數(shù)據(jù)類型及數(shù)組創(chuàng)建
通常將 numpy 庫(kù) 縮寫(xiě)為 np
import numpy as np
1断序、常量
numpy.nan 表示空值
* np.nan != np.nan 兩個(gè)numpy.nan是不相等的
numpy.inf 表示正無(wú)窮大
Inf = inf = infty = Infinity = PINF
numpy.pi 表示圓周率
numpy.e 表示自然常數(shù)
2、常見(jiàn)數(shù)據(jù)類型
3范嘱、時(shí)間日期和時(shí)間增量
numpy.datetime64
從字符串創(chuàng)建datetime64數(shù)組時(shí)滔灶，如果單位不統(tǒng)一普碎，一律轉(zhuǎn)化為最小單位
import numpy
a = np.array(['2020-03', '2020-03-08', '2020-03-08 20:00'], dtype='datetime64')
print(a, a.dtype)
# ['2020-03-01T00:00' '2020-03-08T00:00' '2020-03-08T20:00'] datetime64[m]
用arrange可以創(chuàng)建datetime64數(shù)組，用于生成日期范圍录平。
import numpy as np
a = np.arange('2020-08-01', '2020-08-10', dtype=np.datetime64)
print(a)
# ['2020-08-01' '2020-08-02' '2020-08-03' '2020-08-04' '2020-08-05'
# '2020-08-06' '2020-08-07' '2020-08-08' '2020-08-09']
print(a.dtype) # datetime64[D]
a = np.arange('2020-08-01 20:00', '2020-08-10', dtype=np.datetime64)
print(a)
# ['2020-08-01T20:00' '2020-08-01T20:01' '2020-08-01T20:02' ...
# '2020-08-09T23:57' '2020-08-09T23:58' '2020-08-09T23:59']
print(a.dtype) # datetime64[m]
a = np.arange('2020-05', '2020-12', dtype=np.datetime64)
print(a)
# ['2020-05' '2020-06' '2020-07' '2020-08' '2020-09' '2020-10' '2020-11']
print(a.dtype) # datetime64[M]
timedelta64表示兩個(gè)datetime64的差麻车，于兩個(gè)相減運(yùn)算中的datetime64中較小的一個(gè)保持一致缀皱。
numpy.datetime64 與 datetime.datetime 可以相互轉(zhuǎn)換
import numpy as np
import datetime
dt = datetime.datetime(year=2020, month=6, day=1, hour=20, minute=5, second=30)
dt64 = np.datetime64(dt, 's')
print(dt64, dt64.dtype)
# 2020-06-01T20:05:30 datetime64[s]
dt2 = dt64.astype(datetime.datetime)
print(dt2, type(dt2))
# 2020-06-01 20:05:30 <class 'datetime.datetime'>
numpy.busday_offset?
參數(shù)：offset 偏移量 forward backward 向前后取有效工作日
將指定的偏移量應(yīng)用于工作日，單位天（'D'）动猬。計(jì)算下一個(gè)工作日啤斗，如果當(dāng)前日期為非工作日，默認(rèn)報(bào)錯(cuò)枣察≌迹可以指定 forward 或
backward 規(guī)則來(lái)避免報(bào)錯(cuò)。（一個(gè)是向前取第一個(gè)有效的工作日序目，一個(gè)是向后取第一個(gè)有效的工作日）
import numpy as np
# 2020-07-10 星期五
a = np.busday_offset('2020-07-10', offsets=1)
print(a) # 2020-07-13
a = np.busday_offset('2020-07-11', offsets=1)
print(a)
# ValueError: Non-business day date in busday_offset
a = np.busday_offset('2020-07-11', offsets=0, roll='forward')
b = np.busday_offset('2020-07-11', offsets=0, roll='backward')
print(a) # 2020-07-13
print(b) # 2020-07-10
a = np.busday_offset('2020-07-11', offsets=1, roll='forward')
b = np.busday_offset('2020-07-11', offsets=1, roll='backward')
print(a) # 2020-07-14
print(b) # 2020-07-13
import numpy as np
# 2020-07-10 星期五
a = np.is_busday('2020-07-10')
b = np.is_busday('2020-07-11')
print(a) # True
print(b) # False
# 統(tǒng)計(jì)一個(gè)時(shí)間周期內(nèi)的工作天數(shù)?
import numpy as np?
# 2020-07-10 星期五
begindates = np.datetime64('2020-07-10')
enddates = np.datetime64('2020-07-20')
a = np.arange(begindates, enddates, dtype='datetime64')
b = np.count_nonzero(np.is_busday(a))
print(a)
# ['2020-07-10' '2020-07-11' '2020-07-12' '2020-07-13' '2020-07-14'
# '2020-07-15' '2020-07-16' '2020-07-17' '2020-07-18' '2020-07-19']
print(b) # 6
# 自定義周掩碼值臂痕，即指定一周中哪些星期是工作日。
import numpy as np
# 2020-07-10 星期五
a = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 1, 0, 0])
b = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 0, 0, 1])
print(a) # True
print(b) # False
array 與 asarray 的區(qū)別是當(dāng)數(shù)據(jù)源是ndarray 時(shí)猿涨， array() 仍然會(huì) copy 出一個(gè)副本握童，占用新的內(nèi)存，但不改變 dtype 時(shí)叛赚， asarray() 不會(huì)
numy.fromfuction() 構(gòu)建數(shù)組
import numpy as np
def f(x, y):
return 10 * x + y
x = np.fromfunction(f, (5, 4), dtype=int)
print(x)
# [[ 0 1 2 3]
# [10 11 12 13]
# [20 21 22 23]
# [30 31 32 33]
# [40 41 42 43]]
x = np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
print(x)
# [[ True False False]
# [False True False]
# [False False True]]
numpy.zeros()
numpy.zeros_like()
numpy.ones()
numpy.ones_like()
numpy.empty()
numpy.empty_like()
numpy.full()
numpy.full_like()
import numpy as np
x = np.zeros(5)
print(x) # [0. 0. 0. 0. 0.]
x = np.zeros([2, 3])
print(x)
# [[0. 0. 0.]
# [0. 0. 0.]]
x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.zeros_like(x)
print(y)
# [[0 0 0]
# [0 0 0]]
x = np.ones(5)
print(x) # [1. 1. 1. 1. 1.]
x = np.ones([2, 3])
print(x)
# [[1. 1. 1.]
# [1. 1. 1.]]
x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.ones_like(x)
print(y)
# [[1 1 1]
# [1 1 1]]
numpy.eye() 返回對(duì)角線上為1澡绩，其他地方為零的單位數(shù)組
identity() 返回一個(gè)方的單位數(shù)組
numpy.diag() 提取對(duì)角線
import numpy as np
x = np.arange(9).reshape((3, 3))
print(x)
# [[0 1 2]
# [3 4 5]
# [6 7 8]]
print(np.diag(x)) # [0 4 8]
print(np.diag(x, k=1)) # [1 5]
print(np.diag(x, k=-1)) # [3 7]
v = [1, 3, 5, 7]
x = np.diag(v)
print(x)
# [[1 0 0 0]
# [0 3 0 0]
# [0 0 5 0]
# [0 0 0 7]]
利用數(shù)值范圍來(lái)創(chuàng)建ndarray
1. arange() 函數(shù)：返回給定間隔內(nèi)的均勻間隔的值。
2. linspace() 函數(shù)：返回指定間隔內(nèi)的等間隔數(shù)字俺附。
3. logspace() 函數(shù)：返回?cái)?shù)以對(duì)數(shù)刻度均勻分布肥卡。
4. numpy.random.rand() 返回一個(gè)由[0,1)內(nèi)的隨機(jī)數(shù)組成的數(shù)組。
import numpy as np
x = np.arange(5)
print(x) # [0 1 2 3 4]
x = np.arange(3, 7, 2)
print(x) # [3 5]
x = np.linspace(start=0, stop=2, num=9)
print(x)
# [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]
x = np.logspace(0, 1, 5)
print(np.around(x, 2))
# [ 1. 1.78 3.16 5.62 10. ]
import numpy as np
personType = np.dtype({
'names': ['name', 'age', 'weight'],
'formats': ['U30', 'i8', 'f8']})
a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],
dtype=personType)
print(a, type(a))
# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]
# <class 'numpy.ndarray'>
import numpy as np
personType = np.dtype([('name', 'U30'), ('age', 'i8'), ('weight', 'f8')])
a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],
dtype=personType)
print(a, type(a))
# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]
# <class 'numpy.ndarray'>
# 結(jié)構(gòu)數(shù)組的取值方式和一般數(shù)組差不多事镣，可以通過(guò)下標(biāo)取得元素：
print(a[0])
# ('Liming', 24, 63.9)
print(a[-2:])
# [('Mike', 15, 67. ) ('Jan', 34, 45.8)]
