@文心快码dask.dataframe 读sql 文心快码 可以使用 dask.dataframe 的read_sql_table 或read_sql_query 方法从SQL数据库中读取数据。 使用read_sql_table 方法: 这个方法用于从SQL数据库中读取一个表的数据。 python import dask.dataframe as dd from sqlalchemy import create_engine # 创建数据库连接 engine =...
https://dask-sql.readthedocs.io/en/0.3.1/index.html 参考 https://www.dask.org/get-started https://www.appsloveworld.com/pandas/100/180/reading-an-sql-query-into-a-dask-dataframe https://docs.dask.org/en/stable/dataframe-api.html https://cloud.tencent.com/developer/ask/sof/107228030...
示例4-4. 使用 Dask DataFrame 从 SQL 读取和写入数据 from sqlite3 import connect from sqlalchemy import sql import dask.dataframe as dd #sqlite connection db_conn = "sqlite://fake_school.sql" db = connect(db_conn) col_student_num = sql.column("student_number") col_grade = sql.column("...
deffilters(row):""" For each rowinthe dask bag,only keep the rowifit meets the filter criteriaArgs:row:the rowofthe dataframeReturns:Boolean mask"""return((len(row["id"])<16)and(len(row["categories"])<200)and(len(row["title"])<4096)and(len(row["abstract"])<65535)and("cs."in...
概览 pandas.DataFrame 创建DataFrame 列表 字典 系列（Series） 列选择 列添加 列删除 pop/del 行选择...
安装Dask pip install dask Dask 示例 import dask.dataframe as dddf = dd.read_CSV('Corona_NLP_test.csv')sentiment_counts = df.groupby('Sentiment').size.computeprint(sentiment_counts) Dask 的 API 语法与 Pandas 非常相似,但不同的是,计算只有在调用compute方法时才会真正触发。
Dask’s DataFrame loading andwriting functions start withto_orread_as the prefixes. Each format has its own configuration, but in general, the first positional argument is the location of the data to be read. The location can be a wildcard path of files (e.g.,s3://test-bucket/magic/*...
# query from Snowflake using dask-snowflake connector ddf = dask_snowflake.read_snowflake(query, conn_info) # other query options (in the case you are not querying from Snowflake) # ddf = dask.dataframe.read_csv(...) # ddf = dask.dataframe.read_sql(...) ...
()# Load the data and register it in the context# This will give the table a name, that we can use in queriesdf=dd.read_csv("...")c.create_table("my_data",df)# Now execute a SQL query. The result is again dask dataframe.result=c.sql("""SELECTmy_data.name,SUM(my_data.x)...
read_sql, read_sql_query, read_sql_table, read_table, repartition, to_bag, to_csv, to_datetime, to_hdf, to_json, to_numeric, to_orc, to_parquet, to_records, to_sql, to_timedelta, ) _dask_expr_enabled() import dask.dataframe._dtypes import dask.dataframe._pyarrow_compat from dask...