pandrs 0.1.0-beta.2

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

use pandrs::dataframe::TransformExt;
use pandrs::{DataFrame, MeltOptions, Series, StackOptions, UnstackOptions};
use std::error::Error;

#[allow(clippy::result_large_err)]
fn main() -> Result<(), Box<dyn Error>> {
    println!("=== Example of Data Transformation ===\n");

    // Create sample data
    let mut df = DataFrame::new();

    // ID column
    df.add_column(
        "id".to_string(),
        Series::new(vec!["1", "2", "3"], Some("id".to_string()))?,
    )?;

    // Product category
    df.add_column(
        "category".to_string(),
        Series::new(
            vec!["Food", "Electronics", "Clothing"],
            Some("category".to_string()),
        )?,
    )?;

    // Monthly sales data
    df.add_column(
        "January".to_string(),
        Series::new(vec!["1000", "1500", "800"], Some("January".to_string()))?,
    )?;

    df.add_column(
        "February".to_string(),
        Series::new(vec!["1200", "1300", "1100"], Some("February".to_string()))?,
    )?;

    df.add_column(
        "March".to_string(),
        Series::new(vec!["900", "1800", "1400"], Some("March".to_string()))?,
    )?;

    println!("Original DataFrame:");
    println!("{df:?}");

    // melt operation - convert from wide to long format
    println!("\n----- melt operation (convert from wide to long format) -----");
    let melt_options = MeltOptions {
        id_vars: Some(vec!["id".to_string(), "category".to_string()]),
        value_vars: Some(vec![
            "January".to_string(),
            "February".to_string(),
            "March".to_string(),
        ]),
        var_name: Some("Month".to_string()),
        value_name: Some("Sales".to_string()),
    };

    let melted_df = df.melt(&melt_options)?;
    println!("{melted_df:?}");

    // stack operation
    println!("\n----- stack operation (stack columns to rows) -----");
    let stack_options = StackOptions {
        columns: Some(vec![
            "January".to_string(),
            "February".to_string(),
            "March".to_string(),
        ]),
        var_name: Some("Month".to_string()),
        value_name: Some("Sales".to_string()),
        dropna: false,
    };

    let stacked_df = df.stack(&stack_options)?;
    println!("{stacked_df:?}");

    // unstack operation (using melted_df)
    println!("\n----- unstack operation (convert rows to columns) -----");
    let unstack_options = UnstackOptions {
        var_column: "Month".to_string(),
        value_column: "Sales".to_string(),
        index_columns: Some(vec!["id".to_string(), "category".to_string()]),
        fill_value: None,
    };

    let unstacked_df = melted_df.unstack(&unstack_options)?;
    println!("{unstacked_df:?}");

    // Concatenate DataFrames
    println!("\n----- Concatenate DataFrames (concat) -----");

    // Create additional DataFrame
    let mut df2 = DataFrame::new();
    df2.add_column(
        "id".to_string(),
        Series::new(vec!["4", "5"], Some("id".to_string()))?,
    )?;
    df2.add_column(
        "category".to_string(),
        Series::new(
            vec!["Stationery", "Furniture"],
            Some("category".to_string()),
        )?,
    )?;
    df2.add_column(
        "January".to_string(),
        Series::new(vec!["500", "2000"], Some("January".to_string()))?,
    )?;
    df2.add_column(
        "February".to_string(),
        Series::new(vec!["600", "2200"], Some("February".to_string()))?,
    )?;
    df2.add_column(
        "March".to_string(),
        Series::new(vec!["700", "1900"], Some("March".to_string()))?,
    )?;

    println!("Additional DataFrame:");
    println!("{df2:?}");

    // Concatenate DataFrames
    let concat_df = DataFrame::concat(&[&df, &df2], true)?;
    println!("Concatenated DataFrame:");
    println!("{concat_df:?}");

    // Conditional aggregation
    println!("\n----- Conditional Aggregation -----");

    // Condition: Calculate the total sales for March by category, only for rows where February sales are 1000 or more
    let result = df.conditional_aggregate(
        "category",
        "March",
        |row| {
            if let Some(sales_str) = row.get("February") {
                if let Ok(sales) = sales_str.parse::<i32>() {
                    return sales >= 1000;
                }
            }
            false
        },
        |values| {
            let sum: i32 = values.iter().filter_map(|v| v.parse::<i32>().ok()).sum();
            sum.to_string()
        },
    )?;

    println!("Conditional Aggregation Result (Total March sales by category for rows where February sales are 1000 or more):");
    println!("{result:?}");

    Ok(())
}