Implementing DataFrames in C++: A Custom Approach for High-Performance Data Manipulation

Introduction

When working with data, Python and Pandas dominate the landscape. They provide an intuitive way to load, transform, and analyze datasets. But what if you need the same capabilities inside a C++ pipeline, without switching languages, and with full control over memory and type safety?

That’s the motivation behind Tablo, my implementation of DataFrames in modern C++. This project provides a flexible and performant way to manipulate tabular data directly in C++ — including CSV parsing, type inference, missing value handling, and column/row operations.

In this article, I’ll walk you through the architecture, features, and next steps of this implementation.

Architecture Overview

The core idea of this project is simple:

  • Each column of the DataFrame is stored as a std::vector<T>, where T is the inferred type of the column.
  • Types are detected through semantic analysis when reading a CSV file. Supported types include:
    • std::string
    • char
    • bool
    • int
    • unsigned int
    • double

This allows the system to optimize storage and operations based on the actual data instead of storing everything as strings.

Handling Missing Data (N/A)

Missing values are a common issue when dealing with real-world datasets. In this implementation:

  • N/A is recognized during parsing.
  • Missing entries are properly represented within their respective typed vectors, ensuring consistency and safety.

Core Features

1. Reading CSV Files with Type Inference

The library reads CSV files and builds a DataFrame where each column is typed according to its content. For example:

Tablo df("dataset.csv");

Here, each column will be imported into a typed std::vector<T>.

2. Writing DataFrames Back to CSV

DataFrames can be exported back into a CSV file, with customizable column delimiters:

df.to_csv("output.csv", ';');

This makes it easy to integrate with external tools while preserving type safety inside your C++ code.

3. Selecting and Copying Rows/Columns

You can create a new DataFrame by selecting a subset of rows and columns from an existing one:

Tablo subset = df.copy({0, 2, 4}, {"Name", "Age"});

This produces a new DataFrame with only the chosen rows and columns.

4. Extracting Columns as Typed Vectors

A column can be retrieved directly as a typed vector:

std::vector<int> ages = df.get_column<int>("Age");

This is extremely useful when you want to process the data using C++ algorithms or numerical libraries.

5. Extracting Multiple Columns

You can also extract multiple columns at once as a vector of vectors:

auto selected = df.get_columns<int>({"Height", "Weight"});

This provides direct typed access to the data without conversions.

Roadmap: Implementing Joins

The next big step for this project is implementing joins — allowing DataFrames to be merged based on keys, similar to SQL or Pandas. This will open the door to more advanced relational data operations directly in C++.

Why C++ DataFrames?

You might ask: Why not just use Python and Pandas?
The answer lies in performance and integration.

  • When working in a C++ codebase (e.g., simulation, high-performance computing, embedded analytics), switching to Python introduces overhead.
  • With a native C++ DataFrame, you can:
    • Avoid unnecessary data conversions.
    • Benefit from strong typing and compile-time checks.
    • Leverage modern C++ features for performance.

This makes the library a good fit for projects where speed and memory control matter, but you still want the convenience of high-level tabular abstractions.

Conclusion

With Tablo, I set out to bring the flexibility of Pandas-style DataFrames into the world of C++.
So far, the implementation supports:

  • Reading CSVs with automatic type inference
  • Handling missing values
  • Writing to CSV with custom delimiters
  • Copying subsets of rows and columns
  • Extracting columns or sets of columns as typed vectors

The next milestone is implementing joins, which will significantly expand its capabilities.

If you’re interested, you can explore the full project here:
👉 GitHub Repository: Tablo