在 Pandas DataFrame 的每一列中查找第一个非零值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/50586146/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Find first non-zero value in each column of pandas DataFrame
提问by Konstantin
What is a pandoric way to get a value and index of the first non-zero element in each column of a DataFrame (top to bottom)?
在 DataFrame 的每一列（从上到下）中获取第一个非零元素的值和索引的 pandoric 方法是什么？
import pandas as pd
df = pd.DataFrame([[0, 0, 0],
[0, 10, 0],
[4, 0, 0],
[1, 2, 3]],
columns=['first', 'second', 'third'])
print(df.head())
# first second third
# 0 0 0 0
# 1 0 10 0
# 2 4 0 0
# 3 1 2 3
What I would like to achieve:
我想达到的目标：
# value pos
# first 4 2
# second 10 1
# third 1 3
采纳答案by piRSquared
You're looking for idxmaxwhich gives you the first position of the maximum. However, you need to find the max of "not equal to zero"
您正在寻找idxmax哪个可以为您提供最大值的第一个位置。但是，您需要找到“不等于零”的最大值
df.ne(0).idxmax()
first 2
second 1
third 3
dtype: int64
We can couple this with lookupand assign
df.ne(0).idxmax().to_frame('pos').assign(val=lambda d: df.lookup(d.pos, d.index))
pos val
first 2 4
second 1 10
third 3 3
Same answer packaged slightly differently.
相同的答案包装略有不同。
m = df.ne(0).idxmax()
pd.DataFrame(dict(pos=m, val=df.lookup(m, m.index)))
pos val
first 2 4
second 1 10
third 3 3
回答by jpp
Here's the longwinded way, which should be faster if your non-zero values tend to occur near the start of large arrays:
这是冗长的方式，如果您的非零值倾向于出现在大型数组的开头附近，则应该更快：
import pandas as pd
df = pd.DataFrame([[0, 0, 0],[0, 10, 0],[4, 0, 0],[1, 2, 3]],
columns=['first', 'second', 'third'])
res = [next(((j, i) for i, j in enumerate(df[col]) if j != 0), (0, 0)) for col in df]
df_res = pd.DataFrame(res, columns=['value', 'position'], index=df.columns)
print(df_res)
value position
first 4 2
second 10 1
third 3 3
回答by YOBEN_S
I will using stack, index is for row and column number
我将使用stack，索引用于行号和列号
df[df.eq(df.max(1),0)&df.ne(0)].stack()
Out[252]:
1 second 10.0
2 first 4.0
3 third 3.0
dtype: float64
