Python ——熊猫和类似 mysql 的数据库

As Wes says, io/sql's read_sql will do it, once you've gotten a database connection using a DBI compatible library. We can look at two short examples using the MySQLdb and cx_Oracle libraries to connect to Oracle and MySQL and query their data dictionaries. Here is the example for cx_Oracle:

import pandas as pd
import cx_Oracle


ora_conn = cx_Oracle.connect('your_connection_string')
df_ora = pd.read_sql('select * from user_objects', con=ora_conn)
print 'loaded dataframe from Oracle. # Records: ', len(df_ora)
ora_conn.close()

And here is the equivalent example for MySQLdb:

import MySQLdb
mysql_cn= MySQLdb.connect(host='myhost',
port=3306,user='myusername', passwd='mypassword',
db='information_schema')
df_mysql = pd.read_sql('select * from VIEWS;', con=mysql_cn)
print 'loaded dataframe from MySQL. records:', len(df_mysql)
mysql_cn.close()

The same syntax works for Ms SQL server using podbc also.

import pyodbc
import pandas.io.sql as psql


cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=servername;DATABASE=mydb;UID=username;PWD=password')
cursor = cnxn.cursor()
sql = ("""select * from mytable""")


df = psql.frame_query(sql, cnxn)
cnxn.close()

For Sybase the following works (with http://python-sybase.sourceforge.net)

import pandas.io.sql as psql
import Sybase


df = psql.frame_query("<Query>", con=Sybase.connect("<dsn>", "<user>", "<pwd>"))

For the record, here is an example using a sqlite database:

import pandas as pd
import sqlite3


with sqlite3.connect("whatever.sqlite") as con:
sql = "SELECT * FROM table_name"
df = pd.read_sql_query(sql, con)
print df.shape

And this is how you connect to PostgreSQL using psycopg2 driver (install with "apt-get install python-psycopg2" if you're on Debian Linux derivative OS).

import pandas.io.sql as psql
import psycopg2


conn = psycopg2.connect("dbname='datawarehouse' user='user1' host='localhost' password='uberdba'")


q = """select month_idx, sum(payment) from bi_some_table"""


df3 = psql.frame_query(q, conn)

MySQL example:

import MySQLdb as db
from pandas import DataFrame
from pandas.io.sql import frame_query


database = db.connect('localhost','username','password','database')
data     = frame_query("SELECT * FROM data", database)

I prefer to create queries with SQLAlchemy, and then make a DataFrame from it. SQLAlchemy makes it easier to combine SQL conditions Pythonically if you intend to mix and match things over and over.

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Table
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from pandas import DataFrame
import datetime


# We are connecting to an existing service
engine = create_engine('dialect://user:pwd@host:port/db', echo=False)
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base()


# And we want to query an existing table
tablename = Table('tablename',
Base.metadata,
autoload=True,
autoload_with=engine,
schema='ownername')


# These are the "Where" parameters, but I could as easily
# create joins and limit results
us = tablename.c.country_code.in_(['US','MX'])
dc = tablename.c.locn_name.like('%DC%')
dt = tablename.c.arr_date >= datetime.date.today() # Give me convenience or...


q = session.query(tablename).\
filter(us & dc & dt) # That's where the magic happens!!!


def querydb(query):
"""
Function to execute query and return DataFrame.
"""
df = DataFrame(query.all());
df.columns = [x['name'] for x in query.column_descriptions]
return df


querydb(q)

For recent readers of this question: pandas have the following warning in their docs for version 14.0:

Warning: Some of the existing functions or function aliases have been deprecated and will be removed in future versions. This includes: tquery, uquery, read_frame, frame_query, write_frame.

And:

Warning: The support for the ‘mysql’ flavor when using DBAPI connection objects has been deprecated. MySQL will be further supported with SQLAlchemy engines (GH6900).

This makes many of the answers here outdated. You should use sqlalchemy:

from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('dialect://user:pass@host:port/schema', echo=False)
f = pd.read_sql_query('SELECT * FROM mytable', engine, index_col = 'ID')

pandas.io.sql.frame_query is deprecated. Use pandas.read_sql instead.

import the module

import pandas as pd
import oursql

connect

conn=oursql.connect(host="localhost",user="me",passwd="mypassword",db="classicmodels")
sql="Select customerName, city,country from customers order by customerName,country,city"
df_mysql = pd.read_sql(sql,conn)
print df_mysql

That works just fine and using pandas.io.sql frame_works (with the deprecation warning). Database used is the sample database from mysql tutorial.

This should work just fine.

import MySQLdb as mdb
import pandas as pd
con = mdb.connect(‘127.0.0.1’, ‘root’, ‘password’, ‘database_name’);
with con:
cur = con.cursor()
cur.execute(“select random_number_one, random_number_two, random_number_three from randomness.a_random_table”)
rows = cur.fetchall()
df = pd.DataFrame( [[ij for ij in i] for i in rows] )
df.rename(columns={0: ‘Random Number One’, 1: ‘Random Number Two’, 2: ‘Random Number Three’}, inplace=True);
print(df.head(20))

This helped for me for connecting to AWS MYSQL(RDS) from python 3.x based lambda function and loading into a pandas DataFrame

import json
import boto3
import pymysql
import pandas as pd
user = 'username'
password = 'XXXXXXX'
client = boto3.client('rds')
def lambda_handler(event, context):
conn = pymysql.connect(host='xxx.xxxxus-west-2.rds.amazonaws.com', port=3306, user=user, passwd=password, db='database name', connect_timeout=5)
df= pd.read_sql('select * from TableName limit 10',con=conn)
print(df)
# TODO implement
#return {
#    'statusCode': 200,
#    'df': df
#}

For Postgres users

import psycopg2
import pandas as pd


conn = psycopg2.connect("database='datawarehouse' user='user1' host='localhost' password='uberdba'")


customers = 'select * from customers'


customers_df = pd.read_sql(customers,conn)


customers_df