在pandas数据框中找到条纹

不像jezrael的回答那么优雅，但对我来说更容易理解…

你可以apply自定义函数f，然后cumsum，cumcount和astype：

def f(x):    x['streak'] = x.groupby( (x['stat'] != 0).cumsum()).cumcount() +        ( (x['stat'] != 0).cumsum() == 0).astype(int)     return xdf = df.groupby('loser', sort=False).apply(f)print df   time winner loser  stat  streak0     1      A     B     0       11     2      C     B     0       22     3      D     B     1       03     4      E     B     0       14     5      F     A     0       15     6      G     A     0       26     7      H     A     0       37     8      I     A     1       0

为了更好的理解：

def f(x):    x['c'] = (x['stat'] != 0).cumsum()    x['a'] = (x['c'] == 0).astype(int)    x['b'] = x.groupby( 'c' ).cumcount()    x['streak'] = x.groupby( 'c' ).cumcount() + x['a']    return xdf = df.groupby('loser', sort=False).apply(f)print df   time winner loser  stat  c  a  b  streak0     1      A     B     0  0  1  0       11     2      C     B     0  0  1  1       22     3      D     B     1  1  0  0       03     4      E     B     0  1  0  1       14     5      F     A     0  0  1  0       15     6      G     A     0  0  1  1       26     7      H     A     0  0  1  2       37     8      I     A     1  1  0  0       0

首先，定义一个可以处理单个失败者的函数：

def f(df):    df['streak2'] = (df['stat'] == 0).cumsum()    df['cumsum'] = np.nan    df.loc[df['stat'] == 1, 'cumsum'] = df['streak2']    df['cumsum'] = df['cumsum'].fillna(method='ffill')    df['cumsum'] = df['cumsum'].fillna(0)    df['streak'] = df['streak2'] - df['cumsum']    df.drop(['streak2', 'cumsum'], axis=1, inplace=True)    return df

条纹实际上是a

cumsum

stat

cumsum

stat

然后

groupby

apply

df.groupby('loser').apply(f)

结果是预期的。