如何将pandas DataFrame升级到PostgreSQL表？

Question

我从网络资源中收集了一些数据，并将其全部存储在一个数据框中。现在，为了利用SQLAlchemy提供的强大db工具，我想将所述DataFrame转换为Table（）对象，并最终将所有数据上传到PostgreSQL表中。如果这是可行的，那么完成这项任务的可行方法是什么

Answer 1

如果您使用的是PostgreSQL 9.5或更高版本，则可以使用临时表和INSERT ... ON CONFLICT语句执行UPSERT：

import sqlalchemy as sa

# …

with engine.begin() as conn:
    # step 0.0 - create test environment
    conn.execute(sa.text("DROP TABLE IF EXISTS main_table"))
    conn.execute(
        sa.text(
            "CREATE TABLE main_table (id int primary key, txt varchar(50))"
        )
    )
    conn.execute(
        sa.text(
            "INSERT INTO main_table (id, txt) VALUES (1, 'row 1 old text')"
        )
    )
    # step 0.1 - create DataFrame to UPSERT
    df = pd.DataFrame(
        [(2, "new row 2 text"), (1, "row 1 new text")], columns=["id", "txt"]
    )
    
    # step 1 - create temporary table and upload DataFrame
    conn.execute(
        sa.text(
            "CREATE TEMPORARY TABLE temp_table (id int primary key, txt varchar(50))"
        )
    )
    df.to_sql("temp_table", conn, index=False, if_exists="append")

    # step 2 - merge temp_table into main_table
    conn.execute(
        sa.text("""\
            INSERT INTO main_table (id, txt) 
            SELECT id, txt FROM temp_table
            ON CONFLICT (id) DO
                UPDATE SET txt = EXCLUDED.txt
            """
        )
    )

    # step 3 - confirm results
    result = conn.execute(sa.text("SELECT * FROM main_table ORDER BY id")).fetchall()
    print(result)  # [(1, 'row 1 new text'), (2, 'new row 2 text')]

Answer 2

我已经多次需要它，最后我创建了一个gist for it

函数如下所示，如果是第一次持久化数据帧，它将创建表，如果表已经存在，它将更新表：

import pandas as pd
import sqlalchemy
import uuid
import os

def upsert_df(df: pd.DataFrame, table_name: str, engine: sqlalchemy.engine.Engine):
    """Implements the equivalent of pd.DataFrame.to_sql(..., if_exists='update')
    (which does not exist). Creates or updates the db records based on the
    dataframe records.
    Conflicts to determine update are based on the dataframes index.
    This will set unique keys constraint on the table equal to the index names
    1. Create a temp table from the dataframe
    2. Insert/update from temp table into table_name
    Returns: True if successful
    """

    # If the table does not exist, we should just use to_sql to create it
    if not engine.execute(
        f"""SELECT EXISTS (
            SELECT FROM information_schema.tables 
            WHERE  table_schema = 'public'
            AND    table_name   = '{table_name}');
            """
    ).first()[0]:
        df.to_sql(table_name, engine)
        return True

    # If it already exists...
    temp_table_name = f"temp_{uuid.uuid4().hex[:6]}"
    df.to_sql(temp_table_name, engine, index=True)

    index = list(df.index.names)
    index_sql_txt = ", ".join([f'"{i}"' for i in index])
    columns = list(df.columns)
    headers = index + columns
    headers_sql_txt = ", ".join(
        [f'"{i}"' for i in headers]
    )  # index1, index2, ..., column 1, col2, ...

    # col1 = exluded.col1, col2=excluded.col2
    update_column_stmt = ", ".join([f'"{col}" = EXCLUDED."{col}"' for col in columns])

    # For the ON CONFLICT clause, postgres requires that the columns have unique constraint
    query_pk = f"""
    ALTER TABLE "{table_name}" DROP CONSTRAINT IF EXISTS unique_constraint_for_upsert;
    ALTER TABLE "{table_name}" ADD CONSTRAINT unique_constraint_for_upsert UNIQUE ({index_sql_txt});
    """
    engine.execute(query_pk)

    # Compose and execute upsert query
    query_upsert = f"""
    INSERT INTO "{table_name}" ({headers_sql_txt}) 
    SELECT {headers_sql_txt} FROM "{temp_table_name}"
    ON CONFLICT ({index_sql_txt}) DO UPDATE 
    SET {update_column_stmt};
    """
    engine.execute(query_upsert)
    engine.execute(f"DROP TABLE {temp_table_name}")

    return True

Answer 3

如果您已经有一个pandas数据帧，那么可以使用df.to_sql直接通过SQLAlchemy推送数据

from sqlalchemy import create_engine
#create a connection from Postgre URI
cnxn = create_engine("postgresql+psycopg2://username:password@host:port/database")
#write dataframe to database
df.to_sql("my_table", con=cnxn, schema="myschema")