How can I efficiently populate MySQL databases with lists of dictionaries using Python's executemany() method?

Effectively Populating MySQL Databases with Lists of Dictionaries using Executemany in Python

Inserting large datasets into MySQL databases can often lead to inefficiencies and potential database lockups. To address this issue when working with data represented as lists of dictionaries, consider utilizing the executemany() method.

In the presented scenario, the original approach involves manually inserting each row of the table into the database, which can be cumbersome and error-prone. To optimize this process, executemany() provides a convenient way to execute multiple SQL statements with a single function call.

To use executemany() effectively, restructure the data by extracting the values from the dictionaries and creating a list of tuples. Each tuple represents a row of the table, containing the corresponding values for each column.

itemBank = []
for row in rows:
    itemBank.append((
        tempRow2['Item_Name'],
        tempRow1['Item_Price'],
        tempRow3['Item_In_Stock'],
        tempRow4['Item_Max'], 
        getTimeExtra
        )) #append data

Next, create the SQL query with placeholders for the parameter values, which will be replaced by the values from the tuples in the executemany() function.

q = """ insert ignore into TABLE1 (
        Item_Name, Item_Price, Item_In_Stock, Item_Max, Observation_Date ) 
        values (%s,%s,%s,%s,%s)           
    """

Finally, execute the executemany() function with the modified data and SQL query.

try:
    x.executemany(q, itemBank)
    conn.commit()
except:
    conn.rollback()

This approach significantly improves efficiency by executing multiple rows in a single operation, reducing the overhead associated with individual row insertions. Additionally, it prevents potential database lockups by distributing the load over fewer transactions.

By utilizing executemany() to insert lists of dictionaries into MySQL databases, developers can streamline their data insertion processes, improve database performance, and minimize the risk of lockups.

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