├── @DataFrame
└── DataFrame.m
├── README.md
├── examples
├── DataFrame_example.m
└── html
│ └── DataFrame_example.html
└── tests
├── run_DataFrame_tests.m
├── test_DataFrame.m
└── test_data
└── ugly_data.csv
/@DataFrame/DataFrame.m:
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1 | classdef DataFrame < dynamicprops
2 | %DATAFRAME - an implementation of a DataFrame class for Matlab
3 | % DATAFRAME - inspired by Pandas and R DataFrame, this class wraps the
4 | % Matlab table datatype as an object oriented alternative
5 | %
6 | % SYNTAX:
7 | % df = DataFrame( varargin )
8 | %
9 | % Description:
10 | % df = DataFrame( varargin ) the DataFrame literally passes the input
11 | % arguement on to the table during construction
12 | %
13 | % PROPERTIES:
14 | % (dynamic) - the DataFrame properties will match the
15 | % table.Properties.VariableNames
16 | %
17 | % EXAMPLES:
18 | % df = DataFrame(0, 0, 0, 0, 'VariableNames', ...
19 | % {'test1', 'test2','test3','test4',})
20 | % df.head()
21 | %
22 | % SEE ALSO: table
23 | %
24 | % Author: nick roth
25 | % email: nick.roth@nou-systems.com
26 | % Matlab ver.: 8.3.0.532 (R2014a)
27 | % Date: 01-Sep-2014
28 |
29 | %% Properties
30 | properties
31 | Properties
32 | end
33 |
34 | properties (Access = private)
35 | data
36 | end
37 |
38 | %% Methods
39 | methods
40 | % DATAFRAME Constructor
41 | function self = DataFrame(varargin)
42 |
43 | % Construct dataframe from table
44 | if nargin == 1
45 | tbl = varargin{1};
46 | if istable(tbl)
47 | self.data = tbl;
48 | end
49 | else
50 | % Process inputs as if we are a Matlab table
51 | self.data = table(varargin{:});
52 | end
53 |
54 | self.update_props();
55 | end
56 |
57 | function head(self)
58 | %HEAD - implements the method that Pandas provides to see the
59 | %top rows of the table
60 |
61 | toprows = self.data(min(length(self),10), :);
62 | disp(toprows);
63 | end
64 |
65 | function out = getTable(self)
66 | %GETTABLE - returns a Matlab table from DataFrame object
67 |
68 | out = self.data;
69 | end
70 |
71 | function details(self)
72 | %DETAILS - prints to console a detailed summary of the data
73 | %within the DataFrame
74 |
75 | builtin('disp', self);
76 | self.head();
77 | self.summary();
78 | end
79 |
80 | function bool = is_column(self, col)
81 | %IS_COLUMN - returns boolean value true if the given string is
82 | %a valid column
83 |
84 | bool = false;
85 | cols = self.data.Properties.VariableNames;
86 | if isempty(setdiff(col, cols))
87 | bool = true;
88 | end
89 | end
90 |
91 | function remove_cols(self, cols)
92 | %REMOVE_COLS - removes columns from the DataFrame, given a
93 | %single string, or a cell array of strings
94 |
95 | cols = cellstr(cols);
96 | for i = 1:length(cols)
97 | mp = findprop(self, cols{i});
98 | delete(mp);
99 | end
100 | self.data(:, cols) = [];
101 | end
102 |
103 | function out = columns(self)
104 | %COLUMNS - provides an alternative way to access the column
105 | %names of the DataFrame
106 |
107 | out = self.data.Properties.VariableNames;
108 | end
109 | end
110 |
111 | methods (Access = private)
112 | function update_props(self)
113 | %UDPATE_PROPS - adds the table variables to the class as
114 | %dynamic properties. This way we can make it look like the
115 | %DataFrame object is actually a table
116 |
117 | vars = self.data.Properties.VariableNames;
118 | for i = 1:length(vars)
119 | var = vars{i};
120 | self.addprop(var);
121 | end
122 | end
123 | end
124 |
125 | % Constructors
126 | methods (Static)
127 | function out = fromStruct(varargin)
128 | out = DataFrame(struct2table(varargin{:}));
129 | end
130 |
131 | function out = fromCSV(varargin)
132 | out = DataFrame(readtable(varargin{:}));
133 | end
134 |
135 | function out = fromArray(varargin)
136 | out = DataFrame(array2table(varargin{:}));
137 | end
138 |
139 | function out = fromCell(varargin)
140 | out = DataFrame(cell2table(varargin{:}));
141 | end
142 | end
143 |
144 | %% Pass Throughs
145 | methods
146 | function out = height(self)
147 | out = height(self.data);
148 | end
149 |
150 | function out = width(self)
151 | out = width(self.data);
152 | end
153 |
154 | function summary(self)
155 | summary(self.data);
156 | end
157 |
158 | function out = toStruct(self)
159 | out = table2struct(self.data);
160 | end
161 |
162 | function out = toCell(self)
163 | out = table2cell(self.data);
164 | end
165 |
166 | function out = toArray(self)
167 | out = table2array(self.data);
168 | end
169 |
170 | function writetable(self, varargin)
171 | writetable(self.data, varargin{:})
172 | end
173 |
174 | function out = get.Properties(self)
175 | out = self.data.Properties;
176 | end
177 | end
178 |
179 | %% Static
180 | methods (Static)
181 | function [C,ia,ib] = intersect(A, B, varargin)
182 | [C,ia,ib] = intersect(A, B, varargin{:});
183 | end
184 |
185 | function [Lia, Locb] = ismember(A, B, varargin)
186 | [Lia, Locb] = ismember(A, B, varargin{:});
187 | end
188 |
189 | function out = rowfun(func, A, varargin)
190 | out = rowfun(func, A, varargin{:});
191 | end
192 |
193 | function out = varfun(func, A, varargin)
194 | out = varfun(func, A, varargin{:});
195 | end
196 | end
197 |
198 | %% Overrides
199 | methods
200 |
201 | function self = subsasgn(self, S, B)
202 | %SUBSASGN - overrides the builtin method so that we can
203 | %dynamically attach properties to the object when we are adding
204 | %new data to the DataFrame
205 |
206 | % If this column doesn't exist, add it to the object
207 | valid_prop = setdiff(S.subs, {':'});
208 | if length(valid_prop) == 1 && ~self.is_column(valid_prop)
209 | self.addprop(valid_prop{:});
210 | end
211 |
212 | % Call builtin on the underlying Matlab table
213 | switch S.type
214 | case '.'
215 | self.data = builtin('subsasgn', self.data, S, B);
216 | case '()'
217 | self.data = builtin('subsasgn', self.data, S, B);
218 | case '{}'
219 | self.data = builtin('subsasgn', self.data, S, B);
220 | end
221 | end
222 |
223 | function [varargout] = subsref(self, S)
224 | %SUBSREF - overrides the subscript reference method, which
225 | %provides the way for us to wrap the built in table type
226 |
227 | varargout{1} = [];
228 |
229 | call_type = S.type;
230 | var = S.subs;
231 |
232 | %Catch the subscript behavior so that we can pass through table
233 | %calls directly to it, and methods/class properties to the
234 | %DataFrame class
235 | switch call_type
236 | case '.'
237 | if ismethod(self, var)
238 |
239 | % check for method outputs
240 | mc = metaclass(self);
241 | ml = mc.MethodList;
242 | meth = findobj(ml, 'Name', var);
243 |
244 | if isempty(meth.OutputNames)
245 | % method calls without outputs
246 | builtin('subsref', self, S);
247 | else
248 | varargout{1} = builtin('subsref', self, S);
249 | end
250 | else
251 | %Pass through properties call
252 | varargout{1} = builtin('subsref', self.data, S);
253 | end
254 | case '()'
255 | % Process the '()' subscripting directly on table
256 | tbl = self.data;
257 | varargout{1} = builtin('subsref', tbl, S);
258 | case '{}'
259 | % Process the '{}' subscripting directly on table
260 | tbl = self.data;
261 | varargout{1} = builtin('subsref', tbl, S);
262 | end
263 | end
264 |
265 | function disp(self)
266 | %DISP - overrides the default disp method, which makes our
267 | %DataFrame look like a typical table
268 |
269 | disp(self.data);
270 | end
271 |
272 | function out = numel(~, varargin)
273 | %NUMEL - override the default numel method. It is critical that
274 | %both the first arguement and second arguement, "varargin"
275 | %exists, since this directly affects when Matlab will call the
276 | %method. Otherwise, '{}' subscripting breaks.
277 |
278 | out = 1;
279 | end
280 | end
281 |
282 | end
283 |
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/README.md:
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1 | DataFrame
2 | =========
3 |
4 | Matlab impelementation of DataFrame/Pandas concept. This project wraps and makes use of as much as possible of the Matlab table, but with the intent of providing a class implementation that could be specialized further.
5 |
6 | Create a DataFrame from scratch
The DataFrame is created in the same exact way as a table. The constructor just passes the arguements to the table constructor, then stores the table as as private property. We can still retrieve table properties
df = DataFrame(0, 0, 0, 0, ...
7 | 'VariableNames', {'test1', 'test2','test3','test4',});
8 | df.Properties
9 | ans =
10 |
11 | Description: ''
12 | VariableDescriptions: {}
13 | VariableUnits: {}
14 | DimensionNames: {'Row' 'Variable'}
15 | UserData: []
16 | RowNames: {}
17 | VariableNames: {'test1' 'test2' 'test3' 'test4'}
18 |
19 | View the DataFrame the Same as a Table
DataFrame overrides the display method to show the table instead of the DataFrame object
df
20 |
df =
21 |
22 | test1 test2 test3 test4
23 | _____ _____ _____ _____
24 |
25 | 0 0 0 0
26 |
27 |
Access Table Columns Through DataFrame
The column names are pass throughs into the actual table structure. We can access the column as normal
df.test1
28 | df.test3
29 |
ans =
30 |
31 | 0
32 |
33 |
34 | ans =
35 |
36 | 0
37 |
38 |
Access Methods Attached to the DataFrame Object
You no longer need to know which possible functions you can use on a Matlab table. They are available to use either way.
df.height()
39 | height(df)
40 |
ans =
41 |
42 | 1
43 |
44 |
45 | ans =
46 |
47 | 1
48 |
49 |
Extend DataFrame However We'd Like
Matlab blocks you from extending the table data structure, but we can get around that with this approach
df = DataFrame.fromCSV(which('ugly_data.csv'));
50 | df.head();
51 | Name Zeros Lat Lng Normal Negative
52 | _______________ _____ ________ ________ ___________ ________
53 |
54 | 'Connor Hayden' 0 -2.58201 71.06714 0.006049531 3
55 |
56 |
57 | GUID Date Timestamp
58 | ______________________________________ _________________ __________
59 |
60 | '7B79A197-E85F-2BFF-F37E-727DEDAC9803' 'April 27th 2015' 1394414933
61 |
62 |
63 | AlphaNumeric
64 | _____________
65 |
66 | 'FSU09MJG3ZB'
67 |
68 |
Or, if You Still Want to See the Object
Column data will show empty since we are just dynamically passing through them on each subscript call. Properties shows up with contents, since it uses a getter method
df.details();
69 |
DataFrame with properties:
70 |
71 | Properties: [1x1 struct]
72 | Normal: []
73 | Lng: []
74 | Negative: []
75 | Zeros: []
76 | Name: []
77 | AlphaNumeric: []
78 | Date: []
79 | GUID: []
80 | Lat: []
81 | Timestamp: []
82 |
83 | Name Zeros Lat Lng Normal Negative
84 | _______________ _____ ________ ________ ___________ ________
85 |
86 | 'Connor Hayden' 0 -2.58201 71.06714 0.006049531 3
87 |
88 |
89 | GUID Date Timestamp
90 | ______________________________________ _________________ __________
91 |
92 | '7B79A197-E85F-2BFF-F37E-727DEDAC9803' 'April 27th 2015' 1394414933
93 |
94 |
95 | AlphaNumeric
96 | _____________
97 |
98 | 'FSU09MJG3ZB'
99 |
100 |
101 | Variables:
102 |
103 | Name: 20400x1 cell string
104 |
105 | Zeros: 20400x1 double
106 | Values:
107 |
108 | min 0
109 | median 0
110 | max 0
111 |
112 | Lat: 20400x1 double
113 | Values:
114 |
115 | min -89.42406
116 | median -1.02059
117 | max 88.89347
118 |
119 | Lng: 20400x1 double
120 | Values:
121 |
122 | min -177.18611
123 | median 0.934815
124 | max 176.4439
125 |
126 | Normal: 20400x1 double
127 | Values:
128 |
129 | min -0.543252895
130 | median -0.017611593
131 | max 0.573277103
132 |
133 | Negative: 20400x1 double
134 | Values:
135 |
136 | min -5
137 | median 0.5
138 | max 5
139 |
140 | GUID: 20400x1 cell string
141 |
142 | Date: 20400x1 cell string
143 |
144 | Timestamp: 20400x1 double
145 | Values:
146 |
147 | min 1378967930
148 | median 1403645750.5
149 | max 1439505058
150 |
151 | AlphaNumeric: 20400x1 cell string
152 |
153 |
We Can Try the Same Thing on a Standard Table
tbl = readtable(which('ugly_data.csv'));
154 | try
155 | tbl.head();
156 | head(tbl);
157 | catch err
158 | disp(err.message)
159 | end
160 | Unrecognized variable name 'head'.
161 |
Dynamically add columns to the DataFrame
df.Lat2 = df.Lat;
162 | df.Lat3 = df.Lat;
163 | df.columns()
164 |
ans =
165 |
166 | Columns 1 through 7
167 |
168 | 'Name' 'Zeros' 'Lat' 'Lng' 'Normal' 'Negative' 'GUID'
169 |
170 | Columns 8 through 12
171 |
172 | 'Date' 'Timestamp' 'AlphaNumeric' 'Lat2' 'Lat3'
173 |
174 |
Easily remove columns from the DataFrame
df.remove_cols({'Lat2', 'Lat3'});
175 | df.columns()
176 | ans =
177 |
178 | Columns 1 through 7
179 |
180 | 'Name' 'Zeros' 'Lat' 'Lng' 'Normal' 'Negative' 'GUID'
181 |
182 | Columns 8 through 10
183 |
184 | 'Date' 'Timestamp' 'AlphaNumeric'
185 |
186 |
Initial Start at Providing Complete Wrapper for Table
methods('DataFrame')
187 | Methods for class DataFrame:
188 |
189 | DataFrame disp is_column subsref toStruct
190 | addprop getTable numel summary width
191 | columns head remove_cols toArray writetable
192 | details height subsasgn toCell
193 |
194 | Static methods:
195 |
196 | fromArray fromCell intersect rowfun
197 | fromCSV fromStruct ismember varfun
198 |
199 | Call "methods('handle')" for methods of DataFrame inherited from handle.
200 |