[DEPRECATED] See https://github.com/p-ranav/csv2
This library is now deprecated. Checkout a second implementation of this library here: https://github.com/p-ranav/csv2.
Simply include reader.hpp and you’re good to go.
#include <csv/reader.hpp>
To start parsing CSV files, create a csv::Reader object and call .read(filename).
csv::Reader foo;
foo.read("test.csv");
This .read method is non-blocking. The reader spawns multiple threads to tokenize the file stream and build a “list of dictionaries”. While the reader is doing it’s thing, you can start post-processing the rows it has parsed so far using this iterator pattern:
while(foo.busy()) {
if (foo.ready()) {
auto row = foo.next_row(); // Each row is a csv::unordered_flat_map (github.com/martinus/robin-hood-hashing)
auto foo = row["foo"] // You can use it just like an std::unordered_map
auto bar = row["bar"];
// do something
}
}
If instead you’d like to wait for all the rows to get processed, you can call .rows() which is a convenience method that executes the above while loop
auto rows = foo.rows(); // blocks until the CSV is fully processed
for (auto& row : rows) { // Example: [{"foo": "1", "bar": "2"}, {"foo": "3", "bar": "4"}, ...]
auto foo = row["foo"];
// do something
}
This csv library comes with three standard dialects:
| Name | Description |
|---|---|
| excel | The excel dialect defines the usual properties of an Excel-generated CSV file |
| excel_tab | The excel_tab dialect defines the usual properties of an Excel-generated TAB-delimited file |
| unix | The unix dialect defines the usual properties of a CSV file generated on UNIX systems, i.e. using ‘\n’ as line terminator and quoting all fields |
Custom dialects can be constructed with .configure_dialect(...)
csv::Reader csv;
csv.configure_dialect("my fancy dialect")
.delimiter("")
.quote_character('"')
.double_quote(true)
.skip_initial_space(false)
.trim_characters(' ', '\t')
.ignore_columns("foo", "bar")
.header(true)
.skip_empty_rows(true);
csv.read("foo.csv");
for (auto& row : csv.rows()) {
// do something
}
| Property | Data Type | Description |
|---|---|---|
| delimiter | std::string |
specifies the character sequence which should separate fields (aka columns). Default = "," |
| quote_character | char |
specifies a one-character string to use as the quoting character. Default = '"' |
| double_quote | bool |
controls the handling of quotes inside fields. If true, two consecutive quotes should be interpreted as one. Default = true |
| skip_initial_space | bool |
specifies how to interpret whitespace which immediately follows a delimiter; if false, it means that whitespace immediately after a delimiter should be treated as part of the following field. Default = false |
| trim_characters | std::vector<char> |
specifies the list of characters to trim from every value in the CSV. Default = {} - nothing trimmed |
| ignore_columns | std::vector<std::string> |
specifies the list of columns to ignore. These columns will be stripped during the parsing process. Default = {} - no column ignored |
| header | bool |
indicates whether the file includes a header row. If true the first row in the file is a header row, not data. Default = true |
| column_names | std::vector<std::string> |
specifies the list of column names. This is useful when the first row of the CSV isn’t a header Default = {} |
| skip_empty_rows | bool |
specifies how empty rows should be interpreted. If this is set to true, empty rows are skipped. Default = false |
The line terminator is '\n' by default. I use std::getline and handle stripping out '\r' from line endings. So, for now, this is not configurable in custom dialects.
Consider this strange, messed up log file:
[Thread ID] :: [Log Level] :: [Log Message] :: {Timestamp}
04 :: INFO :: Hello World :: 1555164718
02 :: DEBUG :: Warning! Foo has happened :: 1555463132
To parse this file, simply configure a new dialect that splits on “::” and trims whitespace, braces, and bracket characters.
csv::Reader csv;
csv.configure_dialect("my strange dialect")
.delimiter("::")
.trim_characters(' ', '[', ']', '{', '}');
csv.read("test.csv");
for (auto& row : csv.rows()) {
auto thread_id = row["Thread ID"]; // "04"
auto log_level = row["Log Level"]; // "INFO"
auto message = row["Log Message"]; // "Hello World"
// do something
}
Consider the following CSV. Let’s say you don’t care about the columns age and gender. Here, you can use .ignore_columns and provide a list of columns to ignore.
name, age, gender, email, department
Mark Johnson, 50, M, [email protected], BA
John Stevenson, 35, M, [email protected], IT
Jane Barkley, 25, F, [email protected], MGT
You can configure the dialect to ignore these columns like so:
csv::Reader csv;
csv.configure_dialect("ignore meh and fez")
.delimiter(", ")
.ignore_columns("age", "gender");
csv.read("test.csv");
auto rows = csv.rows();
// Your rows are:
// [{"name": "Mark Johnson", "email": "[email protected]", "department": "BA"},
// {"name": "John Stevenson", "email": "[email protected]", "department": "IT"},
// {"name": "Jane Barkley", "email": "[email protected]", "department": "MGT"}]
Sometimes you have CSV files with no header row:
9 52 1
52 91 0
91 135 0
135 174 0
174 218 0
218 260 0
260 301 0
301 341 0
341 383 0
...
If you want to prevent the reader from parsing the first row as a header, simply:
.header to false.column_names(...)csv.configure_dialect("no headers")
.header(false)
.column_names("foo", "bar", "baz");
The CSV rows will now look like this:
[{"foo": "9", "bar": "52", "baz": "1"}, {"foo": "52", "bar": "91", "baz": "0"}, ...]
If .column_names is not called, then the reader simply generates dictionary keys like so:
[{"0": "9", "1": "52", "2": "1"}, {"0": "52", "1": "91", "2": "0"}, ...]
Sometimes you have to deal with a CSV file that has empty lines; either in the middle or at the end of the file:
a,b,c
1,2,3
4,5,6
10,11,12
Here’s how this get’s parsed by default:
csv::Reader csv;
csv.read("inputs/empty_lines.csv");
auto rows = csv.rows();
// [{"a": 1, "b": 2, "c": 3}, {"a": "", "b": "", "c": ""}, {"a": "4", "b": "5", "c": "6"}, {"a": "", ...}]
If you don’t care for these empty rows, simply call .skip_empty_rows(true)
csv::Reader csv;
csv.configure_dialect()
.skip_empty_rows(true);
csv.read("inputs/empty_lines.csv");
auto rows = csv.rows();
// [{"a": 1, "b": 2, "c": 3}, {"a": "4", "b": "5", "c": "6"}, {"a": "10", "b": "11", "c": "12"}]
If you know exactly how many rows to parse, you can help out the reader by using the .read(filename, num_rows) overloaded method. This saves the reader from trying to figure out the number of lines in the CSV file. You can use this method to parse the first N rows of the file instead of parsing all of it.
csv::Reader foo;
foo.read("bar.csv", 1000);
auto rows = foo.rows();
Note: Do not provide num_rows greater than the actual number of rows in the file - The reader will loop forever till the end of time.
// benchmark.cpp
void parse(const std::string& filename) {
csv::Reader foo;
foo.read(filename);
std::vector<csv::unordered_flat_map<std::string_view, std::string>> rows;
while (foo.busy()) {
if (foo.ready()) {
auto row = foo.next_row();
rows.push_back(row);
}
}
}
$ g++ -pthread -std=c++17 -O3 -Iinclude/ -o test benchmark.cpp
$ time ./test
Each test is run 30 times on an Intel® Core™ i7-6650-U @ 2.20 GHz CPU.
Here are the average-case execution times:
| Dataset | File Size | Rows | Cols | Time |
|---|---|---|---|---|
| Demographic Statistics By Zip Code | 27 KB | 237 | 46 | 0.026s |
| Simple 3-column CSV | 14.1 MB | 761,817 | 3 | 0.523s |
| Majestic Million | 77.7 MB | 1,000,000 | 12 | 1.972s |
| Crimes 2001 - Present | 1.50 GB | 6,846,406 | 22 | 32.411s |
Simply include writer.hpp and you’re good to go.
#include <csv/writer.hpp>
To start writing CSV files, create a csv::Writer object and provide a filename:
csv::Writer foo("test.csv");
Constructing a writer spawns a worker thread that is ready to start writing rows. Using .configure_dialect, configure the dialect to be used by the writer. This is where you can specify the column names:
foo.configure_dialect()
.delimiter(", ")
.column_names("a", "b", "c");
Now it’s time to write rows. You can do this in multiple ways:
foo.write_row("1", "2", "3"); // parameter packing
foo.write_row({"4", "5", "6"}); // std::vector
foo.write_row(std::map<std::string, std::string>{ // std::map
{"a", "7"}, {"b", "8"}, {"c", "9"} });
foo.write_row(std::unordered_map<std::string, std::string>{ // std::unordered_map
{"a", "7"}, {"b", "8"}, {"c", "9"} });
foo.write_row(csv::unordered_flat_map<std::string, std::string>{ // csv::unordered_flat_map
{"a", "7"}, {"b", "8"}, {"c", "9"} });
You can also omit one or more values dynamically when using maps:
foo.write_row(std::map<std::string, std::string>{ // std::map
{"a", "7"}, {"c", "9"} }); // omitting "b"
foo.write_row(std::unordered_map<std::string, std::string>{ // std::unordered_map
{"b", "8"}, {"c", "9"} }); // omitting "a"
foo.write_row(csv::unordered_flat_map<std::string, std::string>{ // csv::unordered_flat_map
{"a", "7"}, {"b", "8"} }); // omitting "c"
Finally, once you’re done writing rows, call .close() to stop the worker thread and close the file stream.
foo.close();
Here’s an example writing 3 million lines of CSV to a file:
csv::Writer foo("test.csv");
foo.configure_dialect()
.delimiter(", ")
.column_names("a", "b", "c");
for (long i = 0; i < 3000000; i++) {
auto x = std::to_string(i % 100);
auto y = std::to_string((i + 1) % 100);
auto z = std::to_string((i + 2) % 100);
foo.write_row(x, y, z);
}
foo.close();
The above code takes about 1.8 seconds to execute on my Surface Pro 4.
Contributions are welcome, have a look at the CONTRIBUTING.md document for more information.
git clone https://github.com/p-ranav/csv.git
cd csv
git submodule update --init --recursive
mkdir build
cd build
cmake .. -DCSV_BUILD_TESTS=ON
cmake --build . --config Debug
ctest --output-on-failure -C Debug
git clone https://github.com/p-ranav/csv.git
cd csv
mkdir build
cd build
cmake ../.
sudo make install
The project is available under the MIT license.