pandas.DataFrame.iterrows#

DataFrame.iterrows()[source]#

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

Each row is yielded as a (index, Series) tuple; the Series has the same index as the DataFrame columns. Note that dtypes may not be preserved across rows. Prefer itertuples() for speed and type consistency.

Yields:
indexlabel or tuple of label

The index of the row. A tuple for a MultiIndex.

dataSeries

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 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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