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# Updating a row while iterating over the rows of a DataFrame in Pandas

schedule Aug 12, 2023
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local_offer
PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[2,3],"B":[4,5]})df A B0 2 41 3 5 ```

# Solution

To update a row while iterating over the rows of `df`:

``` for row in df.itertuples(): df.at[row.Index, "A"] = 10df A B0 10 41 10 5 ```

# Explanation

Firstly, we used the DataFrame's `itertuples()` method to iterate down the rows. Each `row` is a `Series`, and so you have access to the `Index` property. In this case, the `row.Index` returns `0` and `1` for the first and second iteration, respectively.

The reason why it is bad practice to modify the `row` within the loop directly is that row can either be a view or a copy, and so modifying `row` may or may not have an effect on the actual rows of `df`. In order to make direct changes to the actual rows, we used the `df.at[~]` property to directly assign a new value.

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