Cannot cast array data from dtype(‘o’) to dtype(‘float64’) according to the rule ‘safe’

The error message “cannot cast array data from dtype(‘o’) to dtype(‘float64’) according to the rule ‘safe'” occurs when you are trying to convert an array of objects to an array of floating-point numbers in a safe manner, but it’s not possible due to incompatible data types or invalid values. Here is a detailed explanation of the error and an example to illustrate it.

The error usually occurs when you are performing numerical operations on an array that contains mixed data types or missing values (NaNs). By default, NumPy tries to convert the data to a compatible type whenever possible, but when it encounters incompatible data in the array, it raises this error.

To better understand this error, let’s consider an example. Suppose you have an array called “my_array” with elements of different data types:

    import numpy as np
    
    my_array = np.array([1, 2.5, '3', True, None])
  

In this case, the array contains integers, floats, a string, a boolean value, and a missing value. Now, if you try to convert this array to dtype(‘float64’) using the astype() function, you will encounter the error:

    my_float_array = my_array.astype('float64')
  

The reason for the error is that the array contains incompatible data types. It’s not possible to safely cast the string, boolean, or missing value to a float without losing information or resulting in an invalid value. Hence, NumPy raises the error to prevent any unintended data conversions.

To resolve this error, you need to ensure that all the elements in the array are of the same data type or compatible data types. You can achieve this by converting the array elements to a common data type or by removing the elements that are causing the incompatibility. For example, you can remove the string and boolean elements from the array using boolean indexing:

    valid_indices = np.logical_and(~np.isnan(my_array), np.isreal(my_array))
    my_valid_array = my_array[valid_indices].astype('float64')
  

In this updated example, we used the functions np.logical_and() and np.isreal() to identify the indices of valid elements (non-NaN and real elements) in the array. Then, we used boolean indexing to create a new array “my_valid_array” with only the valid elements. Finally, we converted this new array to dtype(‘float64’) without encountering the error.

In summary, the error “cannot cast array data from dtype(‘o’) to dtype(‘float64’) according to the rule ‘safe'” occurs when there are incompatible or invalid data types in the array. To resolve the error, you need to ensure that all the elements are of the same or compatible data types by converting or removing the problematic elements.