使用组约束将列值向左移动 [英] Shift column values to the left with group constraints

问题描述

我有一个潜在的"大型DataFrame，

I have a "potentially" large DataFrame,

     A    B_1    B_2    B_3    C_1    C_2    C_3
0  231  text2  text3    NaN  date4  date1    NaN
1  443  NaN    NaN    text1  date2    NaN    NaN
2  456  text1  text1  text2  NaN    date3  date1

为了最小化某些NaN，我想将所有数据向左移动，从而能够忽略所有NaN列.不过，此移位必须保留在适当的组内，这意味着，只要单元格不在B_1或B_2列中，只要它不移位到C_1等就无关紧要.

In order to minimize some of the NaNs I want to shift all the data to the left and thus be able to disregard all NaN columns. This shift though must remain within the appropriate group, meaning that it does not matter if a cell is in column B_1 or B_2, as long as it does not get shifted to C_1 etc.

我要结束的是这个

     A    B_1    B_2    B_3    C_1    C_2    
0  231  text2  text3    NaN  date4  date1
1  443  text1    NaN    NaN  date2    NaN
2  456  text1  text1  text2  date3  date1

推荐答案

每组使用调整功能，仅必要MultiIndex在列中:

Use justify function per groups, only necessary MultiIndex in columns:

df = df.set_index('A')
df.columns = df.columns.str.split('_', expand=True)

f = lambda x: pd.DataFrame(justify(x.values, invalid_val=np.nan), 
                           index=x.index, columns=x.columns)
df = df.groupby(axis=1, level=0).apply(f)
print (df)
         B                    C            
         1      2      3      1      2    3
A                                          
231  text2  text3    NaN  date4  date1  NaN
443  text1    NaN    NaN  date2    NaN  NaN
456  text1  text1  text2  date3  date1  NaN

然后:

df1.columns = [f'{a}_{b}' for a, b in df1.columns]
df1 = df1.reset_index()

---

结合了先前的答案中的解决方案:

---

Combined with solution from previous answer:

g = df.groupby('A').cumcount() + 1
df1 = df.set_index(['A', g]).unstack()

f = lambda x: pd.DataFrame(justify(x.values, invalid_val=np.nan), 
                           index=x.index, columns=x.columns)
df1 = df.groupby(axis=1, level=0).apply(f)

df1.columns = [f'{a}_{b}' for a, b in df1.columns]
df1 = df1.reset_index()

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