Use length instead of explicit None values

pola-rs · Jun 26, 2024 · 389ccfc · 389ccfc
1 parent 11055c6
commit 389ccfc
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Showing 3 changed files with 5 additions and 8 deletions.
diff --git a/crates/polars-core/src/serde/chunked_array.rs b/crates/polars-core/src/serde/chunked_array.rs
@@ -188,10 +188,7 @@ impl Serialize for NullChunked {
             let mut state = serializer.serialize_map(Some(3))?;
             state.serialize_entry("name", self.name())?;
             state.serialize_entry("datatype", self.dtype())?;
-            state.serialize_entry(
-                "values",
-                &IterSer::new(std::iter::repeat(None::<()>).take(self.len())),
-            )?;
+            state.serialize_entry("values", &IterSer::new(std::iter::once(self.len())))?;
             state.end()
         }
     }

diff --git a/crates/polars-core/src/serde/series.rs b/crates/polars-core/src/serde/series.rs
@@ -281,9 +281,9 @@ impl<'de> Deserialize<'de> for Series {
                         Ok(Series::new(&name, values).cast(&dt).unwrap())
                     },
                     DataType::Null => {
-                        let values: Vec<Option<Cow<[usize]>>> = map.next_value()?;
-                        let len = values.len();
-                        Ok(Series::new_null(&name, len))
+                        let values: Vec<usize> = map.next_value()?;
+                        let len = values.first().unwrap();
+                        Ok(Series::new_null(&name, *len))
                     },
                     dt => {
                         panic!("{dt:?} dtype deserialization not yet implemented")

diff --git a/py-polars/tests/unit/dataframe/test_serde.py b/py-polars/tests/unit/dataframe/test_serde.py
@@ -170,7 +170,7 @@ def test_df_serde_float_inf_nan() -> None:
 
 
 def test_df_serde_null() -> None:
-    df = pl.DataFrame({"a": [None, None]})
+    df = pl.DataFrame({"a": [None, None]}, schema={"a": pl.Null})
     ser = df.serialize()
     result = pl.DataFrame.deserialize(io.StringIO(ser))
     assert_frame_equal(result, df)