace missing or invalid values, reshaping the array to the expected dimensions, and extracting numeric values from objects.**

We hope this article has helped you fix the error and get back to coding.

Thank you! 😊