create ID column in dataframe based on other column values / Pandas -Python

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I have a dataframe like this

L_1  D_1   L_2  D_2    L_3    D_3         C_N
1    Boy                                 Boy||
1    Boy   1-1  play                     Boy|play|
1    Boy   1-1  play  1-1-21  car        Boy|play|car
1    Boy   1-1  play  1-1-1   online     Boy|play|online
2    Girl                                Girl||
2    Girl  2-1  dance                    Girl|dance|

I have created the C_N tab using the code

df['C_N'] = df[['D_1','D_2', 'D_3']].apply(lambda x: '|'.join(x), axis=1)

Now I would like another column where I can also get the IDs of particular group, my ideal output would be :

L_1  D_1   L_2  D_2    L_3    D_3      IDs        C_N
1    Boy                               1         Boy||
1    Boy   1-1  play                   1-1       Boy|play|
1    Boy   1-1  play  1-1-21  car      1-1-21    Boy|play|car
1    Boy   1-1  play  1-1-1   online   1-1-1     Boy|play|online
2    Girl                              2         Girl||
2    Girl  2-1  dance                  2-1       Girl|dance|

can anyone help me in this issue. Thank you in advance!

pandas python-2.7 dataframe python-3.x python
2 Answers

I have defined a custom function to retrieve the required data:

df = pd.DataFrame([
    ['1', 'Boy','','','',''],
    ['1', 'Boy','1-1','play','',''],
    ['1', 'Boy','1-1','play','1-1-21','car'],
    ['1', 'Boy','1-1','play','1-1-1','online'],
    ['2', 'Girl','','','',''],
    ['2', 'Girl','','dance','','']], columns=['L_1','D_1','L_2','D_2','L_3','D_3']
)
df['C_N'] = df[['D_1','D_2', 'D_3']].apply(lambda x: '|'.join(x), axis=1)

def get_data(x,y,z):
    result = []
    if x != '':
        result.append(x)
    if y != '':
        result.append(y)
    if z != '':
        result.append(z)
    return result[-1]

df['IDs'] = ''
df['IDs'] = df.apply(lambda row: get_data(row['L_1'], row['L_2'], row['L_3']), axis=1)

Output df

enter image description here

user_160795390 
df = df.replace("^\s*$", np.nan, regex=True)

id_inds = df.filter(like="L_").agg(pd.Series.last_valid_index, axis=1)

# either this (but deprecated..)
df["IDs"] = df.lookup(df.index, id_inds)

# or this
df["IDs"] = df.to_numpy()[np.arange(len(df)), df.columns.get_indexer(id_inds)]

First we replace empty cells with NaN and then look at the L_* columns. Getting their last_valid_indexes which gives column names. Then we can either lookup (deprecated), or go to numpy values and do fancy indexing with get_indexer,

to get

>>> df
   L_1   D_1  L_2    D_2     L_3     D_3              C_N     IDs
0    1   Boy  NaN    NaN     NaN     NaN            Boy||       1
1    1   Boy  1-1   play     NaN     NaN        Boy|play|     1-1
2    1   Boy  1-1   play  1-1-21     car     Boy|play|car  1-1-21
3    1   Boy  1-1   play   1-1-1  online  Boy|play|online   1-1-1
4    2  Girl  NaN    NaN     NaN     NaN           Girl||       2
5    2  Girl  2-1  dance     NaN     NaN      Girl|dance|     2-1

You can now replace the NaNs back with empty string, if you wish.

user_93321870
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