Probably the most elegant solution (but certainly not the most efficient):

for row in df.values:
    c2 = row[1]
    print(row)
    # ...

for c1, c2 in df.values:
    # ...

Note that:

• the documentation explicitly recommends to use .to_numpy() instead
• the produced NumPy array will have a dtype that fits all columns, in the worst case object
• there are good reasons not to use a loop in the first place

Still, I think this option should be included here, as a straight-forward solution to a (one should think) trivial problem.

Probably the most elegant solution (but certainly not the most efficient):

for row in df.values:
    c2 = row[1]
    print(row)
    # ...

for c1, c2 in df.values:
    # ...

Note that:

• the documentation explicitly recommends to use .to_numpy() instead
• the produced NumPy array will have a dtype that fits all columns, in the worst case object
• there are good reasons not to use a loop in the first place

Still, I think this option should be included here, as a straightforward solution to a (one should think) trivial problem.

Probably the most elegant solution (but certainly not the most efficient):

for row in df.values:
    c2 = row[1]
    print(row)
    # ...

for c1, c2 in df.values:
    # ...

Note that:

• the documentation explicitly recommends to use .to_numpy() instead
• this might be a bad idea for DataFrames with mixed type columns
• there are good reasons not to use a loop in the first place

Still, I think this option should be included here, as a straight-forward solution to a (one should think) trivial problem.

Probably the most elegant solution (but certainly not the most efficient):

for row in df.values:
    c2 = row[1]
    print(row)
    # ...

for c1, c2 in df.values:
    # ...

Note that:

• the documentation explicitly recommends to use .to_numpy() instead
• the produced NumPy array will have a dtype that fits all columns, in the worst case object
• there are good reasons not to use a loop in the first place

Still, I think this option should be included here, as a straight-forward solution to a (one should think) trivial problem.