Pandas：将系列添加到数据框作为列（相同的索引，不同的长度）

Question

I have the following dataframe in pandas (the df below is abbreviated):

    Index: 23253 entries, 7.0 to 30559.0
    Data columns (total 17 columns):
    Epoch         23190  non-null values
    follow        23253  non-null values
    T_Opp         245    non-null values
    T_Dir         171    non-null values
    Teacher       0      non-null values
    Activity      23253  non-null values
    Actor         23253  non-null values
    Recipient1    14608  non-null values
    dtypes: float64(10), object(7)

Columns like T_Opp and T_Dir have dummy (1/0) data in them. When values in these columns are true, I want to add data from the 'Actor' column to the 'Teacher' column. So far, I have this (where the "mask" gives the condition under which the data are true. checked this bit and it works):

    opp_mask = f_acts['Behavior'].str.contains('bp', na=False)
    opp_teacher = f_acts[opp_mask]['Recipient1']

If I were doing this based only on one column, I could simply plug these results into the Teacher column in the dataframe with something like this:

    df['Teacher'] = df[opp_mask]['Actor']

But I need to fill the Teacher column with with data from 6 other columns, without overwriting the earlier columns. I have an idea of how this might work, similar to this toy example:

    list = [1]*len(df.Teacher)
    df['Teacher'] = list

But I can't seem to figure out how to transform the output of the "mask" technique above to the correct format for this approach--it has the same index info but is shorter than the dataframe I need to add it to. What am I missing?

UPDATE: Adding the data below to clarify what I'm trying to do.

   follow   T_Opp   T_Dir   T_Enh   T_SocTol    Teacher    Actor    Recipient1
   7        0       1       0       0           NaN        51608    f 
   8        0       0       0       0           NaN        bla      NaN
   11       0       0       0       0           NaN        51601    NaN
   13       1       0       0       1           NaN        f        51602
   18       0       0       0       0           NaN        f        NaN

So for data like these, what I'm trying to do is check the T_ columns one at a time. If the value in a T_ column is true, fetch the data from the Actor column (if looking at the T_Opp or T_SocTol columns) or from the Recipient column (if looking at T_Enh or T_Dir columns). I want to copy that data into the currently empty Teacher column.

More than one of the T_ columns can be true at a time, but in these cases it will always be "grabbing" the same data twice. (In other words, I never need data from BOTH the Actor and Recipient columns. Only one or the other, for each row).

I want to copy that data into the currently empty Teacher column.

Answer 1

Here's an approach to masking and concatenating multiple columns with Series.where() . If the end result is a column of strings, numeric columns will need to be converted to string first with .astype(str) .

In [23]: df
Out[23]: 
        C0  Mask1  Mask2 Val1 Val2
0  R_l0_g0      0      0   v1   v2
1  R_l0_g1      1      0   v1   v2
2  R_l0_g2      0      1   v1   v2
3  R_l0_g3      1      1   v1   v2

In [24]: df['Other'] = (df.Val1.astype(str).where(df.Mask1, '') + ',' + 
                        df.Val2.astype(str).where(df.Mask2, '')).str.strip(',')

In [25]: df
Out[25]: 
        C0  Mask1  Mask2 Val1 Val2  Other
0  R_l0_g0      0      0   v1   v2       
1  R_l0_g1      1      0   v1   v2     v1
2  R_l0_g2      0      1   v1   v2     v2
3  R_l0_g3      1      1   v1   v2  v1,v2

And here's another approach using DataFrame.where() . .where , like most pandas operations, performs automatic data alignment. Since the column names of the data frame and frame to mask with differ in this case, alignment can be disabled by masking with a raw, un-labeled numpy.ndarray (aka. .values ).

In [23]: masked = df[['Val1', 'Val2']].\
                     where(df[['Mask1', 'Mask2']].values, '') + ','

In [24]: df['Other2'] = masked.sum(axis=1).str.strip(',')

In [25]: df
Out[25]: 
        C0  Mask1  Mask2 Val1 Val2  Other Other2
0  R_l0_g0      0      0   v1   v2              
1  R_l0_g1      1      0   v1   v2     v1     v1
2  R_l0_g2      0      1   v1   v2     v2     v2
3  R_l0_g3      1      1   v1   v2  v1,v2  v1,v2