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Classe « DataFrame »

Méthode pandas.DataFrame.iterrows

Signature de la méthode iterrows

def iterrows(self) -> 'Iterable[tuple[Hashable, Series]]' 

Description

help(DataFrame.iterrows)

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

Yields
------
index : label or tuple of label
    The index of the row. A tuple for a `MultiIndex`.
data : Series
    The data of the row as a Series.

See Also
--------
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
DataFrame.items : Iterate over (column name, Series) pairs.

Notes
-----
1. Because ``iterrows`` returns a Series for each row,
   it does **not** preserve dtypes across the rows (dtypes are
   preserved across columns for DataFrames).

   To preserve dtypes while iterating over the rows, it is better
   to use :meth:`itertuples` which returns namedtuples of the values
   and which is generally faster than ``iterrows``.

2. You should **never modify** something you are iterating over.
   This is not guaranteed to work in all cases. Depending on the
   data types, the iterator returns a copy and not a view, and writing
   to it will have no effect.

Examples
--------

>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

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