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A TypeError: cannot perform reduce with flexible type is an error that occurs when the reduce() method is used to combine the elements of an array with different data types. This is because the reduce() method expects the elements of the array to all be of the same type. In order to fix this error, the array must be checked to make sure all of the elements are of the same type, and any incompatible elements must be converted to the same type.
One error you may encounter when using Python is:
ValueError: cannot perform reduce with flexible type
This error occurs when you attempt to perform some calculation on an object in Python that is not numeric.
The following example shows how to fix this error in practice.
How to Reproduce the Error
Suppose we have the following NumPy array:
import numpy as np #define NumPy array of values data = np.array(['1', '2', '3', '4', '7', '9', '10', '12']) #attempt to calculate median of values np.median(data) TypeError: cannot perform reduce with flexible type
We receive a TypeError because we attempted to calculated the median of a list of string values.
How to Fix the Error
The easiest way to fix this error is to simply convert the NumPy array to a float object so that we can perform mathematical operations on it.
The following code shows how to do so:
#convert NumPy array of string values to float values
data_new = data.astype(float)
#view updated NumPy array
data_new
array([ 1., 2., 3., 4., 7., 9., 10., 12.])
#check data type of array
data_new.dtype
dtype('float64')
We can now perform mathematical operations on the NumPy array:
#calculate median value of array
np.median(data_new)
5.5
#calculate mean value of array
np.mean(data_new)
6.0
#calculate max value of array
np.max(data_new)
12.0
Notice that we don’t receive any errors because the NumPy array is a float object, which means we can perform mathematical operations on it.
The following tutorials explain how to fix other common errors in Python: