安装Polars #

Polars是一个库，安装起来就像调用相应编程语言的包管理器一样简单。

pip install polars

#或者对于那些不支持高级矢量扩展指令集2（AVX2）的旧CPU
pip install polars-lts-cpu

cargo add polars -F lazy

# Or Cargo.toml
[dependencies]
polars = { version = "x", features = ["lazy", ...]}

大索引 #

默认情况下，Polars dataframes的行数限制为2^32(约43亿)行。通过启用大索引扩展功能，可将此限制提升至2^64(约1800京）行：

pip install polars-u64-idx

cargo add polars -F bigidx

# Or Cargo.toml
[dependencies]
polars = { version = "x", features = ["bigidx", ...] }

旧款CPU #

在不支持高级矢量扩展指令集（ AVX）的旧款 CPU 上为 Python 安装 Polars，请运行：

pip install polars-lts-cpu

导入polars #

要使用polars库，只需将其导入到你的项目中即可：

import polars as pl

use polars::prelude::*;

特性标志 #

通过使用上述命令，你可以将 Polars 的核心部分安装到你的系统上。然而，根据你的使用场景，你可能还需要安装一些可选的依赖项。将这些设置为可选的目的是尽量减少占用空间。根据编程语言的不同，相应的标志也有所不同。在整个用户指南中，当所使用的某项功能需要额外的依赖项时，将会提别提醒。

Python #

# 示例
pip install 'polars[numpy,fsspec]'

All #

GPU #

说明

有关更详细的说明和先决条件，请参阅 GPU支持相关内容。

互操作性 #

Excel #

数据库 #

云 #

其他I/O #

其他 #

Rust #

# Cargo.toml
[dependencies]
polars = { version = "0.26.1", features = ["lazy", "temporal", "describe", "json", "parquet", "dtype-datetime"] }

可选择启用的功能如下:

• 额外的数据类型:
  • dtype-date
  • dtype-datetime
  • dtype-time
  • dtype-duration
  • dtype-i8
  • dtype-i16
  • dtype-u8
  • dtype-u16
  • dtype-categorical
  • dtype-struct
• lazy - Lazy API:
  • regex - 在列选择中使用正则表达式.
  • dot_diagram - 根据惰性逻辑计划创建点图。
• sql - 将 SQL 查询传递给 Polars。
• streaming - 能够处理比内存容量更大的数据集。
• random - 生成包含随机采样值的数组
• ndarray- 将DataFrame（数据框）转换为ndarray（多维数组）
• temporal - 针对时间数据类型在 Chrono（时间库）和 Polars（数据处理库）之间进行转换
• timezones - 激活时区支持。
• strings - Extra string utilities for StringChunked:
  • string_pad - for pad_start, pad_end, zfill.
  • string_to_integer - for parse_int.
• object - Support for generic ChunkedArrays called ObjectChunked<T> (generic over T). These are downcastable from Series through the Any trait.
• 性能相关:
  • nightly - Several nightly only features such as SIMD and specialization.
  • performant - more fast paths, slower compile times.
  • bigidx - Activate this feature if you expect » $2^{32}$ rows. This allows polars to scale up way beyond that by using u64 as an index. Polars will be a bit slower with this feature activated as many data structures are less cache efficient.
  • cse - Activate common subplan elimination optimization.
• IO相关:
  • serde - Support for serde serialization and deserialization. Can be used for JSON and more serde supported serialization formats.
  • serde-lazy - Support for serde serialization and deserialization. Can be used for JSON and more serde supported serialization formats.
  • parquet - Read Apache Parquet format.
  • json - JSON serialization.
  • ipc - Arrow’s IPC format serialization.
  • decompress - Automatically infer compression of csvs and decompress them. Supported compressions:
    • gzip
    • zlib
    • zstd
• Dataframe操作:
  • dynamic_group_by - Group by based on a time window instead of predefined keys. Also activates rolling window group by operations.
  • sort_multiple - Allow sorting a dataframe on multiple columns.
  • rows - Create dataframe from rows and extract rows from dataframes. Also activates pivot and transpose operations.
  • join_asof - Join ASOF, to join on nearest keys instead of exact equality match.
  • cross_join - Create the Cartesian product of two dataframes.
  • semi_anti_join - SEMI and ANTI joins.
  • row_hash - Utility to hash dataframe rows to UInt64Chunked.
  • diagonal_concat - Diagonal concatenation thereby combining different schemas.
  • dataframe_arithmetic - Arithmetic between dataframes and other dataframes or series.
  • partition_by - Split into multiple dataframes partitioned by groups.
• Series/表达式操作:
  • is_in - Check for membership in Series.
  • zip_with - Zip two Series / ChunkedArrays.
  • round_series - round underlying float types of series.
  • repeat_by - Repeat element in an array a number of times specified by another array.
  • is_first_distinct - Check if element is first unique value.
  • is_last_distinct - Check if element is last unique value.
  • checked_arithmetic - checked arithmetic returning None on invalid operations.
  • dot_product - Dot/inner product on series and expressions.
  • concat_str - Concatenate string data in linear time.
  • reinterpret - Utility to reinterpret bits to signed/unsigned.
  • take_opt_iter - Take from a series with Iterator<Item=Option<usize>>.
  • mode - Return the most frequently occurring value(s).
  • cum_agg - cum_sum, cum_min, and cum_max, aggregations.
  • rolling_window - rolling window functions, like rolling_mean.
  • interpolate - Interpolate None values.
  • extract_jsonpath - Run jsonpath queries on StringChunked.
  • list - List utils:
    • list_gather - take sublist by multiple indices.
  • rank - Ranking algorithms.
  • moment - Kurtosis and skew statistics.
  • ewma - Exponential moving average windows.
  • abs - Get absolute values of series.
  • arange - Range operation on series.
  • product - Compute the product of a series.
  • diff - diff operation.
  • pct_change - Compute change percentages.
  • unique_counts - Count unique values in expressions.
  • log - Logarithms for series.
  • list_to_struct - Convert List to Struct data types.
  • list_count - Count elements in lists.
  • list_eval - Apply expressions over list elements.
  • cumulative_eval - Apply expressions over cumulatively increasing windows.
  • arg_where - Get indices where condition holds.
  • search_sorted - Find indices where elements should be inserted to maintain order.
  • offset_by - Add an offset to dates that take months and leap years into account.
  • trigonometry - 三角函数.
  • sign - 计算一个序列中每个元素的符号（正、负或零）。
  • propagate_nans - NaN-propagating min/max aggregations.
• Dataframe美化格式化:
  • fmt - 激活Dataframe格式化功能.