Iterate over DataFrame rows as (index, Series) pairs.
This docstring was copied from pandas.core.frame.DataFrame.iterrows.
Some inconsistencies with the Dask version may exist.
- indexlabel or tuple of label
The index of the row. A tuple for a MultiIndex.
The data of the row as a Series.
iterrowsreturns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) >>> row = next(df.iterrows()) >>> 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
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.