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From w...@apache.org
Subject [2/3] arrow git commit: ARROW-618: [Python/C++] Support timestamp+timezone conversion to pandas
Date Mon, 13 Mar 2017 20:15:58 GMT
http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/adapters/pandas.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/adapters/pandas.cc b/python/src/pyarrow/adapters/pandas.cc
index 40079b4..863cf54 100644
--- a/python/src/pyarrow/adapters/pandas.cc
+++ b/python/src/pyarrow/adapters/pandas.cc
@@ -19,7 +19,6 @@
 
 #include <Python.h>
 
-#include "pyarrow/adapters/builtin.h"
 #include "pyarrow/adapters/pandas.h"
 #include "pyarrow/numpy_interop.h"
 
@@ -34,120 +33,39 @@
 #include <thread>
 #include <unordered_map>
 
-#include "arrow/api.h"
+#include "arrow/array.h"
+#include "arrow/column.h"
 #include "arrow/loader.h"
 #include "arrow/status.h"
+#include "arrow/table.h"
 #include "arrow/type_fwd.h"
 #include "arrow/type_traits.h"
 #include "arrow/util/bit-util.h"
 #include "arrow/util/macros.h"
 
+#include "pyarrow/adapters/builtin.h"
 #include "pyarrow/common.h"
 #include "pyarrow/config.h"
+#include "pyarrow/type_traits.h"
 #include "pyarrow/util/datetime.h"
 
-namespace pyarrow {
-
-using arrow::Array;
-using arrow::ChunkedArray;
-using arrow::Column;
-using arrow::DictionaryType;
-using arrow::Field;
-using arrow::DataType;
-using arrow::ListType;
-using arrow::ListBuilder;
-using arrow::Status;
-using arrow::Table;
-using arrow::Type;
-
-namespace BitUtil = arrow::BitUtil;
+namespace arrow {
+namespace py {
 
 // ----------------------------------------------------------------------
 // Utility code
 
-template <int TYPE>
-struct npy_traits {};
-
-template <>
-struct npy_traits<NPY_BOOL> {
-  typedef uint8_t value_type;
-  using TypeClass = arrow::BooleanType;
-  using BuilderClass = arrow::BooleanBuilder;
-
-  static constexpr bool supports_nulls = false;
-  static inline bool isnull(uint8_t v) { return false; }
-};
-
-#define NPY_INT_DECL(TYPE, CapType, T)               \
-  template <>                                        \
-  struct npy_traits<NPY_##TYPE> {                    \
-    typedef T value_type;                            \
-    using TypeClass = arrow::CapType##Type;          \
-    using BuilderClass = arrow::CapType##Builder;    \
-                                                     \
-    static constexpr bool supports_nulls = false;    \
-    static inline bool isnull(T v) { return false; } \
-  };
-
-NPY_INT_DECL(INT8, Int8, int8_t);
-NPY_INT_DECL(INT16, Int16, int16_t);
-NPY_INT_DECL(INT32, Int32, int32_t);
-NPY_INT_DECL(INT64, Int64, int64_t);
+int cast_npy_type_compat(int type_num) {
+// Both LONGLONG and INT64 can be observed in the wild, which is buggy. We set
+// U/LONGLONG to U/INT64 so things work properly.
 
-NPY_INT_DECL(UINT8, UInt8, uint8_t);
-NPY_INT_DECL(UINT16, UInt16, uint16_t);
-NPY_INT_DECL(UINT32, UInt32, uint32_t);
-NPY_INT_DECL(UINT64, UInt64, uint64_t);
-
-#if NPY_INT64 != NPY_LONGLONG
-NPY_INT_DECL(LONGLONG, Int64, int64_t);
-NPY_INT_DECL(ULONGLONG, UInt64, uint64_t);
+#if (NPY_INT64 == NPY_LONGLONG) && (NPY_SIZEOF_LONGLONG == 8)
+  if (type_num == NPY_LONGLONG) { type_num = NPY_INT64; }
+  if (type_num == NPY_ULONGLONG) { type_num = NPY_UINT64; }
 #endif
 
-template <>
-struct npy_traits<NPY_FLOAT32> {
-  typedef float value_type;
-  using TypeClass = arrow::FloatType;
-  using BuilderClass = arrow::FloatBuilder;
-
-  static constexpr bool supports_nulls = true;
-
-  static inline bool isnull(float v) { return v != v; }
-};
-
-template <>
-struct npy_traits<NPY_FLOAT64> {
-  typedef double value_type;
-  using TypeClass = arrow::DoubleType;
-  using BuilderClass = arrow::DoubleBuilder;
-
-  static constexpr bool supports_nulls = true;
-
-  static inline bool isnull(double v) { return v != v; }
-};
-
-template <>
-struct npy_traits<NPY_DATETIME> {
-  typedef int64_t value_type;
-  using TypeClass = arrow::TimestampType;
-  using BuilderClass = arrow::TimestampBuilder;
-
-  static constexpr bool supports_nulls = true;
-
-  static inline bool isnull(int64_t v) {
-    // NaT = -2**63
-    // = -0x8000000000000000
-    // = -9223372036854775808;
-    // = std::numeric_limits<int64_t>::min()
-    return v == std::numeric_limits<int64_t>::min();
-  }
-};
-
-template <>
-struct npy_traits<NPY_OBJECT> {
-  typedef PyObject* value_type;
-  static constexpr bool supports_nulls = true;
-};
+  return type_num;
+}
 
 static inline bool PyObject_is_null(const PyObject* obj) {
   return obj == Py_None || obj == numpy_nan;
@@ -181,8 +99,24 @@ static int64_t ValuesToBitmap(const void* data, int64_t length, uint8_t* bitmap)
   return null_count;
 }
 
+// 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;
+}
+
 template <int TYPE>
-static int64_t ValuesToBytemap(const void* data, int64_t length, uint8_t* valid_bytes) {
+static int64_t ValuesToValidBytes(
+    const void* data, int64_t length, uint8_t* valid_bytes) {
   typedef npy_traits<TYPE> traits;
   typedef typename traits::value_type T;
 
@@ -214,7 +148,7 @@ Status CheckFlatNumpyArray(PyArrayObject* numpy_array, int np_type) {
   return Status::OK();
 }
 
-Status AppendObjectStrings(arrow::StringBuilder& string_builder, PyObject** objects,
+Status AppendObjectStrings(StringBuilder& string_builder, PyObject** objects,
     int64_t objects_length, bool* have_bytes) {
   PyObject* obj;
 
@@ -242,360 +176,561 @@ Status AppendObjectStrings(arrow::StringBuilder& string_builder, PyObject** obje
   return Status::OK();
 }
 
-template <int TYPE>
-struct arrow_traits {};
+template <typename T>
+struct WrapBytes {};
 
 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;
+struct WrapBytes<StringArray> {
+  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
+    return PyUnicode_FromStringAndSize(reinterpret_cast<const char*>(data), length);
+  }
 };
 
-#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_DECL(INT8);
-INT_DECL(INT16);
-INT_DECL(INT32);
-INT_DECL(INT64);
-INT_DECL(UINT8);
-INT_DECL(UINT16);
-INT_DECL(UINT32);
-INT_DECL(UINT64);
-
 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;
+struct WrapBytes<BinaryArray> {
+  static inline PyObject* Wrap(const uint8_t* data, int64_t length) {
+    return PyBytes_FromStringAndSize(reinterpret_cast<const char*>(data), length);
+  }
 };
 
-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;
-};
+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;
+}
 
-static constexpr int64_t kPandasTimestampNull = std::numeric_limits<int64_t>::min();
+// ----------------------------------------------------------------------
+// Conversion from NumPy-in-Pandas to Arrow
 
-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;
-};
+class PandasConverter : public TypeVisitor {
+ public:
+  PandasConverter(
+      MemoryPool* pool, PyObject* ao, PyObject* mo, const std::shared_ptr<DataType>& type)
+      : pool_(pool),
+        type_(type),
+        arr_(reinterpret_cast<PyArrayObject*>(ao)),
+        mask_(nullptr) {
+    if (mo != nullptr and mo != Py_None) { mask_ = reinterpret_cast<PyArrayObject*>(mo); }
+    length_ = PyArray_SIZE(arr_);
+  }
 
-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;
-};
+  bool is_strided() const {
+    npy_intp* astrides = PyArray_STRIDES(arr_);
+    return astrides[0] != PyArray_DESCR(arr_)->elsize;
+  }
 
-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 InitNullBitmap() {
+    int null_bytes = BitUtil::BytesForBits(length_);
 
-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;
-};
+    null_bitmap_ = std::make_shared<PoolBuffer>(pool_);
+    RETURN_NOT_OK(null_bitmap_->Resize(null_bytes));
 
-template <typename T>
-struct WrapBytes {};
+    null_bitmap_data_ = null_bitmap_->mutable_data();
+    memset(null_bitmap_data_, 0, null_bytes);
 
-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);
+    return Status::OK();
   }
-};
 
-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);
-  }
-};
+  // ----------------------------------------------------------------------
+  // Traditional visitor conversion for non-object arrays
 
-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);
+  template <typename ArrowType>
+  Status ConvertData(std::shared_ptr<Buffer>* data);
 
-      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;
+  template <typename ArrowType>
+  Status VisitNative() {
+    using traits = arrow_traits<ArrowType::type_id>;
+
+    if (mask_ != nullptr || traits::supports_nulls) { RETURN_NOT_OK(InitNullBitmap()); }
+
+    std::shared_ptr<Buffer> data;
+    RETURN_NOT_OK(ConvertData<ArrowType>(&data));
+
+    int64_t null_count = 0;
+    if (mask_ != nullptr) {
+      null_count = MaskToBitmap(mask_, length_, null_bitmap_data_);
+    } else if (traits::supports_nulls) {
+      // TODO(wesm): this presumes the NumPy C type and arrow C type are the
+      // same
+      null_count = ValuesToBitmap<traits::npy_type>(
+          PyArray_DATA(arr_), length_, null_bitmap_data_);
     }
+
+    std::vector<FieldMetadata> fields(1);
+    fields[0].length = length_;
+    fields[0].null_count = null_count;
+    fields[0].offset = 0;
+
+    return LoadArray(type_, fields, {null_bitmap_, data}, &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
+#define VISIT_NATIVE(TYPE) \
+  Status Visit(const TYPE& type) override { return VisitNative<TYPE>(); }
 
-    for (int i = 0; i < arr->length(); ++i) {
-      *out_values++ = prim_arr->IsNull(i) ? NAN : in_values[i];
+  VISIT_NATIVE(BooleanType);
+  VISIT_NATIVE(Int8Type);
+  VISIT_NATIVE(Int16Type);
+  VISIT_NATIVE(Int32Type);
+  VISIT_NATIVE(Int64Type);
+  VISIT_NATIVE(UInt8Type);
+  VISIT_NATIVE(UInt16Type);
+  VISIT_NATIVE(UInt32Type);
+  VISIT_NATIVE(UInt64Type);
+  VISIT_NATIVE(FloatType);
+  VISIT_NATIVE(DoubleType);
+  VISIT_NATIVE(TimestampType);
+
+#undef VISIT_NATIVE
+
+  Status Convert(std::shared_ptr<Array>* out) {
+    if (PyArray_NDIM(arr_) != 1) {
+      return Status::Invalid("only handle 1-dimensional arrays");
     }
+    // TODO(wesm): strided arrays
+    if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
+
+    if (type_ == nullptr) { return Status::Invalid("Must pass data type"); }
+
+    // Visit the type to perform conversion
+    RETURN_NOT_OK(type_->Accept(this));
+
+    *out = out_;
+    return Status::OK();
   }
-}
 
-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();
+  // ----------------------------------------------------------------------
+  // Conversion logic for various object dtype arrays
+
+  template <int ITEM_TYPE, typename ArrowType>
+  Status ConvertTypedLists(
+      const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out);
+
+  Status ConvertObjectStrings(std::shared_ptr<Array>* out);
+  Status ConvertBooleans(std::shared_ptr<Array>* out);
+  Status ConvertDates(std::shared_ptr<Array>* out);
+  Status ConvertLists(const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out);
+  Status ConvertObjects(std::shared_ptr<Array>* out);
+
+ protected:
+  MemoryPool* pool_;
+  std::shared_ptr<DataType> type_;
+  PyArrayObject* arr_;
+  PyArrayObject* mask_;
+  int64_t length_;
+
+  // Used in visitor pattern
+  std::shared_ptr<Array> out_;
+
+  std::shared_ptr<ResizableBuffer> null_bitmap_;
+  uint8_t* null_bitmap_data_;
+};
+
+template <typename ArrowType>
+inline Status PandasConverter::ConvertData(std::shared_ptr<Buffer>* data) {
+  using traits = arrow_traits<ArrowType::type_id>;
+
+  // Handle LONGLONG->INT64 and other fun things
+  int type_num_compat = cast_npy_type_compat(PyArray_DESCR(arr_)->type_num);
+
+  if (traits::npy_type != type_num_compat) {
+    return Status::NotImplemented("NumPy type casts not yet implemented");
   }
+
+  *data = std::make_shared<NumPyBuffer>(arr_);
+  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 (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values = in_values[i];
-    }
+template <>
+inline Status PandasConverter::ConvertData<BooleanType>(std::shared_ptr<Buffer>* data) {
+  int nbytes = BitUtil::BytesForBits(length_);
+  auto buffer = std::make_shared<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();
+
+  memset(bitmap, 0, nbytes);
+  for (int i = 0; i < length_; ++i) {
+    if (values[i] > 0) { BitUtil::SetBit(bitmap, i); }
   }
+
+  *data = buffer;
+  return Status::OK();
 }
 
-static Status ConvertBooleanWithNulls(const ChunkedArray& data, PyObject** out_values) {
+Status PandasConverter::ConvertDates(std::shared_ptr<Array>* out) {
   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());
 
-    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 {
-        // False
-        Py_INCREF(Py_False);
-        *out_values++ = Py_False;
-      }
+  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+  DateBuilder date_builder(pool_);
+  RETURN_NOT_OK(date_builder.Resize(length_));
+
+  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 Status::OK();
+  return date_builder.Finish(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 PandasConverter::ConvertObjectStrings(std::shared_ptr<Array>* out) {
+  PyAcquireGIL lock;
+
+  // The output type at this point is inconclusive because there may be bytes
+  // and unicode mixed in the object array
+
+  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+  StringBuilder string_builder(pool_);
+  RETURN_NOT_OK(string_builder.Resize(length_));
+
+  Status s;
+  bool have_bytes = false;
+  RETURN_NOT_OK(AppendObjectStrings(string_builder, objects, length_, &have_bytes));
+  RETURN_NOT_OK(string_builder.Finish(out));
+
+  if (have_bytes) {
+    const auto& arr = static_cast<const StringArray&>(*out->get());
+    *out = std::make_shared<BinaryArray>(arr.length(), arr.value_offsets(), arr.data(),
+        arr.null_bitmap(), arr.null_count());
   }
+  return Status::OK();
 }
 
-template <typename ArrayType>
-inline Status ConvertBinaryLike(const ChunkedArray& data, PyObject** out_values) {
+Status PandasConverter::ConvertBooleans(std::shared_ptr<Array>* out) {
   PyAcquireGIL lock;
-  for (int c = 0; c < data.num_chunks(); c++) {
-    auto arr = static_cast<ArrayType*>(data.chunk(c).get());
 
-    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;
+  PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
+
+  int nbytes = BitUtil::BytesForBits(length_);
+  auto data = std::make_shared<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);
     }
   }
+
+  *out = std::make_shared<BooleanArray>(length_, data, null_bitmap_, null_count);
+
   return Status::OK();
 }
 
-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());
+Status PandasConverter::ConvertObjects(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
 
-  // 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));
+  RETURN_NOT_OK(InitNullBitmap());
 
-  PyAcquireGIL lock;
+  // TODO: mask not supported here
+  if (mask_ != nullptr) {
+    return Status::NotImplemented("mask not supported in object conversions yet");
+  }
 
-  for (int c = 0; c < data.num_chunks(); c++) {
-    auto arr = std::static_pointer_cast<arrow::ListArray>(data.chunk(c));
+  const PyObject** objects;
+  {
+    PyAcquireGIL lock;
+    objects = reinterpret_cast<const PyObject**>(PyArray_DATA(arr_));
+    PyDateTime_IMPORT;
+  }
 
-    const uint8_t* data_ptr;
-    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;
+  if (type_) {
+    switch (type_->type) {
+      case Type::STRING:
+        return ConvertObjectStrings(out);
+      case Type::BOOL:
+        return ConvertBooleans(out);
+      case Type::DATE:
+        return ConvertDates(out);
+      case Type::LIST: {
+        const auto& list_field = static_cast<const ListType&>(*type_);
+        return ConvertLists(list_field.value_field()->type, 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 {
-        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);
+        return Status::TypeError("unhandled python type");
       }
-      ++out_values;
     }
   }
 
-  Py_XDECREF(numpy_array);
-  return Status::OK();
+  return Status::TypeError("Unable to infer type of object array, were all null");
 }
 
-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());
+template <int ITEM_TYPE, typename ArrowType>
+inline Status PandasConverter::ConvertTypedLists(
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out) {
+  typedef npy_traits<ITEM_TYPE> traits;
+  typedef typename traits::value_type T;
+  typedef typename traits::BuilderClass BuilderT;
 
-    const uint8_t* valid_bits = arr->null_bitmap_data();
+  PyAcquireGIL lock;
 
-    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];
+  auto value_builder = std::make_shared<BuilderT>(pool_, 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));
+
+      // TODO(uwe): Support more complex numpy array structures
+      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, ITEM_TYPE));
+
+      int64_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());
+        ValuesToValidBytes<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])) {
+      int64_t size;
+      std::shared_ptr<DataType> inferred_type;
+      RETURN_NOT_OK(list_builder.Append(true));
+      RETURN_NOT_OK(InferArrowType(objects[i], &size, &inferred_type));
+      if (inferred_type->type != type->type) {
+        std::stringstream ss;
+        ss << inferred_type->ToString() << " cannot be converted to " << type->ToString();
+        return Status::TypeError(ss.str());
       }
+      RETURN_NOT_OK(AppendPySequence(objects[i], type, value_builder));
     } else {
-      memcpy(out_values, in_values, sizeof(T) * arr->length());
-      out_values += arr->length();
+      return Status::TypeError("Unsupported Python type for list items");
     }
   }
+  return list_builder.Finish(out);
 }
 
-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());
+template <>
+inline Status PandasConverter::ConvertTypedLists<NPY_OBJECT, StringType>(
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out) {
+  PyAcquireGIL lock;
+  // TODO: If there are bytes involed, convert to Binary representation
+  bool have_bytes = false;
 
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values++ = arr->IsNull(i) ? na_value : static_cast<OutType>(in_values[i]);
+  auto value_builder = std::make_shared<StringBuilder>(pool_);
+  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));
+
+      // TODO(uwe): Support more complex numpy array structures
+      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, NPY_OBJECT));
+
+      int64_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])) {
+      int64_t size;
+      std::shared_ptr<DataType> inferred_type;
+      RETURN_NOT_OK(list_builder.Append(true));
+      RETURN_NOT_OK(InferArrowType(objects[i], &size, &inferred_type));
+      if (inferred_type->type != Type::STRING) {
+        std::stringstream ss;
+        ss << inferred_type->ToString() << " cannot be converted to STRING.";
+        return Status::TypeError(ss.str());
+      }
+      RETURN_NOT_OK(AppendPySequence(objects[i], inferred_type, value_builder));
+    } else {
+      return Status::TypeError("Unsupported Python type for list items");
     }
   }
+  return list_builder.Finish(out);
 }
 
-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;
-    }
+#define LIST_CASE(TYPE, NUMPY_TYPE, ArrowType)                  \
+  case Type::TYPE: {                                            \
+    return ConvertTypedLists<NUMPY_TYPE, ArrowType>(type, out); \
+  }
+
+Status PandasConverter::ConvertLists(
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out) {
+  switch (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");
 }
 
-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());
+Status PandasToArrow(MemoryPool* pool, PyObject* ao, PyObject* mo,
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out) {
+  PandasConverter converter(pool, ao, mo, type);
+  return converter.Convert(out);
+}
 
-    for (int64_t i = 0; i < arr->length(); ++i) {
-      *out_values++ = arr->IsNull(i) ? kPandasTimestampNull
-                                     : (static_cast<int64_t>(in_values[i]) * SHIFT);
+Status PandasObjectsToArrow(MemoryPool* pool, PyObject* ao, PyObject* mo,
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out) {
+  PandasConverter converter(pool, ao, mo, type);
+  return converter.ConvertObjects(out);
+}
+
+Status PandasDtypeToArrow(PyObject* dtype, std::shared_ptr<DataType>* out) {
+  PyArray_Descr* descr = reinterpret_cast<PyArray_Descr*>(dtype);
+
+  int type_num = cast_npy_type_compat(descr->type_num);
+
+#define TO_ARROW_TYPE_CASE(NPY_NAME, FACTORY) \
+  case NPY_##NPY_NAME:                        \
+    *out = FACTORY();                         \
+    break;
+
+  switch (type_num) {
+    TO_ARROW_TYPE_CASE(BOOL, boolean);
+    TO_ARROW_TYPE_CASE(INT8, int8);
+    TO_ARROW_TYPE_CASE(INT16, int16);
+    TO_ARROW_TYPE_CASE(INT32, int32);
+    TO_ARROW_TYPE_CASE(INT64, int64);
+#if (NPY_INT64 != NPY_LONGLONG)
+    TO_ARROW_TYPE_CASE(LONGLONG, int64);
+#endif
+    TO_ARROW_TYPE_CASE(UINT8, uint8);
+    TO_ARROW_TYPE_CASE(UINT16, uint16);
+    TO_ARROW_TYPE_CASE(UINT32, uint32);
+    TO_ARROW_TYPE_CASE(UINT64, uint64);
+#if (NPY_UINT64 != NPY_ULONGLONG)
+    TO_ARROW_CASE(ULONGLONG);
+#endif
+    TO_ARROW_TYPE_CASE(FLOAT32, float32);
+    TO_ARROW_TYPE_CASE(FLOAT64, float64);
+    case NPY_DATETIME: {
+      auto date_dtype =
+          reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(descr->c_metadata);
+      TimeUnit unit;
+      switch (date_dtype->meta.base) {
+        case NPY_FR_s:
+          unit = TimeUnit::SECOND;
+          break;
+        case NPY_FR_ms:
+          unit = TimeUnit::MILLI;
+          break;
+        case NPY_FR_us:
+          unit = TimeUnit::MICRO;
+          break;
+        case NPY_FR_ns:
+          unit = TimeUnit::NANO;
+          break;
+        default:
+          return Status::NotImplemented("Unsupported datetime64 time unit");
+      }
+      *out = timestamp(unit);
+    } break;
+    default: {
+      std::stringstream ss;
+      ss << "Unsupported numpy type " << descr->type_num << std::endl;
+      return Status::NotImplemented(ss.str());
     }
   }
+
+#undef TO_ARROW_TYPE_CASE
+
+  return Status::OK();
 }
 
 // ----------------------------------------------------------------------
 // pandas 0.x DataFrame conversion internals
 
+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<TimestampType*>(datatype);
+
+      switch (timestamp_type->unit) {
+        case TimestampType::Unit::SECOND:
+          date_dtype->meta.base = NPY_FR_s;
+          break;
+        case TimestampType::Unit::MILLI:
+          date_dtype->meta.base = NPY_FR_ms;
+          break;
+        case TimestampType::Unit::MICRO:
+          date_dtype->meta.base = NPY_FR_us;
+          break;
+        case TimestampType::Unit::NANO:
+          date_dtype->meta.base = NPY_FR_ns;
+          break;
+      }
+    } else {
+      // datatype->type == Type::DATE
+      date_dtype->meta.base = NPY_FR_D;
+    }
+  }
+}
+
 class PandasBlock {
  public:
   enum type {
@@ -688,10 +823,219 @@ class PandasBlock {
   DISALLOW_COPY_AND_ASSIGN(PandasBlock);
 };
 
-#define CONVERTLISTSLIKE_CASE(ArrowType, ArrowEnum)                         \
-  case Type::ArrowEnum:                                                     \
-    RETURN_NOT_OK((ConvertListsLike<::arrow::ArrowType>(col, out_buffer))); \
-    break;
+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<PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+    // Upcast to double, set NaN as appropriate
+
+    for (int i = 0; i < arr->length(); ++i) {
+      *out_values++ = prim_arr->IsNull(i) ? NAN : in_values[i];
+    }
+  }
+}
+
+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<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();
+  }
+}
+
+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<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 = in_values[i];
+    }
+  }
+}
+
+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<BooleanArray*>(arr.get());
+
+    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 {
+        // False
+        Py_INCREF(Py_False);
+        *out_values++ = Py_False;
+      }
+    }
+  }
+  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<BooleanArray*>(arr.get());
+    for (int64_t i = 0; i < arr->length(); ++i) {
+      *out_values++ = static_cast<uint8_t>(bool_arr->Value(i));
+    }
+  }
+}
+
+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());
+
+    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 Status::OK();
+}
+
+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());
+
+  // 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<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));
+
+  PyAcquireGIL lock;
+
+  for (int c = 0; c < data.num_chunks(); c++) {
+    auto arr = std::static_pointer_cast<ListArray>(data.chunk(c));
+
+    const uint8_t* data_ptr;
+    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;
+    }
+  }
+
+  Py_XDECREF(numpy_array);
+  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<PrimitiveArray*>(arr.get());
+    auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
+
+    const uint8_t* valid_bits = arr->null_bitmap_data();
+
+    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();
+    }
+  }
+}
+
+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<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) ? na_value : static_cast<OutType>(in_values[i]);
+    }
+  }
+}
+
+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<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;
+    }
+  }
+}
+
+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<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);
+    }
+  }
+}
+
+#define CONVERTLISTSLIKE_CASE(ArrowType, ArrowEnum)                \
+  case Type::ArrowEnum:                                            \
+    RETURN_NOT_OK((ConvertListsLike<ArrowType>(col, out_buffer))); \
+    break;
 
 class ObjectBlock : public PandasBlock {
  public:
@@ -712,9 +1056,9 @@ class ObjectBlock : public PandasBlock {
     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));
+      RETURN_NOT_OK(ConvertBinaryLike<BinaryArray>(data, out_buffer));
     } else if (type == Type::STRING) {
-      RETURN_NOT_OK(ConvertBinaryLike<arrow::StringArray>(data, out_buffer));
+      RETURN_NOT_OK(ConvertBinaryLike<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) {
@@ -880,8 +1224,8 @@ class DatetimeBlock : public PandasBlock {
  public:
   using PandasBlock::PandasBlock;
 
-  Status Allocate() override {
-    RETURN_NOT_OK(AllocateNDArray(NPY_DATETIME));
+  Status AllocateDatetime(int ndim) {
+    RETURN_NOT_OK(AllocateNDArray(NPY_DATETIME, ndim));
 
     PyAcquireGIL lock;
     auto date_dtype = reinterpret_cast<PyArray_DatetimeDTypeMetaData*>(
@@ -890,6 +1234,8 @@ class DatetimeBlock : public PandasBlock {
     return Status::OK();
   }
 
+  Status Allocate() override { return AllocateDatetime(2); }
+
   Status Write(const std::shared_ptr<Column>& col, int64_t abs_placement,
       int64_t rel_placement) override {
     Type::type type = col->type()->type;
@@ -904,15 +1250,15 @@ class DatetimeBlock : public PandasBlock {
       // 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());
+      auto ts_type = static_cast<TimestampType*>(col->type().get());
 
-      if (ts_type->unit == arrow::TimeUnit::NANO) {
+      if (ts_type->unit == TimeUnit::NANO) {
         ConvertNumericNullable<int64_t>(data, kPandasTimestampNull, out_buffer);
-      } else if (ts_type->unit == arrow::TimeUnit::MICRO) {
+      } else if (ts_type->unit == TimeUnit::MICRO) {
         ConvertDatetimeNanos<int64_t, 1000L>(data, out_buffer);
-      } else if (ts_type->unit == arrow::TimeUnit::MILLI) {
+      } else if (ts_type->unit == TimeUnit::MILLI) {
         ConvertDatetimeNanos<int64_t, 1000000L>(data, out_buffer);
-      } else if (ts_type->unit == arrow::TimeUnit::SECOND) {
+      } else if (ts_type->unit == TimeUnit::SECOND) {
         ConvertDatetimeNanos<int64_t, 1000000000L>(data, out_buffer);
       } else {
         return Status::NotImplemented("Unsupported time unit");
@@ -931,6 +1277,9 @@ class DatetimeTZBlock : public DatetimeBlock {
   DatetimeTZBlock(const std::string& timezone, int64_t num_rows)
       : DatetimeBlock(num_rows, 1), timezone_(timezone) {}
 
+  // Like Categorical, the internal ndarray is 1-dimensional
+  Status Allocate() override { return AllocateDatetime(1); }
+
   Status GetPyResult(PyObject** output) override {
     PyObject* result = PyDict_New();
     RETURN_IF_PYERROR();
@@ -977,9 +1326,8 @@ class CategoricalBlock : public PandasBlock {
 
     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());
+      const auto& dict_arr = static_cast<const DictionaryArray&>(*arr);
+      const auto& indices = static_cast<const PrimitiveArray&>(*dict_arr.indices());
       auto in_values = reinterpret_cast<const T*>(indices.data()->data());
 
       // Null is -1 in CategoricalBlock
@@ -1046,28 +1394,6 @@ Status MakeBlock(PandasBlock::type type, int64_t num_rows, int num_columns,
   return (*block)->Allocate();
 }
 
-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;
-}
-
 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
@@ -1168,7 +1494,7 @@ class DataFrameBlockCreator {
           output_type = PandasBlock::DATETIME;
           break;
         case Type::TIMESTAMP: {
-          const auto& ts_type = static_cast<const arrow::TimestampType&>(*col->type());
+          const auto& ts_type = static_cast<const TimestampType&>(*col->type());
           if (ts_type.timezone != "") {
             output_type = PandasBlock::DATETIME_WITH_TZ;
           } else {
@@ -1182,636 +1508,165 @@ class DataFrameBlockCreator {
             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());
-      }
-
-      int block_placement = 0;
-      std::shared_ptr<PandasBlock> block;
-      if (output_type == PandasBlock::CATEGORICAL) {
-        RETURN_NOT_OK(MakeCategoricalBlock(col->type(), table_->num_rows(), &block));
-        categorical_blocks_[i] = block;
-      } else if (output_type == PandasBlock::DATETIME_WITH_TZ) {
-        const auto& ts_type = static_cast<const arrow::TimestampType&>(*col->type());
-        block = std::make_shared<DatetimeTZBlock>(ts_type.timezone, table_->num_rows());
-        RETURN_NOT_OK(block->Allocate());
-        datetimetz_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;
-        }
-      }
-
-      column_types_[i] = output_type;
-      column_block_placement_[i] = block_placement;
-    }
-
-    // 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();
-  }
-
-  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];
-
-      int rel_placement = this->column_block_placement_[i];
-
-      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);
-    };
-
-    nthreads = std::min<int>(nthreads, table_->num_columns());
-
-    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);
-
-      std::mutex error_mtx;
-      bool error_occurred = false;
-      Status error;
-
-      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();
-      }
-
-      if (error_occurred) { return error; }
-    }
-    return Status::OK();
-  }
-
-  Status AppendBlocks(const BlockMap& blocks, PyObject* list) {
-    for (const auto& it : blocks) {
-      PyObject* item;
-      RETURN_NOT_OK(it.second->GetPyResult(&item));
-      if (PyList_Append(list, item) < 0) { RETURN_IF_PYERROR(); }
-    }
-    return Status::OK();
-  }
-
-  Status GetResultList(PyObject** out) {
-    PyAcquireGIL lock;
-
-    PyObject* result = PyList_New(0);
-    RETURN_IF_PYERROR();
-
-    RETURN_NOT_OK(AppendBlocks(blocks_, result));
-    RETURN_NOT_OK(AppendBlocks(categorical_blocks_, result));
-    RETURN_NOT_OK(AppendBlocks(datetimetz_blocks_, result));
-
-    *out = result;
-    return Status::OK();
-  }
-
- private:
-  std::shared_ptr<Table> table_;
-
-  // column num -> block type id
-  std::vector<PandasBlock::type> column_types_;
-
-  // column num -> relative placement within internal block
-  std::vector<int> column_block_placement_;
-
-  // block type -> type count
-  std::unordered_map<int, int> type_counts_;
-
-  // block type -> block
-  BlockMap blocks_;
-
-  // column number -> categorical block
-  BlockMap categorical_blocks_;
-
-  // column number -> datetimetz block
-  BlockMap datetimetz_blocks_;
-};
-
-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_);
-  }
-
-  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();
-  }
-
-  bool is_strided() const {
-    npy_intp* astrides = PyArray_STRIDES(arr_);
-    return astrides[0] != PyArray_DESCR(arr_)->elsize;
-  }
-
- private:
-  Status ConvertData();
-
-  Status ConvertDates(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
-
-    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    arrow::DateBuilder date_builder(pool_);
-    RETURN_NOT_OK(date_builder.Resize(length_));
-
-    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);
-  }
-
-  Status ConvertObjectStrings(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
-
-    // The output type at this point is inconclusive because there may be bytes
-    // and unicode mixed in the object array
-
-    PyObject** objects = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    arrow::StringBuilder string_builder(pool_);
-    RETURN_NOT_OK(string_builder.Resize(length_));
-
-    Status s;
-    bool have_bytes = false;
-    RETURN_NOT_OK(AppendObjectStrings(string_builder, objects, length_, &have_bytes));
-    RETURN_NOT_OK(string_builder.Finish(out));
-
-    if (have_bytes) {
-      const auto& arr = static_cast<const arrow::StringArray&>(*out->get());
-      *out = std::make_shared<arrow::BinaryArray>(arr.length(), arr.value_offsets(),
-          arr.data(), arr.null_bitmap(), arr.null_count());
-    }
-    return Status::OK();
-  }
-
-  Status ConvertBooleans(std::shared_ptr<Array>* out) {
-    PyAcquireGIL lock;
-
-    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);
-      }
-    }
-
-    *out = std::make_shared<arrow::BooleanArray>(length_, data, null_bitmap_, null_count);
-
-    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");
-    }
-
-    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_;
-};
-
-// 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;
-}
-
-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;
-
-  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");
-  }
-
-  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;
-
-  if (mask_ != nullptr || traits::supports_nulls) { RETURN_NOT_OK(InitNullBitmap()); }
-
-  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_);
-  }
-
-  RETURN_NOT_OK(ConvertData());
-  std::shared_ptr<DataType> type;
-  RETURN_NOT_OK(MakeDataType(&type));
-
-  std::vector<arrow::FieldMetadata> fields(1);
-  fields[0].length = length_;
-  fields[0].null_count = null_count;
-  fields[0].offset = 0;
-
-  return arrow::LoadArray(type, fields, {null_bitmap_, data_}, out);
-}
-
-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());
-
-  // TODO: mask not supported here
-  const PyObject** objects = reinterpret_cast<const PyObject**>(PyArray_DATA(arr_));
-  {
-    PyAcquireGIL lock;
-    PyDateTime_IMPORT;
-  }
-
-  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);
+          }
+          output_type = PandasBlock::OBJECT;
+        } break;
+        case Type::DICTIONARY:
+          output_type = PandasBlock::CATEGORICAL;
+          break;
+        default:
+          return Status::NotImplemented(col->type()->ToString());
       }
-      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);
+
+      int block_placement = 0;
+      std::shared_ptr<PandasBlock> block;
+      if (output_type == PandasBlock::CATEGORICAL) {
+        RETURN_NOT_OK(MakeCategoricalBlock(col->type(), table_->num_rows(), &block));
+        categorical_blocks_[i] = block;
+      } else if (output_type == PandasBlock::DATETIME_WITH_TZ) {
+        const auto& ts_type = static_cast<const TimestampType&>(*col->type());
+        block = std::make_shared<DatetimeTZBlock>(ts_type.timezone, table_->num_rows());
+        RETURN_NOT_OK(block->Allocate());
+        datetimetz_blocks_[i] = block;
       } else {
-        return Status::TypeError("unhandled python type");
+        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;
+        }
       }
+
+      column_types_[i] = output_type;
+      column_block_placement_[i] = block_placement;
+    }
+
+    // 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();
   }
 
-  return Status::TypeError("Unable to infer type of object array, were all null");
-}
+  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];
 
-template <int TYPE>
-inline Status ArrowSerializer<TYPE>::ConvertData() {
-  // TODO(wesm): strided arrays
-  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
+      int rel_placement = this->column_block_placement_[i];
 
-  data_ = std::make_shared<NumPyBuffer>(arr_);
-  return Status::OK();
-}
+      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 if (output_type == PandasBlock::DATETIME_WITH_TZ) {
+        auto it = this->datetimetz_blocks_.find(i);
+        if (it == this->datetimetz_blocks_.end()) {
+          return Status::KeyError("No datetimetz 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);
+    };
 
-template <>
-inline Status ArrowSerializer<NPY_BOOL>::ConvertData() {
-  if (is_strided()) { return Status::Invalid("no support for strided data yet"); }
+    nthreads = std::min<int>(nthreads, table_->num_columns());
 
-  int nbytes = BitUtil::BytesForBits(length_);
-  auto buffer = std::make_shared<arrow::PoolBuffer>(pool_);
-  RETURN_NOT_OK(buffer->Resize(nbytes));
+    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);
 
-  const uint8_t* values = reinterpret_cast<const uint8_t*>(PyArray_DATA(arr_));
+      std::mutex error_mtx;
+      bool error_occurred = false;
+      Status error;
 
-  uint8_t* bitmap = buffer->mutable_data();
+      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();
+      }
 
-  memset(bitmap, 0, nbytes);
-  for (int i = 0; i < length_; ++i) {
-    if (values[i] > 0) { BitUtil::SetBit(bitmap, i); }
+      if (error_occurred) { return error; }
+    }
+    return Status::OK();
   }
 
-  data_ = buffer;
-
-  return Status::OK();
-}
-
-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;
-  PyAcquireGIL lock;
-
-  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));
-
-      // TODO(uwe): Support more complex numpy array structures
-      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, ITEM_TYPE));
-
-      int64_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])) {
-      int64_t size;
-      std::shared_ptr<arrow::DataType> type;
-      RETURN_NOT_OK(list_builder.Append(true));
-      RETURN_NOT_OK(InferArrowType(objects[i], &size, &type));
-      if (type->type != field->type->type) {
-        std::stringstream ss;
-        ss << type->ToString() << " cannot be converted to " << field->type->ToString();
-        return Status::TypeError(ss.str());
-      }
-      RETURN_NOT_OK(AppendPySequence(objects[i], field->type, value_builder));
-    } else {
-      return Status::TypeError("Unsupported Python type for list items");
+  Status AppendBlocks(const BlockMap& blocks, PyObject* list) {
+    for (const auto& it : blocks) {
+      PyObject* item;
+      RETURN_NOT_OK(it.second->GetPyResult(&item));
+      if (PyList_Append(list, item) < 0) { RETURN_IF_PYERROR(); }
     }
+    return Status::OK();
   }
-  return list_builder.Finish(out);
-}
 
-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
-  PyAcquireGIL lock;
-  bool have_bytes = false;
+  Status GetResultList(PyObject** out) {
+    PyAcquireGIL lock;
 
-  auto value_builder = std::make_shared<arrow::StringBuilder>(pool_);
-  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));
+    PyObject* result = PyList_New(0);
+    RETURN_IF_PYERROR();
 
-      // TODO(uwe): Support more complex numpy array structures
-      RETURN_NOT_OK(CheckFlatNumpyArray(numpy_array, NPY_OBJECT));
+    RETURN_NOT_OK(AppendBlocks(blocks_, result));
+    RETURN_NOT_OK(AppendBlocks(categorical_blocks_, result));
+    RETURN_NOT_OK(AppendBlocks(datetimetz_blocks_, result));
 
-      int64_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])) {
-      int64_t size;
-      std::shared_ptr<arrow::DataType> type;
-      RETURN_NOT_OK(list_builder.Append(true));
-      RETURN_NOT_OK(InferArrowType(objects[i], &size, &type));
-      if (type->type != Type::STRING) {
-        std::stringstream ss;
-        ss << type->ToString() << " cannot be converted to STRING.";
-        return Status::TypeError(ss.str());
-      }
-      RETURN_NOT_OK(AppendPySequence(objects[i], type, value_builder));
-    } else {
-      return Status::TypeError("Unsupported Python type for list items");
-    }
+    *out = result;
+    return Status::OK();
   }
-  return list_builder.Finish(out);
-}
 
-template <>
-inline Status ArrowSerializer<NPY_OBJECT>::ConvertData() {
-  return Status::TypeError("NYI");
-}
-
-#define TO_ARROW_CASE(TYPE)                                 \
-  case NPY_##TYPE: {                                        \
-    ArrowSerializer<NPY_##TYPE> converter(pool, arr, mask); \
-    RETURN_NOT_OK(converter.Convert(out));                  \
-  } break;
+ private:
+  std::shared_ptr<Table> table_;
 
-Status PandasToArrow(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;
+  // column num -> block type id
+  std::vector<PandasBlock::type> column_types_;
 
-  if (mo != nullptr and mo != Py_None) { mask = reinterpret_cast<PyArrayObject*>(mo); }
+  // column num -> relative placement within internal block
+  std::vector<int> column_block_placement_;
 
-  if (PyArray_NDIM(arr) != 1) {
-    return Status::Invalid("only handle 1-dimensional arrays");
-  }
+  // block type -> type count
+  std::unordered_map<int, int> type_counts_;
 
-  int type_num = PyArray_DESCR(arr)->type_num;
+  // block type -> block
+  BlockMap blocks_;
 
-#if (NPY_INT64 == NPY_LONGLONG) && (NPY_SIZEOF_LONGLONG == 8)
-  // Both LONGLONG and INT64 can be observed in the wild, which is buggy. We set
-  // U/LONGLONG to U/INT64 so things work properly.
-  if (type_num == NPY_LONGLONG) { type_num = NPY_INT64; }
-  if (type_num == NPY_ULONGLONG) { type_num = NPY_UINT64; }
-#endif
+  // column number -> categorical block
+  BlockMap categorical_blocks_;
 
-  switch (type_num) {
-    TO_ARROW_CASE(BOOL);
-    TO_ARROW_CASE(INT8);
-    TO_ARROW_CASE(INT16);
-    TO_ARROW_CASE(INT32);
-    TO_ARROW_CASE(INT64);
-#if (NPY_INT64 != NPY_LONGLONG)
-    TO_ARROW_CASE(LONGLONG);
-#endif
-    TO_ARROW_CASE(UINT8);
-    TO_ARROW_CASE(UINT16);
-    TO_ARROW_CASE(UINT32);
-    TO_ARROW_CASE(UINT64);
-#if (NPY_UINT64 != NPY_ULONGLONG)
-    TO_ARROW_CASE(ULONGLONG);
-#endif
-    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:
-      std::stringstream ss;
-      ss << "Unsupported numpy type " << PyArray_DESCR(arr)->type_num << std::endl;
-      return Status::NotImplemented(ss.str());
-  }
-  return Status::OK();
-}
+  // column number -> datetimetz block
+  BlockMap datetimetz_blocks_;
+};
 
 class ArrowDeserializer {
  public:
@@ -1839,7 +1694,7 @@ class ArrowDeserializer {
   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 prim_arr = static_cast<PrimitiveArray*>(arr.get());
     auto in_values = reinterpret_cast<const T*>(prim_arr->data()->data());
 
     // Zero-Copy. We can pass the data pointer directly to NumPy.
@@ -1988,19 +1843,19 @@ class ArrowDeserializer {
   inline typename std::enable_if<TYPE == Type::STRING, Status>::type ConvertValues() {
     RETURN_NOT_OK(AllocateOutput(NPY_OBJECT));
     auto out_values = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    return ConvertBinaryLike<arrow::StringArray>(data_, out_values);
+    return ConvertBinaryLike<StringArray>(data_, out_values);
   }
 
   template <int T2>
   inline typename std::enable_if<T2 == Type::BINARY, Status>::type ConvertValues() {
     RETURN_NOT_OK(AllocateOutput(NPY_OBJECT));
     auto out_values = reinterpret_cast<PyObject**>(PyArray_DATA(arr_));
-    return ConvertBinaryLike<arrow::BinaryArray>(data_, out_values);
+    return ConvertBinaryLike<BinaryArray>(data_, out_values);
   }
 
 #define CONVERTVALUES_LISTSLIKE_CASE(ArrowType, ArrowEnum) \
   case Type::ArrowEnum:                                    \
-    return ConvertListsLike<::arrow::ArrowType>(col_, out_values);
+    return ConvertListsLike<ArrowType>(col_, out_values);
 
   template <int T2>
   inline typename std::enable_if<T2 == Type::LIST, Status>::type ConvertValues() {
@@ -2051,7 +1906,7 @@ class ArrowDeserializer {
 
  private:
   std::shared_ptr<Column> col_;
-  const arrow::ChunkedArray& data_;
+  const ChunkedArray& data_;
   PyObject* py_ref_;
   PyArrayObject* arr_;
   PyObject* result_;
@@ -2071,4 +1926,11 @@ Status ConvertColumnToPandas(
   return converter.Convert(out);
 }
 
-}  // namespace pyarrow
+Status ConvertTableToPandas(
+    const std::shared_ptr<Table>& table, int nthreads, PyObject** out) {
+  DataFrameBlockCreator helper(table);
+  return helper.Convert(nthreads, out);
+}
+
+}  // namespace py
+}  // namespace arrow

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/adapters/pandas.h
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/adapters/pandas.h b/python/src/pyarrow/adapters/pandas.h
index b548f93..6862339 100644
--- a/python/src/pyarrow/adapters/pandas.h
+++ b/python/src/pyarrow/adapters/pandas.h
@@ -25,28 +25,26 @@
 
 #include <memory>
 
-#include "pyarrow/visibility.h"
+#include "arrow/util/visibility.h"
 
 namespace arrow {
 
 class Array;
 class Column;
-class Field;
+class DataType;
 class MemoryPool;
 class Status;
 class Table;
 
-}  // namespace arrow
-
-namespace pyarrow {
+namespace py {
 
-PYARROW_EXPORT
-arrow::Status ConvertArrayToPandas(
-    const std::shared_ptr<arrow::Array>& arr, PyObject* py_ref, PyObject** out);
+ARROW_EXPORT
+Status ConvertArrayToPandas(
+    const std::shared_ptr<Array>& arr, PyObject* py_ref, PyObject** out);
 
-PYARROW_EXPORT
-arrow::Status ConvertColumnToPandas(
-    const std::shared_ptr<arrow::Column>& col, PyObject* py_ref, PyObject** out);
+ARROW_EXPORT
+Status ConvertColumnToPandas(
+    const std::shared_ptr<Column>& col, PyObject* py_ref, PyObject** out);
 
 struct PandasOptions {
   bool strings_to_categorical;
@@ -58,14 +56,24 @@ struct PandasOptions {
 // BlockManager structure of the pandas.DataFrame used as of pandas 0.19.x.
 //
 // tuple item: (indices: ndarray[int32], block: ndarray[TYPE, ndim=2])
-PYARROW_EXPORT
-arrow::Status ConvertTableToPandas(
-    const std::shared_ptr<arrow::Table>& table, int nthreads, PyObject** out);
+ARROW_EXPORT
+Status ConvertTableToPandas(
+    const std::shared_ptr<Table>& table, int nthreads, PyObject** out);
+
+ARROW_EXPORT
+Status PandasDtypeToArrow(PyObject* dtype, std::shared_ptr<DataType>* out);
 
-PYARROW_EXPORT
-arrow::Status PandasToArrow(arrow::MemoryPool* pool, PyObject* ao, PyObject* mo,
-    const std::shared_ptr<arrow::Field>& field, std::shared_ptr<arrow::Array>* out);
+ARROW_EXPORT
+Status PandasToArrow(MemoryPool* pool, PyObject* ao, PyObject* mo,
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out);
 
-}  // namespace pyarrow
+/// Convert dtype=object arrays. If target data type is not known, pass a type
+/// with nullptr
+ARROW_EXPORT
+Status PandasObjectsToArrow(MemoryPool* pool, PyObject* ao, PyObject* mo,
+    const std::shared_ptr<DataType>& type, std::shared_ptr<Array>* out);
+
+}  // namespace py
+}  // namespace arrow
 
 #endif  // PYARROW_ADAPTERS_PANDAS_H

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/common.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/common.cc b/python/src/pyarrow/common.cc
index d2f5291..c898f63 100644
--- a/python/src/pyarrow/common.cc
+++ b/python/src/pyarrow/common.cc
@@ -24,24 +24,23 @@
 #include "arrow/memory_pool.h"
 #include "arrow/status.h"
 
-using arrow::Status;
-
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
 static std::mutex memory_pool_mutex;
-static arrow::MemoryPool* default_pyarrow_pool = nullptr;
+static MemoryPool* default_pyarrow_pool = nullptr;
 
-void set_default_memory_pool(arrow::MemoryPool* pool) {
+void set_default_memory_pool(MemoryPool* pool) {
   std::lock_guard<std::mutex> guard(memory_pool_mutex);
   default_pyarrow_pool = pool;
 }
 
-arrow::MemoryPool* get_memory_pool() {
+MemoryPool* get_memory_pool() {
   std::lock_guard<std::mutex> guard(memory_pool_mutex);
   if (default_pyarrow_pool) {
     return default_pyarrow_pool;
   } else {
-    return arrow::default_memory_pool();
+    return default_memory_pool();
   }
 }
 
@@ -60,4 +59,5 @@ PyBytesBuffer::~PyBytesBuffer() {
   Py_DECREF(obj_);
 }
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/common.h
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/common.h b/python/src/pyarrow/common.h
index ad65ec7..0b4c6be 100644
--- a/python/src/pyarrow/common.h
+++ b/python/src/pyarrow/common.h
@@ -19,16 +19,16 @@
 #define PYARROW_COMMON_H
 
 #include "pyarrow/config.h"
-#include "pyarrow/visibility.h"
 
 #include "arrow/buffer.h"
 #include "arrow/util/macros.h"
+#include "arrow/util/visibility.h"
 
 namespace arrow {
+
 class MemoryPool;
-}
 
-namespace pyarrow {
+namespace py {
 
 class PyAcquireGIL {
  public:
@@ -98,10 +98,10 @@ struct PyObjectStringify {
   }
 
 // Return the common PyArrow memory pool
-PYARROW_EXPORT void set_default_memory_pool(arrow::MemoryPool* pool);
-PYARROW_EXPORT arrow::MemoryPool* get_memory_pool();
+ARROW_EXPORT void set_default_memory_pool(MemoryPool* pool);
+ARROW_EXPORT MemoryPool* get_memory_pool();
 
-class PYARROW_EXPORT NumPyBuffer : public arrow::Buffer {
+class ARROW_EXPORT NumPyBuffer : public Buffer {
  public:
   NumPyBuffer(PyArrayObject* arr) : Buffer(nullptr, 0) {
     arr_ = arr;
@@ -118,7 +118,7 @@ class PYARROW_EXPORT NumPyBuffer : public arrow::Buffer {
   PyArrayObject* arr_;
 };
 
-class PYARROW_EXPORT PyBytesBuffer : public arrow::Buffer {
+class ARROW_EXPORT PyBytesBuffer : public Buffer {
  public:
   PyBytesBuffer(PyObject* obj);
   ~PyBytesBuffer();
@@ -127,6 +127,7 @@ class PYARROW_EXPORT PyBytesBuffer : public arrow::Buffer {
   PyObject* obj_;
 };
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow
 
 #endif  // PYARROW_COMMON_H

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/config.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/config.cc b/python/src/pyarrow/config.cc
index e1002bf..0be6d96 100644
--- a/python/src/pyarrow/config.cc
+++ b/python/src/pyarrow/config.cc
@@ -19,7 +19,8 @@
 
 #include "pyarrow/config.h"
 
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
 void pyarrow_init() {}
 
@@ -30,4 +31,5 @@ void pyarrow_set_numpy_nan(PyObject* obj) {
   numpy_nan = obj;
 }
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/config.h
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/config.h b/python/src/pyarrow/config.h
index 386ee4b..87fc5c2 100644
--- a/python/src/pyarrow/config.h
+++ b/python/src/pyarrow/config.h
@@ -20,24 +20,27 @@
 
 #include <Python.h>
 
+#include "arrow/util/visibility.h"
+
 #include "pyarrow/numpy_interop.h"
-#include "pyarrow/visibility.h"
 
 #if PY_MAJOR_VERSION >= 3
 #define PyString_Check PyUnicode_Check
 #endif
 
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
-PYARROW_EXPORT
+ARROW_EXPORT
 extern PyObject* numpy_nan;
 
-PYARROW_EXPORT
+ARROW_EXPORT
 void pyarrow_init();
 
-PYARROW_EXPORT
+ARROW_EXPORT
 void pyarrow_set_numpy_nan(PyObject* obj);
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow
 
 #endif  // PYARROW_CONFIG_H

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/helpers.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/helpers.cc b/python/src/pyarrow/helpers.cc
index 78fad16..edebea6 100644
--- a/python/src/pyarrow/helpers.cc
+++ b/python/src/pyarrow/helpers.cc
@@ -19,9 +19,8 @@
 
 #include <arrow/api.h>
 
-using namespace arrow;
-
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
 #define GET_PRIMITIVE_TYPE(NAME, FACTORY) \
   case Type::NAME:                        \
@@ -51,4 +50,5 @@ std::shared_ptr<DataType> GetPrimitiveType(Type::type type) {
   }
 }
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/helpers.h
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/helpers.h b/python/src/pyarrow/helpers.h
index 788c3ee..611e814 100644
--- a/python/src/pyarrow/helpers.h
+++ b/python/src/pyarrow/helpers.h
@@ -18,19 +18,18 @@
 #ifndef PYARROW_HELPERS_H
 #define PYARROW_HELPERS_H
 
-#include <arrow/api.h>
 #include <memory>
 
-#include "pyarrow/visibility.h"
+#include "arrow/type.h"
+#include "arrow/util/visibility.h"
 
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
-using arrow::DataType;
-using arrow::Type;
-
-PYARROW_EXPORT
+ARROW_EXPORT
 std::shared_ptr<DataType> GetPrimitiveType(Type::type type);
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow
 
 #endif  // PYARROW_HELPERS_H

http://git-wip-us.apache.org/repos/asf/arrow/blob/00df40ce/python/src/pyarrow/io.cc
----------------------------------------------------------------------
diff --git a/python/src/pyarrow/io.cc b/python/src/pyarrow/io.cc
index aa4cb7b..0aa61dc 100644
--- a/python/src/pyarrow/io.cc
+++ b/python/src/pyarrow/io.cc
@@ -26,9 +26,8 @@
 
 #include "pyarrow/common.h"
 
-using arrow::Status;
-
-namespace pyarrow {
+namespace arrow {
+namespace py {
 
 // ----------------------------------------------------------------------
 // Python file
@@ -151,7 +150,7 @@ Status PyReadableFile::Read(int64_t nbytes, int64_t* bytes_read, uint8_t* out) {
   return Status::OK();
 }
 
-Status PyReadableFile::Read(int64_t nbytes, std::shared_ptr<arrow::Buffer>* out) {
+Status PyReadableFile::Read(int64_t nbytes, std::shared_ptr<Buffer>* out) {
   PyAcquireGIL lock;
 
   PyObject* bytes_obj;
@@ -214,8 +213,9 @@ Status PyOutputStream::Write(const uint8_t* data, int64_t nbytes) {
 // A readable file that is backed by a PyBytes
 
 PyBytesReader::PyBytesReader(PyObject* obj)
-    : arrow::io::BufferReader(std::make_shared<PyBytesBuffer>(obj)) {}
+    : io::BufferReader(std::make_shared<PyBytesBuffer>(obj)) {}
 
 PyBytesReader::~PyBytesReader() {}
 
-}  // namespace pyarrow
+}  // namespace py
+}  // namespace arrow


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