answer:Here's a one-liner solution that avoids the use of `locals()`, `globals()`, or `try-except`: ``` df_.loc[:, [col1, col2]] = df_.loc[:, [col1, col2]].apply(lambda x: float(str(x).rstrip('0').rstrip('.'))) if 'df_' in vars() else df_ ``` In this example, a ternary operator is used to select either `df_.loc[:, [col1, col2]].apply(lambda x: float(str(x).rstrip('0').rstrip('.')))` (if `df_` exists) or `df_` itself (if `df_` does not exist). The `vars()` function is used to get the names of local variables, and the `loc` function is used to select columns by name. This one-liner applies the lambda function to the selected columns of `df_` using the same syntax as before. If `df_` exists and the lambda function is applied successfully, this one-liner updates `df_` in place. If `df_` does not exist, the original `df_` dataframe is returned. Note that the `[:, [col1, col2]]` syntax selects all rows (`:`) and only the columns named `col1` and `col2`.

answer:Here's a one-liner solution that uses the `globals()` function to check if `df_` exists, but avoids the use of `locals()`, `vars()`, or `try-except`: ``` df_.loc[:, [col1, col2]] = df_.loc[:, [col1, col2]].apply(lambda x: float(str(x).rstrip('0').rstrip('.'))) if 'df_' in globals() else df_ ``` In this one-liner solution, the `globals()` function is used to get the names of global variables, and the `loc` function is used to select columns by name. Like the previous solution, this one-liner applies the lambda function to the selected columns of `df_` using the same syntax as before. If `df_` exists and the lambda function is applied successfully, this one-liner updates `df_` in place. If `df_` does not exist, the original `df_` dataframe is returned. Note that the `[:, [col1, col2]]` syntax selects all rows (`:`) and only the columns named `col1` and `col2`.

answer:Here's a one-liner solution that avoids the use of `locals()`, `globals()`, `vars()`, and `try-except`: ``` df_.loc[df_.index, [col1, col2]] = df_.loc[df_.index, [col1, col2]].apply(lambda x: float(str(x).rstrip('0').rstrip('.'))) ``` In this one-liner solution, the `loc` function is used with the `df_.index` parameter to select all rows, and the `[col1, col2]` parameter to select only the columns named `col1` and `col2`. Like the previous solutions, this one-liner applies the lambda function to the selected columns of `df_` using the same syntax as before. This one-liner updates `df_` in place without relying on any additional modules or functions. Note that the `[:, [col1, col2]]` syntax is replaced with `[df_.index, [col1, col2]]` for simplicity and equivalent functionality.

answer:Here's a one-liner solution that only executes if `df_` exists and avoids the use of `locals()`, `globals()`, `vars()`, and `try-except`: ``` df_.loc[df_.index, [col1, col2]] = df_.loc[df_.index, [col1, col2]].apply(lambda x: float(str(x).rstrip('0').rstrip('.'))) if 'df_' in dir() else df_ ``` In this one-liner solution, the `dir()` function is used to return a list of names in the current local scope, which includes the variable `df_`. If `df_` exists, the one-liner applies the lambda function to the selected columns of `df_` using the same syntax as before. If `df_` does not exist, the original `df_` dataframe is returned. Note that the `[:, [col1, col2]]` syntax is replaced with `[df_.index, [col1, col2]]` for simplicity and equivalent functionality.