pandas.read_sql is extremely slow on python 3 kernel compared to python 2









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I have a simple parameterized select query hitting an Oracle database via pyodbc connection and fetching data in a dataframe via pandas.read_sql.
The code is super efficient and fast in Python 2 kernel whereas, extremely slow in Python 3.



Following is the code:



import pandas
import pyodbc
import time

connection = pyodbc.connect('dsn=oracle;userid=userid;pwd=password')
sql = """
select * from order_table
where
order_key = ?
"""
start_time = time.time()
dataframe = pandas.read_sql(sql=sql, con=connection, params=['key-1'])

print(time.time()-start_time)


Python 2 execution time:
0.193000078201



Python 3 execution time:
53.687000036239624










share|improve this question























  • Which versions of pandas and pypyodbc are you using?
    – Xukrao
    Nov 11 at 18:05










  • Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
    – Jon Clements
    Nov 11 at 18:27











  • Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
    – nikhil
    Nov 11 at 19:27










  • pyodbc version 4.0.24, pandas version 0.23.4
    – nikhil
    Nov 11 at 19:28














up vote
2
down vote

favorite
1












I have a simple parameterized select query hitting an Oracle database via pyodbc connection and fetching data in a dataframe via pandas.read_sql.
The code is super efficient and fast in Python 2 kernel whereas, extremely slow in Python 3.



Following is the code:



import pandas
import pyodbc
import time

connection = pyodbc.connect('dsn=oracle;userid=userid;pwd=password')
sql = """
select * from order_table
where
order_key = ?
"""
start_time = time.time()
dataframe = pandas.read_sql(sql=sql, con=connection, params=['key-1'])

print(time.time()-start_time)


Python 2 execution time:
0.193000078201



Python 3 execution time:
53.687000036239624










share|improve this question























  • Which versions of pandas and pypyodbc are you using?
    – Xukrao
    Nov 11 at 18:05










  • Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
    – Jon Clements
    Nov 11 at 18:27











  • Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
    – nikhil
    Nov 11 at 19:27










  • pyodbc version 4.0.24, pandas version 0.23.4
    – nikhil
    Nov 11 at 19:28












up vote
2
down vote

favorite
1









up vote
2
down vote

favorite
1






1





I have a simple parameterized select query hitting an Oracle database via pyodbc connection and fetching data in a dataframe via pandas.read_sql.
The code is super efficient and fast in Python 2 kernel whereas, extremely slow in Python 3.



Following is the code:



import pandas
import pyodbc
import time

connection = pyodbc.connect('dsn=oracle;userid=userid;pwd=password')
sql = """
select * from order_table
where
order_key = ?
"""
start_time = time.time()
dataframe = pandas.read_sql(sql=sql, con=connection, params=['key-1'])

print(time.time()-start_time)


Python 2 execution time:
0.193000078201



Python 3 execution time:
53.687000036239624










share|improve this question















I have a simple parameterized select query hitting an Oracle database via pyodbc connection and fetching data in a dataframe via pandas.read_sql.
The code is super efficient and fast in Python 2 kernel whereas, extremely slow in Python 3.



Following is the code:



import pandas
import pyodbc
import time

connection = pyodbc.connect('dsn=oracle;userid=userid;pwd=password')
sql = """
select * from order_table
where
order_key = ?
"""
start_time = time.time()
dataframe = pandas.read_sql(sql=sql, con=connection, params=['key-1'])

print(time.time()-start_time)


Python 2 execution time:
0.193000078201



Python 3 execution time:
53.687000036239624







python python-3.x python-2.7 pandas pandasql






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 11 at 17:55









eyllanesc

70.8k93053




70.8k93053










asked Nov 11 at 17:41









nikhil

112




112











  • Which versions of pandas and pypyodbc are you using?
    – Xukrao
    Nov 11 at 18:05










  • Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
    – Jon Clements
    Nov 11 at 18:27











  • Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
    – nikhil
    Nov 11 at 19:27










  • pyodbc version 4.0.24, pandas version 0.23.4
    – nikhil
    Nov 11 at 19:28
















  • Which versions of pandas and pypyodbc are you using?
    – Xukrao
    Nov 11 at 18:05










  • Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
    – Jon Clements
    Nov 11 at 18:27











  • Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
    – nikhil
    Nov 11 at 19:27










  • pyodbc version 4.0.24, pandas version 0.23.4
    – nikhil
    Nov 11 at 19:28















Which versions of pandas and pypyodbc are you using?
– Xukrao
Nov 11 at 18:05




Which versions of pandas and pypyodbc are you using?
– Xukrao
Nov 11 at 18:05












Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
– Jon Clements
Nov 11 at 18:27





Is the environment (except the Python version) exactly the same... eg - you're accessing the same database with the same schema, searching for the same key with the DB at about the same workload, checked there hasn't been any transaction locks preventing your read occurring immediately etc... etc...?
– Jon Clements
Nov 11 at 18:27













Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
– nikhil
Nov 11 at 19:27




Thank you for your reply. Everything remains the same. In the Jupyter Notebook, I go to "Kernel" => "Change Kernel" => "Python 3/2".
– nikhil
Nov 11 at 19:27












pyodbc version 4.0.24, pandas version 0.23.4
– nikhil
Nov 11 at 19:28




pyodbc version 4.0.24, pandas version 0.23.4
– nikhil
Nov 11 at 19:28

















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