导读:每个数据科学专业人员都必须从不同的数据源中提取、转换和加载(Extract-Transform-Load,ETL)数据。
本文将讨论如何使用Python为选定的流行数据库实现数据的ETL。对于关系数据库,选择MySQL,并将Elasticsearch作为文档数据库的例子展开。对于图形数据库,选择Neo4j。对于NoSQL,可参考此前文章中介绍的MongoDB。
作者:萨扬·穆霍帕迪亚(Sayan Mukhopadhyay)
如需转载请联系大数据(ID:hzdashuju)
01 MySQL
MySQLdb是在MySQL C接口上面开发的Python API。
1. 如何安装MySQLdb
首先,需要在计算机上安装Python MySQLdb模块。然后运行以下脚本:
#!/usr/bin/python
import MySQLdb
如果出现导入错误,则表示模块未正确安装。
以下是安装MySQL Python模块的说明:
$gunzip MySQL-python-1.2.2.tar.gz
$tar –xvf MySQL-python-1.2.2.tar
$cd MySQL-python-1.2.2
$python setup.py build
$python setup.py install
2. 数据库连接
在连接到MySQL数据库之前,请确保有以下内容。
3. INSERT操作
以下代码执行SQL INSERT语句,以便在STUDENT表中创建记录:
#!/usr/bin/python
import MySQLdb
# Open database connection
db = MySQLdb.connect("localhost","user","passwd","TEST" )
# prepare a cursor object using cursor() method
cursor = db.cursor()
# Prepare SQL query to INSERT a record into the database.
sql = """INSERT INTO STUDENT(NAME,
SUR_NAME, ROLL_NO)
VALUES ('Sayan', 'Mukhopadhyay', 1)"""
try:
# Execute the SQL command
cursor.execute(sql)
# Commit your changes in the database
db.commit()
except:
# Rollback in case there is any error
db.rollback()
# disconnect from server
db.close()
4. READ操作
以下代码从STUDENT表中提取数据并打印出来:
#!/usr/bin/python
import MySQLdb
# Open database connection
db = MySQLdb.connect("localhost","user","passwd","TEST" )
# prepare a cursor object using cursor() method
cursor = db.cursor()
# Prepare SQL query to INSERT a record into the database.
sql = "SELECT * FROM STUDENT "
try:
# Execute the SQL command
cursor.execute(sql)
# Fetch all the rows in a list of lists.
results = cursor.fetchall()
for row in results:
fname = row[0]
lname = row[1]
id = row[2]
# Now print fetched result
print "name=%s,surname=%s,id=%d" % \
(fname, lname, id )
except:
print "Error: unable to fecth data"
# disconnect from server
db.close()
5. DELETE操作
以下代码从TEST中删除id=1的一行数据:
#!/usr/bin/python
import MySQLdb
# Open database connection
db = MySQLdb.connect("localhost","test","passwd","TEST")
#prepare a cursor object using cursor() method
cursor = db.cursor()
# PrepareSQL query to DELETE required records
sql="DELETE FROM STUDENT WHERE ROLL_NO=1"
try:
#Execute the SQL command
cursor.execute(sql)
#Commit your changes in the database
db.commit()
except:
#Roll back in case there is any error
db.rollback()
#disconnect from server
db.close()
6. UPDATE操作
以下代码将lastname为Mukhopadhyay的记录更改为Mukherjee:
#!/usr/bin/python
import MySQLdb
# Open database connection
db = MySQLdb.connect("localhost","user","passwd","TEST" )
# prepare a cursor object using
cursor() method cursor = db.cursor()
# Prepare SQL query to UPDATE required records
sql = "UPDATE STUDENT SET SUR_NAME="Mukherjee"
WHERE SUR_NAME="Mukhopadhyay""
try:
# Execute the SQL command
cursor.execute(sql)
# Commit your changes in the database
db.commit()
except:
# Rollback in case there is any error
db.rollback()
# disconnect from server
db.close()
7. COMMIT操作
提交操作提供对数据库完成修改命令,并且在此操作之后,无法将其还原。
8. ROLL-BACK操作
如果不能确认对数据的修改同时想要撤回操作,可以使用roll-back()方法。
以下是通过Python访问MySQL数据的完整示例。它将提供将数据存储为CSV文件或MySQL数据库中的数据的完整描述。
import MySQLdb
import sys
out = open('Config1.txt','w')
print "Enter the Data Source Type:"
print "1. MySql"
print "2. Text"
print "3. Exit"
while(1):
data1 = sys.stdin.readline().strip()
if(int(data1) == 1):
out.write("source begin"+"\n"+"type=mysql\n")
print "Enter the ip:"
ip = sys.stdin.readline().strip()
out.write("host=" + ip + "\n")
print "Enter the database name:"
db = sys.stdin.readline().strip()
out.write("database=" + db + "\n")
print "Enter the user name:"
usr = sys.stdin.readline().strip()
out.write("user=" + usr + "\n")
print "Enter the password:"
passwd = sys.stdin.readline().strip()
out.write("password=" + passwd + "\n")
connection = MySQLdb.connect(ip, usr, passwd, db)
cursor = connection.cursor()
query = "show tables"
cursor.execute(query)
data = cursor.fetchall()
tables = []
for row in data:
for field in row:
tables.append(field.strip())
for i in range(len(tables)):
print i, tables[i]
tb = tables[int(sys.stdin.readline().strip())]
out.write("table=" + tb + "\n")
query = "describe " + tb
cursor.execute(query)
data = cursor.fetchall()
columns = []
for row in data:
columns.append(row[0].strip())
for i in range(len(columns)):
print columns[i]
print "Not index choose the exact column names seperated by coma"
cols = sys.stdin.readline().strip()
out.write("columns=" + cols + "\n")
cursor.close()
connection.close()
out.write("source end"+"\n")
print "Enter the Data Source Type:"
print "1. MySql"
print "2. Text"
print "3. Exit"
if(int(data1) == 2):
print "path of text file:"
path = sys.stdin.readline().strip()
file = open(path)
count = 0
for line in file:
print line
count = count + 1
if count > 3:
break
file.close()
out.write("source begin"+"\n"+"type=text\n")
out.write("path=" + path + "\n")
print "enter delimeter:"
dlm = sys.stdin.readline().strip()
out.write("dlm=" + dlm + "\n")
print "enter column indexes seperated by comma:"
cols = sys.stdin.readline().strip()
out.write("columns=" + cols + "\n")
out.write("source end"+"\n")
print "Enter the Data Source Type:"
print "1. MySql"
print "2. Text"
print "3. Exit"
if(int(data1) == 3):
out.close()
sys.exit()
02 Elasticsearch
Elasticsearch(ES)低级客户端提供从Python到ES REST端点的直接映射。Elasticsearch的一大优势是为数据分析提供了全栈解决方案。Elasticsearch作为数据库,有可配置前端Kibana、数据收集工具Logstash以及企业安全工具Shield。
下例具有称为cat、cluster、indices、ingest、nodes、snapshot和tasks的特征,根据任务分别转换为CatClient、ClusterClient、IndicesClient、IngestClient、NodesClient、SnapshotClient和TasksClient实例。这些实例是访问这些类及其方法的唯一方式。
你可以指定自己的连接类,可以通过提供的connection_class参数来使用。
# create connection to local host using the ThriftConnection
Es1=Elasticsearch(connection_class=ThriftConnection)
如果你想打开sniffing,那么有几个选择:
# create connection that will automatically inspect the cluster to get
# the list of active nodes. Start with nodes running on 'esnode1' and
# 'esnode2'
Es1=Elasticsearch(
['esnode1', 'esnode2'],
# sniff before doing anything
sniff_on_start=True,
# refresh nodes after a node fails to respond
sniff_on_connection_fail=True,
# and also every 30 seconds
sniffer_timeout=30
)
不同的主机可以有不同的参数,你可以为每个节点使用一个字典来指定它们。
# connect to localhost directly and another node using SSL on port 443
# and an url_prefix. Note that ``port`` needs to be an int.
Es1=Elasticsearch([
{'host':'localhost'},
{'host':'othernode','port':443,'url_prefix':'es','use_ssl':True},
])
还支持SSL客户端身份验证(有关选项的详细说明,请参阅Urllib3HttpConnection)。
Es1=Elasticsearch(
['localhost:443','other_host:443'],
# turn on SSL
use_ssl=True,
# make sure we verify SSL certificates (off by default)
verify_certs=True,
# provide a path to CA certs on disk
ca_certs='path to CA_certs',
# PEM formatted SSL client certificate
client_cert='path to clientcert.pem',
# PEM formatted SSL client key
client_key='path to clientkey.pem'
)
许多类负责处理Elasticsearch集群。这里可以通过将参数传递给Elasticsearch类来忽略正在使用的默认子类。属于客户端的每个参数都将添加到Transport、ConnectionPool和Connection上。
例如,如果你要使用定制的ConnectionSelector类,只需传入selector_class参数即可。
整个API以很高的精确度包装了原始REST API,其中包括区分调用必需参数和可选参数。这意味着代码区分了按排位的参数和关键字参数。建议读者使用关键字参数来保证所有调用的一致性和安全性。
如果Elasticsearch返回2XX,则API调用成功(并将返回响应)。否则,将引发TransportError(或更具体的子类)的实例。你可以在异常中查看其他异常和错误状态。如果你不希望引发异常,可以通过传入ignore参数忽略状态代码或状态代码列表。
from elasticsearch import Elasticsearch
es=Elasticsearch()
# ignore 400 cause by IndexAlreadyExistsException when creating an index
es.indices.create(index='test-index',ignore=400)
# ignore 404 and 400
es.indices.delete(index='test-index',ignore=[400,404])
03 Neo4j Python驱动
Neo4j支持Neo4j Python驱动,并通过二进制协议与数据库连接。它试图保持简约及Python的惯用方式。
pip install neo4j-driver
from neo4j.v1 import GraphDatabase, basic_auth
driver11 = GraphDatabase.driver("bolt://localhost", auth=basic_auth("neo4j", "neo4j"))
session11 = driver11.session()
session11.run("CREATE (a:Person {name:'Sayan',title:'Mukhopadhyay'})")
result11= session11.run("MATCH (a:Person) WHERE a.name ='Sayan' RETURN a.name AS name, a.title AS title")
for recordi n result11:
print("%s %s"% (record["title"], record["name"]))
session11.close()
04 neo4j-rest-client
neo4j-rest-client的主要目标是确保已经使用本地Neo4j的Python程序员通过python-embedded的方式也能够访问Neo4j REST服务器。因此,neo4j-rest-client API的结构与python-embedded完全同步。但是引入了一种新的结构,以达到更加Python化的风格,并通过Neo4j团队引入的新特性来增强API。
05 内存数据库
另一个重要的数据库类是内存数据库。它在RAM中存储和处理数据。因此,对数据库的操作非常快,并且数据是灵活的。SQLite是内存数据库的一个流行范例。在Python中,需要使用sqlalchemy库来操作SQLite。在第1章的Flask和Falcon示例中,展示了如何从SQLite中选择数据。以下将展示如何在SQLite中存储Pandas数据框架:
from sqlalchemy import create_engine
import sqlite3
conn = sqlite3.connect('multiplier.db')
conn.execute('''CREATE TABLE if not exists multiplier
(domain CHAR(50),
low REAL,
high REAL);''')
conn.close()
db_name = "sqlite:///" + prop + "_" + domain + str(i) + ".db"
disk_engine = create_engine(db_name)
df.to_sql('scores', disk_engine, if_exists='replace')
06 Python版本MongoDB
这部分内容请见此前的文章数据处理入门干货:MongoDB和pandas极简教程。
关于作者:Sayan Mukhopadhyay拥有超过13年的行业经验,并与瑞信、PayPal、CA Technologies、CSC和Mphasis等公司建立了联系。他对投资银行、在线支付、在线广告、IT架构和零售等领域的数据分析应用有着深刻的理解。他的专业领域是在分布式和数据驱动的环境(如实时分析、高频交易等)中,实现高性能计算。
本文摘编自《Python高级数据分析:机器学习、深度学习和NLP实例》,经出版方授权发布。