数据科学
Part II
1.一维数组
（1） 定义和基本操作
- part 1
import numpy as np
names = np.array(['王伟', '小小', '祝福语', '方起问', '刘少胡'])
namesarray(['王伟', '小小', '祝福语', '方起问', '刘少胡'], dtype='- part 2
print(names.ndim) #数据维度
print(names.size) #数据元素个数
print(names.dtype) #数组元素类型1
5
（2）选取元素操作
- part 3
print(names[2], names[-1])祝福语 刘少胡（3）截取操作
- part 4
names[[1, 2, 4]] #抽取元素array(['小小', '祝福语', '刘少胡'], dtype='- part 5
names[1:4] #截断数组array(['小小', '祝福语', '方起问'], dtype='- part 6
names[1:4:2] #设定步长array(['小小', '方起问'], dtype='（4）条件选取操作
- part 7
names[ (names == '王伟') | (names == '小小') ] #设定筛选条件, 不知道为什么不能用or???array(['王伟', '小小'], dtype='- part 8
mask = (names == '王伟') | (names == '小小')
print(mask)
print(names[mask])[ True True False False False]
['王伟' '小小']2.二维数组的操作
（1）基本操作
- part 9
scores = np.array([[70, 85, 77, 90, 82, 84, 89],\
[60, 64, 80, 75, 80, 92, 90],\
[90, 93, 88, 87, 86, 90, 91],\
[80, 82, 91, 88, 83, 86, 80],\
[88, 72, 78, 90, 91, 73, 80]])
print(scores.ndim, scores.size, scores.shape, scores.dtype)
# 操作：数组维度 元素总数 行数列数 元素类型2 35 (5, 7) int32（2）二维数组切片
切片基本知识：arr[row , column]，先行号取再列号取，空运用‘：’占位，可以只写行截取条件
- part 10
scores[1, 0] # 单元素选定60- part 11
scores[[1, 3], [0, 1]] # 先行索引值，再列索引值array([60, 82])- part 12
scores[[1, 3]] # 行选取array([[60, 64, 80, 75, 80, 92, 90],
[80, 82, 91, 88, 83, 86, 80]])- part 13
scores[:, [3, 5]] # 列选取array([[90, 84],
[75, 92],
[87, 90],
[88, 86],
[90, 73]])- part 14
scores[[0, 3], 1:4] # 行选取 + 列截取array([[85, 77, 90],
[82, 91, 88]])- part 15
scores[[1, 3]] [:, [0, 1]] # 某些行的某些列array([[60, 64],
[80, 82]])（3）条件筛选
原理与以上相同，先行后列
- part 16
scores[mask, :]array([[70, 85, 77, 90, 82, 84, 89],
[60, 64, 80, 75, 80, 92, 90]])- part 17
scores[ (names == '王伟') | (names == '小小'), :]array([[70, 85, 77, 90, 82, 84, 89],
[60, 64, 80, 75, 80, 92, 90]])- part 18
# 普通数组不支持条件判断
subjects = ['Chinese', 'Poem', 'Math', 'PS', 'JAVA', 'CAD', 'RSMX']
scores[ (names == '王伟') | (names == '小小') ] [: , (subjects == 'Poem') | (subjects == 'CAD')]array([], shape=(2, 0, 7), dtype=int32)- part 19
subjects = np.array(['Chinese', 'Poem', 'Math', 'PS', 'JAVA', 'CAD', 'RSMX'])
scores[ (names == '王伟') | (names == '小小') ] [: , (subjects == 'Poem') | (subjects == 'CAD')]array([[85, 84],
[64, 92]])（4）创建方法
- part 20
np.arange(0, 11) # 始末array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])- part 21
np.arange(3, 11, 2) # 步长array([3, 5, 7, 9])- part 22
np.arange(0, 15).reshape(3, 5) # 行数 列数array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])- part 23
np.zeros((3, 4))array([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])- part 24
np.ones((4, 3))array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])- part 25
# 玩一玩
subjects > np.array([(2 if one<0 else 'Math') for one in np.ones(subjects.shape)])array([False, True, False, True, False, False, True])3.多维数组运算
（1）广播机制运算
广播机制：会将标量，低维数组转化成高维数组在进行与高维数组的运算
- part 26
# 与标量相加
scores + 1array([[71, 86, 78, 91, 83, 85, 90],
[61, 65, 81, 76, 81, 93, 91],
[91, 94, 89, 88, 87, 91, 92],
[81, 83, 92, 89, 84, 87, 81],
[89, 73, 79, 91, 92, 74, 81]])- part 27
bonus = np.array([-10, -9, -8, -7, -99, 88, 7])
scores + bonusarray([[ 60, 76, 69, 83, -17, 172, 96],
[ 50, 55, 72, 68, -19, 180, 97],
[ 80, 84, 80, 80, -13, 178, 98],
[ 70, 73, 83, 81, -16, 174, 87],
[ 78, 63, 70, 83, -8, 161, 87]])（2）元素运算
- part 28
scores[ names == '小小', subjects == 'Poem'] += 1000
scoresarray([[ 70, 85, 77, 90, 82, 84, 89],
[ 60, 1064, 80, 75, 80, 92, 90],
[ 90, 93, 88, 87, 86, 90, 91],
[ 80, 82, 91, 88, 83, 86, 80],
[ 88, 72, 78, 90, 91, 73, 80]])（3）函数与矩阵运算
- part 29
np.floor(scores/10) # 计算各个元素的floor值，即小于或等于该值的最大整数array([[ 7., 8., 7., 9., 8., 8., 8.],
[ 6., 106., 8., 7., 8., 9., 9.],
[ 9., 9., 8., 8., 8., 9., 9.],
[ 8., 8., 9., 8., 8., 8., 8.],
[ 8., 7., 7., 9., 9., 7., 8.]])- part 30
np.subtract(scores, 3) # 等价于减号array([[ 67, 82, 74, 87, 79, 81, 86],
[ 57, 1061, 77, 72, 77, 89, 87],
[ 87, 90, 85, 84, 83, 87, 88],
[ 77, 79, 88, 85, 80, 83, 77],
[ 85, 69, 75, 87, 88, 70, 77]])- part 31
scores.sum(axis = 0) # 按特定方式求和
# axis = 0 按列求和
# axis = 1 按行求和array([ 388, 1396, 414, 430, 422, 425, 430])- part 32
scores.sum() # 直接求所有元素的和3905- part 33
scores[names == '小小'].mean() # 求平均值220.14285714285714- part 34
# 查询最高成绩， 其中argmax函数寻找最大数的下标
names[scores[:, subjects == 'Math'].argmax() ]'方起问'（4）随机数组
- part 35
np.random.randint(0, 6, 10) # 生成10个数， 取值范围0~5array([1, 4, 4, 3, 0, 3, 2, 0, 5, 1])- part 36
np.random.randint(0, 2, size = (5, 6)) # 随机数， 取值0，1array([[1, 0, 1, 0, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 1],
[1, 0, 1, 1, 1, 1]])- part 37
np.random.normal(0, 1, size = (4, 5)) # 服从正态分布， 均值为0， 方差为1array([[-0.55539109, -1.06996357, -0.48183368, -0.4674807 , -0.20133865],
[ 0.60674675, 0.90978042, -0.06976653, -1.53317462, 1.80177161],
[ 0.76044985, 2.47150792, 0.04590991, 1.24692756, 0.72226678],
[ 1.71904744, -1.25374697, 0.95672962, -1.06480299, -0.41317608]])案例：随机游走
- part 38
steps = 10
rndwlk = np.random.randint(0, 2, size = (2, steps))
rndwlk # 随机二元方向组array([[0, 0, 0, 0, 1, 1, 0, 1, 0, 1],
[1, 0, 0, 1, 1, 1, 1, 0, 1, 1]])- part 39
rndwlk = np.where( rndwlk>0, 1, -1) # 相当于？:
rndwlkarray([[-1, -1, -1, -1, 1, 1, -1, 1, -1, 1],
[ 1, -1, -1, 1, 1, 1, 1, -1, 1, 1]])计算游走位置
- part 40
position = rndwlk.cumsum(axis = 1) # cumsum按行累加求和
positionarray([[-1, -2, -3, -4, -3, -2, -3, -2, -3, -2],
[ 1, 0, -1, 0, 1, 2, 3, 2, 3, 4]], dtype=int32)计算距离
- part 41
dists = np.sqrt(position[0]**2 + position[1]**2) # 计算平方根
distsarray([1.41421356, 2. , 3.16227766, 4. , 3.16227766,
2.82842712, 4.24264069, 2.82842712, 4.24264069, 4.47213595])- part 42
np.set_printoptions(precision = 4) # 保留4位小数
distsarray([1.4142, 2. , 3.1623, 4. , 3.1623, 2.8284, 4.2426, 2.8284,
4.2426, 4.4721])- part 43
dists.max()4.47213595499958- part 44
dists.min()1.4142135623730951- part 45
dists.mean()3.2353040461440385- part 46
# 统计物体游走过程中离原点大于平均距离的次数
(dists>dists.mean()).sum()4绘图展示游走轨迹
- part 47
import matplotlib .pyplot as plt
x = position[0]
y = position[1]
plt.plot(x, y, c='g', marker='*') # 画折线图
plt.scatter(x[0], y[0], c='r', marker='o') # 画起始点
plt.text(x[0]+.1, y[0]-.1, 'origin') # 标记起始点，为了偏离一点，防止遮住
plt.scatter(x[-1], y[-1], c='r', marker='o')# 画终点
plt.text(x[-1]+.1, y[-1]-.1, 'stop') # 标记终点
plt.show() # 显示图