I'm trying to connect to an SQL database and, within a loop, create separate dataframes for each different instance of Id, containing all the data related to that Id. I've tried a number of ways, without any success so far. I'm pretty new to all of this, so I'm probably making some rookie mistakes.
Attempt 1:
import pandas as pd
import pyodbc
conn = pyodbc.connect('Driver={SQL Server};'
'Server=Server_name;'
'Database=Database;'
'UID=Username;'
'PWD=password;'
'Trusted_Connection=yes;')
Name = ['HR','ZA','PR','FW']
for x in Name:
SQL = '''
SELECT *
FROM Database
WHERE Id = {x}'''.format(x = x)
cursor = conn.cursor()
cursor.execute(SQL)
df = pd.read_sql_query(SQL)
On this code, I get an 'invalid column name' programming error on the first Name 'HL'.
Attempt 2:
import pandas as pd
import pyodbc
conn = pyodbc.connect('Driver={SQL Server};'
'Server=Server_name;'
'Database=Database;'
'UID=Username;'
'PWD=password;'
'Trusted_Connection=yes;')
SQL = '''
SELECT *
FROM Database
conn.autocommit = True
cursor.execute(SQL)
for [Id] in cursor:
df = pd.Dataframe(SQL,conn)
On this code, I get a 'ValueError: too many values to unpack (expected 1)' - on the for statement.
I want to put a lot more code in the for loop so I need it to be set up to work through each Id. I hope that makes sense. Any guidance would be greatly appreciated. Thanks
UPDATE:
Thanks for all comments/answers. For some reason I just couldn't get it to work in either of the formats above so I took it back to where I started from now I understand how to include the syntax for the loop variable. The following now works:
import pandas as pd
import pyodbc
conn = pyodbc.connect('Driver={SQL Server};'
'Server=Server_name;'
'Database=Database;'
'UID=Username;'
'PWD=password;'
'Trusted_Connection=yes;')
Name = ['HR','ZA','PR','FW']
for x in Name:
SQL = pd.read_sql_query(
'''
SELECT *
FROM Database_table
WHERE Id = '{x}'
'''.format(x = x), conn)
df = pd.DataFrame(SQL)
I think that if you try a variation on your first attempt like:
for x in Name:
SQL = '''
SELECT *
FROM Database
WHERE Id = ?'''
cursor = conn.cursor()
cursor.execute(SQL)
df = pd.read_sql_query(SQL, params={x})
It should probably work :)
I'm trying to take a csv file and import it into sql server express using Python. I've googled the problem and the solution I'm seeing that seems to be working for everyone else is:
import pymssql
import csv
conn = pymssql.connect(
server="servername",
user='username',
password='password',
database='db'
)
cursor = conn.cursor()
with open('new_csv_file.csv', 'r') as f:
reader = csv.reader(f)
columns = next(reader)
query = 'insert into MyTable({0}) values ({1})'
query = query.format(','.join(columns), ','.join('?' * len(columns)))
cursor = conn.cursor()
for data in reader:
values = map((lambda x: x.strip()), data) # No need for the quote
print(tuple(values))
cursor.execute(query, tuple(values))
cursor.commit()
conn.commit()
cursor.close()
print("Done")
conn.close()
I've confirmed the code works it's just the "execute()" part doesn't.
query is ok, but "values" is giving me the following error:
(102, b"Incorrect syntax near 'Year'.DB-Lib error message 20018, severity 15:\nGeneral SQL Server error: Check messages from the SQL Server\n")
Where 'Year' is the column I'm trying to fit the data in.
Thanks.
OK I found a work-around using sqlalchemy and pandas
import sqlalchemy
print('Creating Engine')
engine = sqlalchemy.create_engine('mssql+pymssql://username:password#localhost/db')
print('Reading Table')
df = pd.read_sql_table("table_name",engine)
print(df)
df_new = pd.read_csv('table_name_new.csv'%)
df_new.to_sql(name="table_name",
con = engine,
if_exists='replace'
)
df_test = pd.read_sql_table("table_name",engine)
If someone did find out why my code doesn't work I'm still interested. I don't know if it's a Python 3 issue (I'm using Python 3).
Cheers
I am trying to execute the following script. but I don't get neither the desired results nor a error message ,and I can't figure out where I'm doing wrong.
import pyodbc
cnxn = pyodbc.connect("Driver={SQL Server Native Client 11.0};"
"Server=mySRVERNAME;"
"Database=MYDB;"
"uid=sa;pwd=MYPWD;"
"Trusted_Connection=yes;")
cursor = cnxn.cursor()
cursor.execute('select DISTINCT firstname,lastname,coalesce(middlename,\' \') as middlename from Person.Person')
for row in cursor:
print('row = %r' % (row,))
any ideas ? any help is appreciated :)
You have to use a fetch method along with cursor. For Example
for row in cursor.fetchall():
print('row = %r' % (row,))
EDIT :
The fetchall function returns all remaining rows in a list.
If there are no rows, an empty list is returned.
If there are a lot of rows, *this will use a lot of memory.*
Unread rows are stored by the database driver in a compact format and are often sent in batches from the database server.
Reading in only the rows you need at one time will save a lot of memory.
If we are going to process the rows one at a time, we can use the cursor itself as an interator
Moreover we can simplify it since cursor.execute() always returns a cursor :
for row in cursor.execute("select bla, anotherbla from blabla"):
print row.bla, row.anotherbla
Documentation
I found this information useful to retrieve data from SQL database to python as a data frame.
import pandas as pd
import pymssql
con = pymssql.connect(server='use-et-aiml-cloudforte-aiops- db.database.windows.net',user='login_username',password='login_password',database='database_name')
cursor = con.cursor()
query = "SELECT * FROM <TABLE_NAME>"
cursor.execute(query)
df = pd.read_sql(query, con)
con.close()
df
import mysql.connector as mc
connection creation
conn = mc.connect(host='localhost', user='root', passwd='password')
print(conn)
#create cursor object
cur = conn.cursor()
print(cur)
cur.execute('show databases')
for i in cur:
print(i)
query = "Select * from employee_performance.employ_mod_recent"
emp_data = pd.read_sql(query, conn)
emp_data
I have some Python code the selects data from Oracle spatial and inserts into Spatialite. My problem is that the cursor contains the geometry in binary and I can’t figure out how to read the binary into the Spatialite insert statement. Just to added this all works if I use WKT but some of the geometries are too long hence the reason for the binary format.
Can anyone help please?
# Import system modules
import cx_Oracle
from pyspatialite import dbapi2 as sl_db
def db_connect():
# Build connect from TNS names
o_db = cx_Oracle.connect("xxxxx", "xxxxx", "xxxxx_gl_dev")
cursor = o_db.cursor()
return cursor
def db_lookup(cursor):
# Select records
sql = "SELECT sdo_util.to_wkbgeometry(a.shape), a.objectid FROM span a WHERE a.objectid = 1382372"
cursor.execute(sql)
row = cursor.fetchall()
return row
def db_insert(row):
# Insert Rows in new spatailite table
database_name = 'C:\\Temp\\MYDATABASE.sqlite'
db_connection = sl_db.connect(database_name)
db_cursor = db_connection.cursor()
sql = 'INSERT INTO "SPAN_OFL" ("geometry", "OBJECTID") Values GeomFromWKB(?,27700),?);'
db_cursor.executemany(sql, row)
db_connection.commit()
db_connection.close()
# main code
cursor = db_connect()
row = db_lookup(cursor)
db_insert(row)
Any help on this problem will be greatly appreciated.
So basically I want to run a query to my SQL database and store the returned data as Pandas data structure.
I have attached code for query.
I am reading the documentation on Pandas, but I have problem to identify the return type of my query.
I tried to print the query result, but it doesn't give any useful information.
Thanks!!!!
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
)
So I sort of want to understand what's the format/datatype of my variable "resoverall" and how to put it with PANDAS data structure.
Here's the shortest code that will do the job:
from pandas import DataFrame
df = DataFrame(resoverall.fetchall())
df.columns = resoverall.keys()
You can go fancier and parse the types as in Paul's answer.
Edit: Mar. 2015
As noted below, pandas now uses SQLAlchemy to both read from (read_sql) and insert into (to_sql) a database. The following should work
import pandas as pd
df = pd.read_sql(sql, cnxn)
Previous answer:
Via mikebmassey from a similar question
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()
If you are using SQLAlchemy's ORM rather than the expression language, you might find yourself wanting to convert an object of type sqlalchemy.orm.query.Query to a Pandas data frame.
The cleanest approach is to get the generated SQL from the query's statement attribute, and then execute it with pandas's read_sql() method. E.g., starting with a Query object called query:
df = pd.read_sql(query.statement, query.session.bind)
Edit 2014-09-30:
pandas now has a read_sql function. You definitely want to use that instead.
Original answer:
I can't help you with SQLAlchemy -- I always use pyodbc, MySQLdb, or psychopg2 as needed. But when doing so, a function as simple as the one below tends to suit my needs:
import decimal
import pyodbc #just corrected a typo here
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
1. Using MySQL-connector-python
# pip install mysql-connector-python
import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
host = 'host',
user = 'username',
passwd = 'pass',
database = 'db_name'
)
query = 'select * from table_name'
df = pd.read_sql(query, con = mydb)
print(df)
2. Using SQLAlchemy
# pip install pymysql
# pip install sqlalchemy
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine('mysql+pymysql://username:password#localhost:3306/db_name')
query = '''
select * from table_name
'''
df = pd.read_sql_query(query, engine)
print(df)
MySQL Connector
For those that works with the mysql connector you can use this code as a start. (Thanks to #Daniel Velkov)
Used refs:
Querying Data Using Connector/Python
Connecting to MYSQL with Python in 3 steps
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())
Here's the code I use. Hope this helps.
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)
This is a short and crisp answer to your problem:
from __future__ import print_function
import MySQLdb
import numpy as np
import pandas as pd
import xlrd
# Connecting to MySQL Database
connection = MySQLdb.connect(
host="hostname",
port=0000,
user="userID",
passwd="password",
db="table_documents",
charset='utf8'
)
print(connection)
#getting data from database into a dataframe
sql_for_df = 'select * from tabledata'
df_from_database = pd.read_sql(sql_for_df , connection)
Like Nathan, I often want to dump the results of a sqlalchemy or sqlsoup Query into a Pandas data frame. My own solution for this is:
query = session.query(tbl.Field1, tbl.Field2)
DataFrame(query.all(), columns=[column['name'] for column in query.column_descriptions])
resoverall is a sqlalchemy ResultProxy object. You can read more about it in the sqlalchemy docs, the latter explains basic usage of working with Engines and Connections. Important here is that resoverall is dict like.
Pandas likes dict like objects to create its data structures, see the online docs
Good luck with sqlalchemy and pandas.
Simply use pandas and pyodbc together. You'll have to modify your connection string (connstr) according to your database specifications.
import pyodbc
import pandas as pd
# MSSQL Connection String Example
connstr = "Server=myServerAddress;Database=myDB;User Id=myUsername;Password=myPass;"
# Query Database and Create DataFrame Using Results
df = pd.read_sql("select * from myTable", pyodbc.connect(connstr))
I've used pyodbc with several enterprise databases (e.g. SQL Server, MySQL, MariaDB, IBM).
This question is old, but I wanted to add my two-cents. I read the question as " I want to run a query to my [my]SQL database and store the returned data as Pandas data structure [DataFrame]."
From the code it looks like you mean mysql database and assume you mean pandas DataFrame.
import MySQLdb as mdb
import pandas.io.sql as sql
from pandas import *
conn = mdb.connect('<server>','<user>','<pass>','<db>');
df = sql.read_frame('<query>', conn)
For example,
conn = mdb.connect('localhost','myname','mypass','testdb');
df = sql.read_frame('select * from testTable', conn)
This will import all rows of testTable into a DataFrame.
Long time from last post but maybe it helps someone...
Shorted way than Paul H:
my_dic = session.query(query.all())
my_df = pandas.DataFrame.from_dict(my_dic)
Here is mine. Just in case if you are using "pymysql":
import pymysql
from pandas import DataFrame
host = 'localhost'
port = 3306
user = 'yourUserName'
passwd = 'yourPassword'
db = 'yourDatabase'
cnx = pymysql.connect(host=host, port=port, user=user, passwd=passwd, db=db)
cur = cnx.cursor()
query = """ SELECT * FROM yourTable LIMIT 10"""
cur.execute(query)
field_names = [i[0] for i in cur.description]
get_data = [xx for xx in cur]
cur.close()
cnx.close()
df = DataFrame(get_data)
df.columns = field_names
pandas.io.sql.write_frame is DEPRECATED.
https://pandas.pydata.org/pandas-docs/version/0.15.2/generated/pandas.io.sql.write_frame.html
Should change to use pandas.DataFrame.to_sql
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html
There is another solution.
PYODBC to Pandas - DataFrame not working - Shape of passed values is (x,y), indices imply (w,z)
As of Pandas 0.12 (I believe) you can do:
import pandas
import pyodbc
sql = 'select * from table'
cnn = pyodbc.connect(...)
data = pandas.read_sql(sql, cnn)
Prior to 0.12, you could do:
import pandas
from pandas.io.sql import read_frame
import pyodbc
sql = 'select * from table'
cnn = pyodbc.connect(...)
data = read_frame(sql, cnn)
best way I do this
db.execute(query) where db=db_class() #database class
mydata=[x for x in db.fetchall()]
df=pd.DataFrame(data=mydata)
If the result type is ResultSet, you should convert it to dictionary first. Then the DataFrame columns will be collected automatically.
This works on my case:
df = pd.DataFrame([dict(r) for r in resoverall])
Here is a simple solution I like:
Put your DB connection info in a YAML file in a secure location (do not version it in the code repo).
---
host: 'hostname'
port: port_number_integer
database: 'databasename'
user: 'username'
password: 'password'
Then load the conf in a dictionary, open the db connection and load the result set of the SQL query in a data frame:
import yaml
import pymysql
import pandas as pd
db_conf_path = '/path/to/db-conf.yaml'
# Load DB conf
with open(db_conf_path) as db_conf_file:
db_conf = yaml.safe_load(db_conf_file)
# Connect to the DB
db_connection = pymysql.connect(**db_conf)
# Load the data into a DF
query = '''
SELECT *
FROM my_table
LIMIT 10
'''
df = pd.read_sql(query, con=db_connection)