For those who are technology people, they are all too familiar with the concept of Python in programming. However, the manipulation process cannot avoid problems, especially young people who are new to running code. So when does a Python error occur and how to handle it? Today we will introduce you to an error that quite a lot of people encounter. It is “ValueError: Object arrays cannot be loaded when allow_pickle=False”. Follow our article to find the solution!

When does the error “ValueError: Object arrays cannot be loaded when allow_pickle=False” occur?

When you encounter this error, it will be very easy to recognize. In this case, an error occurred when you run the command below:

much_data = np.load('muchdata-50-50-20.npy')

and below is the returned result, the system reported an error:

ValueError                                          Traceback (most recent call last)
<ipython-input-6-6710fe7f2bb7> in <module>
----> 1 much_data = np.load('muchdata-50-50-20.npy')

~anaconda3envstf-gpu-cuda8libsite-packagesnumpylibnpyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)
437                     return format.open_memmap(file, mode=mmap_mode)
438                 else:
--> 439                 return format.read_array(fid, allow_pickle=allow_pickle,
440                                              pickle_kwargs=pickle_kwargs)
441             else:

~anaconda3envstf-gpu-cuda8libsite-packagesnumpylibformat.py in read_array(fp, allow_pickle, pickle_kwargs)
725             # The array contained Python objects. We need to unpickle the data.
726             if not allow_pickle:
--> 727             raise ValueError("Object arrays cannot be loaded when "
728                                  "allow_pickle=False")
729             if pickle_kwargs is None:

ValueError: Object arrays cannot be loaded when allow_pickle=False

How to solve “ValueError: Object arrays cannot be loaded when allow_pickle=False” error

Option 1: Understand and distinguish two types of Python errors

To be able to handle unfortunate problems that arise while programming, people need to understand and distinguish two types of Python errors. It’s a syntax or execution error. Execution error is the type of error that will be encountered during program execution after finishing writing the code. It is called Runtime Error, also known as “Exception”. In this case, we can easily identify our error as an exception by the text [ValueError].

The general mechanism in error handling that Python gives is to stop the program as soon as an exception is detected. The system will give a message with details of the problem it is facing. This is considered the default Python exception handling. You can briefly understand that Python will do the following operations:

– Immediately stop program execution when a Python error occurs

– Create Object of corresponding Class Exception in default

– Report an error with full details about the location and type of error encountered.

In this case, we have identified the error we are encountering. With the [ValueError] error, it appears when we build a function that the data type is correct, but when we set the parameter, it is different than when passed. Therefore, to solve the above error, you can adjust the command like below:

much_data = np.load('muchdata-50-50-20.npy', allow_pickle=True)

Option 2: Set up numpy 1.16.2 version

The simplest solution for you to resolve the error “ValueError: Object arrays cannot be loaded when allow_pickle=False” is to set up numpy 1.16.2 version.

The version of Numpy > 1.16.2, so all pickle to default to False.

Let’s run the following command to set up numpy 1.16.2 version.

pip install --force-reinstall numpy==1.16.2

Option 3: Set allow_pickle=True

Exceting two options mentioned above, there is another option for you to handle with your problem. When you use np.load, let’s set allow_pickle=True as the example below:

obj = np.load(file, allow_pickle=True)

Your error will be fixed after you do that.

The options mentioned above are very simple, right? However, their efficiencies are very enormous. They will make your error disappear and your project will work well without any errors. So, what are you waiting without applying them for your problem to get the desired results?

Conclusion

In conclusion, we have provided more information about common errors in Python and how to handle the “ValueError: Object arrays cannot be loaded when allow_pickle=False” error. Hope it helps you to solve your problem!

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