[英]How to count value change during a for loop in python?
I have a dataframe that consists of one column that consists of 0
and 1
.我有一个 dataframe 由一列组成,该列由0
和1
组成。
They are structured in this way [0,0,1,1,0,0,1,1,1,]
.它们以这种方式构造[0,0,1,1,0,0,1,1,1,]
。
My goal is to count only the first 1
in each repeating 1
s in a loop.我的目标是只计算循环中每个重复1
秒中的第一个1
。
So in this example of [0,0,1,1,0,0,1,1,1,]
it should be able to only count a total of 2
.所以在这个[0,0,1,1,0,0,1,1,1,]
的例子中,它应该只能计算总数2
。 How can I use a for loop and use an if
condition and count this?如何使用 for 循环并使用if
条件并计算它?
(As @Erfan mentiond in the comments :) (正如@Erfan 在评论中提到的那样:)
>>> df
col
0 0
1 0
2 1
3 1
4 0
5 0
6 1
7 1
8 1
>>> df['col'].diff().eq(1).sum()
2
Found a messy way to do it where I can create a translated list and count the sum.找到了一种混乱的方法,我可以创建一个翻译列表并计算总和。
def FirstValue(data):
for index, item in enumerate(data):
if item == 1:
if data[index-1] == 1:
counter.append(0)
if item == 1:
if data[index-1] == 0:
counter.append(1)
else:
counter.append(0)
A simple for loop:一个简单的for循环:
out = [0]+[int(j-i==1) for i,j in zip(lst,lst[1:])]
Output: Output:
[0, 0, 1, 0, 0, 0, 1, 0, 0]
Also, you can assign a pd.Series
to a DataFrame column like:此外,您可以将pd.Series
分配给 DataFrame 列,例如:
df.col = (pd.Series(lst).diff()==1).astype(int)
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