Error with Python script when attempting to import CSV into SQL Server

Introduction

In this article, we will explore how to handle errors that occur when trying to import a CSV file into an existing table in SQL Server using Python. We will go over the steps needed to identify and resolve issues related to invalid data being inserted into the database.

Understanding the Error Message

The error message provided indicates that there is an issue with the incoming tabular data stream (TDS) remote procedure call (RPC) protocol stream. The specific error code mentioned is 42000, which corresponds to a general error in SQL Server. Further investigation reveals that the problem lies in attempting to insert invalid values for certain columns.

Determining the Source of the Error

The quickest way to determine what’s wrong with your data is to perform good old-fashioned print debugging. We can add some print statements before executing each cursor.execute() call to see which specific row or column causes the error.

Example Code: Debugging Print Statements

    print("Next insertion:")
    print(f"                  Visit Date: '{row['Visit Date']}'")
    print(f"                    Guest ID: '{row['Guest ID']}'")
    print(f"                   Last Name: '{row['Last Name']}'")
    print(f"                  First Name: '{row['First Name']}'")
    # ... other columns

Running the program with these additional print statements will help you identify which specific row or column is causing the issue.

Inspecting Data Quality

Once we have identified the problematic row, it’s essential to inspect its data quality. We need to ensure that all values in the CSV file are valid and suitable for insertion into the database.

Checking Column Data Types

Before attempting to insert data into SQL Server, we should verify that the column data types match the expected types in the database.

# Define table name
table_name = 'SomeTable'

# Get column information from database
cursor.execute(f"PRAGMA TABLE_INFO({table_name})")
column_info = cursor.fetchall()

# Map column names to their corresponding data types
column_data_types = {
    'Visit Date': 'datetime',
    'Guest ID': 'int',
    # ... other columns
}

for row in df.iterrows():
    for col, val in zip(row[1], df.loc[row[0]]):
        if val is not None and col != 'Taken At (UTC)' and col != 'Recorded By':
            try:
                data_type = column_data_types[col]
                pd.to_numeric(val)
                if data_type == 'datetime':
                    # Convert string to datetime object
                    from dateutil import parser
                    parsed_val = parser.parse(val)
                    df.loc[row[0], col] = parsed_val
                elif data_type == 'int':
                    # Attempt to convert string to integer
                    val = int(val)
            except ValueError:
                print(f"Invalid value '{val}' for column {col}.")

This code maps each column name to its corresponding data type and attempts to validate the values using Python’s built-in pd.to_numeric() function. If a value is invalid, it prints an error message.

Resolving Data Quality Issues

Based on the insights gained from inspecting data quality and identifying problematic rows or columns, we can take steps to resolve these issues:

Clean the CSV file: Review the CSV file for any typos, inconsistencies, or missing values that could be causing errors.
Standardize column names: If necessary, rename columns in the database or CSV file to match exactly.

Automating Data Import

Once we have resolved data quality issues, we can automate the data import process using Python scripts and SQL Server’s built-in tools for importing data.

Best Practices

Validate user input: Before inserting data into the database, ensure that all values are valid and suitable for insertion.
Use try-except blocks: Handle any errors that occur during data import to prevent crashes and provide meaningful error messages.
Log debugging information: Use print statements or log files to track progress and diagnose issues.

Example Code: Automated Data Import

# Convert specific column to float
columns_to_convert = ['Taken By']

for col in columns_to_convert:
    df[col] = pd.to_numeric(df[col], errors='coerce')

df['Recorded By'].fillna(0, inplace=True)

# Establish connection parameters
server = 'sampleServer'
database = 'sampleDatabase'
username = 'sampleUser'
password = 'PW1234!@#$'

# Connection string
conn_str = (
    f"DRIVER={{ODBC Driver 17 for SQL Server}};"
    f"SERVER={server};"
    f"DATABASE={database};"
    f"UID={username};"
    f"PWD={password}"
    )

# Connect to SQL Server
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()

# Define table name
table_name = 'SomeTable'

for index, row in df.iterrows():
  query = """
INSERT INTO {table_name} (Visit Date, Guest ID, Last Name, First Name,
Date of Birth, Guest Age, Guest Gender, Guest Ethnicity,
Guest Ethnicity 2, Street, Line 2, City, County, State, Zip Code,
Housing Type, Latitude, Longitude, Household ID, Household Size, Primary Language,
Diet Restrictions, Taken At (UTC), Taken By, Company Visited,
Company Visited (Short Name), Plan Name, Individuals Served)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
"""
  
  try:
    cursor.execute(query, row['Visit Date'], row['Guest ID'],
                   row['Last Name'], row['First Name'],
                   row['Date of Birth'], row['Guest Age'],
                   row['Guest Gender'], row['Guest Ethnicity'],
                   row['Guest Ethnicity 2'], row['Street'],
                   row['Line 2'], row['City'], row['County'],
                   row['State'], row['Zip Code'], row['Housing Type'],
                   row['Latitude'], row['Longitude'], row['Household ID'],
                   row['Household Size'], row['Primary Language'],
                   row['Diet Restrictions'], row['Taken At (UTC)'],
                   row['Taken By'], row['Company Visited'],
                   row['Company Visited(Short Name)'], row['Plan Name'],
                   row['Individuals Served'])
    conn.commit()
  except pyodbc.Error as e:
    print(f"Error inserting row {index}: {e}")

This script automates the data import process by using try-except blocks to handle any errors that occur during insertion.

Conclusion

In this article, we explored how to address errors when trying to import a CSV file into an existing table in SQL Server using Python. By following best practices and taking steps to validate user input, we can ensure reliable and efficient data import processes.

Last modified on 2024-04-01