二、Polars与Excel的功能比较 1. Polars是一个开源的数据处理和分析库,它可以在Rust语言中使用,能够处理大规模的数据集,支持各种数据操作和计算,并且具有高效的性能。 2. Excel是一款微软公司开发的办公软件,主要用于表格处理、数据分析、图表制作等功能,通常用于中小规模的数据处理和分析。 三、Polars和Excel的
另外 read_excel 还支持同时读取多个 sheet,只需要给 sheet_id 传一个列表即可。 importpolarsaspl# 我们这里只有一个 sheet# 此时会返回一个字典,key 是 sheet 的名称,value 是对应的 DataFramedf_dict = pl.read_excel("girl.xlsx", sheet_id=[1])print(df_dict.__class__)# <class 'dict'># 每个...
DataFrame 是一个二维标签数据结构,可以存储具有不同数据类型的列(类似于 SQL 表或 Excel 的工作表)。 Series 是一维标签数组,可以存储任意数据类型(整数、字符串、浮点数等),所有数据必须是同一类型。 示例:创建 DataFrame 和 Series import polars as pl # 创建 Series s = pl.Series("age", [25, 32, 37...
})# note: when we read back a named table object with a total# row we expect the read to automatically omit that row as# it is *not* part of the actual table datadf.write_excel("demo.xlsx",worksheet="data",table_name="PolarsFrameTable",column_totals=True, ) # optional to specify...
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Load data from CSV, Parquet, Excel files, or a database Write Polars code that takes advantage of query optimization Generate reliable Polars code from large language models Course description Join expert Liam Brannigan to learn to harness Polars for everyday data tasks, from transforming data to...
df.write_csv("1.csv") 1. LazyFrame 使用Lazy接口可以优化查询,超过内存的数据量、提取发现类型错误。 import polars as pl # 新建LazyFrame lf = pl.scan_csv("data.csv", new_columns=["index", "id", "url"]) df = (lf.filter(pl.col("id") != 0) # 过滤数据 ...
In the previous section, you saw how Polars performs lazy queries over CSV files with scan_csv(). Polars can also handle data sources like JSON, Parquet, Avro, Excel, and various databases. You can interact with most of these file types the same way you worked with the CSV file:...
写入文件 写入csv文件 data_csv.write_csv('./data/data_write.csv') 写入excel文件 默认的浮点数为3位,可以通过float_precision参数进行设置 data_csv.write_excel('./data/data_write.xlsx',float_precision=1) 历史相关文章
While Polars excels in data transformation efficiency, it falls short in areas like data exploration and integration into machine learning pipelines, where Pandas remains superior. Polars' incompatibility with most Python data visualization and machine learning libraries, such as scikit-learn and PyTorch...