Question
Answer and Explanation
The TypeError: unhashable type: 'numpy.ndarray' arises when you attempt to use a NumPy array directly as a key in a hashable context, such as with the drop_duplicates() method in Pandas. The drop_duplicates() function relies on hashing to identify and remove duplicate rows, and NumPy arrays, being mutable, are not hashable.
Here's a detailed explanation:
1. Hashability:
- In Python, hashable objects are those that have a hash value that remains consistent throughout their lifetime. This is crucial for them to be used as dictionary keys or elements in sets. Immutable objects like tuples, strings, and numbers are hashable. However, mutable objects, such as lists and NumPy arrays, are not.
2. Pandas drop_duplicates() Behavior:
- The drop_duplicates() method in Pandas is designed to detect and remove duplicate rows in a DataFrame. It works by internally hashing each row (or a subset of columns) to quickly determine if a row has been seen before.
3. Why NumPy arrays are unhashable:
- NumPy arrays are mutable; their content can be changed after they are created. If a NumPy array was hashable, changing its contents could lead to a different hash value, which would cause issues in hash-based structures like sets or Pandas' internal workings. Therefore, they are designed to be unhashable.
4. How the TypeError occurs:
- When your DataFrame or Series contains columns with NumPy arrays, and you call drop_duplicates() on these columns, Pandas attempts to hash these arrays, leading to the TypeError.
5. Solutions to resolve the error:
- Convert NumPy arrays to tuples: If your numpy array is 1-dimensional, converting them to tuples will allow them to be hashable.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [np.array([1, 2]), np.array([1, 2]), np.array([3, 4])]})
df['A'] = df['A'].apply(tuple)
df_no_duplicates = df.drop_duplicates(subset=['A'])
print(df_no_duplicates)
- Convert NumPy arrays to strings: You can convert the NumPy arrays to strings. However, using this solution, you may lose some of the information about the original data if you don't format the string in a way that you can reverse the operation to retrieve the original array.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [np.array([1, 2]), np.array([1, 2]), np.array([3, 4])]})
df['A'] = df['A'].astype(str)
df_no_duplicates = df.drop_duplicates(subset=['A'])
print(df_no_duplicates)
- Drop Duplicates based on another column: Often, we don't have to drop duplicates based on the NumPy arrays, but based on another column.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [np.array([1, 2]), np.array([1, 2]), np.array([3, 4])], 'B':[1,1,2]})
df_no_duplicates = df.drop_duplicates(subset=['B'])
print(df_no_duplicates)
In summary, the TypeError: unhashable type: 'numpy.ndarray' occurs because Pandas drop_duplicates() uses hashing internally, and NumPy arrays are mutable, hence unhashable. Resolving this requires converting them into hashable representations, such as tuples or strings, or dropping the duplicates based on another column, before invoking the drop_duplicates() method.