user="user", password="password", host="host", db="db") sql = text(" id, count(*) AS total FROM `test`") select_stmt = select(sql) print(con) print(select_stmt) df1 = dd.read_sql_query(sql=select_stmt, con=con, index_col="id").compute() print(df1.shape) 1. 2. 3. ...
query = "SELECT * FROM table_name" df = dd.read_sql_query(query, conn) 这里的'table_name'是要读取的SQLite表的名称,可以根据实际情况进行修改。 关闭数据库连接: 代码语言:txt 复制 conn.close() 完成以上步骤后,SQLite表的数据将被读入Dask数据框架df中。可以通过df来进行各种数据操作和分析。
通常,SQL 数据库会有一个主键或数字索引键,您可以用于此目的(例如,read_sql_table("customers", index_col="customer_id"))。示例在示例 4-4 中展示了这一点。 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 ...
I am using dask read sql function. import pandas as pd import dask.dataframe as dd import sqlalchemy as sa from sqlalchemy import MetaData, Table, Column, Integer, Float, String, DateTime metadata = MetaData() shoppingbb = Table ('SHOPPINGBB', metadata, Column('status', String(50),...
可以通过使用variable for each worker将连接数据库设置为get_worker来做到这一点。
read_csv("...") c.create_table("my_data", df) # Now execute a SQL query. The result is again dask dataframe. result = c.sql(""" SELECT my_data.name, SUM(my_data.x) FROM my_data GROUP BY my_data.name """) # Show the result print(result) # Show the result... print(...
# 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(...) ...
PySpark语法类似 SQL: from pyspark.sql import SparkSessionfrom pyspark.sql.functions import col, count, sumspark = SparkSession.builder.appName("example").getOrCreatedf = spark.read.csv('Corona_NLP_test.csv',header=True, inferSchema=True)result = df.groupBy('Location').agg( count('*').alia...
PySpark语法类似 SQL: from pyspark.sql import SparkSessionfrom pyspark.sql.functions import col, count, sumspark = SparkSession.builder.appName("example").getOrCreatedf = spark.read.csv('Corona_NLP_test.csv',header=True, inferSchema=True)result = df.groupBy('Location').agg( count('*').alia...
The reason thatread_sql_tabledoesn't accept arbitrary SQL query strings, is because it needs to be able to partition your query, so each task loads only a chunk of the whole. This is a tricky thing to do for the many dialects out there, so we rely on sqlalchemy to do the fo...