One article is easy to understand: the usage and difference of shape() and reshape() in Python

shape: the English translation of the shape
information of the number of rows and columns in the matrix is read matrix.
reshape: English translation is remodeling ... change the shape of
the matrix is to change the form of a matrix array arr.

The code runs on Python 3.6 version or Pycharm.

1. The usage of shape

import numpy as np
a = np.array([1,2,3,4,4,3,2,8])  #一维数组
a1 = np.array([[1,2,3,4],[4,3,2,8]])  #二维数组
print(a.shape[0])  #值为8，因为有8个数据
print(a1.shape[0])  #值为2（2行）
print(a1.shape[1])  #值为4（4列）

It can be seen from the above code: when
a one-dimensional array: shape is the number of data read in the array.
In the case of a two-dimensional array: shape[0] reads the number of rows of the matrix, shape[1] reads the number of columns of the matrix.

2. The usage of reshape

import numpy as np
a = np.array([1,2,3,4,4,3,2,8])  #一维数组
print(a.reshape(2,4) )
#输出结果为：[[1 2 3 4]
#		     [4 3 2 8]]
print(a.reshape(3,3))
#ValueError: cannot reshape array of size 8 into shape (3,3)

It can be seen from the above code:
reshape(m,n) changes the original matrix to a new matrix with m rows and n columns, but if the new matrix data exceeds the index range of the original data, an error will be reported.

3. Examples of mixed use of shape and reshape:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from matplotlib.font_manager import FontProperties   #导入字体属性模块

font_set = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=20)  #设置字体为宋体


def runplt():    #设置生成画图的函数
    plt.figure()  # 定义figure
    plt.title(u'披萨的价格和直径', fontproperties=font_set)
    plt.xlabel(u'直径（inch）', fontproperties=font_set)
    plt.ylabel(u'价格（美元）', fontproperties=font_set)
    plt.axis([0, 25, 0, 25])
    plt.grid(True)  #画网格线
    return plt


# 训练集和测试集数据
X_train = [[6], [8], [10], [14], [18]]
y_train = [[7], [9], [13], [17.5], [18]]
X_test = [[7], [9], [11], [15]]
y_test = [[8], [12], [15], [18]]

# 画出横纵坐标以及若干散点图
plt1 = runplt()
plt1.scatter(X_train, y_train, s=40) #每个点的size是40

# 给出一些点，并画出线性回归的曲线
xx = np.linspace(0, 26, 100)   #0-26之间等间隔生成100个点作为XX的横轴
regressor = LinearRegression()
regressor.fit(X_train, y_train)
yy = regressor.predict(xx.reshape(xx.shape[0], 1)) #预测100个点的横坐标得到yy
#shape[0] 第一维（行）的长度（行数）
#shape[1]为列数
#reshape((2,4))  改成2行4列矩阵

plt.plot(xx, yy, label="linear equation")