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From w...@apache.org
Subject [2/3] arrow git commit: ARROW-461: [Python] Add Python interfaces to DictionaryArray data, pandas interop
Date Thu, 19 Jan 2017 14:27:41 GMT
http://git-wip-us.apache.org/repos/asf/arrow/blob/9b1b3979/python/src/pyarrow/adapters/pandas.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/adapters/pandas.cc b/python/src/pyarrow/adapters/pandas.cc
index 8c2d350..6623e23 100644
--- a/python/src/pyarrow/adapters/pandas.cc
+++ b/python/src/pyarrow/adapters/pandas.cc
@@ -49,6 +49,7 @@ namespace pyarrow {
 using arrow::Array;
 using arrow::ChunkedArray;
 using arrow::Column;
+using arrow::DictionaryType;
 using arrow::Field;
 using arrow::DataType;
 using arrow::ListType;
@@ -60,7 +61,7 @@ using arrow::Type;
 namespace BitUtil = arrow::BitUtil;
 
 // ----------------------------------------------------------------------
-// Serialization
+// Utility code
 
 template <int TYPE>
 struct npy_traits {};
@@ -242,1577 +243,1730 @@ Status AppendObjectStrings(arrow::StringBuilder& string_builder, PyObject** obje
 }
 
 template <int TYPE>
-class ArrowSerializer {
- public:
-  ArrowSerializer(arrow::MemoryPool* pool, PyArrayObject* arr, PyArrayObject* mask)
-      : pool_(pool), arr_(arr), mask_(mask) {
-    length_ = PyArray_SIZE(arr_);
-  }
+struct arrow_traits {};
 
-  void IndicateType(const std::shared_ptr<Field> field) { field_indicator_ = field; }
+template <>
+struct arrow_traits<Type::BOOL> {
+  static constexpr int npy_type = NPY_BOOL;
+  static constexpr bool supports_nulls = false;
+  static constexpr bool is_boolean = true;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = false;
+};
 
-  Status Convert(std::shared_ptr<Array>* out);
+#define INT_DECL(TYPE)                                      \
+  template <>                                               \
+  struct arrow_traits<Type::TYPE> {                         \
+    static constexpr int npy_type = NPY_##TYPE;             \
+    static constexpr bool supports_nulls = false;           \
+    static constexpr double na_value = NAN;                 \
+    static constexpr bool is_boolean = false;               \
+    static constexpr bool is_numeric_not_nullable = true;   \
+    static constexpr bool is_numeric_nullable = false;      \
+    typedef typename npy_traits<NPY_##TYPE>::value_type T;  \
+  };
 
-  int stride() const { return PyArray_STRIDES(arr_)[0]; }
+INT_DECL(INT8);
+INT_DECL(INT16);
+INT_DECL(INT32);
+INT_DECL(INT64);
+INT_DECL(UINT8);
+INT_DECL(UINT16);
+INT_DECL(UINT32);
+INT_DECL(UINT64);
 
-  Status InitNullBitmap() {
-    int null_bytes = BitUtil::BytesForBits(length_);
+template <>
+struct arrow_traits<Type::FLOAT> {
+  static constexpr int npy_type = NPY_FLOAT32;
+  static constexpr bool supports_nulls = true;
+  static constexpr float na_value = NAN;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = true;
+  typedef typename npy_traits<NPY_FLOAT32>::value_type T;
+};
 
-    null_bitmap_ = std::make_shared<arrow::PoolBuffer>(pool_);
-    RETURN_NOT_OK(null_bitmap_->Resize(null_bytes));
+template <>
+struct arrow_traits<Type::DOUBLE> {
+  static constexpr int npy_type = NPY_FLOAT64;
+  static constexpr bool supports_nulls = true;
+  static constexpr double na_value = NAN;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = true;
+  typedef typename npy_traits<NPY_FLOAT64>::value_type T;
+};
 
-    null_bitmap_data_ = null_bitmap_->mutable_data();
-    memset(null_bitmap_data_, 0, null_bytes);
+static constexpr int64_t kPandasTimestampNull = std::numeric_limits<int64_t>::min();
 
-    return Status::OK();
-  }
+template <>
+struct arrow_traits<Type::TIMESTAMP> {
+  static constexpr int npy_type = NPY_DATETIME;
+  static constexpr bool supports_nulls = true;
+  static constexpr int64_t na_value = kPandasTimestampNull;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = true;
+  typedef typename npy_traits<NPY_DATETIME>::value_type T;
+};
 
-  bool is_strided() const {
-    npy_intp* astrides = PyArray_STRIDES(arr_);
-    return astrides[0] != PyArray_DESCR(arr_)->elsize;
-  }
+template <>
+struct arrow_traits<Type::DATE> {
+  static constexpr int npy_type = NPY_DATETIME;
+  static constexpr bool supports_nulls = true;
+  static constexpr int64_t na_value = kPandasTimestampNull;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = true;
+  typedef typename npy_traits<NPY_DATETIME>::value_type T;
+};
 
- private:
-  Status ConvertData();
+template <>
+struct arrow_traits<Type::STRING> {
+  static constexpr int npy_type = NPY_OBJECT;
+  static constexpr bool supports_nulls = true;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = false;
+};
 
-  Status ConvertDates(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
+template <>
+struct arrow_traits<Type::BINARY> {
+  static constexpr int npy_type = NPY_OBJECT;
+  static constexpr bool supports_nulls = true;
+  static constexpr bool is_boolean = false;
+  static constexpr bool is_numeric_not_nullable = false;
+  static constexpr bool is_numeric_nullable = false;
+};
 
-    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    arrow::TypePtr string_type(new arrow::DateType());
-    arrow::DateBuilder date_builder(pool_, string_type);
-    RETURN_NOT_OK(date_builder.Resize(length_));
+template <typename T>
+struct WrapBytes {};
 
-    Status s;
-    PyObject* obj;
-    for (int64_t i = 0; i < length_; ++i) {
-      obj = objects[i];
-      if (PyDate_CheckExact(obj)) {
-        PyDateTime_Date* pydate = reinterpret_cast<PyDateTime_Date*>(obj);
-        date_builder.Append(PyDate_to_ms(pydate));
-      } else {
-        date_builder.AppendNull();
-      }
-    }
-    return date_builder.Finish(out);
+template <>
+struct WrapBytes<arrow::StringArray> {
+  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
+    return PyUnicode_FromStringAndSize(reinterpret_cast<const char*>(data), length);
   }
+};
 
-  Status ConvertObjectStrings(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
+template <>
+struct WrapBytes<arrow::BinaryArray> {
+  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
+    return PyBytes_FromStringAndSize(reinterpret_cast<const char*>(data), length);
+  }
+};
 
-    // The output type at this point is inconclusive because there may be bytes
-    // and unicode mixed in the object array
+inline void set_numpy_metadata(int type, DataType* datatype, PyArrayObject* out) {
+  if (type == NPY_DATETIME) {
+    PyArray_Descr* descr = PyArray_DESCR(out);
+    auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(descr->c_metadata);
+    if (datatype->type == Type::TIMESTAMP) {
+      auto timestamp_type = static_cast<arrow::TimestampType*>(datatype);
 
-    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    arrow::TypePtr string_type(new arrow::StringType());
-    arrow::StringBuilder string_builder(pool_, string_type);
-    RETURN_NOT_OK(string_builder.Resize(length_));
+      switch (timestamp_type->unit) {
+        case arrow::TimestampType::Unit::SECOND:
+          date_dtype->meta.base = NPY_FR_s;
+          break;
+        case arrow::TimestampType::Unit::MILLI:
+          date_dtype->meta.base = NPY_FR_ms;
+          break;
+        case arrow::TimestampType::Unit::MICRO:
+          date_dtype->meta.base = NPY_FR_us;
+          break;
+        case arrow::TimestampType::Unit::NANO:
+          date_dtype->meta.base = NPY_FR_ns;
+          break;
+      }
+    } else {
+      // datatype->type == Type::DATE
+      date_dtype->meta.base = NPY_FR_D;
+    }
+  }
+}
 
-    Status s;
-    bool have_bytes = false;
-    RETURN_NOT_OK(AppendObjectStrings(string_builder, objects, length_, &have_bytes));
-    RETURN_NOT_OK(string_builder.Finish(out));
+template <typename T>
+inline void ConvertIntegerWithNulls(const ChunkedArray& data, double* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+    // Upcast to double, set NaN as appropriate
 
-    if (have_bytes) {
-      const auto& arr = static_cast<const arrow::StringArray&>(*out->get());
-      *out = std::make_shared<arrow::BinaryArray>(
-          arr.length(), arr.offsets(), arr.data(), arr.null_count(), arr.null_bitmap());
+    for (int i = 0; i < arr->length(); ++i) {
+      *out_values++ = prim_arr->IsNull(i) ? NAN : in_values[i];
     }
-    return Status::OK();
   }
+}
 
-  Status ConvertBooleans(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
+template <typename T>
+inline void ConvertIntegerNoNullsSameType(const ChunkedArray& data, T* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+    memcpy(out_values, in_values, sizeof(T) * arr->length());
+    out_values += arr->length();
+  }
+}
 
-    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+template <typename InType, typename OutType>
+inline void ConvertIntegerNoNullsCast(const ChunkedArray& data, OutType* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
+    for (int32_t i = 0; i < arr->length(); ++i) {
+      *out_values = in_values[i];
+    }
+  }
+}
 
-    int nbytes = BitUtil::BytesForBits(length_);
-    auto data = std::make_shared<arrow::PoolBuffer>(pool_);
-    RETURN_NOT_OK(data->Resize(nbytes));
-    uint8_t* bitmap = data->mutable_data();
-    memset(bitmap, 0, nbytes);
+static Status ConvertBooleanWithNulls(const ChunkedArray& data, PyObject** out_values) {
+  PyAcquireGIL lock;
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto bool_arr = static_cast<arrow::BooleanArray*>(arr.get());
 
-    int64_t null_count = 0;
-    for (int64_t i = 0; i < length_; ++i) {
-      if (objects[i] == Py_True) {
-        BitUtil::SetBit(bitmap, i);
-        BitUtil::SetBit(null_bitmap_data_, i);
-      } else if (objects[i] != Py_False) {
-        ++null_count;
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      if (bool_arr->IsNull(i)) {
+        Py_INCREF(Py_None);
+        *out_values++ = Py_None;
+      } else if (bool_arr->Value(i)) {
+        // True
+        Py_INCREF(Py_True);
+        *out_values++ = Py_True;
       } else {
-        BitUtil::SetBit(null_bitmap_data_, i);
+        // False
+        Py_INCREF(Py_False);
+        *out_values++ = Py_False;
       }
     }
-
-    *out = std::make_shared<arrow::BooleanArray>(length_, data, null_count, null_bitmap_);
-
-    return Status::OK();
   }
+  return Status::OK();
+}
 
-  template <int ITEM_TYPE, typename ArrowType>
-  Status ConvertTypedLists(
-      const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out);
-
-#define LIST_CASE(TYPE, NUMPY_TYPE, ArrowType)                            \
-  case Type::TYPE: {                                                      \
-    return ConvertTypedLists<NUMPY_TYPE, ::arrow::ArrowType>(field, out); \
+static void ConvertBooleanNoNulls(const ChunkedArray& data, uint8_t* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto bool_arr = static_cast<arrow::BooleanArray*>(arr.get());
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      *out_values++ = static_cast<uint8_t>(bool_arr->Value(i));
+    }
   }
+}
 
-  Status ConvertLists(const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
-    switch (field->type->type) {
-      LIST_CASE(UINT8, NPY_UINT8, UInt8Type)
-      LIST_CASE(INT8, NPY_INT8, Int8Type)
-      LIST_CASE(UINT16, NPY_UINT16, UInt16Type)
-      LIST_CASE(INT16, NPY_INT16, Int16Type)
-      LIST_CASE(UINT32, NPY_UINT32, UInt32Type)
-      LIST_CASE(INT32, NPY_INT32, Int32Type)
-      LIST_CASE(UINT64, NPY_UINT64, UInt64Type)
-      LIST_CASE(INT64, NPY_INT64, Int64Type)
-      LIST_CASE(TIMESTAMP, NPY_DATETIME, TimestampType)
-      LIST_CASE(FLOAT, NPY_FLOAT, FloatType)
-      LIST_CASE(DOUBLE, NPY_DOUBLE, DoubleType)
-      LIST_CASE(STRING, NPY_OBJECT, StringType)
-      default:
-        return Status::TypeError("Unknown list item type");
-    }
-
-    return Status::TypeError("Unknown list type");
-  }
-
-  Status MakeDataType(std::shared_ptr<DataType>* out);
-
-  arrow::MemoryPool* pool_;
-
-  PyArrayObject* arr_;
-  PyArrayObject* mask_;
-
-  int64_t length_;
-
-  std::shared_ptr<Field> field_indicator_;
-  std::shared_ptr<arrow::Buffer> data_;
-  std::shared_ptr<arrow::ResizableBuffer> null_bitmap_;
-  uint8_t* null_bitmap_data_;
-};
+template <typename ArrayType>
+inline Status ConvertBinaryLike(const ChunkedArray& data, PyObject** out_values) {
+  PyAcquireGIL lock;
+  for (int c = 0; c < data.num_chunks(); c++) {
+    auto arr = static_cast<ArrayType*>(data.chunk(c).get());
 
-// Returns null count
-static int64_t MaskToBitmap(PyArrayObject* mask, int64_t length, uint8_t* bitmap) {
-  int64_t null_count = 0;
-  const uint8_t* mask_values = static_cast<const uint8_t*>(PyArray_DATA(mask));
-  // TODO(wesm): strided null mask
-  for (int i = 0; i < length; ++i) {
-    if (mask_values[i]) {
-      ++null_count;
-    } else {
-      BitUtil::SetBit(bitmap, i);
+    const uint8_t* data_ptr;
+    int32_t length;
+    const bool has_nulls = data.null_count() > 0;
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      if (has_nulls && arr->IsNull(i)) {
+        Py_INCREF(Py_None);
+        *out_values = Py_None;
+      } else {
+        data_ptr = arr->GetValue(i, &length);
+        *out_values = WrapBytes<ArrayType>::Wrap(data_ptr, length);
+        if (*out_values == nullptr) {
+          PyErr_Clear();
+          std::stringstream ss;
+          ss << "Wrapping "
+             << std::string(reinterpret_cast<const char*>(data_ptr), length) << " failed";
+          return Status::UnknownError(ss.str());
+        }
+      }
+      ++out_values;
     }
   }
-  return null_count;
-}
-
-template <int TYPE>
-inline Status ArrowSerializer<TYPE>::MakeDataType(std::shared_ptr<DataType>* out) {
-  out->reset(new typename npy_traits<TYPE>::TypeClass());
   return Status::OK();
 }
 
-template <>
-inline Status ArrowSerializer<NPY_DATETIME>::MakeDataType(
-    std::shared_ptr<DataType>* out) {
-  PyArray_Descr* descr = PyArray_DESCR(arr_);
-  auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(descr->c_metadata);
-  arrow::TimestampType::Unit unit;
+template <typename ArrowType>
+inline Status ConvertListsLike(
+    const std::shared_ptr<Column>& col, PyObject** out_values) {
+  const ChunkedArray& data = *col->data().get();
+  auto list_type = std::static_pointer_cast<ListType>(col->type());
 
-  switch (date_dtype->meta.base) {
-    case NPY_FR_s:
-      unit = arrow::TimestampType::Unit::SECOND;
-      break;
-    case NPY_FR_ms:
-      unit = arrow::TimestampType::Unit::MILLI;
-      break;
-    case NPY_FR_us:
-      unit = arrow::TimestampType::Unit::MICRO;
-      break;
-    case NPY_FR_ns:
-      unit = arrow::TimestampType::Unit::NANO;
-      break;
-    default:
-      return Status::Invalid("Unknown NumPy datetime unit");
+  // Get column of underlying value arrays
+  std::vector<std::shared_ptr<Array>> value_arrays;
+  for (int c = 0; c < data.num_chunks(); c++) {
+    auto arr = std::static_pointer_cast<arrow::ListArray>(data.chunk(c));
+    value_arrays.emplace_back(arr->values());
   }
+  auto flat_column = std::make_shared<Column>(list_type->value_field(), value_arrays);
+  // TODO(ARROW-489): Currently we don't have a Python reference for single columns.
+  //    Storing a reference to the whole Array would be to expensive.
+  PyObject* numpy_array;
+  RETURN_NOT_OK(ConvertColumnToPandas(flat_column, nullptr, &numpy_array));
 
-  out->reset(new arrow::TimestampType(unit));
-  return Status::OK();
-}
-
-template <int TYPE>
-inline Status ArrowSerializer<TYPE>::Convert(std::shared_ptr<Array>* out) {
-  typedef npy_traits<TYPE> traits;
+  PyAcquireGIL lock;
 
-  if (mask_ != nullptr || traits::supports_nulls) { RETURN_NOT_OK(InitNullBitmap()); }
+  for (int c = 0; c < data.num_chunks(); c++) {
+    auto arr = std::static_pointer_cast<arrow::ListArray>(data.chunk(c));
 
-  int64_t null_count = 0;
-  if (mask_ != nullptr) {
-    null_count = MaskToBitmap(mask_, length_, null_bitmap_data_);
-  } else if (traits::supports_nulls) {
-    null_count = ValuesToBitmap<TYPE>(PyArray_DATA(arr_), length_, null_bitmap_data_);
+    const uint8_t* data_ptr;
+    int32_t length;
+    const bool has_nulls = data.null_count() > 0;
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      if (has_nulls && arr->IsNull(i)) {
+        Py_INCREF(Py_None);
+        *out_values = Py_None;
+      } else {
+        PyObject* start = PyLong_FromLong(arr->value_offset(i));
+        PyObject* end = PyLong_FromLong(arr->value_offset(i + 1));
+        PyObject* slice = PySlice_New(start, end, NULL);
+        *out_values = PyObject_GetItem(numpy_array, slice);
+        Py_DECREF(start);
+        Py_DECREF(end);
+        Py_DECREF(slice);
+      }
+      ++out_values;
+    }
   }
 
-  RETURN_NOT_OK(ConvertData());
-  std::shared_ptr<DataType> type;
-  RETURN_NOT_OK(MakeDataType(&type));
-  RETURN_NOT_OK(MakePrimitiveArray(type, length_, data_, null_count, null_bitmap_, out));
+  Py_XDECREF(numpy_array);
   return Status::OK();
 }
 
-template <>
-inline Status ArrowSerializer<NPY_OBJECT>::Convert(std::shared_ptr<Array>* out) {
-  // Python object arrays are annoying, since we could have one of:
-  //
-  // * Strings
-  // * Booleans with nulls
-  // * Mixed type (not supported at the moment by arrow format)
-  //
-  // Additionally, nulls may be encoded either as np.nan or None. So we have to
-  // do some type inference and conversion
-
-  RETURN_NOT_OK(InitNullBitmap());
+template <typename T>
+inline void ConvertNumericNullable(const ChunkedArray& data, T na_value, T* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
 
-  // TODO: mask not supported here
-  const PyObject** objects = reinterpret_cast<const PyObject**>(PyArray_DATA(arr_));
-  {
-    PyAcquireGIL lock;
-    PyDateTime_IMPORT;
-  }
+    const uint8_t* valid_bits = arr->null_bitmap_data();
 
-  if (field_indicator_) {
-    switch (field_indicator_->type->type) {
-      case Type::STRING:
-        return ConvertObjectStrings(out);
-      case Type::BOOL:
-        return ConvertBooleans(out);
-      case Type::DATE:
-        return ConvertDates(out);
-      case Type::LIST: {
-        auto list_field = static_cast<ListType*>(field_indicator_->type.get());
-        return ConvertLists(list_field->value_field(), out);
-      }
-      default:
-        return Status::TypeError("No known conversion to Arrow type");
-    }
-  } else {
-    for (int64_t i = 0; i < length_; ++i) {
-      if (PyObject_is_null(objects[i])) {
-        continue;
-      } else if (PyObject_is_string(objects[i])) {
-        return ConvertObjectStrings(out);
-      } else if (PyBool_Check(objects[i])) {
-        return ConvertBooleans(out);
-      } else if (PyDate_CheckExact(objects[i])) {
-        return ConvertDates(out);
-      } else {
-        return Status::TypeError("unhandled python type");
+    if (arr->null_count() > 0) {
+      for (int64_t i = 0; i < arr->length(); ++i) {
+        *out_values++ = BitUtil::BitNotSet(valid_bits, i) ? na_value : in_values[i];
       }
+    } else {
+      memcpy(out_values, in_values, sizeof(T) * arr->length());
+      out_values += arr->length();
     }
   }
-
-  return Status::TypeError("Unable to infer type of object array, were all null");
 }
 
-template <int TYPE>
-inline Status ArrowSerializer<TYPE>::ConvertData() {
-  // TODO(wesm): strided arrays
-  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
+template <typename InType, typename OutType>
+inline void ConvertNumericNullableCast(
+    const ChunkedArray& data, OutType na_value, OutType* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
 
-  data_ = std::make_shared<NumPyBuffer>(arr_);
-  return Status::OK();
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      *out_values++ = arr->IsNull(i) ? na_value : static_cast<OutType>(in_values[i]);
+    }
+  }
 }
 
-template <>
-inline Status ArrowSerializer<NPY_BOOL>::ConvertData() {
-  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
-
-  int nbytes = BitUtil::BytesForBits(length_);
-  auto buffer = std::make_shared<arrow::PoolBuffer>(pool_);
-  RETURN_NOT_OK(buffer->Resize(nbytes));
-
-  const uint8_t* values = reinterpret_cast<const uint8_t*>(PyArray_DATA(arr_));
-
-  uint8_t* bitmap = buffer->mutable_data();
+template <typename T>
+inline void ConvertDates(const ChunkedArray& data, T na_value, T* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
 
-  memset(bitmap, 0, nbytes);
-  for (int i = 0; i < length_; ++i) {
-    if (values[i] > 0) { BitUtil::SetBit(bitmap, i); }
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      // There are 1000 * 60 * 60 * 24 = 86400000ms in a day
+      *out_values++ = arr->IsNull(i) ? na_value : in_values[i] / 86400000;
+    }
   }
+}
 
-  data_ = buffer;
+template <typename InType, int SHIFT>
+inline void ConvertDatetimeNanos(const ChunkedArray& data, int64_t* out_values) {
+  for (int c = 0; c < data.num_chunks(); c++) {
+    const std::shared_ptr<Array> arr = data.chunk(c);
+    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
 
-  return Status::OK();
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      *out_values++ = arr->IsNull(i) ? kPandasTimestampNull
+                                     : (static_cast<int64_t>(in_values[i]) * SHIFT);
+    }
+  }
 }
 
-template <int TYPE>
-template <int ITEM_TYPE, typename ArrowType>
-inline Status ArrowSerializer<TYPE>::ConvertTypedLists(
-    const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
-  typedef npy_traits<ITEM_TYPE> traits;
-  typedef typename traits::value_type T;
-  typedef typename traits::BuilderClass BuilderT;
+// ----------------------------------------------------------------------
+// pandas 0.x DataFrame conversion internals
 
-  auto value_builder = std::make_shared<BuilderT>(pool_, field->type);
-  ListBuilder list_builder(pool_, value_builder);
-  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-  for (int64_t i = 0; i < length_; ++i) {
-    if (PyObject_is_null(objects[i])) {
-      RETURN_NOT_OK(list_builder.AppendNull());
-    } else if (PyArray_Check(objects[i])) {
-      auto numpy_array = reinterpret_cast<PyArrayObject*>(objects[i]);
-      RETURN_NOT_OK(list_builder.Append(true));
+class PandasBlock {
+ public:
+  enum type {
+    OBJECT,
+    UINT8,
+    INT8,
+    UINT16,
+    INT16,
+    UINT32,
+    INT32,
+    UINT64,
+    INT64,
+    FLOAT,
+    DOUBLE,
+    BOOL,
+    DATETIME,
+    CATEGORICAL
+  };
 
-      // TODO(uwe): Support more complex numpy array structures
-      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, ITEM_TYPE));
+  PandasBlock(int64_t num_rows, int num_columns)
+      : num_rows_(num_rows), num_columns_(num_columns) {}
+  virtual ~PandasBlock() {}
 
-      int32_t size = PyArray_DIM(numpy_array, 0);
-      auto data = reinterpret_cast<const T*>(PyArray_DATA(numpy_array));
-      if (traits::supports_nulls) {
-        null_bitmap_->Resize(size, false);
-        // TODO(uwe): A bitmap would be more space-efficient but the Builder API doesn't
-        // currently support this.
-        // ValuesToBitmap<ITEM_TYPE>(data, size, null_bitmap_->mutable_data());
-        ValuesToBytemap<ITEM_TYPE>(data, size, null_bitmap_->mutable_data());
-        RETURN_NOT_OK(value_builder->Append(data, size, null_bitmap_->data()));
-      } else {
-        RETURN_NOT_OK(value_builder->Append(data, size));
-      }
-    } else if (PyList_Check(objects[i])) {
-      return Status::TypeError("Python lists are not yet supported");
-    } else {
-      return Status::TypeError("Unsupported Python type for list items");
-    }
-  }
-  return list_builder.Finish(out);
-}
+  virtual Status Allocate() = 0;
+  virtual Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) = 0;
 
-template <>
-template <>
-inline Status
-ArrowSerializer<NPY_OBJECT>::ConvertTypedLists<NPY_OBJECT, ::arrow::StringType>(
-    const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
-  // TODO: If there are bytes involed, convert to Binary representation
-  bool have_bytes = false;
+  PyObject* block_arr() const { return block_arr_.obj(); }
 
-  auto value_builder = std::make_shared<arrow::StringBuilder>(pool_, field->type);
-  ListBuilder list_builder(pool_, value_builder);
-  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-  for (int64_t i = 0; i < length_; ++i) {
-    if (PyObject_is_null(objects[i])) {
-      RETURN_NOT_OK(list_builder.AppendNull());
-    } else if (PyArray_Check(objects[i])) {
-      auto numpy_array = reinterpret_cast<PyArrayObject*>(objects[i]);
-      RETURN_NOT_OK(list_builder.Append(true));
+  virtual Status GetPyResult(PyObject** output) {
+    PyObject* result = PyDict_New();
+    RETURN_IF_PYERROR();
 
-      // TODO(uwe): Support more complex numpy array structures
-      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, NPY_OBJECT));
+    PyDict_SetItemString(result, "block", block_arr_.obj());
+    PyDict_SetItemString(result, "placement", placement_arr_.obj());
 
-      int32_t size = PyArray_DIM(numpy_array, 0);
-      auto data = reinterpret_cast<PyObject**>(PyArray_DATA(numpy_array));
-      RETURN_NOT_OK(AppendObjectStrings(*value_builder.get(), data, size, &have_bytes));
-    } else if (PyList_Check(objects[i])) {
-      return Status::TypeError("Python lists are not yet supported");
+    *output = result;
+
+    return Status::OK();
+  }
+
+ protected:
+  Status AllocateNDArray(int npy_type, int ndim = 2) {
+    PyAcquireGIL lock;
+
+    PyObject* block_arr;
+    if (ndim == 2) {
+      npy_intp block_dims[2] = {num_columns_, num_rows_};
+      block_arr = PyArray_SimpleNew(2, block_dims, npy_type);
     } else {
-      return Status::TypeError("Unsupported Python type for list items");
+      npy_intp block_dims[1] = {num_rows_};
+      block_arr = PyArray_SimpleNew(1, block_dims, npy_type);
     }
-  }
-  return list_builder.Finish(out);
-}
 
-template <>
-inline Status ArrowSerializer<NPY_OBJECT>::ConvertData() {
-  return Status::TypeError("NYI");
-}
+    if (block_arr == NULL) {
+      // TODO(wesm): propagating Python exception
+      return Status::OK();
+    }
 
-#define TO_ARROW_CASE(TYPE)                                 \
-  case NPY_##TYPE: {                                        \
-    ArrowSerializer<NPY_##TYPE> converter(pool, arr, mask); \
-    RETURN_NOT_OK(converter.Convert(out));                  \
-  } break;
+    npy_intp placement_dims[1] = {num_columns_};
+    PyObject* placement_arr = PyArray_SimpleNew(1, placement_dims, NPY_INT64);
+    if (placement_arr == NULL) {
+      // TODO(wesm): propagating Python exception
+      return Status::OK();
+    }
 
-Status PandasMaskedToArrow(arrow::MemoryPool* pool, PyObject* ao, PyObject* mo,
-    const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
-  PyArrayObject* arr = reinterpret_cast<PyArrayObject*>(ao);
-  PyArrayObject* mask = nullptr;
+    block_arr_.reset(block_arr);
+    placement_arr_.reset(placement_arr);
 
-  if (mo != nullptr) { mask = reinterpret_cast<PyArrayObject*>(mo); }
+    block_data_ = reinterpret_cast<uint8_t*>(
+        PyArray_DATA(reinterpret_cast<PyArrayObject*>(block_arr)));
 
-  if (PyArray_NDIM(arr) != 1) {
-    return Status::Invalid("only handle 1-dimensional arrays");
+    placement_data_ = reinterpret_cast<int64_t*>(
+        PyArray_DATA(reinterpret_cast<PyArrayObject*>(placement_arr)));
+
+    return Status::OK();
   }
 
-  switch (PyArray_DESCR(arr)->type_num) {
-    TO_ARROW_CASE(BOOL);
-    TO_ARROW_CASE(INT8);
-    TO_ARROW_CASE(INT16);
-    TO_ARROW_CASE(INT32);
-    TO_ARROW_CASE(INT64);
-    TO_ARROW_CASE(UINT8);
-    TO_ARROW_CASE(UINT16);
-    TO_ARROW_CASE(UINT32);
-    TO_ARROW_CASE(UINT64);
-    TO_ARROW_CASE(FLOAT32);
-    TO_ARROW_CASE(FLOAT64);
-    TO_ARROW_CASE(DATETIME);
-    case NPY_OBJECT: {
-      ArrowSerializer<NPY_OBJECT> converter(pool, arr, mask);
-      converter.IndicateType(field);
-      RETURN_NOT_OK(converter.Convert(out));
-    } break;
-    default:
+  int64_t num_rows_;
+  int num_columns_;
+
+  OwnedRef block_arr_;
+  uint8_t* block_data_;
+
+  // ndarray<int32>
+  OwnedRef placement_arr_;
+  int64_t* placement_data_;
+
+  DISALLOW_COPY_AND_ASSIGN(PandasBlock);
+};
+
+#define CONVERTLISTSLIKE_CASE(ArrowType, ArrowEnum)                         \
+  case Type::ArrowEnum:                                                     \
+    RETURN_NOT_OK((ConvertListsLike<::arrow::ArrowType>(col, out_buffer))); \
+    break;
+
+class ObjectBlock : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
+  virtual ~ObjectBlock() {}
+
+  Status Allocate() override { return AllocateNDArray(NPY_OBJECT); }
+
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
+
+    PyObject** out_buffer =
+        reinterpret_cast<PyObject**>(block_data_) + rel_placement * num_rows_;
+
+    const ChunkedArray& data = *col->data().get();
+
+    if (type == Type::BOOL) {
+      RETURN_NOT_OK(ConvertBooleanWithNulls(data, out_buffer));
+    } else if (type == Type::BINARY) {
+      RETURN_NOT_OK(ConvertBinaryLike<arrow::BinaryArray>(data, out_buffer));
+    } else if (type == Type::STRING) {
+      RETURN_NOT_OK(ConvertBinaryLike<arrow::StringArray>(data, out_buffer));
+    } else if (type == Type::LIST) {
+      auto list_type = std::static_pointer_cast<ListType>(col->type());
+      switch (list_type->value_type()->type) {
+        CONVERTLISTSLIKE_CASE(UInt8Type, UINT8)
+        CONVERTLISTSLIKE_CASE(Int8Type, INT8)
+        CONVERTLISTSLIKE_CASE(UInt16Type, UINT16)
+        CONVERTLISTSLIKE_CASE(Int16Type, INT16)
+        CONVERTLISTSLIKE_CASE(UInt32Type, UINT32)
+        CONVERTLISTSLIKE_CASE(Int32Type, INT32)
+        CONVERTLISTSLIKE_CASE(UInt64Type, UINT64)
+        CONVERTLISTSLIKE_CASE(Int64Type, INT64)
+        CONVERTLISTSLIKE_CASE(TimestampType, TIMESTAMP)
+        CONVERTLISTSLIKE_CASE(FloatType, FLOAT)
+        CONVERTLISTSLIKE_CASE(DoubleType, DOUBLE)
+        CONVERTLISTSLIKE_CASE(StringType, STRING)
+        default: {
+          std::stringstream ss;
+          ss << "Not implemented type for lists: " << list_type->value_type()->ToString();
+          return Status::NotImplemented(ss.str());
+        }
+      }
+    } else {
       std::stringstream ss;
-      ss << "unsupported type " << PyArray_DESCR(arr)->type_num << std::endl;
+      ss << "Unsupported type for object array output: " << col->type()->ToString();
       return Status::NotImplemented(ss.str());
+    }
+
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
   }
-  return Status::OK();
-}
+};
 
-Status PandasToArrow(arrow::MemoryPool* pool, PyObject* ao,
-    const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
-  return PandasMaskedToArrow(pool, ao, nullptr, field, out);
-}
+template <int ARROW_TYPE, typename C_TYPE>
+class IntBlock : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
 
-// ----------------------------------------------------------------------
-// Deserialization
+  Status Allocate() override {
+    return AllocateNDArray(arrow_traits<ARROW_TYPE>::npy_type);
+  }
 
-template <int TYPE>
-struct arrow_traits {};
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
 
-template <>
-struct arrow_traits<arrow::Type::BOOL> {
-  static constexpr int npy_type = NPY_BOOL;
-  static constexpr bool supports_nulls = false;
-  static constexpr bool is_boolean = true;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = false;
-};
+    C_TYPE* out_buffer =
+        reinterpret_cast<C_TYPE*>(block_data_) + rel_placement * num_rows_;
 
-#define INT_DECL(TYPE)                                     \
-  template <>                                              \
-  struct arrow_traits<arrow::Type::TYPE> {                 \
-    static constexpr int npy_type = NPY_##TYPE;            \
-    static constexpr bool supports_nulls = false;          \
-    static constexpr double na_value = NAN;                \
-    static constexpr bool is_boolean = false;              \
-    static constexpr bool is_numeric_not_nullable = true;  \
-    static constexpr bool is_numeric_nullable = false;     \
-    typedef typename npy_traits<NPY_##TYPE>::value_type T; \
-  };
+    const ChunkedArray& data = *col->data().get();
 
-INT_DECL(INT8);
-INT_DECL(INT16);
-INT_DECL(INT32);
-INT_DECL(INT64);
-INT_DECL(UINT8);
-INT_DECL(UINT16);
-INT_DECL(UINT32);
-INT_DECL(UINT64);
+    if (type != ARROW_TYPE) { return Status::NotImplemented(col->type()->ToString()); }
 
-template <>
-struct arrow_traits<arrow::Type::FLOAT> {
-  static constexpr int npy_type = NPY_FLOAT32;
-  static constexpr bool supports_nulls = true;
-  static constexpr float na_value = NAN;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = true;
-  typedef typename npy_traits<NPY_FLOAT32>::value_type T;
+    ConvertIntegerNoNullsSameType<C_TYPE>(data, out_buffer);
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
+  }
 };
 
-template <>
-struct arrow_traits<arrow::Type::DOUBLE> {
-  static constexpr int npy_type = NPY_FLOAT64;
-  static constexpr bool supports_nulls = true;
-  static constexpr double na_value = NAN;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = true;
-  typedef typename npy_traits<NPY_FLOAT64>::value_type T;
-};
+using UInt8Block = IntBlock<Type::UINT8, uint8_t>;
+using Int8Block = IntBlock<Type::INT8, int8_t>;
+using UInt16Block = IntBlock<Type::UINT16, uint16_t>;
+using Int16Block = IntBlock<Type::INT16, int16_t>;
+using UInt32Block = IntBlock<Type::UINT32, uint32_t>;
+using Int32Block = IntBlock<Type::INT32, int32_t>;
+using UInt64Block = IntBlock<Type::UINT64, uint64_t>;
+using Int64Block = IntBlock<Type::INT64, int64_t>;
 
-static constexpr int64_t kPandasTimestampNull = std::numeric_limits<int64_t>::min();
+class Float32Block : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
 
-template <>
-struct arrow_traits<arrow::Type::TIMESTAMP> {
-  static constexpr int npy_type = NPY_DATETIME;
-  static constexpr bool supports_nulls = true;
-  static constexpr int64_t na_value = kPandasTimestampNull;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = true;
-  typedef typename npy_traits<NPY_DATETIME>::value_type T;
-};
+  Status Allocate() override { return AllocateNDArray(NPY_FLOAT32); }
 
-template <>
-struct arrow_traits<arrow::Type::DATE> {
-  static constexpr int npy_type = NPY_DATETIME;
-  static constexpr bool supports_nulls = true;
-  static constexpr int64_t na_value = kPandasTimestampNull;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = true;
-  typedef typename npy_traits<NPY_DATETIME>::value_type T;
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
+
+    if (type != Type::FLOAT) { return Status::NotImplemented(col->type()->ToString()); }
+
+    float* out_buffer = reinterpret_cast<float*>(block_data_) + rel_placement * num_rows_;
+
+    ConvertNumericNullable<float>(*col->data().get(), NAN, out_buffer);
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
+  }
 };
 
-template <>
-struct arrow_traits<arrow::Type::STRING> {
-  static constexpr int npy_type = NPY_OBJECT;
-  static constexpr bool supports_nulls = true;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = false;
-};
+class Float64Block : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
+
+  Status Allocate() override { return AllocateNDArray(NPY_FLOAT64); }
+
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
+
+    double* out_buffer =
+        reinterpret_cast<double*>(block_data_) + rel_placement * num_rows_;
+
+    const ChunkedArray& data = *col->data().get();
+
+#define INTEGER_CASE(IN_TYPE)                         \
+  ConvertIntegerWithNulls<IN_TYPE>(data, out_buffer); \
+  break;
+
+    switch (type) {
+      case Type::UINT8:
+        INTEGER_CASE(uint8_t);
+      case Type::INT8:
+        INTEGER_CASE(int8_t);
+      case Type::UINT16:
+        INTEGER_CASE(uint16_t);
+      case Type::INT16:
+        INTEGER_CASE(int16_t);
+      case Type::UINT32:
+        INTEGER_CASE(uint32_t);
+      case Type::INT32:
+        INTEGER_CASE(int32_t);
+      case Type::UINT64:
+        INTEGER_CASE(uint64_t);
+      case Type::INT64:
+        INTEGER_CASE(int64_t);
+      case Type::FLOAT:
+        ConvertNumericNullableCast<float, double>(data, NAN, out_buffer);
+        break;
+      case Type::DOUBLE:
+        ConvertNumericNullable<double>(data, NAN, out_buffer);
+        break;
+      default:
+        return Status::NotImplemented(col->type()->ToString());
+    }
+
+#undef INTEGER_CASE
 
-template <>
-struct arrow_traits<arrow::Type::BINARY> {
-  static constexpr int npy_type = NPY_OBJECT;
-  static constexpr bool supports_nulls = true;
-  static constexpr bool is_boolean = false;
-  static constexpr bool is_numeric_not_nullable = false;
-  static constexpr bool is_numeric_nullable = false;
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
+  }
 };
 
-template <typename T>
-struct WrapBytes {};
+class BoolBlock : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
 
-template <>
-struct WrapBytes<arrow::StringArray> {
-  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
-    return PyUnicode_FromStringAndSize(reinterpret_cast<const char*>(data), length);
-  }
-};
+  Status Allocate() override { return AllocateNDArray(NPY_BOOL); }
 
-template <>
-struct WrapBytes<arrow::BinaryArray> {
-  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
-    return PyBytes_FromStringAndSize(reinterpret_cast<const char*>(data), length);
-  }
-};
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
 
-inline void set_numpy_metadata(int type, DataType* datatype, PyArrayObject* out) {
-  if (type == NPY_DATETIME) {
-    PyArray_Descr* descr = PyArray_DESCR(out);
-    auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(descr->c_metadata);
-    if (datatype->type == arrow::Type::TIMESTAMP) {
-      auto timestamp_type = static_cast<arrow::TimestampType*>(datatype);
+    if (type != Type::BOOL) { return Status::NotImplemented(col->type()->ToString()); }
 
-      switch (timestamp_type->unit) {
-        case arrow::TimestampType::Unit::SECOND:
-          date_dtype->meta.base = NPY_FR_s;
-          break;
-        case arrow::TimestampType::Unit::MILLI:
-          date_dtype->meta.base = NPY_FR_ms;
-          break;
-        case arrow::TimestampType::Unit::MICRO:
-          date_dtype->meta.base = NPY_FR_us;
-          break;
-        case arrow::TimestampType::Unit::NANO:
-          date_dtype->meta.base = NPY_FR_ns;
-          break;
-      }
-    } else {
-      // datatype->type == arrow::Type::DATE
-      date_dtype->meta.base = NPY_FR_D;
-    }
+    uint8_t* out_buffer =
+        reinterpret_cast<uint8_t*>(block_data_) + rel_placement * num_rows_;
+
+    ConvertBooleanNoNulls(*col->data().get(), out_buffer);
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
   }
-}
+};
 
-template <typename T>
-inline void ConvertIntegerWithNulls(const ChunkedArray& data, double* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
-    // Upcast to double, set NaN as appropriate
+class DatetimeBlock : public PandasBlock {
+ public:
+  using PandasBlock::PandasBlock;
 
-    for (int i = 0; i < arr->length(); ++i) {
-      *out_values++ = prim_arr->IsNull(i) ? NAN : in_values[i];
-    }
-  }
-}
+  Status Allocate() override {
+    RETURN_NOT_OK(AllocateNDArray(NPY_DATETIME));
 
-template <typename T>
-inline void ConvertIntegerNoNullsSameType(const ChunkedArray& data, T* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
-    memcpy(out_values, in_values, sizeof(T) * arr->length());
-    out_values += arr->length();
+    PyAcquireGIL lock;
+    auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(
+        PyArray_DESCR(reinterpret_cast<PyArrayObject*>(block_arr_.obj()))->c_metadata);
+    date_dtype->meta.base = NPY_FR_ns;
+    return Status::OK();
   }
-}
 
-template <typename InType, typename OutType>
-inline void ConvertIntegerNoNullsCast(const ChunkedArray& data, OutType* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
-    for (int32_t i = 0; i < arr->length(); ++i) {
-      *out_values = in_values[i];
-    }
-  }
-}
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    Type::type type = col->type()->type;
 
-static Status ConvertBooleanWithNulls(const ChunkedArray& data, PyObject** out_values) {
-  PyAcquireGIL lock;
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto bool_arr = static_cast<arrow::BooleanArray*>(arr.get());
+    int64_t* out_buffer =
+        reinterpret_cast<int64_t*>(block_data_) + rel_placement * num_rows_;
 
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      if (bool_arr->IsNull(i)) {
-        Py_INCREF(Py_None);
-        *out_values++ = Py_None;
-      } else if (bool_arr->Value(i)) {
-        // True
-        Py_INCREF(Py_True);
-        *out_values++ = Py_True;
+    const ChunkedArray& data = *col.get()->data();
+
+    if (type == Type::DATE) {
+      // DateType is millisecond timestamp stored as int64_t
+      // TODO(wesm): Do we want to make sure to zero out the milliseconds?
+      ConvertDatetimeNanos<int64_t, 1000000L>(data, out_buffer);
+    } else if (type == Type::TIMESTAMP) {
+      auto ts_type = static_cast<arrow::TimestampType*>(col->type().get());
+
+      if (ts_type->unit == arrow::TimeUnit::NANO) {
+        ConvertNumericNullable<int64_t>(data, kPandasTimestampNull, out_buffer);
+      } else if (ts_type->unit == arrow::TimeUnit::MICRO) {
+        ConvertDatetimeNanos<int64_t, 1000L>(data, out_buffer);
+      } else if (ts_type->unit == arrow::TimeUnit::MILLI) {
+        ConvertDatetimeNanos<int64_t, 1000000L>(data, out_buffer);
+      } else if (ts_type->unit == arrow::TimeUnit::SECOND) {
+        ConvertDatetimeNanos<int64_t, 1000000000L>(data, out_buffer);
       } else {
-        // False
-        Py_INCREF(Py_False);
-        *out_values++ = Py_False;
+        return Status::NotImplemented("Unsupported time unit");
       }
+    } else {
+      return Status::NotImplemented(col->type()->ToString());
     }
+
+    placement_data_[rel_placement] = abs_placement;
+    return Status::OK();
   }
-  return Status::OK();
-}
+};
 
-static void ConvertBooleanNoNulls(const ChunkedArray& data, uint8_t* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto bool_arr = static_cast<arrow::BooleanArray*>(arr.get());
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values++ = static_cast<uint8_t>(bool_arr->Value(i));
+template <int ARROW_INDEX_TYPE>
+class CategoricalBlock : public PandasBlock {
+ public:
+  CategoricalBlock(int64_t num_rows) : PandasBlock(num_rows, 1) {}
+
+  Status Allocate() override {
+    constexpr int npy_type = arrow_traits<ARROW_INDEX_TYPE>::npy_type;
+
+    if (!(npy_type == NPY_INT8 || npy_type == NPY_INT16 || npy_type == NPY_INT32 ||
+            npy_type == NPY_INT64)) {
+      return Status::Invalid("Category indices must be signed integers");
     }
+    return AllocateNDArray(npy_type, 1);
   }
-}
 
-template <typename ArrayType>
-inline Status ConvertBinaryLike(const ChunkedArray& data, PyObject** out_values) {
-  PyAcquireGIL lock;
-  for (int c = 0; c < data.num_chunks(); c++) {
-    auto arr = static_cast<ArrayType*>(data.chunk(c).get());
+  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
+      int64_t rel_placement) override {
+    using T = typename arrow_traits<ARROW_INDEX_TYPE>::T;
 
-    const uint8_t* data_ptr;
-    int32_t length;
-    const bool has_nulls = data.null_count() > 0;
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      if (has_nulls && arr->IsNull(i)) {
-        Py_INCREF(Py_None);
-        *out_values = Py_None;
-      } else {
-        data_ptr = arr->GetValue(i, &length);
-        *out_values = WrapBytes<ArrayType>::Wrap(data_ptr, length);
-        if (*out_values == nullptr) {
-          return Status::UnknownError("String initialization failed");
-        }
+    T* out_values = reinterpret_cast<T*>(block_data_) + rel_placement * num_rows_;
+
+    const ChunkedArray& data = *col->data().get();
+
+    for (int c = 0; c < data.num_chunks(); c++) {
+      const std::shared_ptr<Array> arr = data.chunk(c);
+      const auto& dict_arr = static_cast<const arrow::DictionaryArray&>(*arr);
+      const auto& indices =
+          static_cast<const arrow::PrimitiveArray&>(*dict_arr.indices());
+      auto in_values = reinterpret_cast<const T*>(indices.data()->data());
+
+      // Null is -1 in CategoricalBlock
+      for (int i = 0; i < arr->length(); ++i) {
+        *out_values++ = indices.IsNull(i) ? -1 : in_values[i];
       }
-      ++out_values;
     }
-  }
-  return Status::OK();
-}
 
-template <typename ArrowType>
-inline Status ConvertListsLike(
-    const std::shared_ptr<Column>& col, PyObject** out_values) {
-  typedef arrow_traits<ArrowType::type_id> traits;
-  typedef typename ::arrow::TypeTraits<ArrowType>::ArrayType ArrayType;
+    placement_data_[rel_placement] = abs_placement;
 
-  const ChunkedArray& data = *col->data().get();
-  auto list_type = std::static_pointer_cast<ListType>(col->type());
+    auto dict_type = static_cast<const DictionaryType*>(col->type().get());
 
-  // Get column of underlying value arrays
-  std::vector<std::shared_ptr<Array>> value_arrays;
-  for (int c = 0; c < data.num_chunks(); c++) {
-    auto arr = std::static_pointer_cast<arrow::ListArray>(data.chunk(c));
-    value_arrays.emplace_back(arr->values());
+    PyObject* dict;
+    RETURN_NOT_OK(ConvertArrayToPandas(dict_type->dictionary(), nullptr, &dict));
+    dictionary_.reset(dict);
+
+    return Status::OK();
   }
-  auto flat_column = std::make_shared<Column>(list_type->value_field(), value_arrays);
-  // TODO(ARROW-489): Currently we don't have a Python reference for single columns.
-  //    Storing a reference to the whole Array would be to expensive.
-  PyObject* numpy_array;
-  RETURN_NOT_OK(ConvertColumnToPandas(flat_column, nullptr, &numpy_array));
 
-  PyAcquireGIL lock;
+  Status GetPyResult(PyObject** output) override {
+    PyObject* result = PyDict_New();
+    RETURN_IF_PYERROR();
 
-  for (int c = 0; c < data.num_chunks(); c++) {
-    auto arr = std::static_pointer_cast<arrow::ListArray>(data.chunk(c));
+    PyDict_SetItemString(result, "block", block_arr_.obj());
+    PyDict_SetItemString(result, "dictionary", dictionary_.obj());
+    PyDict_SetItemString(result, "placement", placement_arr_.obj());
 
-    const uint8_t* data_ptr;
-    int32_t length;
-    const bool has_nulls = data.null_count() > 0;
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      if (has_nulls && arr->IsNull(i)) {
-        Py_INCREF(Py_None);
-        *out_values = Py_None;
-      } else {
-        PyObject* start = PyLong_FromLong(arr->value_offset(i));
-        PyObject* end = PyLong_FromLong(arr->value_offset(i + 1));
-        PyObject* slice = PySlice_New(start, end, NULL);
-        *out_values = PyObject_GetItem(numpy_array, slice);
-        Py_DECREF(start);
-        Py_DECREF(end);
-        Py_DECREF(slice);
-      }
-      ++out_values;
-    }
-  }
+    *output = result;
 
-  Py_XDECREF(numpy_array);
-  return Status::OK();
-}
+    return Status::OK();
+  }
 
-template <typename T>
-inline void ConvertNumericNullable(const ChunkedArray& data, T na_value, T* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+ protected:
+  OwnedRef dictionary_;
+};
 
-    const uint8_t* valid_bits = arr->null_bitmap_data();
+Status MakeBlock(PandasBlock::type type, int64_t num_rows, int num_columns,
+    std::shared_ptr<PandasBlock>* block) {
+#define BLOCK_CASE(NAME, TYPE)                              \
+  case PandasBlock::NAME:                                   \
+    *block = std::make_shared<TYPE>(num_rows, num_columns); \
+    break;
 
-    if (arr->null_count() > 0) {
-      for (int64_t i = 0; i < arr->length(); ++i) {
-        *out_values++ = BitUtil::BitNotSet(valid_bits, i) ? na_value : in_values[i];
-      }
-    } else {
-      memcpy(out_values, in_values, sizeof(T) * arr->length());
-      out_values += arr->length();
-    }
+  switch (type) {
+    BLOCK_CASE(OBJECT, ObjectBlock);
+    BLOCK_CASE(UINT8, UInt8Block);
+    BLOCK_CASE(INT8, Int8Block);
+    BLOCK_CASE(UINT16, UInt16Block);
+    BLOCK_CASE(INT16, Int16Block);
+    BLOCK_CASE(UINT32, UInt32Block);
+    BLOCK_CASE(INT32, Int32Block);
+    BLOCK_CASE(UINT64, UInt64Block);
+    BLOCK_CASE(INT64, Int64Block);
+    BLOCK_CASE(FLOAT, Float32Block);
+    BLOCK_CASE(DOUBLE, Float64Block);
+    BLOCK_CASE(BOOL, BoolBlock);
+    BLOCK_CASE(DATETIME, DatetimeBlock);
+    default:
+      return Status::NotImplemented("Unsupported block type");
   }
-}
 
-template <typename InType, typename OutType>
-inline void ConvertNumericNullableCast(
-    const ChunkedArray& data, OutType na_value, OutType* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
+#undef BLOCK_CASE
 
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values++ = arr->IsNull(i) ? na_value : static_cast<OutType>(in_values[i]);
-    }
-  }
+  return (*block)->Allocate();
 }
 
-template <typename T>
-inline void ConvertDates(const ChunkedArray& data, T na_value, T* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
-
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      // There are 1000 * 60 * 60 * 24 = 86400000ms in a day
-      *out_values++ = arr->IsNull(i) ? na_value : in_values[i] / 86400000;
-    }
+static inline bool ListTypeSupported(const Type::type type_id) {
+  switch (type_id) {
+    case Type::UINT8:
+    case Type::INT8:
+    case Type::UINT16:
+    case Type::INT16:
+    case Type::UINT32:
+    case Type::INT32:
+    case Type::INT64:
+    case Type::UINT64:
+    case Type::FLOAT:
+    case Type::DOUBLE:
+    case Type::STRING:
+    case Type::TIMESTAMP:
+      // The above types are all supported.
+      return true;
+    default:
+      break;
   }
+  return false;
 }
 
-template <typename InType, int SHIFT>
-inline void ConvertDatetimeNanos(const ChunkedArray& data, int64_t* out_values) {
-  for (int c = 0; c < data.num_chunks(); c++) {
-    const std::shared_ptr<Array> arr = data.chunk(c);
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const InType*>(prim_arr->data()->data());
-
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values++ = arr->IsNull(i) ? kPandasTimestampNull
-                                     : (static_cast<int64_t>(in_values[i]) * SHIFT);
+static inline Status MakeCategoricalBlock(const std::shared_ptr<DataType>& type,
+    int64_t num_rows, std::shared_ptr<PandasBlock>* block) {
+  // All categoricals become a block with a single column
+  auto dict_type = static_cast<const DictionaryType*>(type.get());
+  switch (dict_type->index_type()->type) {
+    case Type::INT8:
+      *block = std::make_shared<CategoricalBlock<Type::INT8>>(num_rows);
+      break;
+    case Type::INT16:
+      *block = std::make_shared<CategoricalBlock<Type::INT16>>(num_rows);
+      break;
+    case Type::INT32:
+      *block = std::make_shared<CategoricalBlock<Type::INT32>>(num_rows);
+      break;
+    case Type::INT64:
+      *block = std::make_shared<CategoricalBlock<Type::INT64>>(num_rows);
+      break;
+    default: {
+      std::stringstream ss;
+      ss << "Categorical index type not implemented: "
+         << dict_type->index_type()->ToString();
+      return Status::NotImplemented(ss.str());
     }
   }
+  return (*block)->Allocate();
 }
 
-class ArrowDeserializer {
+// Construct the exact pandas 0.x "BlockManager" memory layout
+//
+// * For each column determine the correct output pandas type
+// * Allocate 2D blocks (ncols x nrows) for each distinct data type in output
+// * Allocate  block placement arrays
+// * Write Arrow columns out into each slice of memory; populate block
+// * placement arrays as we go
+class DataFrameBlockCreator {
  public:
-  ArrowDeserializer(const std::shared_ptr<Column>& col, PyObject* py_ref)
-      : col_(col), data_(*col->data().get()), py_ref_(py_ref) {}
-
-  Status AllocateOutput(int type) {
-    PyAcquireGIL lock;
-
-    npy_intp dims[1] = {col_->length()};
-    out_ = reinterpret_cast<PyArrayObject*>(PyArray_SimpleNew(1, dims, type));
+  DataFrameBlockCreator(const std::shared_ptr<Table>& table) : table_(table) {}
 
-    if (out_ == NULL) {
-      // Error occurred, trust that SimpleNew set the error state
-      return Status::OK();
-    }
+  Status Convert(int nthreads, PyObject** output) {
+    column_types_.resize(table_->num_columns());
+    column_block_placement_.resize(table_->num_columns());
+    type_counts_.clear();
+    blocks_.clear();
 
-    set_numpy_metadata(type, col_->type().get(), out_);
+    RETURN_NOT_OK(CreateBlocks());
+    RETURN_NOT_OK(WriteTableToBlocks(nthreads));
 
-    return Status::OK();
+    return GetResultList(output);
   }
 
-  template <int TYPE>
-  Status ConvertValuesZeroCopy(int npy_type, std::shared_ptr<Array> arr) {
-    typedef typename arrow_traits<TYPE>::T T;
-
-    auto prim_arr = static_cast<arrow::PrimitiveArray*>(arr.get());
-    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+  Status CreateBlocks() {
+    for (int i = 0; i < table_->num_columns(); ++i) {
+      std::shared_ptr<Column> col = table_->column(i);
+      PandasBlock::type output_type;
 
-    // Zero-Copy. We can pass the data pointer directly to NumPy.
-    void* data = const_cast<T*>(in_values);
+      Type::type column_type = col->type()->type;
+      switch (column_type) {
+        case Type::BOOL:
+          output_type = col->null_count() > 0 ? PandasBlock::OBJECT : PandasBlock::BOOL;
+          break;
+        case Type::UINT8:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::UINT8;
+          break;
+        case Type::INT8:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::INT8;
+          break;
+        case Type::UINT16:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::UINT16;
+          break;
+        case Type::INT16:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::INT16;
+          break;
+        case Type::UINT32:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::UINT32;
+          break;
+        case Type::INT32:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::INT32;
+          break;
+        case Type::INT64:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::INT64;
+          break;
+        case Type::UINT64:
+          output_type = col->null_count() > 0 ? PandasBlock::DOUBLE : PandasBlock::UINT64;
+          break;
+        case Type::FLOAT:
+          output_type = PandasBlock::FLOAT;
+          break;
+        case Type::DOUBLE:
+          output_type = PandasBlock::DOUBLE;
+          break;
+        case Type::STRING:
+        case Type::BINARY:
+          output_type = PandasBlock::OBJECT;
+          break;
+        case Type::DATE:
+          output_type = PandasBlock::DATETIME;
+          break;
+        case Type::TIMESTAMP:
+          output_type = PandasBlock::DATETIME;
+          break;
+        case Type::LIST: {
+          auto list_type = std::static_pointer_cast<ListType>(col->type());
+          if (!ListTypeSupported(list_type->value_type()->type)) {
+            std::stringstream ss;
+            ss << "Not implemented type for lists: "
+               << list_type->value_type()->ToString();
+            return Status::NotImplemented(ss.str());
+          }
+          output_type = PandasBlock::OBJECT;
+        } break;
+        case Type::DICTIONARY:
+          output_type = PandasBlock::CATEGORICAL;
+          break;
+        default:
+          return Status::NotImplemented(col->type()->ToString());
+      }
 
-    PyAcquireGIL lock;
+      int block_placement = 0;
+      if (column_type == Type::DICTIONARY) {
+        std::shared_ptr<PandasBlock> block;
+        RETURN_NOT_OK(MakeCategoricalBlock(col->type(), table_->num_rows(), &block));
+        categorical_blocks_[i] = block;
+      } else {
+        auto it = type_counts_.find(output_type);
+        if (it != type_counts_.end()) {
+          block_placement = it->second;
+          // Increment count
+          it->second += 1;
+        } else {
+          // Add key to map
+          type_counts_[output_type] = 1;
+        }
+      }
 
-    // Zero-Copy. We can pass the data pointer directly to NumPy.
-    npy_intp dims[1] = {col_->length()};
-    out_ = reinterpret_cast<PyArrayObject*>(
-        PyArray_SimpleNewFromData(1, dims, npy_type, data));
+      column_types_[i] = output_type;
+      column_block_placement_[i] = block_placement;
+    }
 
-    if (out_ == NULL) {
-      // Error occurred, trust that SimpleNew set the error state
-      return Status::OK();
+    // Create normal non-categorical blocks
+    for (const auto& it : type_counts_) {
+      PandasBlock::type type = static_cast<PandasBlock::type>(it.first);
+      std::shared_ptr<PandasBlock> block;
+      RETURN_NOT_OK(MakeBlock(type, table_->num_rows(), it.second, &block));
+      blocks_[type] = block;
     }
+    return Status::OK();
+  }
 
-    set_numpy_metadata(npy_type, col_->type().get(), out_);
+  Status WriteTableToBlocks(int nthreads) {
+    auto WriteColumn = [this](int i) {
+      std::shared_ptr<Column> col = this->table_->column(i);
+      PandasBlock::type output_type = this->column_types_[i];
 
-    if (PyArray_SetBaseObject(out_, py_ref_) == -1) {
-      // Error occurred, trust that SetBaseObject set the error state
-      return Status::OK();
-    } else {
-      // PyArray_SetBaseObject steals our reference to py_ref_
-      Py_INCREF(py_ref_);
-    }
+      int rel_placement = this->column_block_placement_[i];
 
-    // Arrow data is immutable.
-    PyArray_CLEARFLAGS(out_, NPY_ARRAY_WRITEABLE);
+      std::shared_ptr<PandasBlock> block;
+      if (output_type == PandasBlock::CATEGORICAL) {
+        auto it = this->categorical_blocks_.find(i);
+        if (it == this->blocks_.end()) {
+          return Status::KeyError("No categorical block allocated");
+        }
+        block = it->second;
+      } else {
+        auto it = this->blocks_.find(output_type);
+        if (it == this->blocks_.end()) { return Status::KeyError("No block allocated"); }
+        block = it->second;
+      }
+      return block->Write(col, i, rel_placement);
+    };
 
-    return Status::OK();
-  }
+    nthreads = std::min<int>(nthreads, table_->num_columns());
 
-  // ----------------------------------------------------------------------
-  // Allocate new array and deserialize. Can do a zero copy conversion for some
-  // types
+    if (nthreads == 1) {
+      for (int i = 0; i < table_->num_columns(); ++i) {
+        RETURN_NOT_OK(WriteColumn(i));
+      }
+    } else {
+      std::vector<std::thread> thread_pool;
+      thread_pool.reserve(nthreads);
+      std::atomic<int> task_counter(0);
 
-  Status Convert(PyObject** out) {
-#define CONVERT_CASE(TYPE)                             \
-  case arrow::Type::TYPE: {                            \
-    RETURN_NOT_OK(ConvertValues<arrow::Type::TYPE>()); \
-  } break;
+      std::mutex error_mtx;
+      bool error_occurred = false;
+      Status error;
 
-    switch (col_->type()->type) {
-      CONVERT_CASE(BOOL);
-      CONVERT_CASE(INT8);
-      CONVERT_CASE(INT16);
-      CONVERT_CASE(INT32);
-      CONVERT_CASE(INT64);
-      CONVERT_CASE(UINT8);
-      CONVERT_CASE(UINT16);
-      CONVERT_CASE(UINT32);
-      CONVERT_CASE(UINT64);
-      CONVERT_CASE(FLOAT);
-      CONVERT_CASE(DOUBLE);
-      CONVERT_CASE(BINARY);
-      CONVERT_CASE(STRING);
-      CONVERT_CASE(DATE);
-      CONVERT_CASE(TIMESTAMP);
-      default: {
-        std::stringstream ss;
-        ss << "Arrow type reading not implemented for " << col_->type()->ToString();
-        return Status::NotImplemented(ss.str());
+      for (int thread_id = 0; thread_id < nthreads; ++thread_id) {
+        thread_pool.emplace_back(
+            [this, &error, &error_occurred, &error_mtx, &task_counter, &WriteColumn]() {
+              int column_num;
+              while (!error_occurred) {
+                column_num = task_counter.fetch_add(1);
+                if (column_num >= this->table_->num_columns()) { break; }
+                Status s = WriteColumn(column_num);
+                if (!s.ok()) {
+                  std::lock_guard<std::mutex> lock(error_mtx);
+                  error_occurred = true;
+                  error = s;
+                  break;
+                }
+              }
+            });
+      }
+      for (auto&& thread : thread_pool) {
+        thread.join();
       }
-    }
-
-#undef CONVERT_CASE
 
-    *out = reinterpret_cast<PyObject*>(out_);
+      if (error_occurred) { return error; }
+    }
     return Status::OK();
   }
 
-  template <int TYPE>
-  inline typename std::enable_if<
-      (TYPE != arrow::Type::DATE) & arrow_traits<TYPE>::is_numeric_nullable, Status>::type
-  ConvertValues() {
-    typedef typename arrow_traits<TYPE>::T T;
-    int npy_type = arrow_traits<TYPE>::npy_type;
+  Status GetResultList(PyObject** out) {
+    PyAcquireGIL lock;
 
-    if (data_.num_chunks() == 1 && data_.null_count() == 0 && py_ref_ != nullptr) {
-      return ConvertValuesZeroCopy<TYPE>(npy_type, data_.chunk(0));
+    auto num_blocks =
+        static_cast<Py_ssize_t>(blocks_.size() + categorical_blocks_.size());
+    PyObject* result = PyList_New(num_blocks);
+    RETURN_IF_PYERROR();
+
+    int i = 0;
+    for (const auto& it : blocks_) {
+      const std::shared_ptr<PandasBlock> block = it.second;
+      PyObject* item;
+      RETURN_NOT_OK(block->GetPyResult(&item));
+      if (PyList_SET_ITEM(result, i++, item) < 0) { RETURN_IF_PYERROR(); }
     }
 
-    RETURN_NOT_OK(AllocateOutput(npy_type));
-    auto out_values = reinterpret_cast<T*>(PyArray_DATA(out_));
-    ConvertNumericNullable<T>(data_, arrow_traits<TYPE>::na_value, out_values);
+    for (const auto& it : categorical_blocks_) {
+      const std::shared_ptr<PandasBlock> block = it.second;
+      PyObject* item;
+      RETURN_NOT_OK(block->GetPyResult(&item));
+      if (PyList_SET_ITEM(result, i++, item) < 0) { RETURN_IF_PYERROR(); }
+    }
 
+    *out = result;
     return Status::OK();
   }
 
-  template <int TYPE>
-  inline typename std::enable_if<TYPE == arrow::Type::DATE, Status>::type
-  ConvertValues() {
-    typedef typename arrow_traits<TYPE>::T T;
+ private:
+  std::shared_ptr<Table> table_;
 
-    RETURN_NOT_OK(AllocateOutput(arrow_traits<TYPE>::npy_type));
-    auto out_values = reinterpret_cast<T*>(PyArray_DATA(out_));
-    ConvertDates<T>(data_, arrow_traits<TYPE>::na_value, out_values);
-    return Status::OK();
-  }
+  // column num -> block type id
+  std::vector<PandasBlock::type> column_types_;
 
-  // Integer specialization
-  template <int TYPE>
-  inline
-      typename std::enable_if<arrow_traits<TYPE>::is_numeric_not_nullable, Status>::type
-      ConvertValues() {
-    typedef typename arrow_traits<TYPE>::T T;
-    int npy_type = arrow_traits<TYPE>::npy_type;
+  // column num -> relative placement within internal block
+  std::vector<int> column_block_placement_;
 
-    if (data_.num_chunks() == 1 && data_.null_count() == 0 && py_ref_ != nullptr) {
-      return ConvertValuesZeroCopy<TYPE>(npy_type, data_.chunk(0));
-    }
+  // block type -> type count
+  std::unordered_map<int, int> type_counts_;
 
-    if (data_.null_count() > 0) {
-      RETURN_NOT_OK(AllocateOutput(NPY_FLOAT64));
-      auto out_values = reinterpret_cast<double*>(PyArray_DATA(out_));
-      ConvertIntegerWithNulls<T>(data_, out_values);
-    } else {
-      RETURN_NOT_OK(AllocateOutput(arrow_traits<TYPE>::npy_type));
-      auto out_values = reinterpret_cast<T*>(PyArray_DATA(out_));
-      ConvertIntegerNoNullsSameType<T>(data_, out_values);
-    }
+  // block type -> block
+  std::unordered_map<int, std::shared_ptr<PandasBlock>> blocks_;
 
-    return Status::OK();
-  }
+  // column number -> categorical block
+  std::unordered_map<int, std::shared_ptr<PandasBlock>> categorical_blocks_;
+};
 
-  // Boolean specialization
-  template <int TYPE>
-  inline typename std::enable_if<arrow_traits<TYPE>::is_boolean, Status>::type
-  ConvertValues() {
-    if (data_.null_count() > 0) {
-      RETURN_NOT_OK(AllocateOutput(NPY_OBJECT));
-      auto out_values = reinterpret_cast<PyObject**>(PyArray_DATA(out_));
-      RETURN_NOT_OK(ConvertBooleanWithNulls(data_, out_values));
-    } else {
-      RETURN_NOT_OK(AllocateOutput(arrow_traits<TYPE>::npy_type));
-      auto out_values = reinterpret_cast<uint8_t*>(PyArray_DATA(out_));
-      ConvertBooleanNoNulls(data_, out_values);
-    }
-    return Status::OK();
+Status ConvertTableToPandas(
+    const std::shared_ptr<Table>& table, int nthreads, PyObject** out) {
+  DataFrameBlockCreator helper(table);
+  return helper.Convert(nthreads, out);
+}
+
+// ----------------------------------------------------------------------
+// Serialization
+
+template <int TYPE>
+class ArrowSerializer {
+ public:
+  ArrowSerializer(arrow::MemoryPool* pool, PyArrayObject* arr, PyArrayObject* mask)
+      : pool_(pool), arr_(arr), mask_(mask) {
+    length_ = PyArray_SIZE(arr_);
   }
 
-  // UTF8 strings
-  template <int TYPE>
-  inline typename std::enable_if<TYPE == arrow::Type::STRING, Status>::type
-  ConvertValues() {
-    RETURN_NOT_OK(AllocateOutput(NPY_OBJECT));
-    auto out_values = reinterpret_cast<PyObject**>(PyArray_DATA(out_));
-    return ConvertBinaryLike<arrow::StringArray>(data_, out_values);
+  void IndicateType(const std::shared_ptr<Field> field) { field_indicator_ = field; }
+
+  Status Convert(std::shared_ptr<Array>* out);
+
+  int stride() const { return PyArray_STRIDES(arr_)[0]; }
+
+  Status InitNullBitmap() {
+    int null_bytes = BitUtil::BytesForBits(length_);
+
+    null_bitmap_ = std::make_shared<arrow::PoolBuffer>(pool_);
+    RETURN_NOT_OK(null_bitmap_->Resize(null_bytes));
+
+    null_bitmap_data_ = null_bitmap_->mutable_data();
+    memset(null_bitmap_data_, 0, null_bytes);
+
+    return Status::OK();
   }
 
-  template <int T2>
-  inline typename std::enable_if<T2 == arrow::Type::BINARY, Status>::type
-  ConvertValues() {
-    RETURN_NOT_OK(AllocateOutput(NPY_OBJECT));
-    auto out_values = reinterpret_cast<PyObject**>(PyArray_DATA(out_));
-    return ConvertBinaryLike<arrow::BinaryArray>(data_, out_values);
+  bool is_strided() const {
+    npy_intp* astrides = PyArray_STRIDES(arr_);
+    return astrides[0] != PyArray_DESCR(arr_)->elsize;
   }
 
  private:
-  std::shared_ptr<Column> col_;
-  const arrow::ChunkedArray& data_;
-  PyObject* py_ref_;
-  PyArrayObject* out_;
-};
+  Status ConvertData();
 
-Status ConvertArrayToPandas(
-    const std::shared_ptr<Array>& arr, PyObject* py_ref, PyObject** out) {
-  static std::string dummy_name = "dummy";
-  auto field = std::make_shared<Field>(dummy_name, arr->type());
-  auto col = std::make_shared<Column>(field, arr);
-  return ConvertColumnToPandas(col, py_ref, out);
-}
+  Status ConvertDates(std::shared_ptr<Array>* out) {
+    PyAcquireGIL lock;
 
-Status ConvertColumnToPandas(
-    const std::shared_ptr<Column>& col, PyObject* py_ref, PyObject** out) {
-  ArrowDeserializer converter(col, py_ref);
-  return converter.Convert(out);
-}
+    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+    arrow::TypePtr string_type(new arrow::DateType());
+    arrow::DateBuilder date_builder(pool_, string_type);
+    RETURN_NOT_OK(date_builder.Resize(length_));
 
-// ----------------------------------------------------------------------
-// pandas 0.x DataFrame conversion internals
+    Status s;
+    PyObject* obj;
+    for (int64_t i = 0; i < length_; ++i) {
+      obj = objects[i];
+      if (PyDate_CheckExact(obj)) {
+        PyDateTime_Date* pydate = reinterpret_cast<PyDateTime_Date*>(obj);
+        date_builder.Append(PyDate_to_ms(pydate));
+      } else {
+        date_builder.AppendNull();
+      }
+    }
+    return date_builder.Finish(out);
+  }
 
-class PandasBlock {
- public:
-  enum type {
-    OBJECT,
-    UINT8,
-    INT8,
-    UINT16,
-    INT16,
-    UINT32,
-    INT32,
-    UINT64,
-    INT64,
-    FLOAT,
-    DOUBLE,
-    BOOL,
-    DATETIME,
-    CATEGORICAL
-  };
+  Status ConvertObjectStrings(std::shared_ptr<Array>* out) {
+    PyAcquireGIL lock;
 
-  PandasBlock(int64_t num_rows, int num_columns)
-      : num_rows_(num_rows), num_columns_(num_columns) {}
-  virtual ~PandasBlock() {}
+    // The output type at this point is inconclusive because there may be bytes
+    // and unicode mixed in the object array
 
-  virtual Status Allocate() = 0;
-  virtual Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
-      int64_t rel_placement) = 0;
+    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+    arrow::TypePtr string_type(new arrow::StringType());
+    arrow::StringBuilder string_builder(pool_, string_type);
+    RETURN_NOT_OK(string_builder.Resize(length_));
 
-  PyObject* block_arr() { return block_arr_.obj(); }
+    Status s;
+    bool have_bytes = false;
+    RETURN_NOT_OK(AppendObjectStrings(string_builder, objects, length_, &have_bytes));
+    RETURN_NOT_OK(string_builder.Finish(out));
 
-  PyObject* placement_arr() { return placement_arr_.obj(); }
+    if (have_bytes) {
+      const auto& arr = static_cast<const arrow::StringArray&>(*out->get());
+      *out = std::make_shared<arrow::BinaryArray>(
+          arr.length(), arr.offsets(), arr.data(), arr.null_count(), arr.null_bitmap());
+    }
+    return Status::OK();
+  }
 
- protected:
-  Status AllocateNDArray(int npy_type) {
+  Status ConvertBooleans(std::shared_ptr<Array>* out) {
     PyAcquireGIL lock;
 
-    npy_intp block_dims[2] = {num_columns_, num_rows_};
-    PyObject* block_arr = PyArray_SimpleNew(2, block_dims, npy_type);
-    if (block_arr == NULL) {
-      // TODO(wesm): propagating Python exception
-      return Status::OK();
+    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+
+    int nbytes = BitUtil::BytesForBits(length_);
+    auto data = std::make_shared<arrow::PoolBuffer>(pool_);
+    RETURN_NOT_OK(data->Resize(nbytes));
+    uint8_t* bitmap = data->mutable_data();
+    memset(bitmap, 0, nbytes);
+
+    int64_t null_count = 0;
+    for (int64_t i = 0; i < length_; ++i) {
+      if (objects[i] == Py_True) {
+        BitUtil::SetBit(bitmap, i);
+        BitUtil::SetBit(null_bitmap_data_, i);
+      } else if (objects[i] != Py_False) {
+        ++null_count;
+      } else {
+        BitUtil::SetBit(null_bitmap_data_, i);
+      }
     }
 
-    npy_intp placement_dims[1] = {num_columns_};
-    PyObject* placement_arr = PyArray_SimpleNew(1, placement_dims, NPY_INT64);
-    if (placement_arr == NULL) {
-      // TODO(wesm): propagating Python exception
-      return Status::OK();
+    *out = std::make_shared<arrow::BooleanArray>(length_, data, null_count, null_bitmap_);
+
+    return Status::OK();
+  }
+
+  template <int ITEM_TYPE, typename ArrowType>
+  Status ConvertTypedLists(
+      const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out);
+
+#define LIST_CASE(TYPE, NUMPY_TYPE, ArrowType)                            \
+  case Type::TYPE: {                                                      \
+    return ConvertTypedLists<NUMPY_TYPE, ::arrow::ArrowType>(field, out); \
+  }
+
+  Status ConvertLists(const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
+    switch (field->type->type) {
+      LIST_CASE(UINT8, NPY_UINT8, UInt8Type)
+      LIST_CASE(INT8, NPY_INT8, Int8Type)
+      LIST_CASE(UINT16, NPY_UINT16, UInt16Type)
+      LIST_CASE(INT16, NPY_INT16, Int16Type)
+      LIST_CASE(UINT32, NPY_UINT32, UInt32Type)
+      LIST_CASE(INT32, NPY_INT32, Int32Type)
+      LIST_CASE(UINT64, NPY_UINT64, UInt64Type)
+      LIST_CASE(INT64, NPY_INT64, Int64Type)
+      LIST_CASE(TIMESTAMP, NPY_DATETIME, TimestampType)
+      LIST_CASE(FLOAT, NPY_FLOAT, FloatType)
+      LIST_CASE(DOUBLE, NPY_DOUBLE, DoubleType)
+      LIST_CASE(STRING, NPY_OBJECT, StringType)
+      default:
+        return Status::TypeError("Unknown list item type");
     }
 
-    block_arr_.reset(block_arr);
-    placement_arr_.reset(placement_arr);
-
-    block_data_ = reinterpret_cast<uint8_t*>(
-        PyArray_DATA(reinterpret_cast<PyArrayObject*>(block_arr)));
-
-    placement_data_ = reinterpret_cast<int64_t*>(
-        PyArray_DATA(reinterpret_cast<PyArrayObject*>(placement_arr)));
-
-    return Status::OK();
+    return Status::TypeError("Unknown list type");
   }
 
-  int64_t num_rows_;
-  int num_columns_;
-
-  OwnedRef block_arr_;
-  uint8_t* block_data_;
+  Status MakeDataType(std::shared_ptr<DataType>* out);
 
-  // ndarray<int32>
-  OwnedRef placement_arr_;
-  int64_t* placement_data_;
+  arrow::MemoryPool* pool_;
 
-  DISALLOW_COPY_AND_ASSIGN(PandasBlock);
-};
+  PyArrayObject* arr_;
+  PyArrayObject* mask_;
 
-#define CONVERTLISTSLIKE_CASE(ArrowType, ArrowEnum)                         \
-  case Type::ArrowEnum:                                                     \
-    RETURN_NOT_OK((ConvertListsLike<::arrow::ArrowType>(col, out_buffer))); \
-    break;
+  int64_t length_;
 
-class ObjectBlock : public PandasBlock {
- public:
-  using PandasBlock::PandasBlock;
-  virtual ~ObjectBlock() {}
+  std::shared_ptr<Field> field_indicator_;
+  std::shared_ptr<arrow::Buffer> data_;
+  std::shared_ptr<arrow::ResizableBuffer> null_bitmap_;
+  uint8_t* null_bitmap_data_;
+};
 
-  Status Allocate() override { return AllocateNDArray(NPY_OBJECT); }
+// Returns null count
+static int64_t MaskToBitmap(PyArrayObject* mask, int64_t length, uint8_t* bitmap) {
+  int64_t null_count = 0;
+  const uint8_t* mask_values = static_cast<const uint8_t*>(PyArray_DATA(mask));
+  // TODO(wesm): strided null mask
+  for (int i = 0; i < length; ++i) {
+    if (mask_values[i]) {
+      ++null_count;
+    } else {
+      BitUtil::SetBit(bitmap, i);
+    }
+  }
+  return null_count;
+}
 
-  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
-      int64_t rel_placement) override {
-    Type::type type = col->type()->type;
+template <int TYPE>
+inline Status ArrowSerializer<TYPE>::MakeDataType(std::shared_ptr<DataType>* out) {
+  out->reset(new typename npy_traits<TYPE>::TypeClass());
+  return Status::OK();
+}
 
-    PyObject** out_buffer =
-        reinterpret_cast<PyObject**>(block_data_) + rel_placement * num_rows_;
+template <>
+inline Status ArrowSerializer<NPY_DATETIME>::MakeDataType(
+    std::shared_ptr<DataType>* out) {
+  PyArray_Descr* descr = PyArray_DESCR(arr_);
+  auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(descr->c_metadata);
+  arrow::TimestampType::Unit unit;
 
-    const ChunkedArray& data = *col->data().get();
+  switch (date_dtype->meta.base) {
+    case NPY_FR_s:
+      unit = arrow::TimestampType::Unit::SECOND;
+      break;
+    case NPY_FR_ms:
+      unit = arrow::TimestampType::Unit::MILLI;
+      break;
+    case NPY_FR_us:
+      unit = arrow::TimestampType::Unit::MICRO;
+      break;
+    case NPY_FR_ns:
+      unit = arrow::TimestampType::Unit::NANO;
+      break;
+    default:
+      return Status::Invalid("Unknown NumPy datetime unit");
+  }
 
-    if (type == Type::BOOL) {
-      RETURN_NOT_OK(ConvertBooleanWithNulls(data, out_buffer));
-    } else if (type == Type::BINARY) {
-      RETURN_NOT_OK(ConvertBinaryLike<arrow::BinaryArray>(data, out_buffer));
-    } else if (type == Type::STRING) {
-      RETURN_NOT_OK(ConvertBinaryLike<arrow::StringArray>(data, out_buffer));
-    } else if (type == Type::LIST) {
-      auto list_type = std::static_pointer_cast<ListType>(col->type());
-      switch (list_type->value_type()->type) {
-        CONVERTLISTSLIKE_CASE(UInt8Type, UINT8)
-        CONVERTLISTSLIKE_CASE(Int8Type, INT8)
-        CONVERTLISTSLIKE_CASE(UInt16Type, UINT16)
-        CONVERTLISTSLIKE_CASE(Int16Type, INT16)
-        CONVERTLISTSLIKE_CASE(UInt32Type, UINT32)
-        CONVERTLISTSLIKE_CASE(Int32Type, INT32)
-        CONVERTLISTSLIKE_CASE(UInt64Type, UINT64)
-        CONVERTLISTSLIKE_CASE(Int64Type, INT64)
-        CONVERTLISTSLIKE_CASE(TimestampType, TIMESTAMP)
-        CONVERTLISTSLIKE_CASE(FloatType, FLOAT)
-        CONVERTLISTSLIKE_CASE(DoubleType, DOUBLE)
-        CONVERTLISTSLIKE_CASE(StringType, STRING)
-        default: {
-          std::stringstream ss;
-          ss << "Not implemented type for lists: " << list_type->value_type()->ToString();
-          return Status::NotImplemented(ss.str());
-        }
-      }
-    } else {
-      std::stringstream ss;
-      ss << "Unsupported type for object array output: " << col->type()->ToString();
-      return Status::NotImplemented(ss.str());
-    }
+  out->reset(new arrow::TimestampType(unit));
+  return Status::OK();
+}
 
-    placement_data_[rel_placement] = abs_placement;
-    return Status::OK();
-  }
-};
+template <int TYPE>
+inline Status ArrowSerializer<TYPE>::Convert(std::shared_ptr<Array>* out) {
+  typedef npy_traits<TYPE> traits;
 
-template <int ARROW_TYPE, typename C_TYPE>
-class IntBlock : public PandasBlock {
- public:
-  using PandasBlock::PandasBlock;
+  if (mask_ != nullptr || traits::supports_nulls) { RETURN_NOT_OK(InitNullBitmap()); }
 
-  Status Allocate() override {
-    return AllocateNDArray(arrow_traits<ARROW_TYPE>::npy_type);
+  int64_t null_count = 0;
+  if (mask_ != nullptr) {
+    null_count = MaskToBitmap(mask_, length_, null_bitmap_data_);
+  } else if (traits::supports_nulls) {
+    null_count = ValuesToBitmap<TYPE>(PyArray_DATA(arr_), length_, null_bitmap_data_);
   }
 
-  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
-      int64_t rel_placement) override {
-    Type::type type = col->type()->type;
-
-    C_TYPE* out_buffer =
-        reinterpret_cast<C_TYPE*>(block_data_) + rel_placement * num_rows_;
+  RETURN_NOT_OK(ConvertData());
+  std::shared_ptr<DataType> type;
+  RETURN_NOT_OK(MakeDataType(&type));
+  RETURN_NOT_OK(MakePrimitiveArray(type, length_, data_, null_count, null_bitmap_, out));
+  return Status::OK();
+}
 
-    const ChunkedArray& data = *col->data().get();
+template <>
+inline Status ArrowSerializer<NPY_OBJECT>::Convert(std::shared_ptr<Array>* out) {
+  // Python object arrays are annoying, since we could have one of:
+  //
+  // * Strings
+  // * Booleans with nulls
+  // * Mixed type (not supported at the moment by arrow format)
+  //
+  // Additionally, nulls may be encoded either as np.nan or None. So we have to
+  // do some type inference and conversion
 
-    if (type != ARROW_TYPE) { return Status::NotImplemented(col->type()->ToString()); }
+  RETURN_NOT_OK(InitNullBitmap());
 
-    ConvertIntegerNoNullsSameType<C_TYPE>(data, out_buffer);
-    placement_data_[rel_placement] = abs_placement;
-    return Status::OK();
+  // TODO: mask not supported here
+  const PyObject** objects = reinterpret_cast<const PyObject**>(PyArray_DATA(arr_));
+  {
+    PyAcquireGIL lock;
+    PyDateTime_IMPORT;
   }
-};
-
-using UInt8Block = IntBlock<Type::UINT8, uint8_t>;
-using Int8Block = IntBlock<Type::INT8, int8_t>;
-using UInt16Block = IntBlock<Type::UINT16, uint16_t>;
-using Int16Block = IntBlock<Type::INT16, int16_t>;
-using UInt32Block = IntBlock<Type::UINT32, uint32_t>;
-using Int32Block = IntBlock<Type::INT32, int32_t>;
-using UInt64Block = IntBlock<Type::UINT64, uint64_t>;
-using Int64Block = IntBlock<Type::INT64, int64_t>;
 
-class Float32Block : public PandasBlock {
- public:
-  using PandasBlock::PandasBlock;
+  if (field_indicator_) {
+    switch (field_indicator_->type->type) {
+      case Type::STRING:
+        return ConvertObjectStrings(out);
+      case Type::BOOL:
+        return ConvertBooleans(out);
+      case Type::DATE:
+        return ConvertDates(out);
+      case Type::LIST: {
+        auto list_field = static_cast<ListType*>(field_indicator_->type.get());
+        return ConvertLists(list_field->value_field(), out);
+      }
+      default:
+        return Status::TypeError("No known conversion to Arrow type");
+    }
+  } else {
+    for (int64_t i = 0; i < length_; ++i) {
+      if (PyObject_is_null(objects[i])) {
+        continue;
+      } else if (PyObject_is_string(objects[i])) {
+        return ConvertObjectStrings(out);
+      } else if (PyBool_Check(objects[i])) {
+        return ConvertBooleans(out);
+      } else if (PyDate_CheckExact(objects[i])) {
+        return ConvertDates(out);
+      } else {
+        return Status::TypeError("unhandled python type");
+      }
+    }
+  }
 
-  Status Allocate() override { return AllocateNDArray(NPY_FLOAT32); }
+  return Status::TypeError("Unable to infer type of object array, were all null");
+}
 
-  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
-      int64_t rel_placement) override {
-    Type::type type = col->type()->type;
+template <int TYPE>
+inline Status ArrowSerializer<TYPE>::ConvertData() {
+  // TODO(wesm): strided arrays
+  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
 
-    if (type != Type::FLOAT) { return Status::NotImplemented(col->type()->ToString()); }
+  data_ = std::make_shared<NumPyBuffer>(arr_);
+  return Status::OK();
+}
 
-    float* out_buffer = reinterpret_cast<float*>(block_data_) + rel_placement * num_rows_;
+template <>
+inline Status ArrowSerializer<NPY_BOOL>::ConvertData() {
+  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
 
-    ConvertNumericNullable<float>(*col->data().get(), NAN, out_buffer);
-    placement_data_[rel_placement] = abs_placement;
-    return Status::OK();
-  }
-};
+  int nbytes = BitUtil::BytesForBits(length_);
+  auto buffer = std::make_shared<arrow::PoolBuffer>(pool_);
+  RETURN_NOT_OK(buffer->Resize(nbytes));
 
-class Float64Block : public PandasBlock {
- public:
-  using PandasBlock::PandasBlock;
+  const uint8_t* values = reinterpret_cast<const uint8_t*>(PyArray_DATA(arr_));
 
-  Status Allocate() override { return AllocateNDArray(NPY_FLOAT64); }
+  uint8_t* bitmap = buffer->mutable_data();
 
-  Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
-      int64_t rel_placement) override {
-    Type::type type = col->type()->type;
+  memset(bitmap, 0, nbytes);
+  for (int i = 0; i < length_; ++i) {
+    if (values[i] > 0) { BitUtil::SetBit(bitmap, i); }
+  }
 
-    double* out_buffer =
-        reinterpret_cast<double*>(block_data_) + rel_placement * num_rows_;
+  data_ = buffer;
 
-    const ChunkedArray& data = *col->data().get();
+  return Status::OK();
+}
 
-#define INTEGER_CASE(IN_TYPE)                         \
-  ConvertIntegerWithNulls<IN_TYPE>(data, out_buffer); \
-  break;
+template <int TYPE>
+template <int ITEM_TYPE, typename ArrowType>
+inline Status ArrowSerializer<TYPE>::ConvertTypedLists(
+    const std::shared_ptr<Field>& field, std::shared_ptr<Array>* out) {
+  typedef npy_traits<ITEM_TYPE> traits;
+  typedef typename traits::value_type T;
+  typedef typename traits::BuilderClass BuilderT;
 
-    switch (type) {
-      case Type::UINT8:
-        INTEGER_CASE(uint8_t);
-      case Type::INT8:
-        INTEGER_CASE(int8_t);
-      case Type::UINT16:
-        INTEGER_CASE(uint16_t);
-      case Type::INT16:
-        INTEGER_CASE(int16_t);
-      case Type::UINT32:
-        INTEGER_CASE(uint32_t);
-      case Type::INT32:
-        INTEGER_CASE(int32_t);
-      case Type::UINT64:
-        INTEGER_CASE(uint64_t);
-      case Type::INT64:
-        INTEGER_CASE(int64_t);
-      case Type::FLOAT:
-        ConvertNumericNullableCast<float, double>(data, NAN, out_buffer);
-        break;
-      case Type::DOUBLE:
-        ConvertNumericNullable<double>(data, NAN, out_buffer);
-        break;
-      default:
-        return Status::NotImplemented(col->type()->ToString());
-    }
+  auto value_builder = std::make_shared<BuilderT>(pool_, field->type);
+  ListBuilder list_builder(pool_, value_builder);
+  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+  for (int64_t i = 0; i < length_; ++i) {
+    if (PyObject_is_null(objects[i])) {
+      RETURN_NOT_OK(list_builder.AppendNull());
+    } else if (PyArray_Check(objects[i])) {
+      auto numpy_array = reinterpret_cast<PyArrayObject*>(objects[i]);
+      RETURN_NOT_OK(list_builder.Append(true));
 
-#undef INTEGER_CASE
+      // TODO(uwe): Support more complex numpy array structures
+      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, ITEM_TYPE));
 
-    placement_data_[rel_placement] = abs_placement;
-    return Status::OK();
+      int32_t size = PyArray_DIM(numpy_array, 0);
+      auto data = reinterpret_cast<const T*>(PyArray_DATA(numpy_arra

<TRUNCATED>

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