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Published 4 months ago • Latest: 0.3.4
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DartFrame is a flexible data manipulation library for Dart, inspired by pandas. It simplifies working with structured data like tables, CSVs, and JSON, offering intuitive tools for filtering, transfor [...]

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example/example.dart

import 'package:dartframe/dartframe.dart';

void main() {
  dataframe1();
  dataframe2();
}

void dataframe1() {
  // Load the CSV data into a DataFrame and tries to convert string to the right type
  var df = DataFrame.fromCSV(
    csv: csvData,
    formatData: true,
    missingDataIndicator: ['<NA>', 'NA'],
    replaceMissingValueWith: null, //'NaN'
  );

  // Data Exploration
  print('\nShape: ${df.shape}');
  print('List Columns:\n${df.columns}');
  print(df.tail(5)); // View the last 5 rows
  print(df.head(5)); // View the first 5 rows
  print(df.limit(10,
      startIndex: 5)); // View some number of rows starting at an index
  print('Describe Data:\n${df.describe()}\n'); // Summary statistics
  print(
      'Structure:\n${df.structure()}\n'); // Get the structure of the dataframe

  // Data Cleaning
  df.fillna('Unknown'); // Fill missing values with 'Unknown'
  df.replace('<NA>', null); // Replace the missing values with null

  // Data Analysis
  print(df.groupBy('area'));
  print(df.valueCounts('area')); //Count the freq. of each value in a column
  print(df.groupBy('area')['Brent']); // Get Brent data

  // Average price per area
  df.groupBy('area').forEach((key, value) {
    print('$key Mean: ${value['price'].mean()}');
  });

  // Add a new column using calculations
  df['delivery_time_over_30'] = df['delivery_min'] > 30;

  print('\nDelivery Mins > 30:\n${df['delivery_min'] > 30}\n');

  print(df);

  // Get specific column
  print(df['date']);
  print(df[1]);

  df.toJSON();
  //String jsonString = jsonEncode(df.toJSON());
  //print(jsonString);

  // Table manipulation
  print(df.head(5));
  df.rename({"date": "Date"});
  print(df.columns);
  df.drop('index');
  print(df.head(5));
  df.fillna('Charles');
  print(df.head(5));
}

void dataframe2() {
  Series numbers = Series([1, 2, 3, 4], name: "Numbers");

  // Update a single element
  numbers[1] = 10;

  // Update multiple elements
  numbers[[0, 2]] = [5, 6];

  // Update based on a condition
  numbers[numbers > 7] = 99;

  print(numbers);

  // Series concatenation
  Series s1 = Series([1, 2, 3], name: 'A');
  Series s2 = Series([4, 5, 6], name: 'B');

// Vertical concatenation
  Series sVertical = s1.concatenate(s2);
  print(sVertical);

// Horizontal concatenation
  Series sHorizontal = s1.concatenate(s2, axis: 1);
  print(sHorizontal);

// DataFrame concatenation
  var df1 = DataFrame(columns: [
    'A',
    'B'
  ], data: [
    [1, 2],
    [3, 4]
  ]);
  var df2 = DataFrame(columns: [
    'A',
    'B'
  ], data: [
    [5, 6],
    [7, 8]
  ]);
  var df3 = DataFrame(columns: [
    'C',
    'D'
  ], data: [
    [10, 11],
    [12, 13]
  ]);

// Vertical concatenation
  var dfVertical = df1.concatenate(df2);

// Horizontal concatenation
  var dfHorizontal = df1.concatenate(df3, axis: 1);
  print(dfHorizontal);
  print(dfVertical);

  // print(s_horizontal);

  var df = DataFrame(
    columns: ['A', 'B', 'C', 'D'],
    data: [
      [1, 2.5, 3, 4],
      [2, 3.5, 4, 5],
      [3, 4.5, 5, 6],
      [4, 5.5, 6, 7],
    ],
  );

  // Describe the DataFrame
  print(df.describe());

  df = DataFrame(
    allowFlexibleColumns: true,
    data: [
      [1, 'A'],
      [2, 'B'],
      [3, 'C'],
      [4, 'D'],
    ],
  );

  print('Before shuffle:');
  print(df);

// new columns
  df.columns = ['ID', 'Letter', 'Free'];

  // Shuffle without a seed
  var newDf = df.shuffle();
  print('After random shuffle:');
  print(newDf);

  df.columns = ['id', 'alpha'];

  // Shuffle with a seed for reproducibility
  newDf = df.shuffle(seed: 123);
  print('After shuffle with seed:');
  print(newDf);
}

var csvData = """
index,date,week,weekday,area,count,rabate,price,operator,driver,delivery_min,temperature,wine_ordered,wine_delivered,wrongpizza,quality
1,01/03/2014,9,6,Camden,5,TRUE,65.655,Rhonda,Taylor,20,53,0,0,FALSE,medium
2,01/03/2014,9,6,Westminster,2,FALSE,26.98,Rhonda,Butcher,19.6,56.4,0,0,FALSE,high
3,01/03/2014,9,6,Westminster,3,FALSE,40.97,Allanah,Butcher,17.8,36.5,0,0,FALSE,NA
4,01/03/2014,9,6,Brent,2,FALSE,25.98,Allanah,Taylor,37.3,NA,0,0,FALSE,NA
5,01/03/2014,9,6,Brent,5,TRUE,57.555,Rhonda,Carter,21.8,50,0,0,FALSE,medium
6,01/03/2014,9,6,Camden,1,FALSE,13.99,Allanah,Taylor,48.7,27,0,0,FALSE,low
7,01/03/2014,9,6,Camden,4,TRUE,89.442,Rhonda,Taylor,49.3,33.9,1,1,FALSE,low
8,01/03/2014,9,6,Brent,NA,NA,NA,Allanah,Taylor,25.6,54.8,NA,NA,FALSE,high
9,01/03/2014,9,6,Westminster,3,FALSE,40.97,Allanah,Taylor,26.4,48,0,0,FALSE,high
10,01/03/2014,9,6,Brent,6,TRUE,84.735,Rhonda,Carter,24.3,54.4,1,1,FALSE,medium
11,01/03/2014,9,6,Westminster,3,FALSE,66.41,Allanah,Miller,11.7,28.8,1,1,FALSE,low
12,01/03/2014,9,6,Brent,5,TRUE,62.955,Rhonda,Carter,19.5,51.3,0,0,FALSE,medium
13,01/03/2014,9,6,Camden,4,TRUE,46.764,Allanah,Taylor,32.7,24.05,0,0,FALSE,low
14,01/03/2014,9,6,Camden,1,FALSE,49.95,Rhonda,Carter,38.8,35.7,1,1,FALSE,low
15,01/03/2014,9,6,Brent,6,TRUE,73.746,Rhonda,Carter,23,53.6,0,0,FALSE,medium
16,01/03/2014,9,6,Westminster,5,TRUE,57.555,Rhonda,Miller,30.8,51.3,0,0,FALSE,<NA>
17,<NA>,NA,NA,Brent,2,FALSE,26.98,Allanah,Carter,27.7,51,0,0,FALSE,high
18,01/03/2014,9,6,Brent,2,FALSE,27.98,Rhonda,Butcher,29.7,47.7,0,0,FALSE,medium
19,01/03/2014,9,6,Brent,3,FALSE,41.97,Rhonda,Carter,9.1,52.8,0,0,FALSE,medium
20,01/03/2014,9,6,Westminster,1,FALSE,11.99,Rhonda,Miller,37.3,20,0,0,FALSE,low
21,01/03/2014,9,6,Brent,4,TRUE,46.764,Allanah,Butcher,21.2,52.4,0,0,FALSE,<NA>
22,01/03/2014,9,6,Brent,4,TRUE,52.164,Rhonda,Butcher,10.6,55.1,0,0,FALSE,medium
23,01/03/2014,9,6,Westminster,3,FALSE,44.97,Rhonda,Butcher,19.5,26.7,0,0,FALSE,low
24,01/03/2014,9,6,Camden,4,TRUE,51.264,Rhonda,Taylor,31,49.8,0,0,FALSE,medium
25,01/03/2014,9,6,Westminster,3,FALSE,38.97,Allanah,Miller,20.8,32.5,0,0,FALSE,low
26,01/03/2014,9,6,Brent,5,TRUE,58.455,Allanah,Taylor,18,36.8,0,0,FALSE,medium
27,01/03/2014,9,6,Brent,5,TRUE,97.065,Rhonda,Butcher,25.6,51,1,1,FALSE,medium
28,01/03/2014,9,6,Westminster,6,TRUE,75.546,Allanah,Miller,11.8,54.4,0,0,FALSE,high
29,01/03/2014,9,6,Camden,5,TRUE,65.655,Rhonda,Taylor,36.5,46.2,0,0,FALSE,medium
30,01/03/2014,9,6,Westminster,2,FALSE,30.98,Rhonda,Butcher,17.4,56.4,0,0,FALSE,<NA>
31,01/03/2014,9,6,Camden,2,FALSE,26.98,Allanah,Carter,23.5,35.7,0,0,FALSE,medium
32,01/03/2014,9,6,Westminster,4,TRUE,51.264,Rhonda,Butcher,24.2,49.1,0,0,FALSE,medium
33,01/03/2014,9,6,Brent,5,TRUE,61.155,Allanah,Carter,15,53.3,0,0,FALSE,<NA>
34,01/03/2014,9,6,Camden,3,FALSE,42.97,Allanah,Carter,36.2,41.7,0,0,FALSE,<NA>
35,01/03/2014,9,6,Westminster,2,FALSE,25.98,Rhonda,Taylor,25.9,47.6,0,0,FALSE,<NA>
36,01/03/2014,9,6,Westminster,5,TRUE,57.555,Allanah,Butcher,32.4,30.3,0,0,FALSE,low
37,01/03/2014,9,6,Brent,3,FALSE,44.97,Allanah,Butcher,15.4,37.8,0,0,FALSE,medium
38,01/03/2014,9,6,Brent,4,TRUE,48.564,Rhonda,Taylor,30.6,23.55,0,0,FALSE,low
39,01/03/2014,9,6,Brent,3,FALSE,40.97,Allanah,Taylor,27.9,49.9,0,0,FALSE,high
40,01/03/2014,9,6,Camden,4,TRUE,49.464,Allanah,Taylor,23,50.7,0,0,FALSE,high
41,01/03/2014,9,6,Brent,2,FALSE,24.98,Rhonda,Taylor,39.7,20.45,0,0,FALSE,low
42,01/03/2014,9,6,Westminster,6,TRUE,74.646,Allanah,Miller,29.1,25.85,0,0,FALSE,low
43,01/03/2014,9,6,Camden,3,FALSE,38.97,Rhonda,Taylor,34.4,43.8,0,0,FALSE,medium
44,02/03/2014,9,7,Camden,4,TRUE,92.466,Rhonda,Farmer,9.5,54.2,1,0,FALSE,medium
45,02/03/2014,9,7,Camden,1,FALSE,56.43,Allanah,Farmer,11.2,55.2,1,1,FALSE,high
46,02/03/2014,9,7,Westminster,4,TRUE,44.964,Rhonda,Farmer,14.4,55.9,0,0,FALSE,high
47,02/03/2014,9,7,Brent,4,TRUE,78.957,Allanah,Hunter,9.2,22.2,1,1,FALSE,low
48,02/03/2014,9,7,Brent,1,FALSE,13.99,Allanah,Hunter,11.9,52.8,0,0,FALSE,high
49,02/03/2014,9,7,Brent,4,TRUE,89.154,Allanah,Butcher,9.4,41,1,1,FALSE,medium
50,02/03/2014,9,7,Brent,3,FALSE,37.97,Allanah,Taylor,21.4,33.2,0,0,FALSE,low
51,02/03/2014,9,7,Brent,2,FALSE,29.98,Rhonda,Hunter,11.5,54.5,0,0,FALSE,medium
52,02/03/2014,9,7,Brent,3,FALSE,47.97,Allanah,Hunter,15.7,54.4,0,0,FALSE,<NA>
53,02/03/2014,9,7,Camden,1,FALSE,13.99,Allanah,Taylor,27.2,46,0,0,FALSE,<NA>
54,02/03/2014,9,7,Camden,3,FALSE,75.47,Rhonda,Taylor,27.7,26.3,1,1,FALSE,low
55,02/03/2014,9,7,Camden,6,TRUE,74.646,Rhonda,Farmer,23.7,51.1,0,0,FALSE,medium
56,02/03/2014,9,7,Camden,3,FALSE,36.97,Rhonda,Farmer,18.2,26.35,0,0,FALSE,low
57,02/03/2014,9,7,Westminster,4,TRUE,49.464,Allanah,Hunter,17.1,37.8,0,0,FALSE,medium
58,02/03/2014,9,7,Camden,1,FALSE,13.99,Rhonda,Farmer,19.9,51.5,0,0,FALSE,<NA>
59,02/03/2014,9,7,Camden,3,FALSE,79.67,Rhonda,Farmer,21.9,53.1,1,1,FALSE,<NA>
60,02/03/2014,9,7,Westminster,2,FALSE,63.78,Rhonda,Farmer,15.5,54.5,1,1,FALSE,medium
61,02/03/2014,9,7,Camden,4,TRUE,54.864,Allanah,Taylor,25.4,48.8,0,0,FALSE,high
62,02/03/2014,9,7,Camden,2,FALSE,25.98,Rhonda,Taylor,25.6,50.3,0,0,FALSE,medium
12,01/03/2014,9,6,Brent,5,TRUE,62.955,Rhonda,Carter,19.5,51.3,0,0,FALSE,medium
13,01/03/2014,9,6,Camden,4,TRUE,46.764,Allanah,Taylor,32.7,24.05,0,0,FALSE,low
14,01/03/2014,9,6,Camden,1,FALSE,49.95,Rhonda,Carter,38.8,35.7,1,1,FALSE,low
15,01/03/2014,9,6,Brent,6,TRUE,73.746,Rhonda,Carter,23,53.6,0,0,FALSE,medium
16,01/03/2014,9,6,Westminster,5,TRUE,57.555,Rhonda,Miller,30.8,51.3,0,0,FALSE,<NA>
17,<NA>,NA,NA,Brent,2,FALSE,26.98,Allanah,Carter,27.7,51,0,0,FALSE,high
18,01/03/2014,9,6,Brent,2,FALSE,27.98,Rhonda,Butcher,29.7,47.7,0,0,FALSE,medium
19,01/03/2014,9,6,Brent,3,FALSE,41.97,Rhonda,Carter,9.1,52.8,0,0,FALSE,medium
20,01/03/2014,9,6,Westminster,1,FALSE,11.99,Rhonda,Miller,37.3,20,0,0,FALSE,low
21,01/03/2014,9,6,Brent,4,TRUE,46.764,Allanah,Butcher,21.2,52.4,0,0,FALSE,<NA>
22,01/03/2014,9,6,Brent,4,TRUE,52.164,Rhonda,Butcher,10.6,55.1,0,0,FALSE,medium
23,01/03/2014,9,6,Westminster,3,FALSE,44.97,Rhonda,Butcher,19.5,26.7,0,0,FALSE,low
24,01/03/2014,9,6,Camden,4,TRUE,51.264,Rhonda,Taylor,31,49.8,0,0,FALSE,medium
25,01/03/2014,9,6,Westminster,3,FALSE,38.97,Allanah,Miller,20.8,32.5,0,0,FALSE,low
26,01/03/2014,9,6,Brent,5,TRUE,58.455,Allanah,Taylor,18,36.8,0,0,FALSE,medium
27,01/03/2014,9,6,Brent,5,TRUE,97.065,Rhonda,Butcher,25.6,51,1,1,FALSE,medium
28,01/03/2014,9,6,Westminster,6,TRUE,75.546,Allanah,Miller,11.8,54.4,0,0,FALSE,high
29,01/03/2014,9,6,Camden,5,TRUE,65.655,Rhonda,Taylor,36.5,46.2,0,0,FALSE,medium
30,01/03/2014,9,6,Westminster,2,FALSE,30.98,Rhonda,Butcher,17.4,56.4,0,0,FALSE,<NA>
31,01/03/2014,9,6,Camden,2,FALSE,26.98,Allanah,Carter,23.5,35.7,0,0,FALSE,medium
32,01/03/2014,9,6,Westminster,4,TRUE,51.264,Rhonda,Butcher,24.2,49.1,0,0,FALSE,medium
33,01/03/2014,9,6,Brent,5,TRUE,61.155,Allanah,Carter,15,53.3,0,0,FALSE,<NA>
34,01/03/2014,9,6,Camden,3,FALSE,42.97,Allanah,Carter,36.2,41.7,0,0,FALSE,<NA>
35,01/03/2014,9,6,Westminster,2,FALSE,25.98,Rhonda,Taylor,25.9,47.6,0,0,FALSE,<NA>
36,01/03/2014,9,6,Westminster,5,TRUE,57.555,Allanah,Butcher,32.4,30.3,0,0,FALSE,low
37,01/03/2014,9,6,Brent,3,FALSE,44.97,Allanah,Butcher,15.4,37.8,0,0,FALSE,medium
38,01/03/2014,9,6,Brent,4,TRUE,48.564,Rhonda,Taylor,30.6,23.55,0,0,FALSE,low
39,01/03/2014,9,6,Brent,3,FALSE,40.97,Allanah,Taylor,27.9,49.9,0,0,FALSE,high
40,01/03/2014,9,6,Camden,4,TRUE,49.464,Allanah,Taylor,23,50.7,0,0,FALSE,high
41,01/03/2014,9,6,Brent,2,FALSE,24.98,Rhonda,Taylor,39.7,20.45,0,0,FALSE,low
42,01/03/2014,9,6,Westminster,6,TRUE,74.646,Allanah,Miller,29.1,25.85,0,0,FALSE,low
43,01/03/2014,9,6,Camden,3,FALSE,38.97,Rhonda,Taylor,34.4,43.8,0,0,FALSE,medium
44,02/03/2014,9,7,Camden,4,TRUE,92.466,Rhonda,Farmer,9.5,54.2,1,0,FALSE,medium
45,02/03/2014,9,7,Camden,1,FALSE,56.43,Allanah,Farmer,11.2,55.2,1,1,FALSE,high
46,02/03/2014,9,7,Westminster,4,TRUE,44.964,Rhonda,Farmer,14.4,55.9,0,0,FALSE,high
47,02/03/2014,9,7,Brent,4,TRUE,78.957,Allanah,Hunter,9.2,22.2,1,1,FALSE,low
48,02/03/2014,9,7,Brent,1,FALSE,13.99,Allanah,Hunter,11.9,52.8,0,0,FALSE,high
49,02/03/2014,9,7,Brent,4,TRUE,89.154,Allanah,Butcher,9.4,41,1,1,FALSE,medium
50,02/03/2014,9,7,Brent,3,FALSE,37.97,Allanah,Taylor,21.4,33.2,0,0,FALSE,low
51,02/03/2014,9,7,Brent,2,FALSE,29.98,Rhonda,Hunter,11.5,54.5,0,0,FALSE,medium
52,02/03/2014,9,7,Brent,3,FALSE,47.97,Allanah,Hunter,15.7,54.4,0,0,FALSE,<NA>
53,02/03/2014,9,7,Camden,1,FALSE,13.99,Allanah,Taylor,27.2,46,0,0,FALSE,<NA>
54,02/03/2014,9,7,Camden,3,FALSE,75.47,Rhonda,Taylor,27.7,26.3,1,1,FALSE,low
55,02/03/2014,9,7,Camden,6,TRUE,74.646,Rhonda,Farmer,23.7,51.1,0,0,FALSE,medium
56,02/03/2014,9,7,Camden,3,FALSE,36.97,Rhonda,Farmer,18.2,26.35,0,0,FALSE,low
57,02/03/2014,9,7,Westminster,4,TRUE,49.464,Allanah,Hunter,17.1,37.8,0,0,FALSE,medium
58,02/03/2014,9,7,Camden,1,FALSE,13.99,Rhonda,Farmer,19.9,51.5,0,0,FALSE,<NA>
59,02/03/2014,9,7,Camden,3,FALSE,79.67,Rhonda,Farmer,21.9,53.1,1,1,FALSE,<NA>
60,02/03/2014,9,7,Westminster,2,FALSE,63.78,Rhonda,Farmer,15.5,54.5,1,1,FALSE,medium
61,02/03/2014,9,7,Camden,4,TRUE,54.864,Allanah,Taylor,25.4,48.8,0,0,FALSE,high
62,02/03/2014,9,7,Camden,2,FALSE,25.98,Rhonda,Taylor,25.6,50.3,0,0,FALSE,medium
""";

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DartFrame is a flexible data manipulation library for Dart, inspired by pandas. It simplifies working with structured data like tables, CSVs, and JSON, offering intuitive tools for filtering, transforming, and analyzing data. Perfect for Dart and Flutter projects

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