The provided code can be optimized to improve its performance. Here are a few suggestions to make it faster:

Use defaultdict from the collections module: Instead of using a regular dictionary, you can use defaultdict which is more efficient for this scenario. It eliminates the need for checking if a key exists before performing an operation. When a key is accessed for the first time, it is automatically initialized with a default value, which in this case can be a tuple (0, 0.0). This change avoids multiple dictionary lookups and reduces the overall time complexity.

Use a more efficient method to read the file: Reading a large file line by line using a for loop can be slow. You can use the readlines() method to read all the lines at once and process them. This change minimizes the number of I/O operations and improves performance.

ChatGPT :

Batch pop operations: Instead of popping each key individually from the dictionary, you can accumulate all the keys to be removed in a set and then perform a batch removal operation after processing the file. This approach reduces the number of dictionary modifications and speeds up the code.

Here's an optimized version of your code that incorporates these suggestions:

from collections import defaultdict

d = defaultdict(lambda: (0, 0.0))

keys_to_remove = set()

with open('d.txt', 'r') as f:

lines = f.readlines()

for line in lines:

v = line.split(" ")

b, i, o = int(v[0]), int(v[1]), int(v[2])

for x in range(4 + i, 4 + i + o):

d[str(v[x + o])] = (b, float(v[x]))

for x in range(4, 4 + i):

keys_to_remove.add(str(v[x]))

for key in keys_to_remove:

d.pop(key, None)

print(dict(d))

These optimizations should help improve the performance of your code when processing a large dataset. However, note that working with such massive amounts of data may still pose challenges due to memory constraints or disk I/O limitations.