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I'm trying to built a Python model within MS SQL SERVER 2017. I've attempted to use some tutorials but the result was far from expected. I'm wondering what's wrong with this script (loading SQL Table into pandas dataframe):


EXEC sp_execute_external_script
dataset = pandas.DataFrame(input_data)  
@input_data_1 = N'Select * FROM dbo.Rests_GO'


The error message is:

STDOUT message(s) from external script: 

Express Edition will continue to be enforced.
SqlSatelliteCall function failed. Please see the console output for more information.
Traceback (most recent call last):
  File "C:\Program Files\Microsoft SQL 
Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\lib\site-packages\revoscalepy\computecontext\RxInSqlServer.py", line 406, in 
    rx_native_call("SqlSatelliteCall", params)
  File "C:\Program Files\Microsoft SQL 
Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\lib\site-packages\revoscalepy\RxSerializable.py", line 291, in rx_native_call
ret = px_call(functionname, params)
RuntimeError: revoscalepy function failed.

I'll be glad to accept any help.

You're using Express Edition right? See this: https://docs.microsoft.com/en-us/sql/sql-server/editions-and-components-of-sql-server-2017?view=sql-server-2017. There's nothing wrong with your code though you should include the input data to be clear as below

use AdventureWorksDW2014 go

EXEC sp_execute_external_script @language =N'Python', @script= N' import pandas as pd from pandas import DataFrame OutputDataSet = pd.DataFrame(InputDataSet.describe()) ', @input_data_1 = N'SELECT CAST(TotalProductCost AS float) , CAST(UnitPrice AS Float) , CAST(OrderQuantity AS FLOAT) FROM FactInternetSales' with result sets (( TotalProductCost float , UnitPrice Float , OrderQuantity FLOAT ))

Tutorial: Inserting Records and DataFrames Into a SQL Database, Extracting data from Microsoft SQL Server database using SQL query and storing it in pandas (or numpy) objects. With following code: ## From  Loading data from SQL Server database to Python Pandas Dataframe. To perform data analysis most of the data analyst, data scientist, statistician, and data engineer use python pandas module. Here will learn Extracting data from Microsoft SQL Server database using SQL query and storing it in pandas objects.

Although I can't answer your errors, you do need another parameter "input_data_x_name" for the code to work:

EXEC sp_execute_external_script
@script=N'dataset = pandas.DataFrame(input_data)',
@input_data_1 = N'Select * FROM dbo.Rests_GO',
@input_data_1_name = N'input_data' 

Using Python Pandas dataframe to read and insert data to Microsoft , In this tutorial, I'll show you how to get from SQL to pandas DataFrame. Next, I established a connection between Python and MS Access using the pyodbc package. import pyodbc conn = pyodbc.connect(r'Driver={Microsoft Access Driver  Load the file into your Python workbook using the Pandas read_csv function like so: Load CSV files into Python to create Pandas Dataframes using the read_csv function.

Although Express Edition is one of the SQL Server editions, this message is subtly different and it's telling you that Python client libraries are licensed under the limitations of SQL Server's Express Edition. You can safely ignore it unless you're concerned about scalability further down the line. You'll actually see the same error message about limits (in-memory data sets and a maximum of 2-core processing) on SQL Server Standard Edition - I've seen it numerous times, and have just double checked it for accuracy. In summary the first message is an informational message, not an error message, so you can safely ignore that part of the output.

The second part of the message is what you want to focus on (almost invariably when debugging this type of TSQL/Python code).

Therefore, user3912517's solution given above is the correct one, though it might be useful to understand why.

Simple explanation: Python needs to reference the data set passed into it from SQL Server with a name.

It does this via the @input_data_1_name parameter, which effectively "names" the data returned by the query defined in @input_data_1.

This provides the link between the data set returned by the query that's run in the SQL Server environment and the dataframe that the Python code needs to reference the same data set in the Python environment.

SQL to Pandas DataFrame (with examples), But if your goal is to just get the csv data into the SQL database, you could also import urllib import pyodbc import pandas as pd df = pd.read_csv(". You can store your data frame as csv file (use tabs for separator if your data doesn't If you want to stay within Python, the following code should work. I am trying to understand how python could pull data from an FTP server into pandas then move this into SQL server. My code here is very rudimentary to say the least and I am looking for any advice or help at all. I have tried to load the data from the FTP server first which works fine.

Get data from pandas into a SQL server with PYODBC, Loading data into a Pandas DataFrame - a performance study the Python programmers will often load a full dataset into a Pandas dataframe, MSSQL_pymssql : Pandas' read_sql() with MS SQL and a pymssql connection  In this tutorial, I’ll show you how to get from SQL to pandas DataFrame using an example. For illustration purposes, I created a simple database using MS Access, but the same principles would apply if you’re using other platforms, such as MySQL, SQL Server, or Oracle. Steps to get from SQL to Pandas DataFrame Step 1: Create a database

Loading data into a Pandas DataFrame, Python and Pandas are excellent tools for munging data but if you want to store it long term a DataFrame is not the solution, especially if you  I can connect to my local mysql database from python, and I can create, select from, and insert individual rows. My question is: can I directly instruct mysqldb to take an entire dataframe and ins

A Better Way To Load Data into Microsoft SQL Server from Pandas , Write records stored in a DataFrame to a SQL database. append: Insert new values to the existing table. https://www.python.org/dev/peps/pep-0249/. The cars table will be used to store the cars information from the DataFrame. Step 3: Get from Pandas DataFrame to SQL. You can use the following syntax to get from pandas DataFrame to SQL: df.to_sql('CARS', conn, if_exists='replace', index = False) Where CARS is the table name created in step 2.