Linux command
polars 命令
文件
复制后可按需替换文件名、目录或参数。
常用示例
Read and display CSV file
polars read [file.csv]
Query with SQL
polars sql "SELECT * FROM '[file.csv]' WHERE value > 100"
Convert CSV to Parquet
polars convert [input.csv] [output.parquet]
Show schema of file
polars schema [file.parquet]
Filter and output as JSON
polars sql "SELECT name, score FROM '[data.csv]' ORDER BY score DESC LIMIT 10" -o json
Join two files
polars sql "SELECT * FROM '[a.csv]' JOIN '[b.csv]' ON a.id = b.id"
说明
polars is the command-line interface for Polars, a fast DataFrame library. It provides SQL querying and format conversion for data files without writing code. The sql command executes SQL queries directly on files. Reference files as table names in quotes within the query. Polars' query engine optimizes execution for large datasets. Supported formats include CSV, Parquet, JSON, and Arrow. The convert command transforms between formats, useful for creating optimized Parquet files from CSV sources. Polars uses Apache Arrow columnar format internally, enabling efficient processing of large datasets with minimal memory overhead. Query optimization includes predicate pushdown and projection.
参数
- -o, --output _format_
- Output format: csv, json, parquet, table.
- --delimiter _char_
- CSV delimiter character.
- --no-header
- CSV has no header row.
- -n, --limit _rows_
- Limit output rows.
- -h, --help
- Display help information.
- -V, --version
- Display version information.
FAQ
What is the polars command used for?
polars is the command-line interface for Polars, a fast DataFrame library. It provides SQL querying and format conversion for data files without writing code. The sql command executes SQL queries directly on files. Reference files as table names in quotes within the query. Polars' query engine optimizes execution for large datasets. Supported formats include CSV, Parquet, JSON, and Arrow. The convert command transforms between formats, useful for creating optimized Parquet files from CSV sources. Polars uses Apache Arrow columnar format internally, enabling efficient processing of large datasets with minimal memory overhead. Query optimization includes predicate pushdown and projection.
How do I run a basic polars example?
Run `polars read [file.csv]` in a terminal, then adjust file names, paths, flags, or remote targets for your system.
What does -o, --output _format_ do in polars?
Output format: csv, json, parquet, table.