modin.pandas.DataFrame.itertuples¶

DataFrame.itertuples(index: bool = True, name: str | None = 'Pandas') → Iterable[tuple[Any, ...]][source]¶

Iterate over DataFrame rows as namedtuples.

Parameters:

• index (bool, default True) – If True, return the index as the first element of the tuple.

• name (str or None, default "Pandas") – The name of the returned namedtuples or None to return regular tuples.

Returns:

An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

Return type:

iterator

See also

DataFrame.iterrows

Iterate over DataFrame rows as (index, Series) pairs.

DataFrame.items

Iterate over (column name, Series) pairs.

Notes

1. Iterating over rows is an antipattern in Snowpark pandas and pandas. Use df.apply() or other aggregation methods when possible instead of iterating over a DataFrame. Iterators and for loops do not scale well.

2. The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore (follows namedtuple rules).

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]}, index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

Copy

By setting the index parameter to False we can remove the index as the first element of the tuple: >>> for row in df.itertuples(index=False): … print(row) … Pandas(num_legs=4, num_wings=0) Pandas(num_legs=2, num_wings=2)

>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...      index=['cobra', 'viper', 'sidewinder'],
...      columns=['max_speed', 'shield'])
>>> df
            max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8

Copy

>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='cobra', max_speed=1, shield=2)
Pandas(Index='viper', max_speed=4, shield=5)
Pandas(Index='sidewinder', max_speed=7, shield=8)

Copy

Rename the namedtuple and create it without the index values. >>> for row in df.itertuples(name=”NewName”, index=False): … print(row) … NewName(max_speed=1, shield=2) NewName(max_speed=4, shield=5) NewName(max_speed=7, shield=8)

When name is None, return a regular tuple. >>> for row in df.itertuples(name=None): … print(row) … (‘cobra’, 1, 2) (‘viper’, 4, 5) (‘sidewinder’, 7, 8)