To fix the AttributeError: Can’t Get Attribute ‘new_block’ in Pandas, we need to understand what is causing this issue. This usually occurs when there’s a mismatch in the versions of the pickle file and the environment trying to load it. Specifically, it often happens when a Pandas DataFrame object has been pickled with one version of Pandas and then unpickled with another version where internal classes such as ‘BlockManager’ have changed.
Below we will explore steps to troubleshoot and fix this issue:
1. Verify the Pandas Version
First, you should check the versions of Pandas used to pickle and unpickle the DataFrame. To do this, you can use the following code snippet:
import pandas as pd
print(pd.__version__)
Ensure that the version of Pandas in both environments (the one where the DataFrame was pickled and the one where it is being unpickled) is the same. You can install the particular version of Pandas using:
pip install pandas==<version>
2. Updating the ‘new_block’ Attribute
If updating the Pandas version does not resolve the issue, you can manually map the missing ‘new_block’ attribute. Here’s a detailed example:
import pandas as pd
import pickle
# A workaround to map the 'new_block'
def map_new_block(obj):
from pandas.core.internals.blocks import new_block
obj['new_block'] = new_block
return obj
# Load the pickle file
with open('your_pickle_file.pkl', 'rb') as f:
data = pickle.load(f, fix_imports=True, encoding="latin1", errors="strict",
buffers=None, strict=False, object_hook=map_new_block)
# Assuming the pickle contains a DataFrame
df = pd.DataFrame(data)
print(df)
3. Creating a Custom Unpickler
Another advanced solution would be to create a custom unpickler which handles the missing ‘new_block’ attribute. Below is an example:
import pandas.core.internals.managers as managers
import pickle
# Define a custom Unpickler
class CustomUnpickler(pickle.Unpickler):
def find_class(self, module, name):
if name == 'new_block':
from pandas.core.internals.blocks import new_block
return new_block
return super().find_class(module, name)
# Use the Custom Unpickler to load the pickle file
with open('your_pickle_file.pkl', 'rb') as f:
data = CustomUnpickler(f).load()
# Assuming the pickle contains a DataFrame
df = pd.DataFrame(data)
print(df)
The CustomUnpickler overrides the behavior for certain missing attributes and maps them to the correct imports in the current version of Pandas.
4. Re-pickling After Migration
If you have access to the original environment where the DataFrame was pickled, another approach is to unpickle and re-pickle the DataFrame in the current environment. Then, you can load it without encountering the ‘new_block’ error:
import pandas as pd
# Load the DataFrame from the original pickle
with open('your_old_pickle_file.pkl', 'rb') as f:
df = pd.read_pickle(f)
# Save the DataFrame in a new pickle file
with open('your_new_pickle_file.pkl', 'wb') as f:
pickle.dump(df, f)
# Now loading the new pickle file in the current environment
with open('your_new_pickle_file.pkl', 'rb') as f:
df_new = pd.read_pickle(f)
print(df_new)
Always keep your environments synchronized whenever possible, and avoid pickling and unpickling across different versions to prevent such issues.