pyspark.pandas.DataFrame.iterrows¶

DataFrame.iterrows() → Iterator[Tuple[Union[Any, Tuple[Any, …]], pandas.core.series.Series]][source]¶

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

Yields

indexlabel or tuple of label: The index of the row. A tuple for a MultiIndex.
datapandas.Series: The data of the row as a Series.
itgenerator: A generator that iterates over the rows of the frame.

Notes

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
```
>>> df = ps.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
```
To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.
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.