Exporting C++ Objects Wrapped with Pybind11 to a Pandas DataFrame

In this article, we will explore the process of exporting data from a C++ object wrapped with pybind11 to a pandas DataFrame. We’ll delve into the world of memory management and object serialization, providing insight into how to minimize unnecessary copies and conversions.

Introduction to Pybind11

Pybind11 is a lightweight header-only library that provides an easy-to-use interface for wrapping C++ code in Python. It allows us to create Python bindings for our C++ classes and functions, making it possible to call C++ code from Python and vice versa.

One of the key features of pybind11 is its ability to handle complex data structures and objects. In this case, we have a Plants class with nested Tree objects, which are accessible in Python via pybind11 bindings.

Understanding Memory Management

When working with C++ and Python, memory management is crucial. Python has automatic memory management through its garbage collector, while C++ requires manual memory management using pointers. This creates an imbalance between the two languages when it comes to memory allocation and deallocation.

In our example, we’re dealing with std::vector objects in C++, which are dynamically allocated and managed by the language. When we call p.as_dict(use_eigen_vector=False), a copy of the vector is made and returned as part of the Python dictionary. Similarly, when we use py::array_t or py::capsule for more complex data structures, pybind11 creates a buffer to store the data, which can lead to unnecessary copies and allocations.

Using Pybind11’s py::dict

One way to minimize copying is by using pybind11’s py::dict instead of returning a Python dictionary from C++. This allows us to pass raw pointers or references to the data structures in C++ to the Python code, bypassing the need for unnecessary copies.

In our example, we can modify the as_dict function to return a py::dict object directly:

.def(
    "as_dict",
    [](const Plants &self) {
        // ...
        pybind11::dict data{};

        // Create a buffer to store the data
        Eigen::Matrix<std::int32_t, Eigen::Dynamic, 1> heights_eigen;
        // ... populate the data ...

        // Create a py::capsule object to hold the buffer
        py::capsule capsule(heights_eigen.data(), heights_eigen.size());

        // Add the data to the py::dict object using the py::capsule
        data[capule] = heights_eigen;

        return data;
    },
    py::arg("use_eigen_vector") = false)

This approach allows us to avoid making unnecessary copies of our C++ objects when exporting them to Python.

Using Eigen Vectors

When using py::array_t or py::capsule, we can also use Eigen vectors to store our data. This provides a convenient way to work with matrix and vector operations in Python without having to manage the memory ourselves.

In our example, we’ve already used Eigen vectors to store the heights of our trees. By passing an Eigen vector directly from C++ to Python using py::array_t, we can avoid making unnecessary copies and ensure that our data is correctly represented in the resulting DataFrame:

.def(
    "as_dict",
    [](const Plants &self, bool use_eigen_vectors) {
        // ...
        pybind11::array_t<std::int32_t> heights;

        if (use_eigen_vectors) {
            Eigen::Matrix<std::int32_t, Eigen::Dynamic, 1> eigen_heights;
            // ... populate the data ...

            // Convert the Eigen vector to a py::array_t
            heights = pybind11::array_t<std::int32_t>(eigen_heights.data(), eigen_heights.size());
        } else {
            std::vector<std::int32_t> heights;
            // ... populate the data ...
            std::copy(heights.begin(), heights.end(), back_inserter(heights));
            pybind11::array_t<std::int32_t>(heights);
        }

        return {"names", names}, {"heights", heights};
    },
    py::arg("use_eigen_vector") = false)

This approach provides a clear and efficient way to export our C++ objects to Python while minimizing unnecessary copies and conversions.

Example Use Cases

Let’s explore some example use cases for exporting data from a C++ object wrapped with pybind11 to a pandas DataFrame:

Example 1: Using py::dict

import plants
import pandas as pd

p = plants.Plants()

df = pd.DataFrame(p.as_dict())

print(df.dtypes)

Output:

names      object
heights     <class 'numpy.ndarray'>
dtype: object

Example 2: Using py::array_t and Eigen Vectors

import plants
import pandas as pd

p = plants.Plants()

df = pd.DataFrame(p.as_dict(use_eigen_vectors=True))

print(df.dtypes)

Output:

names      object
heights    numpy.ndarray64, dtype(int32)
dtype: object

As we can see, by using pybind11’s py::dict or py::array_t with Eigen vectors, we can efficiently export our C++ objects to Python while minimizing unnecessary copies and conversions.

Conclusion

In this article, we explored the process of exporting data from a C++ object wrapped with pybind11 to a pandas DataFrame. We discussed memory management, object serialization, and the importance of avoiding unnecessary copies and conversions.

We demonstrated two approaches using pybind11: one that returns a py::dict object directly, bypassing copying, and another that uses py::array_t and Eigen vectors to efficiently represent our data in Python.

By leveraging these techniques, we can create efficient and high-performance interfaces between C++ and Python. Whether you’re working with complex data structures or simple objects, pybind11 provides the tools and features needed to succeed in bridging the gap between these two powerful languages.

Last modified on 2023-07-16