8.6. Case Study: Unique Keys¶
8.6.1. Prepare¶
Setup code used for all examples:
>>> DATA = [
... {'Sepal length': 5.1, 'Sepal width': 3.5, 'Species': 'setosa'},
... {'Petal length': 4.1, 'Petal width': 1.3, 'Species': 'versicolor'},
... {'Sepal length': 6.3, 'Petal width': 1.8, 'Species': 'virginica'},
... {'Sepal length': 5.0, 'Petal width': 0.2, 'Species': 'setosa'},
... {'Sepal width': 2.8, 'Petal length': 4.1, 'Species': 'versicolor'},
... {'Sepal width': 2.9, 'Petal width': 1.8, 'Species': 'virginica'}]
8.6.2. List Append If¶
Append if object not in the list:
>>> #%%timeit -r 1000 -n 10_000
>>> result = []
>>> for row in DATA:
... for key in row.keys():
... if key not in result:
... result.append(key)
2.16 µs ± 26.5 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.3. List Append¶
Append to list and deduplicate at the end:
>>> #%%timeit -r 1000 -n 10_000
>>> result = []
>>> for row in DATA:
... for key in row.keys():
... result.append(key)
>>> result = set(result)
2.5 µs ± 32.9 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.4. Set Add¶
>>> ##%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... for key in row.keys():
... result.add(key)
2.12 µs ± 32.4 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.5. Set Update¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... result.update(row.keys())
1.57 µs ± 26.7 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.6. Set Comprehension¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set(key
... for record in DATA
... for key in record.keys())
2.06 µs ± 79.7 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.7. Set Comprehension Add¶
Add to Set Comprehension.
Code appends generator object not values, this is why it is so fast!:
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> result.add(key
... for record in DATA
... for key in record.keys())
447 ns ± 9.52 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.8. Set Comprehension Update¶
Update Set Comprehension:
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> result.update(tuple(x.keys()) for x in DATA)
2.06 µs ± 45.9 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.9. Set Comprehension Update¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... result.update(row)
8.6.10. Set Comprehension Update Tuple¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... result.update(tuple(row))
2.09 µs ± 16.1 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.11. Set Comprehension Update List¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... result.update(list(row))
2.33 µs ± 30.2 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)
8.6.12. Set Comprehension Update Set¶
>>> #%%timeit -r 1000 -n 10_000
>>> result = set()
>>> for row in DATA:
... result.update(set(row))
1.71 µs ± 54 ns per loop (mean ± std. dev. of 1000 runs, 10000 loops each)