💡 Problem Formulation: When working with matrices in Python, you may encounter the need to convert a 2D list (a matrix) into a dictionary where each key is an index of the matrix and the value is a list of elements in that row or column. For instance, given a matrix [[1, 2], [3, 4]], the desired output could be a dictionary {0: [1, 3], 1: [2, 4]}, effectively transforming row and column elements into dictionary value lists.
Method 1: Using Dictionary Comprehension with Range
This method involves using a dictionary comprehension with the range function to iterate over the column indices and create a list for each column index that contains the corresponding elements from each row of the matrix.
Here’s an example:
matrix = [[1, 2], [3, 4]]
column_dict = {i: [row[i] for row in matrix] for i in range(len(matrix[0]))}Output: {0: [1, 3], 1: [2, 4]}
In this snippet, the dictionary comprehension iterates over each column index and uses a list comprehension to gather elements from each row that belong to that column index. It’s a concise method but relies on uniform row lengths.
Method 2: Using zip() Function
The zip() function can be employed cleverly to transpose the matrix, effectively turning rows into columns and vice versa, and then using a dictionary comprehension to create the final dictionary.
Here’s an example:
matrix = [[1, 2], [3, 4]] column_dict = dict(enumerate(zip(*matrix)))
Output: {0: (1, 3), 1: (2, 4)}
The code uses zip(*matrix) to transpose the matrix and enumerate to generate keys, then it constructs a dictionary. The result is a dictionary with tuple values instead of lists, which is a small difference but could be significant depending on the use case.
Method 3: Using Iteration and setdefault
Another approach is iterating over the matrix and using the setdefault method of dictionaries to append elements to lists corresponding to each key, handling the initialization of the list if the key does not already exist.
Here’s an example:
matrix = [[1, 2], [3, 4]]
column_dict = {}
for row in matrix:
for i, value in enumerate(row):
column_dict.setdefault(i, []).append(value)Output: {0: [1, 3], 1: [2, 4]}
This snippet carefully constructs the dictionary without assuming the lengths of the rows. It works well for matrices with uneven row lengths, which makes it a versatile choice.
Method 4: Using pandas DataFrame
For those already working within a data analysis context, converting a matrix to a dictionary is conveniently handled using the pandas library’s DataFrame data structure and its built-in to_dict method.
Here’s an example:
import pandas as pd matrix = [[1, 2], [3, 4]] df = pd.DataFrame(matrix).transpose() column_dict = df.to_dict(orient='list')
Output: {0: [1, 3], 1: [2, 4]}
Converting the matrix to a pandas DataFrame and then transposing it simplifies the operation, then to_dict(orient='list') outputs the desired dictionary. This method is powerful but requires pandas, which is a heavy dependency for a simple task.
Bonus One-Liner Method 5: Using a Lambda and map
If you fancy a more Pythonic one-liner, using a lambda function in combination with the map function can make for a succinct way to convert matrix columns into dictionary value lists.
Here’s an example:
matrix = [[1, 2], [3, 4]]
column_dict = {i: list(col) for i, col in enumerate(map(lambda *row: row, *matrix))}Output: {0: [1, 3], 1: [2, 4]}
The lambda function unpacks the rows of the matrix and the map function aggregates each column across the rows, which the dictionary comprehension then turns into a dictionary. This one-liner is elegant but might be harder to decode for novice Python users.
Summary/Discussion
- Method 1: Dictionary Comprehension with Range. Simple and pythonic. Best for matrices with uniform row lengths. Less readable with complex comprehensions.
- Method 2: Using zip() Function. Elegant and concise. Results in tuple values, which might require conversion for some use cases. Assumes uniform lengths.
- Method 3: Using Iteration and setdefault. Flexible and adaptable. Handles uneven row lengths. Might be less efficient and verbose compared to other methods.
- Method 4: Using pandas DataFrame. Easy for data analysts familiar with pandas. Brings unnecessary overhead for simple tasks or when not already using pandas.
- Method 5: Using a Lambda and map. A Pythonic one-liner. May be obscure for less experienced programmers. Assumed knowledge of functional programming concepts.