Die Vorteile von NumPy-Arrays
Effizienten Python-Code schreiben
Logan Thomas
Scientific Software Technical Trainer, Enthought
NumPy-Array-Übersicht
- Alternative zur Python Listen
nums_list = list(range(5))
[0, 1, 2, 3, 4]
import numpy as np
nums_np = np.array(range(5))
array([0, 1, 2, 3, 4])
# NumPy array homogeneity
nums_np_ints = np.array([1, 2, 3])
array([1, 2, 3])
nums_np_ints.dtype
dtype('int64')
nums_np_floats = np.array([1, 2.5, 3])
array([1. , 2.5, 3. ])
nums_np_floats.dtype
dtype('float64')
NumPy-Array-Broadcasting
- Python-Listen unterstützen kein Broadcasting.
nums = [-2, -1, 0, 1, 2]
nums ** 2
TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
- Listenansatz
# For loop (inefficient option)
sqrd_nums = []
for num in nums:
sqrd_nums.append(num ** 2)
print(sqrd_nums)
[4, 1, 0, 1, 4]
# List comprehension (better option but not best)
sqrd_nums = [num ** 2 for num in nums]
print(sqrd_nums)
[4, 1, 0, 1, 4]
NumPy-Array-Broadcasting
- NumPy-Array-Broadcasting kommt zur Rettung!
nums_np = np.array([-2, -1, 0, 1, 2])
nums_np ** 2
array([4, 1, 0, 1, 4])
Einfache 1-D-Indizierung (Listen)
nums = [-2, -1, 0, 1, 2]
nums[2]
0
nums[-1]
2
nums[1:4]
[-1, 0, 1]
Einfache 1-D-Indizierung (Arrays)
nums_np = np.array(nums)
nums_np[2]
0
nums_np[-1]
2
nums_np[1:4]
array([-1, 0, 1])
# 2-D list
nums2 = [ [1, 2, 3],
[4, 5, 6] ]
- Einfache 2D-Indizierung (Listen)
nums2[0][1]
2
[row[0] for row in nums2]
[1, 4]
# 2-D array
nums2_np = np.array(nums2)
- Einfache 2D-Indizierung (Arrays)
nums2_np[0,1]
2
nums2_np[:,0]
array([1, 4])
NumPy-Array-Boolesche Indizierung
nums = [-2, -1, 0, 1, 2]
nums_np = np.array(nums)
- Boolesche Indizierung
nums_np > 0
array([False, False, False, True, True])
nums_np[nums_np > 0]
array([1, 2])
- Keine boolesche Indizierung für Listen
# For loop (inefficient option)
pos = []
for num in nums:
if num > 0:
pos.append(num)
print(pos)
[1, 2]
# List comprehension (better option but not best)
pos = [num for num in nums if num > 0]
print(pos)
[1, 2]
Lass uns mit den leistungsstarken NumPy-Arrays üben!
Effizienten Python-Code schreiben
