Python--Numpy简单了解

• Numpy 高效的运算工具
• Numpy的优势
• ndarray属性
• 基本操作
  • ndarray.方法()
  • numpy.函数名()
• ndarray运算
  • 逻辑运算
  • 统计运算
  • 数组间运算
• 合并、分割、IO操作、数据处理

1. Numpy优势

1.1 Numpy介绍 - 数值计算库

• num - numerical 数值化的
• py - python
• ndarray
  • n - 任意个
  • d - dimension 维度
  • array - 数组

1.2 ndarray介绍

import numpy as np
score = np.array([[80, 89, 86, 67, 79],
[78, 97, 89, 67, 81],
[90, 94, 78, 67, 74],
[91, 91, 90, 67, 69],
[76, 87, 75, 67, 86],
[70, 79, 84, 67, 84],
[94, 92, 93, 67, 64],
[86, 85, 83, 67, 80]])

1.3 ndarray与Python原生list运算效率对比

import random
import time
# 生成一个大数组
python_list = []
 
for i in range(100000000):
    python_list.append(random.random())
ndarray_list = np.array(python_list)
 
# 原生pythonlist求和
t1 = time.time()
a = sum(python_list)
t2 = time.time()
d1 = t2 - t1
 
# ndarray求和
t3 = time.time()
b = np.sum(ndarray_list)
t4 = time.time()
d2 = t4 - t3

d1= 0.7309620380401611
d2= 0.12980318069458008

1.4 ndarray的优势

1. 存储风格
   ndarray - 相同类型 - 通用性不强
   list - 不同类型 - 通用性很强
2. 并行化运算
   ndarray支持向量化运算
3. 底层语言
   C语言，解除了GIL
   

2. 认识N维数组-ndarray属性

2.1 ndarray的属性

• shape
  • ndim ：看看维度
  • size ：看看大小
• dtype
  • itemsize ：一个元素所占大小
• 在创建ndarray的时候，如果没有指定类型
• 默认
  • 整数 int64
  • 浮点数 float64

array([[80, 89, 86, 67, 79],
       [78, 97, 89, 67, 81],
       [90, 94, 78, 67, 74],
       [91, 91, 90, 67, 69],
       [76, 87, 75, 67, 86],
       [70, 79, 84, 67, 84],
       [94, 92, 93, 67, 64],
       [86, 85, 83, 67, 80]])
 
score.shape # (8, 5)
score.ndim  # 2
score.size # 40
score.dtype # dtype('int64')
score.itemsize # 8

2.2 ndarray的形状

a = np.array([[1,2,3],[4,5,6]])
b = np.array([1,2,3,4])
c = np.array([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
 
a # array([[1, 2, 3],
b # array([1, 2, 3, 4])
c # array([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6]]])
a.shape # (2, 3)
b.shape # (4,)
c.shape # (2, 2, 3)

2.3 ndarray的类型

type(score.dtype)
<type 'numpy.dtype'>
 
# 指定类型
# 创建数组的时候指定类型
np.array([1.1, 2.2, 3.3], dtype="float32")

dtype是numpy是numpy.dtype类型，先看看对数组来说都有哪些类型

3. 基本操作

• adarray.方法()
• np.函数名()
  • np.array()

3.1 生成数组的方法

3.1.1 生成0和1

• np.zeros(shape)
• np.ones(shape)

# 1 生成0和1的数组
np.zeros(shape=(3, 4), dtype="float32")
-----------------------------------------
array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]], dtype=float32)

np.ones(shape=[2, 3], dtype=np.int32)
-----------------------------------------
array([[1, 1, 1],
       [1, 1, 1]], dtype=int32)

3.1.2 从现有数组中生成

• np.array() np.copy() 深拷贝
• np.asarray() 浅拷贝

data1 = np.array(score)
data2 = np.asarray(score)
data3 = np.copy(score)
score[3, 1] = 10000

修改source，data2改变，data1，data3不改变

3.1.3 生成固定范围的数组

• np.linspace(0, 10, 100)

  • [0, 10] 等距离 生成个数

• np.arange(a, b, c)

  • range(a, b, c)
    • [a, b) c是步长

np.linspace(0, 10, 5)
# array([ 0. ,  2.5,  5. ,  7.5, 10. ])
np.arange(0, 11, 5)
# array([ 0,  5, 10])

3.1.4 生成随机数组

分布状况 - 直方图

1. 均匀分布
   每组的可能性相等
2. 正态分布
   σ 幅度、波动程度、集中程度、稳定性、离散程度

• 均匀分布uniform
  • low：float类型，此概率的均值(对应着整个分布的中心centre)
  • scale：float类型，此概率分布的标准差(对应于分布的宽度，scale越大越矮胖，越小越瘦高)
  • size：int or tuple of ints 输出的shape，默认位None，只输出一个值

import matplotlib.pyplot as plt
import numpy as np
data1 = np.random.uniform(low=-1, high=1, size=1000000)
array([-0.49795073, -0.28524454,  0.56473937, ...,  0.6141957 ,
        0.4149972 ,  0.89473129])
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)
 
# 2、绘制直方图
plt.hist(data1, 1000)
 
# 3、显示图像
plt.show()

• 正态分布normal
  • low：此概率的均值(对应着整个分布的中心centre)
  • scale：float此概率分布的标准差(对应于分布的宽度，scale越大越矮胖，越小越瘦高)
  • size：int or tuple of ints 输出的shape，默认位None，只输出一个值

# 正态分布
data2 = np.random.normal(loc=1.75, scale=0.1, size=1000000)
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)
 
# 2、绘制直方图
plt.hist(data2, 1000)
 
# 3、显示图像
plt.show()

3.2 数组的索引、切片

stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
# 返回结果
array([[-0.03469926,  1.68760014,  0.05915316,  2.4473136 , -0.61776756, -0.56253866, -1.24738637,  0.48320978,  1.01227938, -1.44509723],[-1.8391253 , -1.10142576,  0.09582268,  1.01589092, -1.20262068, 0.76134643, -0.76782097, -1.11192773,  0.81609586,  0.07659056],[-0.74293074, -0.7836588 ,  1.32639574, -0.52735663,  1.4167841 , 2.10286726, -0.21687665, -0.33073563, -0.46648617,  0.07926839],[ 0.45914676, -0.78330377, -1.10763289,  0.10612596, -0.63375855,-1.88121415,  0.6523779 , -1.27459184, -0.1828502 , -0.76587891],[-0.50413407, -1.35848099, -2.21633535, -1.39300681,  0.13159471, 0.65429138,  0.32207255,  1.41792558,  1.12357799, -0.68599018],[ 0.3627785 ,  1.00279706, -0.68137875, -2.14800075, -2.82895231,-1.69360338,  1.43816168, -2.02116677,  1.30746801,  1.41979011],[-2.93762047,  0.22199761,  0.98788788,  0.37899235,  0.28281886,-1.75837237, -0.09262863, -0.92354076,  1.11467277,  0.76034531],[-0.39473551,  0.28402164, -0.15729195, -0.59342945, -1.0311294 ,-1.07651428,  0.18618331,  1.5780439 ,  1.31285558,  0.10777784]])
 
# 获取第一个股票的前3个交易日的涨跌幅数据
stock_change[0, :3]
# 返回结果
array([-0.03469926,  1.68760014,  0.05915316])

一维、二维、三维的数组如何索引？

# 三维，一维
a1 = np.array([ [[1,2,3],[4,5,6]], [[12,3,34],[5,6,7]]])
# 返回结果
array([[[ 1,  2,  3],
        [ 4,  5,  6]],
 
       [[12,  3, 34],
        [ 5,  6,  7]]])
# 索引、切片
a1.shape # (2, 2, 3)
a1[1, 0, 2] # 34
 
# 修改
a1[1, 0, 2] = 100000
# 返回结果
array([[[     1,      2,      3],
        [     4,      5,      6]],
 
       [[    12,      3, 100000],
        [     5,      6,      7]]])

3.3 形状修改

• ndarray.reshape(shape) 返回新的ndarray，原始数据没有改变
• ndarray.resize(shape) 没有返回值，对原始的ndarray进行了修改
• ndarray.T 转置 行变成列，列变成行

• ndarray.reshape(shape)返回新的ndarray，原始数据没有改变

# 需求：让刚才的股票行、日期列反过来，变成日期行，股票列
stock_change
# 返回结果
array(
[[-0.03469926,  1.68760014,  0.05915316,  2.4473136 , -0.61776756,
-0.56253866, -1.24738637,  0.48320978,  1.01227938, -1.44509723],
[-1.8391253 , -1.10142576,  0.09582268,  1.01589092, -1.20262068,
0.76134643, -0.76782097, -1.11192773,  0.81609586,  0.07659056],
[-0.74293074, -0.7836588 ,  1.32639574, -0.52735663,  1.4167841 ,
2.10286726, -0.21687665, -0.33073563, -0.46648617,  0.07926839],
[ 0.45914676, -0.78330377, -1.10763289,  0.10612596, -0.63375855,
-1.88121415,  0.6523779 , -1.27459184, -0.1828502 , -0.76587891],
[-0.50413407, -1.35848099, -2.21633535, -1.39300681,  0.13159471,
0.65429138,  0.32207255,  1.41792558,  1.12357799, -0.68599018],
[ 0.3627785 ,  1.00279706, -0.68137875, -2.14800075, -2.82895231,
-1.69360338,  1.43816168, -2.02116677,  1.30746801,  1.41979011],
[-2.93762047,  0.22199761,  0.98788788,  0.37899235,  0.28281886,
-1.75837237, -0.09262863, -0.92354076,  1.11467277,  0.76034531],
[-0.39473551,  0.28402164, -0.15729195, -0.59342945, -1.0311294 ,
-1.07651428,  0.18618331,  1.5780439 ,  1.31285558,  0.10777784]])
 
stock_change.reshape((10, 8))
# 返回结果
array(
[[-0.03469926,  1.68760014,  0.05915316,  2.4473136 , -0.61776756,
-0.56253866, -1.24738637,  0.48320978],
[ 1.01227938, -1.44509723, -1.8391253 , -1.10142576,  0.09582268,
1.01589092, -1.20262068,  0.76134643],
[-0.76782097, -1.11192773,  0.81609586,  0.07659056, -0.74293074,
-0.7836588 ,  1.32639574, -0.52735663],
[ 1.4167841 ,  2.10286726, -0.21687665, -0.33073563, -0.46648617,
0.07926839,  0.45914676, -0.78330377],
[-1.10763289,  0.10612596, -0.63375855, -1.88121415,  0.6523779 ,
-1.27459184, -0.1828502 , -0.76587891],
[-0.50413407, -1.35848099, -2.21633535, -1.39300681,  0.13159471,
0.65429138,  0.32207255,  1.41792558],
[ 1.12357799, -0.68599018,  0.3627785 ,  1.00279706, -0.68137875,
-2.14800075, -2.82895231, -1.69360338],
[ 1.43816168, -2.02116677,  1.30746801,  1.41979011, -2.93762047,
0.22199761,  0.98788788,  0.37899235],
[ 0.28281886, -1.75837237, -0.09262863, -0.92354076,  1.11467277,
0.76034531, -0.39473551,  0.28402164],
[-0.15729195, -0.59342945, -1.0311294 , -1.07651428,  0.18618331,
1.5780439 ,  1.31285558,  0.10777784]])

• ndarray.resize(shape)没有返回值，对原始的ndarray进行了修改

stock_change.shape # (8, 10)
stock_change.resize((10, 8))
stock_change.shape # (10, 8)

• ndarray.T转置 行变成列，列变成行

stock_change.T

3.4 类型修改

• ndarray.astype(type)
• ndarray序列化到本地
• ndarray.tostring()

stock_change.astype("int32")
# 返回结果
array([[ 0,  1,  0,  2,  0,  0, -1,  0,  1, -1],
       [-1, -1,  0,  1, -1,  0,  0, -1,  0,  0],
       [ 0,  0,  1,  0,  1,  2,  0,  0,  0,  0],
       [ 0,  0, -1,  0,  0, -1,  0, -1,  0,  0],
       [ 0, -1, -2, -1,  0,  0,  0,  1,  1,  0],
       [ 0,  1,  0, -2, -2, -1,  1, -2,  1,  1],
       [-2,  0,  0,  0,  0, -1,  0,  0,  1,  0],
       [ 0,  0,  0,  0, -1, -1,  0,  1,  1,  0]], dtype=int32)
 
stock_change.tobytes()
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3.5 数组的去重

• set()：只能处理一维
• np.unique()

temp = np.array([[1, 2, 3, 4],[3, 4, 5, 6]])
# 返回结果
array([[1, 2, 3, 4],
       [3, 4, 5, 6]])
np.unique(temp)
# 返回结果
array([1, 2, 3, 4, 5, 6])
 
set(temp.flatten()) # 将多维降维成一维，然后用set去重 只能处理一维
# 返回结果
{1, 2, 3, 4, 5, 6}

4. ndarray运算

4.1 逻辑运算

• 布尔索引
• 通用判断函数
  • np.all(布尔值)
    • 只要有一个False就返回False，只有全是True才返回True
  • np.any()
    • 只要有一个True就返回True，只有全是False才返回False
• np.where（三元运算符）
  • np.where(布尔值, True的位置的值, False的位置的值)

stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
# 返回结果
array([[ 1.46338968, -0.45576704,  0.29667843,  0.16606916,  0.46446682,0.83167611, -1.35770374, -0.65001192,  1.38319911, -0.93415832],[ 0.36775845,  0.24078108,  0.122042  ,  1.19314047,  1.34072589,0.09361683,  1.19030379,  1.4371421 , -0.97829363, -0.11962767],[-1.48252741, -0.69347186,  0.91122464, -0.30606473,  0.41598897,0.79542753, -0.01447862, -1.49943117, -0.23285809,  0.42806777],[ 0.39438905, -1.31770556,  1.7344868 , -1.52812773, -0.47703227,-0.3795497 , -0.88422651,  1.37510973, -0.93622775,  0.49257673],[-0.9822216 , -1.09482936, -0.81834523,  0.57335311,  0.97390091,0.05314952, -0.58316743,  0.19264426,  0.02081861,  0.84445247],[ 0.41739964, -0.26826893, -0.70003442, -0.58593912,  0.86546709,-1.30304864,  0.05254567, -1.73976785, -0.43532247,  0.4760526 ],[-0.21739882,  0.52007085, -0.60160491,  0.57108639,  1.03303301,-0.69172579,  1.04716985, -0.22985706, -0.11125069,  0.87722923],[-0.183266  ,  0.56273065,  0.29357786, -0.19343363, -1.54547303,-0.31977163, -0.00659025,  0.48160678,  0.88443604, -0.48456825]])
--------------------------------------------------
# 逻辑判断, 如果涨跌幅大于0.5就标记为True 否则为False
stock_change > 0.5
# 返回结果
array([[ True, False, False, False, False,  True, False, False,  True,False],[False, False, False,  True,  True, False,  True,  True, False,False],[False, False,  True, False, False,  True, False, False, False,False],[False, False,  True, False, False, False, False,  True, False,False],[False, False, False,  True,  True, False, False, False, False,True],[False, False, False, False,  True, False, False, False, False,False],[False,  True, False,  True,  True, False,  True, False, False,True],[False,  True, False, False, False, False, False, False,  True,False]])
--------------------------------------------------
stock_change[stock_change > 0.5] = 1.1
# 返回结果
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916,  0.46446682,1.1       , -1.35770374, -0.65001192,  1.1       , -0.93415832],[ 0.36775845,  0.24078108,  0.122042  ,  1.1       ,  1.1       ,0.09361683,  1.1       ,  1.1       , -0.97829363, -0.11962767],[-1.48252741, -0.69347186,  1.1       , -0.30606473,  0.41598897,1.1       , -0.01447862, -1.49943117, -0.23285809,  0.42806777],[ 0.39438905, -1.31770556,  1.1       , -1.52812773, -0.47703227,-0.3795497 , -0.88422651,  1.1       , -0.93622775,  0.49257673],[-0.9822216 , -1.09482936, -0.81834523,  1.1       ,  1.1       ,0.05314952, -0.58316743,  0.19264426,  0.02081861,  1.1       ],[ 0.41739964, -0.26826893, -0.70003442, -0.58593912,  1.1       ,-1.30304864,  0.05254567, -1.73976785, -0.43532247,  0.4760526 ],[-0.21739882,  1.1       , -0.60160491,  1.1       ,  1.1       ,-0.69172579,  1.1       , -0.22985706, -0.11125069,  1.1       ],[-0.183266  ,  1.1       ,  0.29357786, -0.19343363, -1.54547303,-0.31977163, -0.00659025,  0.48160678,  1.1       , -0.48456825]])

# 判断stock_change[0:2, 0:5]是否全是上涨的
stock_change[0:2, 0:5] > 0
# 返回结果
array([[ True, False,  True,  True,  True],
       [ True,  True,  True,  True,  True]])
--------------------------------------------------      
np.all(stock_change[0:2, 0:5] > 0)
# 返回结果
False
--------------------------------------------------
# 判断前5只股票这段期间是否有上涨的
np.any(stock_change[:5, :] > 0)
# 返回结果
True

# 判断前四个股票前四天的涨跌幅 大于0的置为1，否则为0
temp = stock_change[:4, :4]
# 返回结果
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ],
       [-1.48252741, -0.69347186,  1.1       , -0.30606473],
       [ 0.39438905, -1.31770556,  1.1       , -1.52812773]])
--------------------------------------------------
np.where(temp > 0, 1, 0)
# 返回结果
array([[1, 0, 1, 1],
       [1, 1, 1, 1],
       [0, 0, 1, 0],
       [1, 0, 1, 0]])
--------------------------------------------------
temp > 0
# 返回结果
array([[ True, False,  True,  True],
       [ True,  True,  True,  True],
       [False, False,  True, False],
       [ True, False,  True, False]])
--------------------------------------------------
np.where([[ True, False,  True,  True],
       [ True,  True,  True,  True],
       [False, False,  True, False],
       [ True, False,  True, False]], 1, 0)
# 返回结果
array([[1, 0, 1, 1],
       [1, 1, 1, 1],
       [0, 0, 1, 0],
       [1, 0, 1, 0]])
--------------------------------------------------
# 判断前四个股票前四天的涨跌幅 大于0.5并且小于1的，换为1，否则为0
# 判断前四个股票前四天的涨跌幅 大于0.5或者小于-0.5的，换为1，否则为0
# (temp > 0.5) and (temp < 1)
np.logical_and(temp > 0.5, temp < 1)
# 返回结果
array([[False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False]])
--------------------------------------------------
np.where([[False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False]], 1, 0)
# 返回结果
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])
--------------------------------------------------
np.where(np.logical_and(temp > 0.5, temp < 1), 1, 0)
# 返回结果
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])
--------------------------------------------------
np.logical_or(temp > 0.5, temp < -0.5)
# 返回结果
array([[ True, False, False, False],
       [False, False, False,  True],
       [ True,  True,  True, False],
       [False,  True,  True,  True]])
--------------------------------------------------
np.where(np.logical_or(temp > 0.5, temp < -0.5), 11, 3)
# 返回结果
array([[11,  3,  3,  3],
       [ 3,  3,  3, 11],
       [11, 11, 11,  3],
       [ 3, 11, 11, 11]])

4.2 统计运算

axis轴的取值并不一定，Numpy中不同的API轴的值不一样，
在这里，axis 0代表行，1代表列

• 统计指标函数
  • min, max, mean, median, var, std
  • np.函数名
  • ndarray.方法名
• 返回最大值、最小值所在位置
  • np.argmax(temp, axis=)
  • np.argmin(temp, axis=)

# 前四只股票前四天的最大涨幅
temp # shape: (4, 4) 0  1
# 返回结果
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ],
       [-1.48252741, -0.69347186,  1.1       , -0.30606473],
       [ 0.39438905, -1.31770556,  1.1       , -1.52812773]])
--------------------------------------------------
temp.max(axis=0)# 按列求最大值
# 返回结果
array([1.1       , 0.24078108, 1.1       , 1.1       ])
--------------------------------------------------
np.max(temp, axis=-1)
# 返回结果
array([1.1, 1.1, 1.1, 1.1])
--------------------------------------------------
np.argmax(temp, axis=-1)
# 返回结果
array([0, 3, 2, 2])

5. 数组间运算

5.1 场景

5.2 数组与数的运算

• +-*/

arr = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr / 10
# 返回结果
array([[0.1, 0.2, 0.3, 0.2, 0.1, 0.4],
       [0.5, 0.6, 0.1, 0.2, 0.3, 0.1]])

5.3 数组与数组的运算

arr1 = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr2 = np.array([[1, 2, 3, 4], [3, 4, 5, 6]])
 
array([[1, 2, 3, 2, 1, 4],
       [5, 6, 1, 2, 3, 1]])

5.4 广播机制

执行broadcast的前提在于，两个ndarray执行的是element-wise的运算，Broadcast机制的功能是为了方便不同形状的ndarray(numpy库的核心数据结构)进行数学运算

• 维度相等
• shape(其中相对应的一个地方为1)
  广播的原则：如果两个数组的后缘维度（trailing dimension，即从末尾开始算起的维度）的轴长度相符，或其中的一方的长度为1，则认为它们是广播兼容的。广播会在缺失和（或）长度为1的维度上进行。

5.5 矩阵运算

1 什么是矩阵
 矩阵matrix 二维数组
 矩阵 & 二维数组
 两种方法存储矩阵
     1）ndarray 二维数组
         矩阵乘法：
             np.matmul
             np.dot
     2）matrix数据结构
2 矩阵乘法运算
 形状
     (m, n) * (n, l) = (m, l)
 运算规则
     A (2, 3) B(3, 2)
     A * B = (2, 2)

# ndarray存储矩阵
data = np.array([[80, 86],
[82, 80],
[85, 78],
[90, 90],
[86, 82],
[82, 90],
[78, 80],
[92, 94]])

# matrix存储矩阵
data_mat = np.mat([[80, 86],
[82, 80],
[85, 78],
[90, 90],
[86, 82],
[82, 90],
[78, 80],
[92, 94]])
type(data_mat)
numpy.matrixlib.defmatrix.matrix

data # (8, 2) * (2, 1) = (8, 1)
np.matmul(data, weights)
array([[84.2],
       [80.6],
       [80.1],
       [90. ],
       [83.2],
       [87.6],
       [79.4],
       [93.4]])
np.dot(data, weights)       
array([[84.2],
       [80.6],
       [80.1],
       [90. ],
       [83.2],
       [87.6],
       [79.4],
       [93.4]])
data_mat * weights_mat
matrix([[84.2],
        [80.6],
        [80.1],
        [90. ],
        [83.2],
        [87.6],
        [79.4],
        [93.4]])
data @ weights
array([[84.2],
       [80.6],
       [80.1],
       [90. ],
       [83.2],
       [87.6],
       [79.4],
       [93.4]])

6. 合并、分割

6.1 合并

• numpy.hstack(tup)
  
• numpy.vstack(tup)
  
• numpy.concatenate((a1, a2 , ...), axis=0)
  

a = stock_change[:2, 0:4]
b = stock_change[4:6, 0:4]
 
a
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ]])
       
a.shape # (2, 4)
 
a.reshape((-1, 2))
array([[ 1.1       , -0.45576704],
       [ 0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108],
       [ 0.122042  ,  1.1       ]])
 
b
array([[-0.9822216 , -1.09482936, -0.81834523,  1.1       ],
       [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
 
np.hstack((a, b))
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916, -0.9822216 ,
        -1.09482936, -0.81834523,  1.1       ],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ,  0.41739964,
        -0.26826893, -0.70003442, -0.58593912]])
 
np.concatenate((a, b), axis=1)
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916, -0.9822216 ,
        -1.09482936, -0.81834523,  1.1       ],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ,  0.41739964,
        -0.26826893, -0.70003442, -0.58593912]])
 
np.vstack((a, b))
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ],
       [-0.9822216 , -1.09482936, -0.81834523,  1.1       ],
       [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
 
np.concatenate((a, b), axis=0)
array([[ 1.1       , -0.45576704,  0.29667843,  0.16606916],
       [ 0.36775845,  0.24078108,  0.122042  ,  1.1       ],
       [-0.9822216 , -1.09482936, -0.81834523,  1.1       ],
       [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])

6.2 分割

7. IO操作与数据处理

7.1 Numpy读取

data = np.genfromtxt("test.csv", delimiter=",")
 
array([[  nan,   nan,   nan,   nan],
       [  1. , 123. ,   1.4,  23. ],
       [  2. , 110. ,   nan,  18. ],

7.2 如何处理缺失值

两种思路：

• 直接删除含有缺失值的样本
• 替换/插补
  • 按列求平均，用平均python教程值进行填补