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# Converting a row to column labels 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":[3,"A"],"b":[4,"B"]}, index=["c","d"])df a bc 3 4d A B ```

# Solution

To convert the second row to column labels:

``` df.columns = df.loc["d"].valuesdf A Bc 3 4d A B ```

# Explanation

Here, `loc["d"]` returns row `d` as a `Series`:

``` df.loc["d"] a Ab BName: d, dtype: object ```

Directly assigning this to the `columns` of `df` yields:

``` df.columns = df.loc["d"]df d A Bc 3 4d A B ```

This output, which is taken directly out of Jupyter Notebook, is misleading since the `d` at the top-left is actually the name assigned to the new columns of `df`.

In most cases, you don't need this name, and so our solution converts this Series into a NumPy array via the `values` property, which removes the name:

``` df.columns = df.loc["d"].valuesdf A Bc 3 4d A B ```

# Supplement - dropping a row

After converting the row to column labels, we no longer need that row. To drop that row, use `drop(~)` like so:

``` df.drop("d", inplace=True)df A Bc 3 4 ```

Here, `inplace=True` means that we want to modify the `df` directly without creating a new DataFrame.

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