numpy基础知识

- 日理万妓 2023-05-31 10:17 6阅读 0赞

首先要导入numpy的包

- 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]])

- type(score)

### 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
    d2

### ndarray的属性 ###

- score.shape # (8, 5)
     - score.ndim
     - score.size
     - score.dtype 
     - score.itemsize

### 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.shape

### ndarray的类型 ###

a.type

### 生成数组的方法 ###

1 生成0和1的数组  np.zeros(shape=(3, 4), dtype="float32")
    2.np.ones(shape=[2, 3], dtype=np.int32)

### 从现有数组生成 ###

1.np.array()
    2. data1 = np.array(score)

### 生成固定范围的数组 ###

np.linspace(0, 10, 5)
    np.arange(0, 11, 5)

### 随机生成8只股票2周的交易日涨幅数据 ###

stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))

### 形状修改 ###

stock_change.reshape((10, 8))
    stock_change.T

### 类型修改 ###

stock_change.astype("int32")

### 数组的去重 ###

temp = np.array([[1, 2, 3, 4],[3, 4, 5, 6]])
    np.unique(temp)np.

### 逻辑运算 ###

stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
    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
    np.all(stock_change[0:2, 0:5] > 0)
    #判断前5只股票这段期间是否有上涨的
    np.any(stock_change[:5, :] > 0)

### np.where（三元运算符） ###

#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]
    np.where(temp > 0, 1, 0)
    np.where(np.logical_and(temp > 0.5, temp < 1), 1, 0)
    np.logical_or(temp > 0.5, temp < -0.5)
    np.where(np.logical_or(temp > 0.5, temp < -0.5), 11, 3)

### 股票涨跌幅统计运算 ###

temp.max(axis=0)
    np.max(temp, axis=-1)
    np.argmax(temp, axis=-1)

### 数组与数的运算 ###

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

### 数组与数组的运算 ###

arr = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
    arr / 10

### 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], [3]])
    arr1 + arr2
    arr1 * arr2
    arr1 / arr2

### 矩阵运算 ###

### 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]])
    weights = np.array([[0.3], [0.7]])
    np.matmul(data, weights)

### 合并 ###

a = stock_change[:2, 0:4]
    b = stock_change[4:6, 0:4]
    a.shape
    a.reshape((-1, 2))
    np.hstack((a, b))
    np.concatenate((a, b), axis=1)
    np.vstack((a, b))
    np.concatenate((a, b), axis=0)

### Numpy读取 ###

data = np.genfromtxt("test.csv", delimiter=",")