ml_dataframe
A way to store and manipulate data
The library exposes in-memory storage for dynamically typed data. The storage is represented by DataFrame class.
Table of contents
- Usage example
- DataFrame API
- Get the header
- Get the rows
- Get the series
- Get the shape
- Add a series
- Drop a series by a name
- Drop a series by an index
- Sample a dataframe from rows
- Sample a dataframe from series indices
- Sample a dataframe from series names
- Save a dataframe
- Shuffle rows of a dataframe
- Get a JSON representation
- Convert to Matrix
- Get a series by name
- Get a series by index
- Map values
- Map values of a series
- Ways to create a dataframe
- Prefilled dataframes
- Contacts
Usage example:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = [
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
];
final dataframe = DataFrame(data);
print(dataframe);
// DataFrame (5 x 6)
// Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
// 1 5.1 3.5 1.4 0.2 Iris-setosa
// 2 4.9 3.0 1.4 0.2 Iris-setosa
// 89 5.6 3.0 4.1 1.3 Iris-versicolor
// 90 5.5 2.5 4.0 1.3 Iris-versicolor
// 91 5.5 2.6 4.4 1.2 Iris-versicolor
}
DataFrame API with examples:
Get the header of the data
By default, the very first row is considered a header, unless one specify their own header or autogenerated one. More on this is here
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final header = dataframe.header;
print(header);
// ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']
}
Get the rows of the data
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final rows = dataframe.rows;
print(rows);
// [
// [1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
// [2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
// [89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
// [90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
// [91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
// ],
}
Get the series collection (columns) of the data
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final series = dataframe.series;
print(series);
// [
// 'Id': [1, 2, 89, 90, 91],
// 'SepalLengthCm': [5.1, 4.9, 5.6, 5.5, 5.5],
// 'SepalWidthCm': [3.5, 3.0, 3.0, 2.5, 2.6],
// 'PetalLengthCm': [1.4, 1.4, 4.1, 4.0, 4.4],
// 'PetalWidthCm': [0.2, 0.2, 1.3, 1.3, 1.2],
// 'Species': ['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor'],
// ],
}
Get the shape of the data
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final shape = dataframe.shape;
print(shape);
// [5, 6] - 5 rows, 6 columns
}
Add a series
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final firstSeries = Series('super_series', [1, 2, 3, 4, 5, 6]);
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final modifiedDataframe = dataframe.addSeries([firstSeries]); // The method doesn't mutate the original dataframe
print(modifiedDataframe.series.first);
// 'super_series': [1, 2, 3, 4, 5, 6]
}
Drop a series by a series name
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
print(dataframe.shape);
// [5, 6] - 6 rows, 6 columns
final modifiedDataframe = dataframe.dropSeries(names: ['Id']); // The method doesn't mutate the original dataframe
print(modifiedDataframe.shape);
// [5, 5] - after a series had been dropped, the number of columns became one lesser
}
Drop a series by a series index
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
print(dataframe.shape);
// [5, 6] - 5 rows, 6 columns
final modifiedDataframe = dataframe.dropSeries(indices: [0]); // The method doesn't mutate the original dataframe
print(modifiedDataframe.shape);
// [5, 5] - after a series had been dropped, the number of columns became one lesser
}
Sample a new dataframe from rows of an existing dataframe
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final sampled = dataframe.sampleFromRows([0, 5]);
print(sampled);
// DataFrame (2 x 6)
// Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
// 1 5.1 3.5 1.4 0.2 Iris-setosa
// 91 5.5 2.6 4.4 1.2 Iris-versicolor
}
Sample a new dataframe from series indices of an existing dataframe
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final sampled = dataframe.sampleFromSeries(indices: [0, 1]);
print(sampled);
// DataFrame (5 x 2)
// Id SepalLengthCm
// 1 5.1
// 2 4.9
// 89 5.6
// 90 5.5
// 91 5.5
}
Sample a new dataframe from series names of an existing dataframe
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final sampled = dataframe.sampleFromSeries(names: ['Id', 'SepalLengthCm']);
print(sampled);
// DataFrame (5 x 2)
// Id SepalLengthCm
// 1 5.1
// 2 4.9
// 89 5.6
// 90 5.5
// 91 5.5
}
Save a dataframe to a JSON file
import 'package:ml_dataframe/ml_dataframe.dart';
void main() async {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
await dataframe.saveAsJson('path/to/json/file.json');
}
Shuffle rows in a dataframe
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
print(dataframe);
// DataFrame (5 x 6)
// Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
// 1 5.1 3.5 1.4 0.2 Iris-setosa
// 2 4.9 3.0 1.4 0.2 Iris-setosa
// 89 5.6 3.0 4.1 1.3 Iris-versicolor
// 90 5.5 2.5 4.0 1.3 Iris-versicolor
// 91 5.5 2.6 4.4 1.2 Iris-versicolor
final shuffled = dataframe.shuffle(); // keep in mind that `shuffle` like other methods returns a new dataframe, the method doesn't mutate the source dataframe
print(shuffled);
// DataFrame (5 x 6)
// Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
// 89 5.6 3.0 4.1 1.3 Iris-versicolor
// 1 5.1 3.5 1.4 0.2 Iris-setosa
// 91 5.5 2.6 4.4 1.2 Iris-versicolor
// 2 4.9 3.0 1.4 0.2 Iris-setosa
// 90 5.5 2.5 4.0 1.3 Iris-versicolor
}
One can use seed parameter to keep the order of rows disregard the number of shuffle calls:
dataframe.shuffle(seed: 10);
Get a json-serializable representation
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final json = dataframe.toJson(); // json contains a serializable map
}
Convert a dataframe to a matrix:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'],
[ 1, 5.1, 3.5, 1.4, 0.2],
[ 2, 4.9, 3.0, 1.4, 0.2],
[ 89, 5.6, 3.0, 4.1, 1.3],
[ 90, 5.5, 2.5, 4.0, 1.3],
[ 91, 5.5, 2.6, 4.4, 1.2],
]);
final matrix = dataframe.toMatrix();
print(matrix); // because of internal representation of Float32 numbers there are some round-off errors in the output
// Matrix 5 x 5:
// (1.0, 5.099999904632568, 3.5, 1.399999976158142, 0.20000000298023224)
// (2.0, 4.900000095367432, 3.0, 1.399999976158142, 0.20000000298023224)
// (89.0, 5.599999904632568, 3.0, 4.099999904632568, 1.2999999523162842)
// (90.0, 5.5, 2.5, 4.0, 1.2999999523162842)
// (91.0, 5.5, 2.5999999046325684, 4.400000095367432, 1.2000000476837158)
}
the method throws an error if there are inconvertible to a number values in the dataframe.
Get a series by its index
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final series = dataframe[0];
print(series);
// Id: [1, 2, 89, 90, 91]
}
Get a series by its name
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final dataframe = DataFrame([
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
]);
final series = dataframe['Id'];
print(series);
// Id: [1, 2, 89, 90, 91]
}
Map values of a dataframe
import 'package:ml_dataframe/ml_dataframe';
void main() {
final data = DataFrame([
['col_1', 'col_2', 'col_3'],
[ 2, 20, 200],
[ 3, 30, 300],
[ 4, 40, 400],
]);
// the first generic type ia a type of the source value, the second generic type is a type of the mapped value
final modifiedData = data.map<num, num>((value) => value * 2);
print(modifiedData);
// DataFrame (3 x 3)
// col_1 col_2 col_3
// 4 40 400
// 6 60 600
// 8 80 800
}
Map values of a specific dataframe series
import 'package:ml_dataframe/ml_dataframe';
void main() {
final data = DataFrame([
['col_1', 'col_2', 'col_3'],
[ 2, 20, 200],
[ 3, 30, 300],
[ 4, 40, 400],
]);
// the first generic type ia a type of the source value, the second generic type is a type of the mapped value
final modifiedData = data.mapSeries<num, num>((value) => value * 2, name: 'col_2');
print(modifiedData);
// DataFrame (3 x 3)
// col_1 col_2 col_3
// 2 40 200
// 3 60 300
// 4 80 400
}
Ways to create a dataframe
DataFrame constructor
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = [
['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],
[ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
];
final dataframe = DataFrame(data);
}
By default, the very first row is considered a header. If the data does not have a header, one can use autogenerated
header by providing headerExists: false config to the constructor:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = [
[1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
];
final dataframe = DataFrame(data, headerExists: false);
print(dataframe.header);
}
It outputs ['col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6']. col_ is a default prefix for the autogenerated
columns.
Also, if there are no header row in the data, one can use a predefined header:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = [
[1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],
[2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],
[89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],
[90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],
[91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],
];
final dataframe = DataFrame(data, header: ['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6']);
}
fromCsv function
import 'package:ml_dataframe/ml_dataframe.dart';
void main() async {
final data = await fromCsv('path/to/csv/file.csv');
}
If the csv file does not have a header row, it's needed to provide the corresponding flag:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() async {
final data = await fromCsv('path/to/csv/file.csv', headerExists: false);
}
Restore a dataframe previously persisted as a json file - fromJson function
import 'package:ml_dataframe/ml_dataframe.dart';
void main() async {
final data = await fromJson('path/to/json/file.json');
}
This function works in conjunction with DataFrame saveAsJson method.
Dataframes with prefilled data
In order to test data processing algorithms, one can use "toy" datasets. The library exposes several of them:
Iris dataset - function getIrisDataFrame
One can create a dataframe filled with Iris data:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = getIrisDataFrame();
print(data);
// DataFrame (150 x 6)
// Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
// ...
}
Pima Indians diabetes dataset - function getPimaIndiansDiabetesDataFrame
One can create a dataframe filled with Pima Indians diabetes data:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = getPimaIndiansDiabetesDataFrame();
print(data);
// DataFrame (768 x 9)
// Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
// ...
}
Red wine quality dataset - function getWineQualityDataframe
One can create a dataframe filled with Red wine quality data:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = getWineQualityDataframe();
print(data);
// DataFrame (1599 x 12)
// fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality
// ...
}
Boston housing dataset - function getHousingDataframe
One can create a dataframe filled with Boston housing data:
import 'package:ml_dataframe/ml_dataframe.dart';
void main() {
final data = getHousingDataframe();
print(data);
// DataFrame (506 x 14)
// CRIM ZN INDUS CHAS NOX RM ... MEDV
// 0.00632 18.0 2.31 0 0.538 6.575 ... 24.0
// 0.02731 0.0 7.07 0 0.469 6.421 ... 21.6
// 0.02729 0.0 7.07 0 0.469 7.185 ... 34.7
// 0.03237 0.0 2.18 0 0.458 6.998 ... 33.4
// 0.06905 0.0 2.18 0 0.458 7.147 ... 36.2
// ... ... ... ... ... ... ... ...
// 0.06263 0.0 11.93 0 0.573 6.593 ... 22.4
// 0.04527 0.0 11.93 0 0.573 6.12 ... 20.6
// 0.06076 0.0 11.93 0 0.573 6.976 ... 23.9
// 0.10959 0.0 11.93 0 0.573 6.794 ... 22.0
// 0.04741 0.0 11.93 0 0.573 6.03 ... 11.9
}
Contacts
If you have questions, feel free to text me on