TypeError: ufunc ‘add’ did not contain a loop with signature matching types

If you are working with pandas and numpy, you might encounter a TypeError like this:

TypeError: ufunc ‘add’ did not contain a loop with signature matching types dtype (‘S21’) dtype (‘S21’) dtype (‘S21’)

This error means that you are trying to add two arrays with incompatible data types. For example, you might have an array of strings and an array of numbers, and you want to concatenate them with a separator character.

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One way to fix this error is to convert your arrays to the same data type before adding them. You can use the .astype() method to change the data type of a pandas Series or a numpy array. For example, if you have a Series of Order_IDs and a Series of Dates, and you want to create a key by joining them with an underscore, you can do this:

df1['key'] = df1['Order_ID'].astype(str) + '_' + df1['Date'].astype(str)

This will convert both Series to strings and then add them element-wise. Alternatively, you can use the .apply() method to apply a function to each element of a Series. For example, you can use str() to convert each element to a string:

df1['key'] = df1['Order_ID'].apply(str) + '_' + df1['Date'].apply(str)

This will achieve the same result as the previous code. However, using .astype() is more idiomatic and faster than using .apply().

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By converting your arrays to the same data type, you can avoid the TypeError and perform the desired operation.

See also:
How to resolve TypeError: Cannot perform reduce with flexible type
How to solve TypeError: ‘numpy.int64’ object is not callable
How to solve TypeError: ‘numpy.float64’ object is not iterable
How to solve TypeError: only integer scalar arrays can be converted to a scalar index
How to solve TypeError: ‘set’ object is not subscriptable

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