Self split geopandas Linestring geodataframe in a fast way without loosing all attributes

I found a solution.

Using my example:

a) The original shapefile

import geopandas as gpd
df = gpd.read_file("stac-graphe.shp")
df
id   test                geometry
1   test1   LINESTRING (10.244 -273.317, 784.201 -222.924)
2   test2   LINESTRING (210.484 -553.461, 324.991 -4.534)
3   test3   LINESTRING (169.970 -134.276, 126.511 -218.533...
4   test4   LINESTRING (100.000 -433.317, 724.390 -112.341...
5   test5   LINESTRING (232.683 -113.317, 694.146 -445.024...
6   test6   LINESTRING (563.415 -552.341, 559.512 -22.585)

b) Buffer the original geometry to avoid float arithmetic problems (in intersects or within)

df2 = df.copy()
df2.geometry = df2.geometry.buffer(0.01)

c) Use unary_union to split all the self-intersected LineStrings

un = df.geometry.unary_union
geom = [i for i in un]
id = [j for j in range(len(geom))]
unary = gpd.GeoDataFrame({"id":id,"geometry":geom})
unary.head()
id                   geometry
0   LINESTRING (10.244 -273.317, 192.920 -261.423)
1   LINESTRING (192.920 -261.423, 272.484 -256.242)
2   LINESTRING (272.484 -256.242, 418.308 -246.748)
3   LINESTRING (418.308 -246.748, 469.403 -243.421)
4   LINESTRING (469.403 -243.421, 561.095 -237.451)

d) Use a spatial join (with within or intersect) to join the two dataframes and retrieve the original attributes

from geopandas.tools import sjoin
result =sjoin(unary, df2, how="inner",op='within')
result.head()
id_left                   geometry               index_right id_right   test
0   LINESTRING (10.244 -273.317, 192.920 -261.423)   0         1       test1
1   LINESTRING (192.920 -261.423, 272.484 -256.242)  0         1       test1
2   LINESTRING (272.484 -256.242, 418.308 -246.748)  0         1       test1
3   LINESTRING (418.308 -246.748, 469.403 -243.421)  0         1       test1
4   LINESTRING (469.403 -243.421, 561.095 -237.451)  0         1       test1