polars_rows_iter/iter_from_column/
iter_from_column_string.rs

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
use super::iter_from_column_str::create_iter;
use super::*;
use iter_from_column_trait::IterFromColumn;
use polars::prelude::*;

impl<'a> IterFromColumn<'a> for String {
    type RawInner = &'a str;
    fn create_iter(column: &'a Column) -> PolarsResult<Box<dyn Iterator<Item = Option<&'a str>> + 'a>> {
        create_iter(column)
    }

    #[inline]
    fn get_value(polars_value: Option<&'a str>, column_name: &str, _dtype: &DataType) -> PolarsResult<Self>
    where
        Self: Sized,
    {
        Ok(polars_value
            .ok_or_else(|| <&'a str as IterFromColumn<'a>>::unexpected_null_value_error(column_name))?
            .to_string())
    }
}

impl<'a> IterFromColumn<'a> for Option<String> {
    type RawInner = &'a str;
    fn create_iter(column: &'a Column) -> PolarsResult<Box<dyn Iterator<Item = Option<&'a str>> + 'a>> {
        create_iter(column)
    }

    #[inline]
    fn get_value(polars_value: Option<&'a str>, _column_name: &str, _dtype: &DataType) -> PolarsResult<Self>
    where
        Self: Sized,
    {
        Ok(polars_value.map(|s| s.to_string()))
    }
}

#[cfg(test)]
mod tests {
    use crate::*;
    use itertools::{izip, Itertools};
    use polars::prelude::*;
    use rand::{rngs::StdRng, SeedableRng};
    use shared_test_helpers::*;

    const ROW_COUNT: usize = 64;

    #[test]
    fn str_test() {
        let mut rng = StdRng::seed_from_u64(0);
        let height = ROW_COUNT;
        let dtype = DataType::String;

        let col = create_column("col", dtype.clone(), false, height, &mut rng);
        let col_opt = create_column("col_opt", dtype, true, height, &mut rng);

        let col_values = col.str().unwrap().iter().map(|v| v.unwrap().to_owned()).collect_vec();
        let col_opt_values = col_opt
            .str()
            .unwrap()
            .iter()
            .map(|v| v.map(|s| s.to_owned()))
            .collect_vec();

        let df = DataFrame::new(vec![col, col_opt]).unwrap();

        let col_iter = col_values.into_iter();
        let col_opt_iter = col_opt_values.into_iter();

        let expected_rows = izip!(col_iter, col_opt_iter)
            .map(|(col, col_opt)| TestRow { col, col_opt })
            .collect_vec();

        #[derive(Debug, FromDataFrameRow, PartialEq)]
        struct TestRow {
            col: String,
            col_opt: Option<String>,
        }

        let rows = df.rows_iter::<TestRow>().unwrap().map(|v| v.unwrap()).collect_vec();

        assert_eq!(rows, expected_rows)
    }

    #[cfg(feature = "dtype-categorical")]
    #[test]
    fn cat_test() {
        let mut rng = StdRng::seed_from_u64(0);
        let height = ROW_COUNT;
        let dtype = DataType::Categorical(None, CategoricalOrdering::Physical);

        let col = create_column("col", dtype.clone(), false, height, &mut rng);
        let col_opt = create_column("col_opt", dtype, true, height, &mut rng);

        let col_values = col
            .categorical()
            .unwrap()
            .iter_str()
            .map(|v| v.unwrap().to_owned())
            .collect_vec();
        let col_opt_values = col_opt
            .categorical()
            .unwrap()
            .iter_str()
            .map(|v| v.map(|s| s.to_owned()))
            .collect_vec();

        let df = DataFrame::new(vec![col, col_opt]).unwrap();

        let col_iter = col_values.into_iter();
        let col_opt_iter = col_opt_values.into_iter();

        let expected_rows = izip!(col_iter, col_opt_iter)
            .map(|(col, col_opt)| TestRow { col, col_opt })
            .collect_vec();

        #[derive(Debug, FromDataFrameRow, PartialEq)]
        struct TestRow {
            col: String,
            col_opt: Option<String>,
        }

        let rows = df.rows_iter::<TestRow>().unwrap().map(|v| v.unwrap()).collect_vec();

        assert_eq!(rows, expected_rows)
    }

    #[cfg(feature = "dtype-categorical")]
    #[test]
    fn enum_test() {
        let mut rng = StdRng::seed_from_u64(0);
        let height = ROW_COUNT;

        let enum_value_series = Series::new("enum".into(), &["A", "B", "C", "D", "E"]);
        let categories = enum_value_series.str().unwrap().downcast_iter().next().unwrap().clone();
        let dtype = create_enum_dtype(categories);

        let col = create_column("col", dtype.clone(), false, height, &mut rng);
        let col_opt = create_column("col_opt", dtype, true, height, &mut rng);

        let col_values = col
            .categorical()
            .unwrap()
            .iter_str()
            .map(|v| v.unwrap().to_owned())
            .collect_vec();
        let col_opt_values = col_opt
            .categorical()
            .unwrap()
            .iter_str()
            .map(|v| v.map(|s| s.to_owned()))
            .collect_vec();

        let df = DataFrame::new(vec![col, col_opt]).unwrap();

        let col_iter = col_values.into_iter();
        let col_opt_iter = col_opt_values.into_iter();

        let expected_rows = izip!(col_iter, col_opt_iter)
            .map(|(col, col_opt)| TestRow { col, col_opt })
            .collect_vec();

        #[derive(Debug, FromDataFrameRow, PartialEq)]
        struct TestRow {
            col: String,
            col_opt: Option<String>,
        }

        let rows = df.rows_iter::<TestRow>().unwrap().map(|v| v.unwrap()).collect_vec();

        assert_eq!(rows, expected_rows)
    }
}