PS 2

dframe/README.md

@@ -70,7 +70,7 @@ Using Arrow should also allow seamless interoperability with other data wranglin
 Operations on a `QFrame`, such as copying columns, dropping columns, sorting them or applying some kind of operation on columns, return a new `QFrame`, leaving the original untouched.
 
 Arrow uses a ref-counting mechanism for all the types that involve memory allocation (mainly to address workloads involving memory allocated on a GPGPU, by a SQL database or a mmap-file.)
-This ref-counting mechanism is presented to the user as a pair of methods `Retain/Release` that resp. increment and decrement that reference count.
+This ref-counting mechanism is presented to the user as a pair of methods `Retain`/`Release` that increment and decrement that reference count.
 At first, it would seem this mechanism would prevent to expose an API with "chained methods", as the intermediate `Frame` would be "leaked":
 
 ```go

dframe/dfmat/dfmat.go

@@ -49,19 +49,20 @@ func newConfig(n int) *config {
 func FromMatrix(m mat.Matrix, opts ...Option) *dframe.Frame {
 	var (
 		mem = memory.NewGoAllocator()
-		bld = array.NewFloat64Builder(mem)
 
 		r, c   = m.Dims()
 		arrs   = make([]array.Interface, c)
 		fields = make([]arrow.Field, c)
 	)
-	defer bld.Release()
 
 	cfg := newConfig(c)
 	for _, opt := range opts {
 		opt(cfg)
 	}
 
+	bld := array.NewFloat64Builder(mem)
+	defer bld.Release()
+
 	switch m := m.(type) {
 	case mat.RawColViewer:
 		for i := 0; i < c; i++ {
@@ -99,19 +100,20 @@ func FromMatrix(m mat.Matrix, opts ...Option) *dframe.Frame {
 func FromVector(vec mat.Vector, opts ...Option) *dframe.Frame {
 	var (
 		mem = memory.NewGoAllocator()
-		bld = array.NewFloat64Builder(mem)
 
 		rows   = vec.Len()
 		arrs   = make([]array.Interface, 1)
 		fields = make([]arrow.Field, 1)
 	)
-	defer bld.Release()
 
 	cfg := newConfig(1)
 	for _, opt := range opts {
 		opt(cfg)
 	}
 
+	bld := array.NewFloat64Builder(mem)
+	defer bld.Release()
+
 	switch vec := vec.(type) {
 	case mat.RawColViewer:
 		col := vec.RawColView(0)

dframe/dframe.go

@@ -35,6 +35,8 @@ type Frame struct {
 	rows int64
 }
 
+var _ array.Table = (*Frame)(nil)
+
 // Dict is a map of string to array of data.
 type Dict map[string]interface{}
 
@@ -271,14 +273,12 @@ func FromCols(cols []array.Column, opts ...Option) (*Frame, error) {
 		}
 	}
 
-	{
-		fields := make([]arrow.Field, len(cols))
-		for i, col := range cols {
-			fields[i].Name = col.Name()
-			fields[i].Type = col.DataType()
-		}
-		df.schema = arrow.NewSchema(fields, nil)
+	fields := make([]arrow.Field, len(cols))
+	for i, col := range cols {
+		fields[i].Name = col.Name()
+		fields[i].Type = col.DataType()
 	}
+	df.schema = arrow.NewSchema(fields, nil)
 
 	// validate the data frame and its constituents.
 	// note we retain the columns after having validated the data frame
@@ -622,7 +622,3 @@ func (tx *Tx) Drop(cols ...string) *Tx {
 	tx.df.schema = sc
 	return tx
 }
-
-var (
-	_ array.Table = (*Frame)(nil)
-)

dframe/dframe_example_test.go

@@ -15,7 +15,6 @@ import (
 )
 
 func ExampleFrame_fromTable() {
-
 	pool := memory.NewGoAllocator()
 
 	schema := arrow.NewSchema(
@@ -90,7 +89,6 @@ func ExampleFrame_fromTable() {
 }
 
 func ExampleFrame_fromArrays() {
-
 	pool := memory.NewGoAllocator()
 
 	schema := arrow.NewSchema(
@@ -149,7 +147,6 @@ func ExampleFrame_fromArrays() {
 }
 
 func ExampleFrame_fromCols() {
-
 	pool := memory.NewGoAllocator()
 
 	schema := arrow.NewSchema(
@@ -220,7 +217,6 @@ func ExampleFrame_fromCols() {
 }
 
 func ExampleFrame_fromFrame() {
-
 	pool := memory.NewGoAllocator()
 
 	schema := arrow.NewSchema(