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DataFrame.iterrows()
[source] -
Iterate over DataFrame rows as (index, Series) pairs.
Returns: it : generator
A generator that iterates over the rows of the frame.
See also
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itertuples
- Iterate over DataFrame rows as namedtuples of the values.
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iteritems
- Iterate over (column name, Series) pairs.
Notes
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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 = 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
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 thaniterrows
. - 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.
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DataFrame.iterrows()
2017-01-12 04:46:01
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