Pythonic计算pandas数据帧条纹的方法
发布时间:2020-12-15 11:16:36  所属栏目:Python  来源:互联网 
            导读:给定df df = pd.DataFrame([[1, 5, 2, 8, 2], [2, 4, 4, 20, 2], [3, 3, 1, 20, 2], [4, 2, 2, 1, 3], [5, 1, 4, -5, -4], [1, 5, 2, 2, -20], [2, 4, 4, 3, -8], [3, 3, 1, -1, -1], [4, 2, 2, 0, 12]
                
                
                
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                         给定df df = pd.DataFrame([[1,5,2,8,2],[2,4,20,[3,3,1,[4,3],[5,-5,-4],[1,-20],-8],-1,-1],12],-2]],columns=['A','B','C','D','E'],index=[1,6,7,9,10]) 基于this answer,我创建了一个计算条纹(向上,向下)的函数. def streaks(df,column):
    #Create sign column
    df['sign'] = 0
    df.loc[df[column] > 0,'sign'] = 1
    df.loc[df[column] < 0,'sign'] = 0
    # Downstreak
    df['d_streak2'] = (df['sign'] == 0).cumsum()
    df['cumsum'] = np.nan
    df.loc[df['sign'] == 1,'cumsum'] = df['d_streak2']
    df['cumsum'] = df['cumsum'].fillna(method='ffill')
    df['cumsum'] = df['cumsum'].fillna(0)
    df['d_streak'] = df['d_streak2'] - df['cumsum']
    df.drop(['d_streak2','cumsum'],axis=1,inplace=True)
    # Upstreak
    df['u_streak2'] = (df['sign'] == 1).cumsum()
    df['cumsum'] = np.nan
    df.loc[df['sign'] == 0,'cumsum'] = df['u_streak2']
    df['cumsum'] = df['cumsum'].fillna(method='ffill')
    df['cumsum'] = df['cumsum'].fillna(0)
    df['u_streak'] = df['u_streak2'] - df['cumsum']
    df.drop(['u_streak2',inplace=True)
    del df['sign']
    return df 
 功能很好,但很长.我确信写这个有更好的方法.我尝试了另一个答案,但效果不佳. 这是所需的输出 streaks(df,'E')
    A   B   C    D     E    d_streak    u_streak
1   1   5   2    8     2         0.0    1.0
2   2   4   4   20     2         0.0    2.0
3   3   3   1   20     2         0.0    3.0
4   4   2   2    1     3         0.0    4.0
5   5   1   4   -5    -4         1.0    0.0
6   1   5   2    2   -20         2.0    0.0
7   2   4   4    3    -8         3.0    0.0
8   3   3   1   -1    -1         4.0    0.0
9   4   2   2    0    12         0.0    1.0
10  5   1   4   20    -2         1.0    0.0
解决方法您可以简化功能,如下所示:def streaks(df,col):
    sign = np.sign(df[col])
    s = sign.groupby((sign!=sign.shift()).cumsum()).cumsum()
    return df.assign(u_streak=s.where(s>0,0.0),d_streak=s.where(s<0,0.0).abs()) 
 使用它: streaks(df,'E') 首先,使用np.sign计算所考虑的列中存在的每个单元的符号.这些将1分配给正数,将-1分配给负数. 接下来,使用sign!= sign.shift()来识别相邻值的集合(比较当前单元格和它的下一个)并获取它将在分组过程中使用的累积和. 执行groupby,将这些作为键/条件,并再次获取子组元素的累积和. 最后,将正计算的cumsum值分配给ustreak,将负的值(取其模数后的绝对值)分配给dstreak. (编辑:莱芜站长网) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容!  | 
                  
