Python len（）与arange（）的行为

使用此数据帧，dff：

   A  B
0  0  a
1  1  a
2  2  b
3  3  b
4  4  b
5  5  b
6  6  c
7  7  c

我知道如何

len（dff）==8

但是，我不理解以下方面的答案：

dff['counts'] = np.arange(len(dff)) 

那是

     A  B  counts  
  0  0  a       0  
  1  1  a       1  
  2  2  b       2  
  3  3  b       3  
  4  4  b       4  
  5  5  b       5  
  6  6  c       6  
  7  7  c       7  

---

dff['counts']

每行不应该是8吗？引擎盖下面发生了什么事？

您似乎误解了：

此处df的长度用于设置

停止

参数：

从文档中：

numpy.arange([start, ]stop, [step, ]dtype=None)
Return evenly spaced values within a given interval.

Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list.

When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.

Parameters: 
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.
step : number, optional
Spacing between values. For any output out, this is the distance between two adjacent values, out[i+1] - out[i]. The default step size is 1. If step is specified, start must also be given.
dtype : dtype
The type of the output array. If dtype is not given, infer the data type from the other input arguments.
Returns:    
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is ceil((stop - start)/step). Because of floating point overflow, this rule may result in the last element of out being greater than stop.

如果您想将每一行设置为相同的值，您可以

In [34]:

dff['counts'] = len(dff)
dff
Out[34]:
   A  B  counts
0  0  a       8
1  1  a       8
2  2  b       8
3  3  b       8
4  4  b       8
5  5  b       8
6  6  c       8
7  7  c       8

---

谢谢你的澄清。我想我不习惯数据帧和列表在长度相同时自动合并。如果传递标量，我会将所有行设置为相同的值；如果长度相同，我会对齐类似数组的对象；如果是序列/数据帧，我会沿索引/列对齐。这是预期的行为，例如，如果您尝试

dff['counts']=np.arange（7）

您能给出一个“如果是一个系列/数据帧，则沿索引/列对齐”的快速示例吗？基本上，如果执行了以下操作，您将看到我的意思dff['c']=pd.Series（np.arange（8），index=np.arange（3,11））`您将看到，由于另一个系列索引从3开始，因此该索引值中的值被分配，前3行最终为

NaN

In [34]:

dff['counts'] = len(dff)
dff
Out[34]:
   A  B  counts
0  0  a       8
1  1  a       8
2  2  b       8
3  3  b       8
4  4  b       8
5  5  b       8
6  6  c       8
7  7  c       8