数据框python中列的嵌套循环

Question

我有以下数据框

print(df1)

        Date    start          end    delta d1   x_s    y_s      z_s    x_f      y_f    z_f
0   09/01/2017  09/01/2017  06/02/2017  28  28  0.989   0.945   0.626   0.191   0.932   0.280
1   10/01/2017  09/01/2017  06/02/2017  27  28  0.989   0.945   0.626   0.191   0.932   0.280
2   11/01/2017  09/01/2017  06/02/2017  26  28  0.989   0.945   0.626   0.191   0.932   0.280
3   12/01/2017  09/01/2017  06/02/2017  25  28  0.989   0.945   0.626   0.191   0.932   0.280
4   13/01/2017  09/01/2017  06/02/2017  24  28  0.989   0.945   0.626   0.191   0.932   0.280
5   14/01/2017  09/01/2017  06/02/2017  23  28  0.989   0.945   0.626   0.191   0.932   0.280
6   15/01/2017  09/01/2017  06/02/2017  22  28  0.989   0.945   0.626   0.191   0.932   0.280
7   16/01/2017  09/01/2017  06/02/2017  21  28  0.989   0.945   0.626   0.191   0.932   0.280
8   17/01/2017  09/01/2017  06/02/2017  20  28  0.989   0.945   0.626   0.191   0.932   0.280
9   18/01/2017  09/01/2017  06/02/2017  19  28  0.989   0.945   0.626   0.191   0.932   0.280

df1['delta'] = df1['end'] - df1['Date'] 和 df1['d1'] = df['end']-df1['start'] 我想创建 3 个新列，其中显示对 (x_s, x_f)、(y_s, y_f) 和 (z_s, z_f) 之间的插值。

我试过下面的代码

def mapper (name):
     return name+'_i'

ss = list(df1[['x_s', 'y_s', 'z_s']])
fs = list(df1[['x_f', 'y_f', 'z_f' ]])
df2 = pd.DataFrame

for s in ss :
    for f in fs:
         df2[s] = df1[s] + (((df1[f] - df1[s])/df1['d1'])*df1['delta'])

df_conc = pd.concat((df1, df2_new), axis=1)

但是，当我尝试运行嵌套循环时，出现以下错误：

TypeError: 'type' object does not support item assignment

我想知道我做错了什么。我将不胜感激任何提示或建议。提前非常感谢！

第二次尝试：

ss = ('x', 'y', 'z') 

for s in ss: 
   df1[mapper(s)] = pd.Series((df1[s+'_s'] + ((df1[s+'_f'] - df1[s+'_s'])/(df1['d1']))*df1['delta']), name=mapper(s), index=df1.index)  

但我仍然没有得到 3 个新列，它们循环通过以下对 (x_s, x_f)、(y_s, y_f)、(z_s, z_f)。

如果您发现我做错了什么，请告诉我，非常感谢！

Answer 1

这应该可以解决它：

for s in ss :
    for f in fs:
        df1[mapper(s)] = pd.Series(df1[s] + (((df1[f] - df1[s])/df1['d1'])*df1['delta']), name=mapper(s), index=df1.index)

我认为这就是你想要的，丢掉最后的concat 行。当您添加类似的新列时，Pandas 希望将索引传递给它，请参阅here

您可能还需要检查列的.dtypes，并根据需要使用pd.to_datetime。 This 也可能有帮助。

我运行了以下内容：

df1.end = pd.to_datetime(df1.end)
df1.start = pd.to_datetime(df1.start)
df1.Date = pd.to_datetime(df1.Date)


df1.delta = df1.delta / pd.offsets.Second(1)
df1.d1 = df1.d1 / pd.offsets.Second(1)

Answer 2

我认为你不应该循环播放。让 numpy 以矢量化的方式为您完成这一切。

ss = df[['x_s', 'y_s', 'z_s']].values
fs = df[['x_f', 'y_f', 'z_f' ]].values
ss2 = ss + ((ss - fs)/df[['d1']].values)*df[['delta']].values

请注意，我确信您可以摆脱上面的一些 .values 但这应该说明原理

Answer 3

def mapper (name):
     return name+'_i'

ss = ('x', 'y', 'z') 

for s in ss: 
   df1[mapper(s)] = pd.Series((df1[s+'_s'] + ((df1[s+'_f'] - df1[s+'_s'])/(df1['d1']))*df1['delta']), name=mapper(s), index=df1.index)