Question

df = pd.DataFrame({"name":["A", "A","A", "A", "B" ,"B","B" ,"B", "C", "C","C", "C"],
                   "nickname":["X","Y","X","Z","X","Y","X","Y","Y", "X","Y", "Y"]})

如何将df按“名称”分组，并在每行中将“ X”后紧跟“ Y”的行中删除行？即在这种情况下应删除“ X”。

必需的输出：

1     A        Y
2     A        X
3     A        Z
5     B        Y
7     B        Y
8     C        Y
10    C        Y
11    C        Y

Answer 1

使用DataFrameGroupBy.shift进行每组移位，并用ne比较!=，按位OR-|进行链接，并按boolean indexing进行过滤：

m = df.groupby('name')['nickname'].shift(-1).ne('Y') | df['nickname'].ne('X')

df = df[m]
print (df)
   name nickname
1     A        Y
2     A        X
3     A        Z
5     B        Y
7     B        Y
8     C        Y
10    C        Y
11    C        Y

编辑：

df = pd.DataFrame({"name":["A", "A","A", "A", "B" ,"B","B" ,"B", "C", "C","C", "C"],
                   "nickname":["X","Y","X","Z","X","Y","X","X","Y", "X","Y", "Y"]})

print (df)
   name nickname
0     A        X
1     A        Y
2     A        X
3     A        Z
4     B        X
5     B        Y
6     B        X
7     B        X
8     C        Y
9     C        X
10    C        Y
11    C        Y

m = df.groupby('name')['nickname'].shift(-1).ne('Y') | df['nickname'].ne('X')

df1 = df[m]
print (df1)
   name nickname
1     A        Y
2     A        X
3     A        Z
5     B        Y
6     B        X
7     B        X
8     C        Y
10    C        Y
11    C        Y

print(df[(df['nickname']!='X') | (df['nickname'].shift(-1)!='Y')])
   name nickname
1     A        Y
2     A        X
3     A        Z
5     B        Y
6     B        X
8     C        Y
10    C        Y
11    C        Y

Answer 2

为此使用df[...]，过滤掉不需要的，并不需要分组：

print(df[(df['nickname']!='X') | (df['nickname'].shift(-1)!='Y')])

输出：

   name nickname
1     A        Y
2     A        X
3     A        Z
5     B        Y
7     B        Y
8     C        Y
10    C        Y
11    C        Y

更新：

print(df[(df['nickname']!='X') | (df['nickname'].shift(-1).isin(['Y','Z'])==0)])

熊猫：删除组行，其中该组的特定值后跟另一个值

2 个答案: