python高級應用與數(shù)據(jù)分析學習筆記 07

# -*- coding: utf-8 -*-
import numpy as np
"""
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   File Name：     numpy1
   Description :
   Author :       Lenovo
   date：          2018/1/6
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   Change Activity:
                   2018/1/6:
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"""
__author__ = 'Lenovo'

# 查看numpy的版本
print(np.__version__)  #1.13.3

# ndarray創(chuàng)建數(shù)組的8種方式
# 1、array
a = np.array([1,2,3],dtype=np.float)
print(a)  #[ 1.  2.  3.]
print(a.dtype)   #float64
aa = np.array([[1,2,3],[4,5,6]],dtype=np.float)
print(aa)   #[[ 1.  2.  3.]
            #[ 4.  5.  6.]]

# 2、zeros   #數(shù)值是0
b = np.zeros((2,3),dtype=np.int)
print(b)   #[[0 0 0]
            #[0 0 0]]

# 3、ones
c = np.ones((2,3))   #數(shù)值是1
print(c)   #[[ 1.  1.  1.]
            #[ 1.  1.  1.]]

# 4、empty
d = np.empty((2,2))  #數(shù)值是隨機數(shù)
print(d)   #[[  3.13408242e-312   3.13408236e-312]
            #[  3.13408242e-312   3.13408236e-312]]

# 5、arange
e = np.arange(1,10,2)          #效果等同于下面的一行代碼
ee = np.array(range(1,10,2))
print(e)    #[1 3 5 7 9]
print(ee)   #[1 3 5 7 9]

# 6、linspace
f = np.linspace(1,10,5)   #等差數(shù)列
print(f)    #[  1.     3.25   5.5    7.75  10.  ]
ff = np.linspace(1,10,6,endpoint=False)  #endpoint=False 不包括最后一個  默認為True
print(ff)  #[ 1.   2.5  4.   5.5  7.   8.5]
fff = np.linspace(1,10,7)
print(fff)  #[  1.    2.5   4.    5.5   7.    8.5  10. ]

# 7、logspace   等積數(shù)列
h = np.logspace(1,10,5,endpoint=False)
print(h)
# [  1.00000000e+01   6.30957344e+02   3.98107171e+04   2.51188643e+06
#    1.58489319e+08]

hh = np.logspace(1,10,6)
print(hh)
# [  1.00000000e+01   6.30957344e+02   3.98107171e+04   2.51188643e+06
#    1.58489319e+08   1.00000000e+10]

# 8、logspace   等積數(shù)列
g = np.random.random((2,3,4))
print(g)
# [[[ 0.68460976  0.39046828  0.88349152  0.82786386]
#   [ 0.41709914  0.04807389  0.25544836  0.03965502]
#   [ 0.27632234  0.27974446  0.77059336  0.58248951]]
#
#  [[ 0.0792647   0.88046797  0.02701309  0.97778673]
#   [ 0.3007913   0.52267062  0.81059741  0.94046282]
#   [ 0.15352565  0.02459086  0.30466552  0.19771866]]]
gg = np.random.randint(1,10,(2,3,4))
print(gg)
# [[[8 1 3 4]
#   [8 1 1 7]
#   [2 2 7 3]]
#
#  [[4 5 9 7]
#   [1 9 5 4]
#   [8 3 5 1]]]

# 屬性的訪問
# dtype  數(shù)據(jù)類型
# shape   內(nèi)容類型
# size    個數(shù)
# ndim  維度
print('屬性的訪問：',g.dtype,g.shape,g.size,g.ndim)