Numpy是Python做数据分析必须掌握的基础库之一,非常适合刚学习完Numpy基础的同学,完成以下习题可以帮助你更好的掌握这个基础库。
Python版本:Python 3.6.2
Numpy版本:Numpy 1.13.1
(提示: np.tile)
Z = np.tile(np.array([[1, 0], [0, 1]]), (4, 4))
print (Z)
(提示: (x – min) / (max – min))
Z = np.random.random((5, 5))
Zmax, Zmin = Z.max(), Z.min()
Z = (Z-Zmin)/(Zmax-Zmin)
print (Z)
(提示: np.dtype)
color = np.dtype([("r", np.ubyte, 1),
("g", np.ubyte, 1),
("b", np.ubyte, 1),
("a", np.ubyte, 1)])
c = np.array((255, 255, 255, 1), dtype=color)
print (c)
Out[80]:
array((255, 255, 255, 1),
dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1')])
(提示: np.dot | @)
Z = np.dot(np.zeros((5, 3)), np.zeros((3, 2)))
# 或者
Z = np.zeros((5, 3))@ np.zeros((3, 2))
print (Z)
(提示: >, <=)
Z = np.arange(11)
Z[(3 <= Z) & (Z < 8)] *= -1
print (Z)
(提示: np.sum)
# Author: Jake VanderPlas # 结果
print(sum(range(5),-1)) 9
from numpy import *
print(sum(range(5),-1)) 10 #numpy.sum(a, axis=None)
Z**Z True
2 << Z >> 2 False
Z <- Z True
1j*Z True #复数
Z/1/1 True
Z<Z>Z False
np.array(0) / np.array(0) nan
np.array(0) // np.array(0) 0
np.array([np.nan]).astype(int).astype(float) -2.14748365e+09
(提示: np.uniform, np.copysign, np.ceil, np.abs)
# Author: Charles R Harris
Z = np.random.uniform(-10,+10,10)
print (np.copysign(np.ceil(np.abs(Z)), Z))
(提示: np.intersect1d)
Z1 = np.random.randint(0, 10, 10)
Z2 = np.random.randint(0, 10, 10)
print (np.intersect1d(Z1, Z2))
(提示: np.seterr, np.errstate)
# Suicide mode on
defaults = np.seterr(all="ignore")
Z = np.ones(1) / 0
# Back to sanity
_ = np.seterr(**defaults)
# 另一个等价的方式, 使用上下文管理器(context manager)
with np.errstate(divide='ignore'):
Z = np.ones(1) / 0
(提示: 虚数)
np.sqrt(-1) == np.emath.sqrt(-1) Faslse
(提示: np.datetime64, np.timedelta64)
yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')
today = np.datetime64('today', 'D')
tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')
(提示: np.arange(dtype=datetime64[’D’]))
Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')
print (Z)
(提示: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=))
A = np.ones(3) * 1
B = np.ones(3) * 1
C = np.ones(3) * 1
np.add(A, B, out=B)
np.divide(A, 2, out=A)
np.negative(A, out=A)
np.multiply(A, B, out=A)
(提示: %, np.floor, np.ceil, astype, np.trunc)
Z = np.random.uniform(0, 10, 10)
print (Z - Z % 1)
print (np.floor(Z))
print (np.cell(Z)-1)
print (Z.astype(int))
print (np.trunc(Z))
(提示: np.arange)
Z = np.zeros((5, 5))
Z += np.arange(5)
print (Z)
(提示: np.fromiter)
def generate():
for x in range(10):
yield x
Z = np.fromiter(generate(), dtype=float, count=-1)
print (Z)
(提示: np.linspace)
Z = np.linspace(0, 1, 12, endpoint=True)[1: -1]
print (Z)
(提示: sort)
Z = np.random.random(10)
Z.sort()
print (Z)
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。