importnumpyasnp# 创建一个原始数组original_array=np.array([[1,2],[3,4]])new_column=np.array([5,6])# 广播新列new_column_broadcasted=np.broadcast_to(new_column[:,np.newaxis],(2,1))# 使用 hstack 添加广播后的列result_array=np.hstack((original_array,new_column_broadcasted))print(resul...
np.r_[ ... ]andnp.c_[ ... ]are useful alternatives tovstackandhstack, with square brackets [] instead of round (). 几个例子: : import numpy as np : N = 3 : A = np.eye(N) : np.c_[ A, np.ones(N) ] # add a column array([[ 1., 0., 0., 1.], [ 0., 1.,...
x = np.array([1,2,3]) #2 dimensional y = np.array([(1,2,3),(4,5,6)]) x = np.arange(3) >>> array([0, 1, 2]) y = np.arange(3.0) >>> array([ 0., 1., 2.]) x = np.arange(3,7) >>> array([3, 4, 5, 6]) y ...
EN当您需要调试这类事情时，将其分解为更简单的步骤是有用的。您是否弄错了切片，添加了两种不兼容的...
b = np.array([1, 2, 3, 4], dtype=int) print(b) forx, yinnp.nditer([a, b]): print(x, y) [[ 0 5 10 15] [20 25 30 35] [40 45 50 55]] [1 2 3 4] 0 1 5 2 10 3 15 4 20 1 25 2 30 3 35 4 40 1 ...
data={'A':[1,2,3]}df=pd.DataFrame(data)# Define a customfunctiondefsquare(x):returnx**2# Applying the'square'functionto the'A'column df['A_squared']=df['A'].apply(square)print(df['A_squared'])Output:011429 使用.apply()将平方函数应用于整个'A'列。不需要显式循环。
>>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]]) 1. 2. 3. 4. 数组常用属性——ndim、shape、dtype、itemsize、data 示例: >>> import numpy as np >>> a = np.arange(15).reshape(3, 5) ...
[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:, b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1, b2] # a weird thing to do array([ 4,...
axis=1)# along the sencond axis (column) array([[2, 1, 3, 4], [6, 5, 7, 8]])...
array([[1, 2, 3], [4, 5, 6]]) # 计算二维数组所有元素的和 sum_b = np.sum(b) print("Sum of array b:", sum_b) # 输出: 21 # 计算二维数组每列的和 sum_b_axis_0 = np.sum(b, axis=0) print("Sum of each column in array b:", sum_b_axis_0) # 输出: [5 7 9] #...