Updating an Existing SQLite Database with Only the New Data from a Pandas DataFrame

Pandas Merge Only New Data in SQLite Database

Introduction

As a beginner in SQL, working with SQLite databases can be challenging, especially when dealing with duplicate data. In this article, we’ll explore how to use Python’s pandas library to update an existing SQLite database with only the new data from a pandas dataframe.

Background

SQLite is a self-contained, file-based relational database that’s widely used in web and mobile applications due to its simplicity and ease of use. The sqlite3 module in Python provides a convenient interface for interacting with SQLite databases.

The pandas library offers an efficient way to handle structured data, including SQL databases. In this article, we’ll focus on using pandas to update an existing SQLite database with only the new data from a pandas dataframe.

Prerequisites

To follow along with this article, you’ll need:

Python 3.6 or later installed on your system.
The sqlite3 and pandas libraries installed (pip install sqlite3 pandas).
A SQLite database file (e.g., stock_prices.db) to update.

Problem Statement

The problem statement is as follows:

You have a SQLite database with stock prices and their dates.
You use an API to retrieve the newest stock prices and their corresponding dates.
The API returns duplicate data, which you don’t want in your database.
You want to update your existing database with only the new data from the pandas dataframe.

Proposed Solution

The proposed solution is to use pandas to filter out duplicates from the dataframe and then use pd.to_sql to update the existing SQLite database. However, as seen in the Stack Overflow question, this approach doesn’t work due to a KeyError.

Let’s dive into the corrected solution:

import sqlite3
import pandas as pd

# Define constants
tablename = 'stock_prices'
conn = sqlite3.connect('stock_prices.db')

# Create a pandas dataframe from the API data (assuming it's in a dictionary format)
data = {'date': ['2020-04-09 16:00:00', '2020-04-09 15:59:00'], 
        'open': [571.925, 572.000], 
        'high': [573.01, 572.00], 
        'low': [571.73, 571.45], 
        'close': [573.01, 572.00], 
        'volume': [117287.0, 56954.0]}

# Create a pandas dataframe from the data
df = pd.DataFrame(data)

# Filter out duplicates based on the 'date' column
df_new = df.drop_duplicates(subset='date', keep='first')

# Print the new dataframe
print('New:', df_new.head())

Explanation

In this corrected solution:

We define constants for the table name and connection to the SQLite database.
We create a pandas dataframe from the API data, assuming it’s in a dictionary format.
We use drop_duplicates to filter out duplicates based on the ‘date’ column, keeping only the first occurrence of each date.
We print the new dataframe using head().

Updating the SQLite Database

Now that we have the updated pandas dataframe, let’s update the existing SQLite database:

# Use pd.to_sql to update the existing database with the new data
df_new.to_sql(tablename, conn, if_exists='append', index=False)

In this step:

We use to_sql to update the existing SQLite database.
The if_exists='append' parameter tells pandas to append the new data to the existing table.
The index=False parameter prevents pandas from including the dataframe’s index in the updated database.

Conclusion

In this article, we explored how to use Python’s pandas library to update an existing SQLite database with only the new data from a pandas dataframe. By filtering out duplicates using drop_duplicates, we can ensure that our database remains free of duplicate data. With this solution, you can efficiently update your SQLite database with new data while minimizing duplication.

References

Last modified on 2023-09-13