fix: address pandas chained-assignment FutureWarning ahead of pandas 3.0

pixelmap/backend.py

@@ -150,15 +150,17 @@ def format_wiring_df(wiring_df):
     """
     # add shank id to electrode id, in dataframe's cells
     df = wiring_df.copy()
+    new_columns = {}
     for column in df.columns[1:]:
         shank_id = int(column[5])
         electrode_ids = df.loc[:, column].values
         shank_electrode_ids = np.vstack([np.zeros(len(electrode_ids)) + shank_id, electrode_ids]).T
-        shank_electrode_ids = [tuple(se) for se in shank_electrode_ids]
-        df[column] = df[column].astype(object)
-        df[column] = shank_electrode_ids
+        # object-dtype Series so each cell holds a (shank_id, electrode_id) tuple
+        new_columns[column] = pd.Series(
+            [tuple(se) for se in shank_electrode_ids], index=df.index, dtype=object
+        )
 
-    return df
+    return df.assign(**new_columns)
 
 
 def _imro_format_for(probe_subtype, probe_type):

pixelmap/utils/survey.py

@@ -43,10 +43,13 @@ def parse_survey_file(content: str) -> pd.DataFrame:
             f"Expected header: Shank\\tXum\\tZum\\tVal."
         )
 
-    df = df[list(SURVEY_COLUMNS)].copy()
-    for col in ("shank", "xum", "zum"):
-        df[col] = pd.to_numeric(df[col], errors="raise").astype(int)
-    df["val"] = pd.to_numeric(df["val"], errors="raise").astype(float)
+    df = df[list(SURVEY_COLUMNS)]
+    df = df.assign(
+        shank=pd.to_numeric(df["shank"], errors="raise"),
+        xum=pd.to_numeric(df["xum"], errors="raise"),
+        zum=pd.to_numeric(df["zum"], errors="raise"),
+        val=pd.to_numeric(df["val"], errors="raise"),
+    ).astype({"shank": int, "xum": int, "zum": int, "val": float})
     return df
 
 

pyproject.toml

@@ -27,7 +27,7 @@ dependencies = [
     "panel>=1.3.0",
     "matplotlib>=3.7.0",
     "numpy>=1.24.0",
-    "pandas>=2.0.0",
+    "pandas>=2.3",
     "param>=1.13.0",
     "bokeh>=3.0.0",
     "dotenv>=0.9.9",