cherry picks dataframe handling

Author: spjuhel

climada/util/dataframe_handling.py

@@ -0,0 +1,63 @@
+"""
+This file is part of CLIMADA.
+
+Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
+
+CLIMADA is free software: you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free
+Software Foundation, version 3.
+
+CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along
+with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
+
+---
+
+Define functions to handle with coordinates
+"""
+
+import pandas as pd
+
+
+def reorder_dataframe_columns(
+    df: pd.DataFrame, priority_order: list[str], keep_remaining: bool = True
+) -> pd.DataFrame | pd.Series:
+    """
+    Applies a column priority list to a DataFrame to reorder its columns.
+
+    This function is robust to cases where:
+    1. Columns in 'priority_order' are not in the DataFrame (they are ignored).
+    2. Columns in the DataFrame are not in 'priority_order'.
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        The input DataFrame.
+    priority_order: list[str]
+        A list of strings defining the desired column
+        order. Columns listed first have higher priority.
+    keep_remaining: bool
+        If True, any columns in the DataFrame but NOT in
+        'priority_order' will be appended to the end in their
+        original relative order. If False, these columns
+        are dropped.
+
+    Returns:
+        pd.DataFrame: The DataFrame with columns reordered according to the priority list.
+    """
+
+    present_priority_columns = [col for col in priority_order if col in df.columns]
+
+    new_column_order = present_priority_columns
+
+    if keep_remaining:
+        remaining_columns = [
+            col for col in df.columns if col not in present_priority_columns
+        ]
+
+        new_column_order.extend(remaining_columns)
+
+    return df[new_column_order]