qwiklabs assessment performance tuning in python scripts

performance tuning in python scripts: a qwiklabs assessment guide

performance tuning in python scripts involves optimizing code to improve execution speed and reduce resource consumption. below is a guide that covers essential concepts and practical methods for performance tuning, complete with code examples.

key concepts

1. **profiling**: measure where time is being spent in your code.
2. **data structures**: choose the right data structures for efficiency.
3. **algorithm optimization**: use the most efficient algorithms for your task.
4. **built-in functions**: utilize python’s built-in functions and libraries.
5. **concurrency**: leverage multi-threading or async processing for i/o-bound tasks.

step-by-step tutorial

step 1: profiling your code

before optimizing, identify bottlenecks using profiling tools like `cprofile`.

```python
import cprofile

def my_function():
total = 0
for i in range(1000000):
total += i
return total

```

step 2: choosing the right data structures

using the appropriate data structure can significantly enhance performance. for example, using a set for membership tests is faster than using a list.

```python
inefficient membership test
my_list = [i for i in range(1000000)]
if 999999 in my_list:
print("found in list")

efficient membership test with a set
my_set = set(my_list)
if 999999 in my_set:
print("found in set")
```

step 3: algorithm optimization

consider the algorithm you are using. for example, if you're sorting a large list, python's built-in sort is highly optimized.

```python
using built-in sort
my_list = [5, 3, 2, 4, 1]
print(my_list)
```

step 4: utilizing built-in functions

using built-in functions can be faster than writing your own loops.

```python
using sum() instead of a loop
numbers = range(1000000)
total = sum(numbers) faster than manually summing
print(total)
```

step 5: implementing concurre ...

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