Renaming Columns Using The Rename Function

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Rename Columns in a Pandas Dataframe

Renaming Columns using the `rename` Function

One way of renaming the columns in a Pandas Dataframe is by using the `rename` function. This method is quite useful when we need to rename specific columns with new names. The `rename` function takes the following parameters:

• mapper: A dictionary or a function that maps the old column names to the new column names.
• index: A boolean value that indicates whether the index should be renamed or not. The default value is `False`.
• columns: A boolean value that indicates whether the columns should be renamed or not. The default value is `True`.
• axis: The axis along which the renaming should be done. The default value is `0` (index).
• copy: A boolean value that indicates whether a copy of the DataFrame should be returned or not. The default value is `False`.
• inplace: A boolean value that indicates whether the renaming should be done inplace or not. The default value is `False`.
• level: The level of the MultiIndex to rename. The default value is `None`.

Renaming Columns Using the `dfrename` Function

Use the `dfrename` function and refer the columns to be renamed. Not all the columns have to be renamed. For example, to rename the `age` column to `age_group`, use the following code:

```python import pandas as pd df = pd.DataFrame({'name': ['John', 'Mary', 'Peter'], 'age': [20, 25, 30]}) df = dfrename(df, {'age': 'age_group'}) print(df) ```

Renaming Columns Inplace

You can use one of the following three methods to rename columns in a pandas DataFrame inplace:

1. Using the `rename` function with the `inplace` parameter set to `True`:
```python df.rename(columns={'age': 'age_group'}, inplace=True) ```
2. Using the `dfrename` function with the `inplace` parameter set to `True`:
```python dfrename(df, {'age': 'age_group'}, inplace=True) ```
3. Using the `assign` function to create a new DataFrame with the renamed columns:
```python df = df.assign(age_group=df['age']) ```

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