首先,确保导入pandas和numpy。 importpandasaspdimportnumpyasnp 1. 2. 创建样本数据框 使用pandas创建一个简单的数据框以进行操作。 data={'A':[1,2,3],'B':[[4,5],[6],[7,8]]}df=pd.DataFrame(data) 1. 2. 应用expand函数 对数据框的某列应用expand()函数。 df['B_expanded']=df['B']....
print(sum(g)) with open('b.txt') as f: # 不建议使用这种方式,代码不易观察 print(sum((float(line.split()[-1])*float(line.split()[-2]) for line in f))) # 应用4: res=[] with open('b.txt') as f: for line in f: # print(line) l=line.split() # print(l) d={} d[...
import pandas as pd df = xw.Range('B3').expand().options(pd.DataFrame).value df.reset_index(inplace=True) .expand()自动检测数据的维度,.options()指定我们需要pandas数据框架。我们在末尾重置了索引,因此x轴将被视为列,而不是数据框架索引。 图8 数据已经读入到Python,我们可以生成一个图形,然后将其...
h = self.default_hidden() v = v.type_as(h) score = self.score(torch.cat([h.view(-1), v.view(-1)])) x =torch.cat([v.view(-1), (v * (s > 0.5).type_as(v). expand_as(v).type_as(v)).view(-1)]) _, h = self.rnn(x.view(1, 1, -1), h) return score.view...
import pandas as pd df = xw.Range('B3').expand().options(pd.DataFrame).value df.reset_index(inplace=True) .expand()自动检测数据的维度,.options()指定我们需要pandas数据框架。我们在末尾重置了索引,因此x轴将被视为列,而不是数据框...
import numpy as np >>> a = np.arange(5) >>> a.shape (5L,) # 方式一：利用 np.expand_...
按钮演示", command=callback)button.pack(ipadx=5, ipady=5, expand=True)root.mainloop()也可以直接调用部分命令，不使用自定义函数。import tkinter as tkroot = tk.Tk()root.geometry('300x200+200+200')root.title('Button 按钮演示')# 此处设置按钮button = tk.Button( root, text="退出...
expand mat1.lastgroup mat1.regs mat1.group mat1.lastindex mat1.span In [62]: help(mat1.group) Help on built-in function group: group(...) group([group1, ...]) -> str or tuple. Return subgroup(s) of the match by indices or names. For 0 returns the entire match. In [63]...
expand_dims ( a, axis ) [source] Expand the shape of an array. Insert a new axis that will appear at the axis position in the expanded array shape. Note Previous to NumPy 1.13.0, neither axis < -a.ndim - 1 nor axis > a.ndim raised errors or put the new axis where documented....
return X / np.expand_dims(lp_norm, axis) # 标准化数据集 X def standardize(X): X_std = np.zeros(X.shape) mean = X.mean(axis=0) std = X.std(axis=0) # 做除法运算时请永远记住分母不能等于 0 的情形 # X_std = (X - X.mean(axis=0...