Convert List of Tuples To Multiple Lists in Python

When working with data in Python, it's common to encounter situations where we need to convert a list of tuples into separate lists. For example, if we have a list of tuples where each tuple represents a pair of related data points, we may want to split this into individual lists for easier processing. Let's explore different methods to achieve this.

Using zip() with unpacking

This method uses the zip() function along with the unpacking operator to directly group the tuple elements into separate lists.

# Input list of tuples
li = [(1, 'x'), (2, 'y'), (3, 'z')]

# Unpack and group the elements into separate lists
a, b = zip(*li)

# Convert the zip objects to lists
a, b = list(a), list(b)
print(a)  
print(b) 

[1, 2, 3]
['x', 'y', 'z']

Explanation:

• The asterisk operator unpacks the list of tuples.
• The zip() function groups the elements from the tuples by their positions.
• We convert the grouped elements into lists for further use.

Let's explore some more methods and see how we can convert list of tuples to multiple lists in Python.

Using list comprehension

In this, list comprehension is used to extract specific elements from each tuple into separate lists along with indexing.

# Import numpy
import numpy as np

# Input list of tuples
li = [(1, 'x'), (2, 'y'), (3, 'z')]

# Convert to a numpy array
arr = np.array(li)

# Extract and convert columns into separate lists
a, b = list(arr[:, 0]), list(arr[:, 1])
print(a)  
print(b) 

['1', '2', '3']
['x', 'y', 'z']

Explanation:

• We iterate over the list of tuples and extract the required elements using their index positions.
• Each comprehension generates a separate list.

Using numpy for multidimensional data

This method uses numpy, a library optimized for numerical and array-based operations. It can split the tuples into columns of a numpy array and then convert them to lists.

# Import numpy
import numpy as np

# Input list of tuples
li = [(1, 'x'), (2, 'y'), (3, 'z')]

# Convert to a numpy array
arr = np.array(li)

# Extract and convert columns into separate lists
a, b = list(arr[:, 0]), list(arr[:, 1])
print(a)  
print(b)  

['1', '2', '3']
['x', 'y', 'z']

Explanation:

• We convert the list of tuples into a numpy array for better handling of multidimensional data.
• By slicing the array, we extract the columns, which are then converted back into lists.

Using for loop

For loop can also be used to manually append each element to separate lists.

# Input list of tuples
data = [(1, 'x'), (2, 'y'), (3, 'z')]

# Initialize empty lists
a, b = [], []

# Iterate through the tuples
for t in data:
    a.append(t[0])
    b.append(t[1])

# Print the resulting lists
print(a)  # Output: [1, 2, 3]
print(b)  # Output: ['x', 'y', 'z']

[1, 2, 3]
['x', 'y', 'z']

Explanation:

• Each tuple is iterated over and its elements are appended to the respective lists.
• This method can be a bit slower for large datasets as compared to the other methods.

Using itertools for extended unpacking

The itertools module provides a handy method to handle grouped data. We use starmap() function for a clean unpacking solution.

# Import starmap from itertools
from itertools import starmap

# Input list of tuples
data = [(1, 'x'), (2, 'y'), (3, 'z')]

# Use starmap to unpack and group
a, b = zip(*starmap(lambda x, y: (x, y), data))

# Convert to lists
a, b = list(a), list(b)

print(a)  
print(b)  

[1, 2, 3]
['x', 'y', 'z']

Explanation: The starmap() function allows applying a function to unpacked arguments which makes it flexible and easy for tuple operations.