pandrs 0.3.0

A high-performance DataFrame library for Rust, providing pandas-like API with advanced features including SIMD optimization, parallel processing, and distributed computing capabilities
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317

#![allow(clippy::result_large_err)]
use pandrs::error::Error;
use pandrs::{BooleanColumn, Column, Float64Column, Int64Column, OptimizedDataFrame, StringColumn};
use std::collections::HashMap;

#[test]
fn test_optimized_dataframe_creation() {
    // Create empty DataFrame
    let df = OptimizedDataFrame::new();
    assert_eq!(df.column_count(), 0);
    assert_eq!(df.row_count(), 0);
    assert!(df.column_names().is_empty());
}

#[test]
fn test_optimized_dataframe_add_column() {
    // Add column to DataFrame
    let mut df = OptimizedDataFrame::new();
    let values = Int64Column::new(vec![10, 20, 30]);

    // Add column
    df.add_column("values", Column::Int64(values)).unwrap();

    // Validation
    assert_eq!(df.column_count(), 1);
    assert_eq!(df.row_count(), 3);
    assert_eq!(df.column_names(), &["values"]);
}

#[test]
fn test_optimized_dataframe_add_multiple_columns() {
    // Create DataFrame with multiple columns
    let mut df = OptimizedDataFrame::new();

    let ages = Int64Column::new(vec![25, 30, 35]);
    let heights = Int64Column::new(vec![170, 180, 175]);

    df.add_column("age", Column::Int64(ages)).unwrap();
    df.add_column("height", Column::Int64(heights)).unwrap();

    // Validation
    assert_eq!(df.column_count(), 2);
    assert_eq!(df.row_count(), 3);
    assert!(df.contains_column("age"));
    assert!(df.contains_column("height"));
    assert!(!df.contains_column("weight"));
}

#[test]
fn test_optimized_dataframe_column_length_mismatch() {
    // Test error when adding columns with different lengths
    let mut df = OptimizedDataFrame::new();

    let ages = Int64Column::new(vec![25, 30, 35]);
    df.add_column("age", Column::Int64(ages)).unwrap();

    // Add column with different length
    let heights = Int64Column::new(vec![170, 180]);
    let result = df.add_column("height", Column::Int64(heights));

    // Should error
    assert!(result.is_err());

    // Check specific error type
    match result {
        Err(Error::InconsistentRowCount { .. }) => (),
        _ => panic!("Expected an InconsistentRowCount error"),
    }
}

#[test]
fn test_optimized_dataframe_duplicate_column() {
    // Test error when adding duplicate column name
    let mut df = OptimizedDataFrame::new();

    let ages1 = Int64Column::new(vec![25, 30, 35]);
    df.add_column("age", Column::Int64(ages1)).unwrap();

    // Add column with same name
    let ages2 = Int64Column::new(vec![40, 45, 50]);
    let result = df.add_column("age", Column::Int64(ages2));

    // Should error
    assert!(result.is_err());

    // Check specific error type
    match result {
        Err(Error::DuplicateColumnName(_)) => (),
        _ => panic!("Expected a DuplicateColumnName error"),
    }
}

#[test]
fn test_optimized_dataframe_mixed_types() {
    let mut df = OptimizedDataFrame::new();

    // Add columns of different types
    let int_col = Int64Column::new(vec![1, 2, 3]);
    let float_col = Float64Column::new(vec![1.1, 2.2, 3.3]);
    let str_col = StringColumn::new(vec![
        "one".to_string(),
        "two".to_string(),
        "three".to_string(),
    ]);
    let bool_col = BooleanColumn::new(vec![true, false, true]);

    df.add_column("int", Column::Int64(int_col)).unwrap();
    df.add_column("float", Column::Float64(float_col)).unwrap();
    df.add_column("str", Column::String(str_col)).unwrap();
    df.add_column("bool", Column::Boolean(bool_col)).unwrap();

    // Validation
    assert_eq!(df.column_count(), 4);
    assert_eq!(df.row_count(), 3);

    // Check column types
    let int_view = df.column("int").unwrap();
    let float_view = df.column("float").unwrap();
    let str_view = df.column("str").unwrap();
    let bool_view = df.column("bool").unwrap();

    assert!(int_view.as_int64().is_some());
    assert!(float_view.as_float64().is_some());
    assert!(str_view.as_string().is_some());
    assert!(bool_view.as_boolean().is_some());
}

#[test]
fn test_optimized_dataframe_select_columns() {
    let mut df = OptimizedDataFrame::new();

    let id_col = Int64Column::new(vec![1, 2, 3, 4]);
    let name_col = StringColumn::new(vec![
        "Alice".to_string(),
        "Bob".to_string(),
        "Charlie".to_string(),
        "Dave".to_string(),
    ]);
    let age_col = Int64Column::new(vec![25, 30, 35, 40]);

    df.add_column("id", Column::Int64(id_col)).unwrap();
    df.add_column("name", Column::String(name_col)).unwrap();
    df.add_column("age", Column::Int64(age_col)).unwrap();

    // Select columns to create new DataFrame
    let selected = df.select(&["id", "name"]).unwrap();

    // Validation
    assert_eq!(selected.column_count(), 2);
    assert_eq!(selected.row_count(), 4);
    assert!(selected.contains_column("id"));
    assert!(selected.contains_column("name"));
    assert!(!selected.contains_column("age"));

    // When selecting a non-existent column
    let result = df.select(&["id", "nonexistent"]);
    assert!(result.is_err());
}

#[test]
fn test_optimized_dataframe_filter() {
    let mut df = OptimizedDataFrame::new();

    let id_col = Int64Column::new(vec![1, 2, 3, 4]);
    let filter_col = BooleanColumn::new(vec![true, false, true, false]);

    df.add_column("id", Column::Int64(id_col)).unwrap();
    df.add_column("filter", Column::Boolean(filter_col))
        .unwrap();

    // Filter using boolean column
    let filtered = df.filter("filter").unwrap();

    // Validation
    assert_eq!(filtered.row_count(), 2);

    // When filter column doesn't exist
    let result = df.filter("nonexistent");
    assert!(result.is_err());

    // When filter column is not boolean
    let mut df2 = OptimizedDataFrame::new();
    let id_col = Int64Column::new(vec![1, 2, 3, 4]);
    df2.add_column("id", Column::Int64(id_col)).unwrap();

    let result = df2.filter("id");
    assert!(result.is_err());
}

#[test]
fn test_optimized_dataframe_rename_columns() {
    // Create a DataFrame with test columns
    let mut df = OptimizedDataFrame::new();

    df.add_int_column("old_col1", [1, 2, 3].to_vec()).unwrap();
    df.add_string_column(
        "old_col2",
        ["a".to_string(), "b".to_string(), "c".to_string()].to_vec(),
    )
    .unwrap();
    df.add_float_column("old_col3", vec![1.0, 2.0, 3.0])
        .unwrap();

    // Verify original column names
    let original_names = df.column_names();
    assert_eq!(original_names, &["old_col1", "old_col2", "old_col3"]);

    // Test rename_columns method
    let mut rename_map = HashMap::new();
    rename_map.insert("old_col1".to_string(), "new_col1".to_string());
    rename_map.insert("old_col2".to_string(), "new_col2".to_string());

    df.rename_columns(&rename_map).unwrap();

    // Verify column names after renaming
    let renamed_names = df.column_names();
    assert_eq!(renamed_names, &["new_col1", "new_col2", "old_col3"]);

    // Verify that columns still contain the correct data
    assert!(df.contains_column("new_col1"));
    assert!(df.contains_column("new_col2"));
    assert!(df.contains_column("old_col3"));
    assert!(!df.contains_column("old_col1"));
    assert!(!df.contains_column("old_col2"));
}

#[test]
fn test_optimized_dataframe_rename_columns_errors() {
    let mut df = OptimizedDataFrame::new();
    df.add_int_column("col1", [1, 2, 3].to_vec()).unwrap();
    df.add_string_column(
        "col2",
        ["a".to_string(), "b".to_string(), "c".to_string()].to_vec(),
    )
    .unwrap();

    // Test renaming non-existent column
    let mut rename_map = HashMap::new();
    rename_map.insert("non_existent".to_string(), "new_name".to_string());

    let result = df.rename_columns(&rename_map);
    assert!(result.is_err());

    // Test duplicate new names
    let mut duplicate_map = HashMap::new();
    duplicate_map.insert("col1".to_string(), "duplicate".to_string());
    duplicate_map.insert("col2".to_string(), "duplicate".to_string());

    let result = df.rename_columns(&duplicate_map);
    assert!(result.is_err());

    // Test renaming to existing column name
    let mut conflict_map = HashMap::new();
    conflict_map.insert("col1".to_string(), "col2".to_string());

    let result = df.rename_columns(&conflict_map);
    assert!(result.is_err());
}

#[test]
fn test_optimized_dataframe_set_column_names() {
    // Create a DataFrame with test columns
    let mut df = OptimizedDataFrame::new();

    df.add_int_column("col1", [1, 2, 3].to_vec()).unwrap();
    df.add_string_column(
        "col2",
        ["a".to_string(), "b".to_string(), "c".to_string()].to_vec(),
    )
    .unwrap();
    df.add_float_column("col3", vec![1.0, 2.0, 3.0]).unwrap();

    // Verify original column names
    let original_names = df.column_names();
    assert_eq!(original_names, &["col1", "col2", "col3"]);

    // Test set_column_names method
    let new_names = vec![
        "final_col1".to_string(),
        "final_col2".to_string(),
        "final_col3".to_string(),
    ];
    df.set_column_names(new_names).unwrap();

    // Verify column names after setting
    let final_names = df.column_names();
    assert_eq!(final_names, &["final_col1", "final_col2", "final_col3"]);

    // Verify that columns still contain the correct data
    assert!(df.contains_column("final_col1"));
    assert!(df.contains_column("final_col2"));
    assert!(df.contains_column("final_col3"));
    assert!(!df.contains_column("col1"));
    assert!(!df.contains_column("col2"));
    assert!(!df.contains_column("col3"));
}

#[test]
fn test_optimized_dataframe_set_column_names_errors() {
    let mut df = OptimizedDataFrame::new();
    df.add_int_column("col1", [1, 2, 3].to_vec()).unwrap();
    df.add_string_column(
        "col2",
        ["a".to_string(), "b".to_string(), "c".to_string()].to_vec(),
    )
    .unwrap();

    // Test wrong number of names
    let wrong_count_names = vec!["new1".to_string()]; // Only 1 name for 2 columns
    let result = df.set_column_names(wrong_count_names);
    assert!(result.is_err());

    // Test duplicate names
    let duplicate_names = vec!["duplicate".to_string(), "duplicate".to_string()];
    let result = df.set_column_names(duplicate_names);
    assert!(result.is_err());
}