Melt And Merge On Substring - Python & Pandas

I have data which has data like id name model_# ms bp1 cd1 sf1 sa1 rq1 bp2 cd2 sf2 sa2 rq2 ... 1 John 23984 1 23 234 124

Solution 1:

Solution

Set the index...
Use a clever column groupby...
Another clever function to apply...

d1 = df.set_index(['id', 'name', 'model_#', 'ms'])

def melt_(df):
    id_vars = df.index.names
    return df.reset_index().melt(id_vars=id_vars).set_index(id_vars)


d2 = d1.groupby(d1.columns.str.extract('(\D+)', expand=False), axis=1).apply(melt_)

d2.columns = d2.columns.swaplevel(0, 1).map('_'.join)
d2.reset_index()

   id  name  model_#  ms variable_bp  value_bp variable_cd  value_cd variable_rq  value_rq variable_sa  value_sa variable_sf  value_sf
0   1  John    23984   1         bp1        23         cd1       234         rq1       252         sa1        25         sf1       124
1   2  John    23984   2         bp1       234         cd1       234         rq1       262         sa1        62         sf1       242
2   1  John    23984   1         bp2       252         cd2        62         rq2       234         sa2       234         sf2       194
3   2  John    23984   2         bp2       622         cd2       262         rq2       262         sa2        26         sf2       622

---

Overly Functionalized

e = lambda d, n: dict(zip(n, d.dtypes))
i = lambda d, n: pd.DataFrame(d.values, d.index, n).astype(e(d, n))
h = lambda d: i(d, d.columns.map(fmt)).reset_index()
m = lambda d: d.reset_index().melt(cols).set_index(cols)
fmt = '{0[1]}_{0[0]}'.format

cols = ['id', 'name', 'model_#', 'ms']

d1 = df.set_index(cols)
g = d1.columns.str.extract('(\D+)', expand=False)
d1.groupby(g, axis=1).apply(m).pipe(h)

   id  name  model_#  ms variable_bp  value_bp variable_cd  value_cd variable_rq  value_rq variable_sa  value_sa variable_sf  value_sf
0   1  John    23984   1         bp1        23         cd1       234         rq1       252         sa1        25         sf1       124
1   2  John    23984   2         bp1       234         cd1       234         rq1       262         sa1        62         sf1       242
2   1  John    23984   1         bp2       252         cd2        62         rq2       234         sa2       234         sf2       194
3   2  John    23984   2         bp2       622         cd2       262         rq2       262         sa2        26         sf2       622

---

Old Answer

This is far from pretty and I'm not even sure this is what you want.

d1 = df.set_index(['id', 'name', 'model_#', 'ms'])

cidx = pd.MultiIndex.from_tuples(
    d1.columns.to_series().str.extract('(\D+)(\d+)', expand=False).values.tolist(),
    names=[None, 'variable']
)

d1.columns = cidx

d2 = d1.sort_index(axis=1).stack()

variables = pd.DataFrame(
    (d2.columns + d2.index.get_level_values('variable')[:, None]).tolist(),
    d2.index, d2.columns
)

d3 = pd.concat(
    [variables, d2], axis=1, keys=['variable', 'value']
).reset_index('variable', drop=True).sort_index(axis=1, level=1, sort_remaining=False)

d3.columns = d3.columns.map('_'.join)

d3.reset_index()

   id  name  model_#  ms variable_bp  value_bp variable_cd  value_cd variable_rq  value_rq variable_sa  value_sa variable_sf  value_sf
0   1  John    23984   1         bp1        23         cd1       234         rq1       252         sa1        25         sf1       124
1   1  John    23984   1         bp2       252         cd2        62         rq2       234         sa2       234         sf2       194
2   2  John    23984   2         bp1       234         cd1       234         rq1       262         sa1        62         sf1       242
3   2  John    23984   2         bp2       622         cd2       262         rq2       262         sa2        26         sf2       622