[Python] How can I optimize Python code for faster execution?

I'm working on a data processing application in Python where performance is becoming a bottleneck. As my dataset grows larger, the execution time increases significantly, and I need to find ways to optimize my code.

I've identified a particular function that processes large lists and dictionaries which seems to be the main performance culprit. Here's a simplified version of the code:

def process_data(data_list):
results = []
for item in data_list:
# Extract values from item (dictionary)
value1 = item.get('field1', 0)
value2 = item.get('field2', 0)

# Perform calculations
intermediate_result = 0
for i in range(value1):
intermediate_result += i * value2

# Additional processing
transformed_value = {}
for key, val in item.items():
if isinstance(val, (int, float)):
transformed_value[key] = val * 1.5
else:
transformed_value[key] = val

results.append({
'original': item,
'calculated': intermediate_result,
'transformed': transformed_value
})

return results

# Usage with a large dataset
large_data = [{'field1': i, 'field2': i*2, 'text': f'item_{i}'} for i in range(10000)]
result = process_data(large_data) # This takes a long time to execute


I've already tried:

1. Using PyPy instead of CPython
2. Basic code optimizations like moving operations outside of loops where possible

I'm looking for:

1. General strategies to optimize Python code
2. Specific improvements for this type of data processing
3. Alternative approaches or libraries that might help
4. Any profiling tools that would help identify bottlenecks more precisely

Any insights or advice would be greatly appreciated!

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