如何将SQL查询结果转换为PANDAS数据结构DataFrame？
简单来说，我想对我的SQL数据库运行查询并将返回的数据存储为Pandas数据结构。
我正在阅读有关Pandas的文档，但是在识别查询的返回类型时遇到了问题。
我试图打印查询结果，但没有得到任何有用的信息。查询代码如下：
from sqlalchemy import create_engine
engine2 = create_engine('mysql://THE DATABASE I AM ACCESSING')
connection2 = engine2.connect()
dataid = 1022
resoverall = connection2.execute("
SELECT
sum(BLABLA) AS BLA,
sum(BLABLABLA2) AS BLABLABLA2,
sum(SOME_INT) AS SOME_INT,
sum(SOME_INT2) AS SOME_INT2,
100*sum(SOME_INT2)/sum(SOME_INT) AS ctr,
sum(SOME_INT2)/sum(SOME_INT) AS cpc
FROM daily_report_cooked
WHERE campaign_id = '%s'", %dataid)
如代码所示，我想了解变量”resoverall”的格式/数据类型是什么，以及如何将其与PANDAS数据结构一起使用。
最佳办法
这是完成任务的最短代码：
from pandas import DataFrame
df = DataFrame(resoverall.fetchall())
df.columns = resoverall.keys()
次佳办法
如下所述， Pandas 现在可以使用SQLAlchemy进行数据库的读取(read_sql)和插入(to_sql)。以下方法有效：
import pandas as pd
df = pd.read_sql(sql, cnxn) # 令 cnxn = connection2老版的做法：
import pyodbc
import pandas.io.sql as psql
cnxn = pyodbc.connect(connection_info)
cursor = cnxn.cursor()
sql = "SELECT * FROM TABLE"
df = psql.frame_query(sql, cnxn)
cnxn.close()第三种办法
SQLAlchemy之外，如果需要使用pyodbc，MySQLdb或psychopg2，像下面这样一个简单的函数往往可以满足我的需求：
import decimal
import pydobc
import numpy as np
import pandas
cnn, cur = myConnectToDBfunction()
cmd = "SELECT * FROM myTable"
cur.execute(cmd)
dataframe = __processCursor(cur, dataframe=True)
def __processCursor(cur, dataframe=False, index=None):
'''
Processes a database cursor with data on it into either
a structured numpy array or a pandas dataframe.
input:
cur - a pyodbc cursor that has just received data
dataframe - bool. if false, a numpy record array is returned
if true, return a pandas dataframe
index - list of column(s) to use as index in a pandas dataframe
'''
datatypes = []
colinfo = cur.description
for col in colinfo:
if col[1] == unicode:
datatypes.append((col[0], 'U%d' % col[3]))
elif col[1] == str:
datatypes.append((col[0], 'S%d' % col[3]))
elif col[1] in [float, decimal.Decimal]:
datatypes.append((col[0], 'f4'))
elif col[1] == datetime.datetime:
datatypes.append((col[0], 'O4'))
elif col[1] == int:
datatypes.append((col[0], 'i4'))
data = []
for row in cur:
data.append(tuple(row))
array = np.array(data, dtype=datatypes)
if dataframe:
output = pandas.DataFrame.from_records(array)
if index is not None:
output = output.set_index(index)
else:
output = array
return output
第四种办法
对于mysql:
import pandas as pd
import mysql.connector
# Setup MySQL connection
db = mysql.connector.connect(
host="<IP>", # your host, usually localhost
user="<USER>", # your username
password="<PASS>", # your password
database="<DATABASE>" # name of the data base
)
# You must create a Cursor object. It will let you execute all the queries you need
cur = db.cursor()
# Use all the SQL you like
cur.execute("SELECT * FROM <TABLE>")
# Put it all to a data frame
sql_data = pd.DataFrame(cur.fetchall())
sql_data.columns = cur.column_names
# Close the session
db.close()
# Show the data
print(sql_data.head())
第五种办法
对于SQL Server。
import pandas as pd
from sqlalchemy import create_engine
def getData():
# Parameters
ServerName = "my_server"
Database = "my_db"
UserPwd = "user:pwd"
Driver = "driver=SQL Server Native Client 11.0"
# Create the connection
engine = create_engine('mssql+pyodbc://' + UserPwd + '@' + ServerName + '/' + Database + "?" + Driver)
sql = "select * from mytable"
df = pd.read_sql(sql, engine)
return df
df2 = getData()
print(df2)
参考资料