| Linux + Mac | Windows |
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Rapidcsv is a C++ header-only library for CSV parsing. While the name admittedly was inspired by the rapidjson project, the objectives are not the same. The goal of rapidcsv is to be an easy-to-use CSV library enabling rapid development. For optimal performance (be it CPU or memory usage) a CSV parser implemented for the specific use-case is likely to be more performant.
Here is a simple example reading a CSV file and getting 'Close' column as a vector of floats.
colhdr.csv content:
Open,High,Low,Close,Volume,Adj Close
64.529999,64.800003,64.139999,64.620003,21705200,64.620003
64.419998,64.730003,64.190002,64.620003,20235200,64.620003
64.330002,64.389999,64.050003,64.360001,19259700,64.360001
64.610001,64.949997,64.449997,64.489998,19384900,64.489998
64.470001,64.690002,64.300003,64.620003,21234600,64.620003
ex001.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/colhdr.csv");
std::vector<float> col = doc.GetColumn<float>("Close");
std::cout << "Read " << col.size() << " values." << std::endl;
}Refer to section More Examples below for more examples. The tests directory also contains many simple usage examples.
Rapidcsv is implemented using C++11 with the intention of being portable. It's been tested on:
- macOS Catalina 10.15
- Ubuntu 20.04 LTS
- Windows 10 / Visual Studio 2015
Simply copy src/rapidcsv.h to your project/include directory and include it.
Several of the following examples are also provided in the examples/
directory and can be executed directly under Linux and macOS. Example running
ex001.cpp:
./examples/ex001.cpp
By default rapidcsv treats the first row as column headers, and the first column is treated as data. This allows accessing columns using their labels, but not rows or cells (only using indices). In order to treat the first column as row headers one needs to use LabelParams and set pRowNameIdx to 0.
colrowhdr.csv content:
Date,Open,High,Low,Close,Volume,Adj Close
2017-02-24,64.529999,64.800003,64.139999,64.620003,21705200,64.620003
2017-02-23,64.419998,64.730003,64.190002,64.620003,20235200,64.620003
2017-02-22,64.330002,64.389999,64.050003,64.360001,19259700,64.360001
2017-02-21,64.610001,64.949997,64.449997,64.489998,19384900,64.489998
2017-02-17,64.470001,64.690002,64.300003,64.620003,21234600,64.620003
ex002.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/colrowhdr.csv", rapidcsv::LabelParams(0, 0));
std::vector<float> close = doc.GetRow<float>("2017-02-22");
std::cout << "Read " << close.size() << " values." << std::endl;
long long volume = doc.GetCell<long long>("Volume", "2017-02-22");
std::cout << "Volume " << volume << " on 2017-02-22." << std::endl;
}rowhdr.csv content:
2017-02-24,64.529999,64.800003,64.139999,64.620003,21705200,64.620003
2017-02-23,64.419998,64.730003,64.190002,64.620003,20235200,64.620003
2017-02-22,64.330002,64.389999,64.050003,64.360001,19259700,64.360001
2017-02-21,64.610001,64.949997,64.449997,64.489998,19384900,64.489998
2017-02-17,64.470001,64.690002,64.300003,64.620003,21234600,64.620003
ex003.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/rowhdr.csv", rapidcsv::LabelParams(-1, 0));
std::vector<std::string> row = doc.GetRow<std::string>("2017-02-22");
std::cout << "Read " << row.size() << " values." << std::endl;
}nohdr.csv content:
64.529999,64.800003,64.139999,64.620003,21705200,64.620003
64.419998,64.730003,64.190002,64.620003,20235200,64.620003
64.330002,64.389999,64.050003,64.360001,19259700,64.360001
64.610001,64.949997,64.449997,64.489998,19384900,64.489998
64.470001,64.690002,64.300003,64.620003,21234600,64.620003
ex004.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/nohdr.csv", rapidcsv::LabelParams(-1, -1));
std::vector<float> close = doc.GetColumn<float>(5);
std::cout << "Read " << close.size() << " values." << std::endl;
long long volume = doc.GetCell<long long>(4, 2);
std::cout << "Volume " << volume << " on 2017-02-22." << std::endl;
}For reading of files with custom separator (i.e. not comma), one need to specify the SeparatorParams argument. The following example reads a file using semi-colon as separator.
semi.csv content:
Date;Open;High;Low;Close;Volume;Adj Close
2017-02-24;64.529999;64.800003;64.139999;64.620003;21705200;64.620003
2017-02-23;64.419998;64.730003;64.190002;64.620003;20235200;64.620003
2017-02-22;64.330002;64.389999;64.050003;64.360001;19259700;64.360001
2017-02-21;64.610001;64.949997;64.449997;64.489998;19384900;64.489998
2017-02-17;64.470001;64.690002;64.300003;64.620003;21234600;64.620003
ex005.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/semi.csv", rapidcsv::LabelParams(0, 0),
rapidcsv::SeparatorParams(';'));
std::vector<float> close = doc.GetColumn<float>("Close");
std::cout << "Read " << close.size() << " values." << std::endl;
long long volume = doc.GetCell<long long>("Volume", "2017-02-22");
std::cout << "Volume " << volume << " on 2017-02-22." << std::endl;
}The internal cell representation in the Document class is using std::string
and when other types are requested, standard conversion routines are used.
All standard conversions are relatively straight-forward, with the
exception of char for which rapidcsv interprets the cell's (first) byte
as a character. The following example illustrates the supported data types.
colrowhdr.csv content:
Date,Open,High,Low,Close,Volume,Adj Close
2017-02-24,64.529999,64.800003,64.139999,64.620003,21705200,64.620003
2017-02-23,64.419998,64.730003,64.190002,64.620003,20235200,64.620003
2017-02-22,64.330002,64.389999,64.050003,64.360001,19259700,64.360001
2017-02-21,64.610001,64.949997,64.449997,64.489998,19384900,64.489998
2017-02-17,64.470001,64.690002,64.300003,64.620003,21234600,64.620003
ex006.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
rapidcsv::Document doc("examples/colrowhdr.csv", rapidcsv::LabelParams(0, 0));
std::cout << doc.GetCell<std::string>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<int>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<long>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<long long>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<unsigned>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<unsigned long>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<unsigned long long>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<float>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<double>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<long double>("Volume", "2017-02-22") << std::endl;
std::cout << doc.GetCell<char>("Volume", "2017-02-22") << std::endl;
}
One may override conversion routines (or add new ones) by implementing ToVal() and/or ToStr(). Below is an example overriding int conversion, to instead provide two decimal fixed-point numbers. Also see tests/test035.cpp for a test overriding ToVal() and ToStr().
ex008.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
namespace rapidcsv
{
template<>
void Converter<int>::ToVal(const std::string& pStr, int& pVal) const
{
pVal = static_cast<int>(roundf(100.0f * std::stof(pStr)));
}
}
int main()
{
rapidcsv::Document doc("examples/colrowhdr.csv", rapidcsv::LabelParams(0, 0));
std::vector<int> close = doc.GetColumn<int>("Close");
std::cout << "close[0] = " << close[0] << std::endl;
std::cout << "close[1] = " << close[1] << std::endl;
}It is also possible to override conversions on a per-call basis, enabling more flexibility. This is illustrated in the following example. Additional conversion override usage can be found in the test tests/test063.cpp
ex009.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
void ConvFixPoint(const std::string& pStr, int& pVal)
{
pVal = static_cast<int>(roundf(100.0f * std::stof(pStr)));
}
struct MyStruct
{
int val = 0;
};
void ConvMyStruct(const std::string& pStr, MyStruct& pVal)
{
pVal.val = static_cast<int>(roundf(100.0f * std::stof(pStr)));
}
int main()
{
rapidcsv::Document doc("examples/colrowhdr.csv", rapidcsv::LabelParams(0, 0));
std::cout << "regular = " << doc.GetCell<int>("Close", "2017-02-21") << "\n";
std::cout << "fixpointfunc = " << doc.GetCell<int>("Close", "2017-02-21", ConvFixPoint) << "\n";
auto convFixLambda = [](const std::string& pStr, int& pVal) { pVal = static_cast<int>(roundf(100.0f * stof(pStr))); };
std::cout << "fixpointlambda = " << doc.GetCell<int>("Close", "2017-02-21", convFixLambda) << "\n";
std::cout << "mystruct = " << doc.GetCell<MyStruct>("Close", "2017-02-21", ConvMyStruct).val << "\n";
}In addition to specifying a filename, rapidcsv supports constructing a Document from a stream and, indirectly through stringstream, from a string. Here is a simple example reading CSV data from a string:
ex007.cpp content:
#include <iostream>
#include <vector>
#include "rapidcsv.h"
int main()
{
const std::string& csv =
"Date,Open,High,Low,Close,Volume,Adj Close\n"
"2017-02-24,64.529999,64.800003,64.139999,64.620003,21705200,64.620003\n"
"2017-02-23,64.419998,64.730003,64.190002,64.620003,20235200,64.620003\n"
"2017-02-22,64.330002,64.389999,64.050003,64.360001,19259700,64.360001\n"
"2017-02-21,64.610001,64.949997,64.449997,64.489998,19384900,64.489998\n"
"2017-02-17,64.470001,64.690002,64.300003,64.620003,21234600,64.620003\n"
;
std::stringstream sstream(csv);
rapidcsv::Document doc(sstream, rapidcsv::LabelParams(0, 0));
std::vector<float> close = doc.GetColumn<float>("Close");
std::cout << "Read " << close.size() << " values." << std::endl;
long long volume = doc.GetCell<long long>("Volume", "2017-02-22");
std::cout << "Volume " << volume << " on 2017-02-22." << std::endl;
}By default rapidcsv throws an exception if one tries to access non-numeric data as a numeric data type, as it basically propagates the underlying conversion routines' exceptions to the calling application.
The reason for this is to ensure data correctness. If one wants to be able to read data with invalid numbers as numeric data types, one can use ConverterParams to configure the converter to default to a numeric value. The value is configurable and by default it's std::numeric_limits::signaling_NaN() for float types, and 0 for integer types. Example:
rapidcsv::Document doc("file.csv", rapidcsv::LabelParams(),
rapidcsv::SeparatorParams(),
rapidcsv::ConverterParams(true));Rapidcsv provides the methods GetColumnNames() and GetRowNames() to retrieve the column and row names. To check whether a particular column name exists one can for example do:
rapidcsv::Document doc("file.csv");
std::vector<std::string> columnNames = doc.GetColumnNames();
bool columnExists =
(std::find(columnNames.begin(), columnNames.end(), "A") != columnNames.end());By default rapidcsv automatically dequotes quoted cells (i.e. removes the encapsulating
" characters from "example quoted cell"). This functionality may be disabled by
passing pAutoQuote = false in SeparatorParams, example:
rapidcsv::Document doc("file.csv", rapidcsv::LabelParams(),
rapidcsv::SeparatorParams(',' /* pSeparator */,
false /* pTrim */,
rapidcsv::sPlatformHasCR /* pHasCR */,
false /* pQuotedLinebreaks */,
false /* pAutoQuote */));Rapidcsv's preferred encoding for non-ASCII text is UTF-8. UTF-16 LE and UTF-16 BE can be read and written by rapidcsv on systems where codecvt header is present. Define HAS_CODECVT before including rapidcsv.h in order to enable the functionality. Rapidcsv unit tests automatically detects the presence of codecvt and sets HAS_CODECVT as needed, see CMakeLists.txt for reference. When enabled, the UTF-16 encoding of any loaded file is automatically detected.
The following classes makes up the Rapidcsv interface:
- class rapidcsv::Document
- class rapidcsv::SeparatorParams
- class rapidcsv::LabelParams
- class rapidcsv::ConverterParams
- class rapidcsv::no_converter
- class rapidcsv::Converter< T >
Rapidcsv uses cmake for its tests. Commands to build and execute the test suite:
mkdir -p build && cd build && cmake .. && make && ctest -C unit --output-on-failure && ctest -C perf --verbose ; cd -
Rapidcsv uses doxyman2md to generate its API documentation:
doxyman2md src doc
Rapidcsv uses Uncrustify to ensure consistent code formatting:
uncrustify -c uncrustify.cfg --no-backup src/rapidcsv.h
There are many CSV parsers for C++, for example:
Rapidcsv is distributed under the BSD 3-Clause license. See LICENSE file.
Bugs, PRs, etc are welcome on the GitHub project page https://github.com/d99kris/rapidcsv
c++, c++11, csv parser, comma separated values, single header library.