如何标记满足某些标准的每个组的第一个条目?
How to mark first entry per group satisfying some criterion?
假设我有一些数据框,其中一列有一些值多次出现形成组(片段中的列 A
)。现在我想创建一个新列,例如每个组的第一个 x
(第 C
列)条目为 1
,其他条目为 0
。
我设法完成了第一部分,但我没有找到将条件包含在 x
es 中的好方法,有没有好的方法可以做到这一点?
import pandas as pd
df = pd.DataFrame(
{
"A": ["0", "0", "1", "2", "2", "2"], # data to group by
"B": ["a", "b", "c", "d", "e", "f"], # some other irrelevant data to be preserved
"C": ["y", "x", "y", "x", "y", "x"], # only consider the 'x'
}
)
target = pd.DataFrame(
{
"A": ["0", "0", "1", "2", "2", "2"],
"B": ["a", "b", "c", "d", "e", "f"],
"C": ["y", "x", "y", "x", "y", "x"],
"D": [ 0, 1, 0, 1, 0, 0] # first entry per group of 'A' that has an 'C' == 'x'
}
)
# following partial solution doesn't account for filtering by 'x' in 'C'
df['D'] = df.groupby('A')['C'].transform(lambda x: [1 if i == 0 else 0 for i in range(len(x))])
在你的情况下先切片然后 drop_duplicates
然后分配回来
df['D'] = df.loc[df.C=='x'].drop_duplicates('A').assign(D=1)['D']
df['D'].fillna(0,inplace=True)
df
Out[149]:
A B C D
0 0 a y 0.0
1 0 b x 1.0
2 1 c y 0.0
3 2 d x 1.0
4 2 e y 0.0
5 2 f x 0.0
假设我有一些数据框,其中一列有一些值多次出现形成组(片段中的列 A
)。现在我想创建一个新列,例如每个组的第一个 x
(第 C
列)条目为 1
,其他条目为 0
。
我设法完成了第一部分,但我没有找到将条件包含在 x
es 中的好方法,有没有好的方法可以做到这一点?
import pandas as pd
df = pd.DataFrame(
{
"A": ["0", "0", "1", "2", "2", "2"], # data to group by
"B": ["a", "b", "c", "d", "e", "f"], # some other irrelevant data to be preserved
"C": ["y", "x", "y", "x", "y", "x"], # only consider the 'x'
}
)
target = pd.DataFrame(
{
"A": ["0", "0", "1", "2", "2", "2"],
"B": ["a", "b", "c", "d", "e", "f"],
"C": ["y", "x", "y", "x", "y", "x"],
"D": [ 0, 1, 0, 1, 0, 0] # first entry per group of 'A' that has an 'C' == 'x'
}
)
# following partial solution doesn't account for filtering by 'x' in 'C'
df['D'] = df.groupby('A')['C'].transform(lambda x: [1 if i == 0 else 0 for i in range(len(x))])
在你的情况下先切片然后 drop_duplicates
然后分配回来
df['D'] = df.loc[df.C=='x'].drop_duplicates('A').assign(D=1)['D']
df['D'].fillna(0,inplace=True)
df
Out[149]:
A B C D
0 0 a y 0.0
1 0 b x 1.0
2 1 c y 0.0
3 2 d x 1.0
4 2 e y 0.0
5 2 f x 0.0