Cannot perform reduce with flexible type

If you are trying to perform a mathematical operation on NumPy arrays containing numerical strings that invoke the universal function ufunc.reduce(), you'll get the TypeError: "cannot execute reduce with flexible type." To fix this problem, use astype to cast the array's values to astype(float). You can place the values in a DataFrame and conduct operations on the DataFrame columns if you have a multidimensional array.

This article will walk you through the error and how to fix it with code errors.

What is “Connot perform reduce with flexible type” (Explanation)

Let's dissect the error message to figure out what it implies. When you try to use an unlawful operation on a given data type, you get a TypeError. The phrase "cannot perform reduce" indicates that we are using a method that invokes reduce. the reduce() is a universal function (ufunc). A vectorized wrapper for a function that accepts a fixed number of specified inputs and outputs a fixed number of specific outputs is called a ufunc. The reduce method reduces the dimension of an array by one. Reduce is a NumPy function that is called by some NumPy functions, such as mean(). Numerical strings, which are both strings and numbers, are referred to as "flexible type." For the reduction function, only numerical values are acceptable.

What are the reasons that can trigger this error

When you are trying to execute mathematical operations that invoke ufunc.reduce() on strings instead of integers or floats, you'll get the TypeError: "cannot perform reduce with flexible type".

If you are trying to apply mathematical operations on a multidimensional array in NumPy and you did not use DataFrame that is available in the pandas' library, you will see the same error message.

Solution

Let's look at a NumPy array with numerical strings as an example. To get the average value of the array, we'll use the mean() function on it. Let's take a closer look at the code:

import numpy as np
data = np.array(['2', '4', '6', '8', '10', '12'])
mean = np.mean(data)
print(mean)

Let's see what goes on when we run the test: Interpreter will throw an Error message TypeError: “cannot perform reduce with flexible type"

Because we try to calculate the mean on an array of numerical strings, our code returns a TypeError. We can see that when we call the mean() function, we are contacting the umr_sum method, which executes reduce, which is why the error is referring to reduce.

To fix this error, we can use the astype() method to cast the array values to floats. Let's take a look at the new code:

data_float = data.astype(float)
print(data_float)
print(data_float.dtype)

Let's see once again what goes on when we run the test:

[ 2. 4. 6. 8. 10. 12.]

float64

We can calculate the mean now that we have an array of floats. Let's explore what goes on when we run the code:

mean = np.mean(data_float)
print(mean)

7.0

Instead of a two-dimensional NumPy array, we can use a Pandas DataFrame. Let's have a look at the code:

import pandas as pd
scores = pd.DataFrame({'Player':['Harry', 'Ron', 'Severus'],
'Position':['seeker', 'keeper', 'beater'],
'Score':[5, 8, 3]})

print(scores)
print(scores.Score)

To see the DataFrame and the dtype of the Score column, run the following code:

Player                    Position                Score
Harry                     seeker                   5
Ron                       keeper                   8
Severus                   beater                   3

5 8 3

Name: Score, dtype: int64

In the Score column, the values are all integers. Let's figure out what the average of the scores is:

print(scores.mean())
Score 5.333333

dtype: float64
With the help of this, we can successfully calculate the average (means)

Conclusion

The only thing worse than having an error like this is not knowing what it means. The good news is that there are plenty of resources online to help you understand what’s going on, and how to resolve it. For example, if you search for “Cannot perform reduce with flexible type” in Google then you will find all about this error message and how it can be fixed by converting your data types before calling a mathematical operation If this isn’t enough for you, write in the comment section, I will address your questions as soon as possible.