Easily resolved (&, |) and the difference between (and, or)

(&, |), And (and, or) are similar between the two groups of operators, used in a "and" / "or" upper, in use slightly different.
(&, |), And (and, or) are used to compare two sets of variables, the format is essentially:

a & b
a | b
a and b
a or b

If a, b is a number variable, &, | indicates a bit operation, and, or non-zero depending on whether the output is determined,

1. &， |：

   	```
   	# 1&2，2在二进制里面是10,1在二进制中是01，那么01与运算10得到是0 
   
   	1 & 2         # 输出为 0， 
   	1 | 2          # 输出为3
   	```
   

2. and, or:

    # 判断变量是否为0， 是0则为False，非0判断为True，
    # and中含0，返回0； 均为非0时，返回后一个值， 
   2 and 0   # 返回0
   2 and 1   # 返回1
   1 and 2   # 返回2
   
   # or中， 至少有一个非0时，返回第一个非0,
   2 or 0   # 返回2
   2 or 1   # 返回2
   0 or 1   # 返回1 
   

How a, b are logical variables, the use of two types of basically the same

	```
	In[103]:(3>0) | (3<1)
	Out[103]: True
	In[104]:(3>0) and (3<1)
	Out[104]: False
	In[105]:(3>0) or (3<1)
	Out[105]: True
	In[106]:(3>0) & (3<1)
	Out[106]: False
	```

It is worth mentioning during the slicing process DataFrame, pay attention to the use of a logical variable,
you need to be found to meet the logical condition when a plurality of data, and to use the & |, under certain conditions with and / or being given 'ValueError: The truth value of a Series is ambiguous

target_url = "http://aima.cs.berkeley.edu/data/iris.csv"
data = pd.read_csv(target_url, header=None, columns=['s_line', 's_wid', 'p_line', 'p_wid', 'kind'])
data.columns = ['s_line', 's_wid', 'p_line', 'p_wid', 'kind']
x_data = data.iloc[:, :-1]

# 在多个逻辑条件下，用& 或者|，
x_1 = x_data[x_data['s_line'] > 6 & x_data['p_wid'] > 0]