I have a user-defined function that uses pymysql to connect to a mysql database and then it interrogates the database and reads the results into a Pandas dataframe.
import pandas as pd
import pymysql
import getpass
def myGetData(myQuery):
myServer = 'xxx.xxx.xxx.xxx'
myUser = input("Enter MySQL database username: ")
myPwd = getpass.getpass("Enter password: ")
myConnection = pymysql.connect(host=myServer,user=myUser,password=myPwd)
myTempDF = pd.io.sql.read_sql(myQuery, con=myConnection)
myConnection.close()
return myTempDF
myDF = myGetData("SELECT * FROM `myDB`.`myTable`")
I have written code to catch exceptions arising from pymysql.connect() although I've not shown it here for clarity. I also want to be able to catch any exceptions that might arise from read_sql(). Where can I find a list of exceptions that might be raised? It's not in the Pandas documentation (http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.read_sql.html) and I can't find any hints online. I could just catch all exceptions but that seems to be generally frowned upon by the Python community. How should I catch exceptions raised by read_sql()?
EDIT
I've done some more work on this and it seems that even when I know what error is being generated, it's not straight-forward to catch the exception. So, for example, in the code given above, if I enter the username and/or password incorrectly, an operational error is generated. The final line or the error report reads something like:
OperationalError: (1045, "Access denied for user 'yyy'@'xxx.xxx.xxx.xxx' (using password: YES)")
I've been able to catch this error using:
try:
phjConnection = pymysql.connect(host=phjServer, user=phjUser, password=phjPwd)
except pymysql.OperationalError as e:
print("\nAn OperationalError occurred. Error number {0}: {1}.".format(e.args[0],e.args[1]))
That works fine (although discovering that the OperationalError needed to be caught using pymysql.OperationalError was by chance).
Now, in the next part of the function, Pandas function real_sql() uses the connection created above to run a SQL query. If I include a purposely incorrect query that has an incorrect table name, then another OperationalError occurs followed by a DatabaseError:
OperationalError: (1142, "SELECT command denied to user 'yyy'@'xxx.xxx.xxx.xxx' for table 'table'")
During handling of the above exception, another exception occurred:
DatabaseError: Execution failed on sql 'SELECT * FROM `db`.`table`': (1142, "SELECT command denied to user 'yyy'@'xxx.xxx.xxx.xxx' for table 'table'")
But I am now completely mystified as to how I catch this second OperationalError. The pymysql.OperationalError used previously doesn't work. I've tried almost everything I can think of and still can't catch the error. Shouldn't the error message be a little more informative about how the error was generated and how it can be caught? Clearly, I'm missing something obvious but I just can't find the solution. Any suggestions would be appreciated.
EDIT 2
In response to the comment, I am now catching exceptions as follows:
import pandas as pd
import pymysql
import getpass
def myGetData(myQuery):
myServer = 'xxx.xxx.xxx.xxx'
myUser = input("Enter MySQL database username: ")
myPwd = getpass.getpass("Enter password: ")
try:
myConnection = pymysql.connect(host=myServer,user=myUser,password=myPwd)
except pymysql.OperationalError as e:
# Catching this exception works fine if, for example,
# I enter the wrong username and password
print("\nAn OperationalError occurred. Error number {0}: {1}.".format(e.args[0],e.args[1]))
try:
myTempDF = pd.io.sql.read_sql(myQuery, con=myConnection)
except pymysql.OperationalError as e:
# However, this error isn't picked up following an incorrect
# SQL query despite the error message saying that an
# OperationalError has occurred.
# Many variations on this theme have been tried but failed.
print("\nAn error occurred. Error number {0}: {1}.".format(e.args[0],e.args[1]))
myConnection.close()
return myTempDF
myDF = myGetData("SELECT * FROM `myDB`.`myTable`")
read_sql. Read SQL query or database table into a DataFrame. This function is a convenience wrapper around read_sql_table and read_sql_query (for backward compatibility).
Connect to the MSSQL server by using the server name and database name using pdb. connect(). And then read SQL query using read_sql() into the pandas data frame and print the data.
pandasql allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pandasql seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.
Good question, note, read_sql
is a wrapper around 'read_sql_table and read_sql_query. Reading through the source, a ValueError
is consistently thrown inside the parent and the helper functions. So you can safely catch a ValueError
and handle appropriately. (Do have a look at the source)
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