feat: support custom values for fillNull

README.md

@@ -114,10 +114,9 @@ The DataFrame class supports also methods for filtering out or changing null val
 const df = readCsv('test.csv')
 df.dropNull() // drop null
 df.fillNull() // fill null values with the zero value of their type
+df.fillNull('a string', 0.5, 4, true) // fill null values with custom values
 ```
 
-> NOTE: `fillNull` works with zero values, which means: `""` for string, `0` for integer and float, `false` for boolean
-
 You can filter out and change NaN values as well (only applies if there are float-typed columns):
 
 ```typescript

__test__/index.spec.ts

@@ -227,7 +227,7 @@ test('Reading null values works correctly', (t) => {
   t.deepEqual(col4, [true, false, true, false, null])
 })
 
-test('Filling null values works correctly', (t) => {
+test('Filling null values with zero values works correctly', (t) => {
   const df = readCsv('testfiles/with-nulls.csv')
   df.fillNull()
   t.is(df.len, 5)
@@ -245,6 +245,24 @@ test('Filling null values works correctly', (t) => {
   t.deepEqual(col4, [true, false, true, false, false])
 })
 
+test('Filling null values with custom values works correctly', (t) => {
+  const df = readCsv('testfiles/with-nulls.csv')
+  df.fillNull('test', 99.3, 77, true)
+  t.is(df.len, 5)
+  const col1 = asStringArray(df.get('col1')!)!
+  t.is(col1.filter((x) => x === null).length, 0)
+  t.deepEqual(col1, ['hello', 'test', 'bye', 'test', 'ciao'])
+  const col2 = asIntArray(df.get('col2')!)!
+  t.is(col2.filter((x) => x === null).length, 0)
+  t.deepEqual(col2, [1, 2, 77, 3, 4])
+  const col3 = asFloatArray(df.get('col3')!)!
+  t.is(col3.filter((x) => x === null).length, 0)
+  t.deepEqual(col3, [0.1, 0.2, 0.3, 99.3, 0.4])
+  const col4 = asBooleanArray(df.get('col4')!)!
+  t.is(col4.filter((x) => x === null).length, 0)
+  t.deepEqual(col4, [true, false, true, false, true])
+})
+
 test('Dropping NaN values works correctly', (t) => {
   const col1 = toStringColumn(['hello', 'world', null, 'bye', 'moon', 'hey'])
   const col2 = toFloatColumn([1.2, 2.3, 0.3, NaN, 0.5, 8.9])

index.d.ts

@@ -10,7 +10,12 @@ export declare class DataFrame {
   dropNan(): void
   fillNan(fillValue?: number | undefined | null): void
   dropNull(): void
-  fillNull(): void
+  fillNull(
+    fillString?: string | undefined | null,
+    fillFloat?: number | undefined | null,
+    fillInt?: number | undefined | null,
+    fillBool?: boolean | undefined | null,
+  ): void
   writeCsv(path: string): void
 }
 

src/lib.rs

@@ -343,34 +343,44 @@ impl DataFrame {
   }
 
   #[napi]
-  pub fn fill_null(&mut self) {
+  pub fn fill_null(
+    &mut self,
+    fill_string: Option<String>,
+    fill_float: Option<f64>,
+    fill_int: Option<i64>,
+    fill_bool: Option<bool>,
+  ) {
+    let str_val = fill_string.unwrap_or_default();
+    let f64_val = fill_float.unwrap_or_default();
+    let i64_val = fill_int.unwrap_or_default();
+    let bool_val = fill_bool.unwrap_or_default();
     for col in self.columns.values_mut() {
       match col {
         ColumnData::Boolean(b) => {
           for item in b.iter_mut().take(self.len as usize) {
             if item.is_none() {
-              *item = Some(false);
+              *item = Some(bool_val);
             }
           }
         }
         ColumnData::Float(f) => {
           for item in f.iter_mut().take(self.len as usize) {
             if item.is_none() {
-              *item = Some(0_f64);
+              *item = Some(f64_val);
             }
           }
         }
         ColumnData::String(s) => {
           for item in s.iter_mut().take(self.len as usize) {
             if item.is_none() {
-              *item = Some(String::new());
+              *item = Some(str_val.to_owned());
             }
           }
         }
         ColumnData::Integer(j) => {
           for item in j.iter_mut().take(self.len as usize) {
             if item.is_none() {
-              *item = Some(0_i64);
+              *item = Some(i64_val);
             }
           }
         }