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

//! # Polars: *<small>DataFrames in Rust</small>*
//!
//! Polars is a DataFrame library for Rust. It is based on [Apache Arrows](https://arrow.apache.org/) memory model.
//! This means that operations on Polars array's *(called `Series` or `ChunkedArray<T>` {if the type `T` is known})* are
//! optimally aligned cache friendly operations and SIMD. Sadly, Apache Arrow needs **nightly Rust**,
//! which means that Polars cannot run on stable.
//!
//! Read more in the pages of the [DataFrame](frame/struct.DataFrame.html), [Series](series/enum.Series.html), and
//! [ChunkedArray](chunked_array/struct.ChunkedArray.html) data structures.
//!
//! ## Read and write CSV/ JSON
//!
//! ```
//! use polars::prelude::*;
//! use std::fs::File;
//!
//! fn example() -> Result<DataFrame> {
//!     let file = File::open("iris.csv").expect("could not open file");
//!
//!     CsvReader::new(file)
//!             .infer_schema(None)
//!             .has_header(true)
//!             .finish()
//! }
//! ```
//!
//! For more IO examples see:
//!
//! * [the csv module](frame/ser/csv/index.html)
//! * [the json module](frame/ser/json/index.html)
//! * [the IPC module](frame/ser/ipc/index.html)
//!
//!
//! ## Joins
//!
//! ```
//! use polars::prelude::*;
//!
//! // Create first df.
//! let s0 = Series::new("days", &[0, 1, 2, 3, 4]);
//! let s1 = Series::new("temp", &[22.1, 19.9, 7., 2., 3.]);
//! let temp = DataFrame::new(vec![s0, s1]).unwrap();
//!
//! // Create second df.
//! let s0 = Series::new("days", &[1, 2]);
//! let s1 = Series::new("rain", &[0.1, 0.2]);
//! let rain = DataFrame::new(vec![s0, s1]).unwrap();
//!
//! // Left join on days column.
//! let joined = temp.left_join(&rain, "days", "days");
//! println!("{}", joined.unwrap())
//! ```
//!
//! ```text
//! +------+------+------+
//! | days | temp | rain |
//! | ---  | ---  | ---  |
//! | i32  | f64  | f64  |
//! +======+======+======+
//! | 0    | 22.1 | null |
//! +------+------+------+
//! | 1    | 19.9 | 0.1  |
//! +------+------+------+
//! | 2    | 7    | 0.2  |
//! +------+------+------+
//! | 3    | 2    | null |
//! +------+------+------+
//! | 4    | 3    | null |
//! +------+------+------+
//! ```
//!
//! ## GroupBys
//!
//! ```
//! use polars::prelude::*;
//! fn groupby_sum(df: &DataFrame) -> Result<DataFrame> {
//!     df.groupby("column_name")?
//!     .select("agg_column_name")
//!     .sum()
//! }
//! ```
//!
//! ## Arithmetic
//! ```
//! use polars::prelude::*;
//! let s: Series = [1, 2, 3].iter().collect();
//! let s_squared = &s * &s;
//! ```
//!
//! ## Rust iterators
//!
//! ```
//! use polars::prelude::*;
//!
//! let s: Series = [1, 2, 3].iter().collect();
//! let s_squared: Series = s.i32()
//!      .expect("datatype mismatch")
//!      .into_iter()
//!      .map(|optional_v| {
//!          match optional_v {
//!              Some(v) => Some(v * v),
//!              None => None, // null value
//!          }
//!  }).collect();
//! ```
//!
//! ## Apply custom closures
//!
//! Besides running custom iterators, custom closures can be applied on the values of [ChunkedArray](chunked_array/struct.ChunkedArray.html)
//! by using the [apply](chunked_array/apply/trait.Apply.html) method. This method accepts
//! a closure that will be applied on all values of `Option<T>` that are non null. Note that this is the
//! **fastest** way to apply a custom closure on `ChunkedArray`'s.
//! ```
//! # use polars::prelude::*;
//! let s: Series = Series::new("values", [Some(1.0), None, Some(3.0)]);
//! // null values are ignored automatically
//! let squared = s.f64()
//!     .unwrap()
//!     .apply(|value| value.powf(2.0))
//!     .into_series();
//!
//! assert_eq!(Vec::from(squared.f64().unwrap()), &[Some(1.0), None, Some(9.0)])
//! ```
//!
//! ## Comparisons
//!
//! ```
//! use polars::prelude::*;
//! use itertools::Itertools;
//! let s = Series::new("dollars", &[1, 2, 3]);
//! let mask = s.eq(1);
//! let valid = [true, false, false].iter();
//!
//! assert_eq!(Vec::from(mask.bool().unwrap()), &[Some(true), Some(false), Some(false)]);
//! ```
//!
//! ## And more...
//!
//! * [DataFrame](frame/struct.DataFrame.html)
//! * [Series](series/enum.Series.html)
//! * [ChunkedArray](chunked_array/struct.ChunkedArray.html)
//!     - [Operations implemented by Traits](chunked_array/ops/index.html)
//! * [Time/ DateTime utilities](doc/time/index.html)
//! * [Groupby and aggregations](frame/group_by/struct.GroupBy.html)
//!
//! ## Features
//!
//! Additional cargo features:
//!
//! * `pretty` (default)
//!     - pretty printing of DataFrames
//! * `simd`
//!     - SIMD operations
//! * `temporal`
//!     - Conversions between Chrono and Polars for temporal data
#![allow(dead_code)]
#![feature(iterator_fold_self)]
#[macro_use]
pub mod series;
#[macro_use]
pub(crate) mod utils;
pub mod chunked_array;
pub mod datatypes;
#[cfg(feature = "docs")]
pub mod doc;
pub mod error;
mod fmt;
pub mod frame;
pub mod prelude;
pub mod testing;