数据分析之Numpy
一、创建ndarray
1、使用np.array()创建
一维数组
1 import numpy as np 2 3 np.array([1,2,3,4,5])
二维数组
1 import numpy as np 2 3 np.array([[1,2,3],['a','b',1.1]])
注意：
- numpy默认ndarray的所有元素的类型是相同的
- 如果传进来的列表中包含不同的类型，则统一为同一类型，优先级：str>float>int
2、使用np的routines函数创建
1、 np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) 等差数列
1 2 3 np.linspace(1,100,num=50) 4 5 array([ 1. , 3.02040816, 5.04081633, 7.06122449, 6 9.08163265, 11.10204082, 13.12244898, 15.14285714, 7 17.16326531, 19.18367347, 21.20408163, 23.2244898 , 8 25.24489796, 27.26530612, 29.28571429, 31.30612245, 9 33.32653061, 35.34693878, 37.36734694, 39.3877551 , 10 41.40816327, 43.42857143, 45.44897959, 47.46938776, 11 49.48979592, 51.51020408, 53.53061224, 55.55102041, 12 57.57142857, 59.59183673, 61.6122449 , 63.63265306, 13 65.65306122, 67.67346939, 69.69387755, 71.71428571, 14 73.73469388, 75.75510204, 77.7755102 , 79.79591837, 15 81.81632653, 83.83673469, 85.85714286, 87.87755102, 16 89.89795918, 91.91836735, 93.93877551, 95.95918367, 17 97.97959184, 100. ])
2、np.arange([start, ]stop, [step, ]dtype=None)
1 np.arange(1,100,2) 2 3 # 执行结果 4 array([ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 5 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 6 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99])
3、np.random.randint(low, high=None, size=None, dtype='l') # (常用)
1 np.random.seed(10) 2 arr = np.random.randint(0,100,size=(5,6))
二、ndarray的属性
4个必记参数： ndim：维度 shape：形状（各维度的长度） size：总长度
np.ndim , np.shape, np.size
三、ndarray的基本操作
1 np.random.seed(10) 2 arr = np.random.randint(0,100,size=(5,6)) 3 4 array([[ 9, 15, 64, 28, 89, 93], 5 [29, 8, 73, 0, 40, 36], 6 [16, 11, 54, 88, 62, 33], 7 [72, 78, 49, 51, 54, 77], 8 [69, 13, 25, 13, 92, 86]])
1、索引
取索引为1、2的两行数据
arr[ [1,2] ]
1 arr[[1,2]] 2 3 array([[29, 8, 73, 0, 40, 36], 4 [16, 11, 54, 88, 62, 33]])
2、切片
获取二维数组前两行
1 arr[0:2] 2 3 array([[ 9, 15, 64, 28, 89, 93], 4 [29, 8, 73, 0, 40, 36]])
获取二维数组前两列
arr[ :,0:2] (逗号前边代表行切片，后面代表列切片)
1 arr[:,0:2] 2 3 array([[ 9, 15], 4 [29, 8], 5 [16, 11], 6 [72, 78], 7 [69, 13]])
#获取二维数组前两行和前两列数据
1 arr[0:2,0:2] 2 3 array([[ 9, 15], 4 [29, 8]])
反转
#将数组的行倒序
1 arr[::-1] 2 3 array([[69, 13, 25, 13, 92, 86], 4 [72, 78, 49, 51, 54, 77], 5 [16, 11, 54, 88, 62, 33], 6 [29, 8, 73, 0, 40, 36], 7 [ 9, 15, 64, 28, 89, 93]])
#列倒序
1 arr[:,::-1] 2 3 array([[93, 89, 28, 64, 15, 9], 4 [36, 40, 0, 73, 8, 29], 5 [33, 62, 88, 54, 11, 16], 6 [77, 54, 51, 49, 78, 72], 7 [86, 92, 13, 25, 13, 69]])
#全部倒序
1 arr[::-1,::-1] 2 3 array([[86, 92, 13, 25, 13, 69], 4 [77, 54, 51, 49, 78, 72], 5 [33, 62, 88, 54, 11, 16], 6 [36, 40, 0, 73, 8, 29], 7 [93, 89, 28, 64, 15, 9]])
三、变形
使用arr.reshape()函数，注意参数是一个tuple！
1.将一维数组变形成多维数组
1 arr_1.reshape((-1,15)) 2 3 array([[ 9, 15, 64, 28, 89, 93, 29, 8, 73, 0, 40, 36, 16, 11, 54], 4 [88, 62, 33, 72, 78, 49, 51, 54, 77, 69, 13, 25, 13, 92, 86]])
2.将多维数组变形成一维数组
arr_1 = arr.reshape((30,))
4、级联
np.concatenate() # 实际操作中级联多为二维数组
（jupyter）
1 arr 2 3 array([[ 9, 15, 64, 28, 89, 93], 4 [29, 8, 73, 0, 40, 36], 5 [16, 11, 54, 88, 62, 33], 6 [72, 78, 49, 51, 54, 77], 7 [69, 13, 25, 13, 92, 86]])
np.concatenate((arr,arr),axis=1)
1 np.concatenate((arr,arr),axis=1) # 按照行级联 2 3 array([[ 9, 15, 64, 28, 89, 93, 9, 15, 64, 28, 89, 93], 4 [29, 8, 73, 0, 40, 36, 29, 8, 73, 0, 40, 36], 5 [16, 11, 54, 88, 62, 33, 16, 11, 54, 88, 62, 33], 6 [72, 78, 49, 51, 54, 77, 72, 78, 49, 51, 54, 77], 7 [69, 13, 25, 13, 92, 86, 69, 13, 25, 13, 92, 86]])
# 将axis参数改为0表示按列级联，要保证对齐
四、ndarray的聚合操作
1. 求和np.sum
1 arr.sum(axis=0) # 按照列求和 2 3 array([195, 125, 265, 180, 337, 325])
2. 最大最小值：np.max/ np.min
同理
3.平均值：np.mean()
五、ndarray的排序
np.sort()与ndarray.sort()都可以，但有区别：
- np.sort()不改变输入
- ndarray.sort()本地处理，不占用空间，但改变输入
1 np.sort(arr,axis=0) 2 3 array([[ 9, 8, 25, 0, 40, 33], 4 [16, 11, 49, 13, 54, 36], 5 [29, 13, 54, 28, 62, 77], 6 [69, 15, 64, 51, 89, 86], 7 [72, 78, 73, 88, 92, 93]])
1 arr.sort(axis=0) 2 3 array([[ 9, 8, 25, 0, 40, 33], 4 [16, 11, 49, 13, 54, 36], 5 [29, 13, 54, 28, 62, 77], 6 [69, 15, 64, 51, 89, 86], 7 [72, 78, 73, 88, 92, 93]])
二、使用matplotlib.pyplot获取一个numpy数组，数据来源于一张图片
import matplotlib.pyplot as plt img_arr = plt.imread('./bobo.jpg') plt.imshow(img_arr) # 这里展示图片
