To loop all rows in a dataframe and use values of each row conveniently, namedtuples can be converted to ndarrays. For example:

df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])

Iterating over the rows:

for row in df.itertuples(index=False, name='Pandas'):
    print np.asarray(row)

results in:

[ 1.   0.1]
[ 2.   0.2]

Please note that if index=True, the index is added as the first element of the tuple, which may be undesirable for some applications.

To loop all rows in a dataframe and use values of each row conveniently, namedtuples can be converted to ndarrays. For example:

df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])

Iterating over the rows:

for row in df.itertuples(index=False, name='Pandas'):
    print np.asarray(row)

results in:

[ 1.   0.1]
[ 2.   0.2]

Please note that if index=True, the index is added as the first element of the tuple, which may be undesirable for some applications.

To loop all rows in a dataframe and use values of each row conveniently, namedtuples can be converted to ndarrays. For example:

df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])

Iterating over the rows:

for row in df.itertuples(index=False, name='Pandas'):
    print np.asarray(row)

results in:

[ 1.   0.1]
[ 2.   0.2]

To loop all rows in a dataframe and use values of each row conveniently, namedtuples can be converted to ndarrays. For example:

df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])

Iterating over the rows:

for row in df.itertuples(index=False, name='Pandas'):
    print np.asarray(row)

results in:

[ 1.   0.1]
[ 2.   0.2]

Please note that if index=True, the index is added as the first element of the tuple, which may be undesirable for some applications.