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Ignoring parts with total degree greater than $d$ suggest that power series with precision $d$ are more suitable in this setting:

Q = PowerSeriesRing(QQ, n, 'x', default_prec=d) 
x = Q.gens()

Ignoring parts with total degree greater than $d$ suggest suggests that power series with precision $d$ are more suitable in this setting:

Q = PowerSeriesRing(QQ, n, 'x', default_prec=d) 
x = Q.gens()

Ignoring parts with total degree greater than $d$ suggests that power series with precision $d$ are more suitable in this setting:

Q = PowerSeriesRing(QQ, n, 'x', default_prec=d) 
x = Q.gens()
[g.add_bigoh(d) for g in Q.gens()]

Ignoring parts with total degree greater than $d$ $\geq d$ suggests that power series with precision $d$ are more suitable in this setting:

Q = PowerSeriesRing(QQ, n, 'x', default_prec=d) 
x = [g.add_bigoh(d) for g in Q.gens()]

Ignoring parts with total degree $\geq d$ suggests that power series with precision $d$ are more suitable in this setting:

Q = PowerSeriesRing(QQ, n, 'x', default_prec=d) 
x = [g.add_bigoh(d) for g in Q.gens()]
f = x[0]^4
print( f.add_bigoh(3).polynomial() )

Ignoring parts with total degree $\geq d$ suggests that power series with precision $d$ are more suitable in this setting:

d = 3
Q = PowerSeriesRing(QQ, n, 3, 'x', default_prec=d) 
x = [g.add_bigoh(d) for g in Q.gens()]
f = x[0]^4
print( f.add_bigoh(3).polynomial() f.add_bigoh(d).polynomial() )