Скачать или смотреть Resolving the Issue of Only One Row Being Inserted into SQLite from Pandas DataFrame in Python

Discover how to successfully insert multiple rows from a Pandas DataFrame into an SQLite database when dealing with inconsistent spacing in your data.
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Resolving the Issue of Only One Row Being Inserted into SQLite from Pandas DataFrame in Python

When working with data in Python, a common issue arises when attempting to load data from a text file and push it into a database. If you've ever tried to read data from a file, load it into a Pandas DataFrame, and subsequently insert it into an SQLite database, you may have found that only one row gets inserted despite having multiple rows available in your dataframe. Let’s walk through this problem and provide a clear resolution!

Understanding the Problem

In your scenario, you’re trying to read data from a text file (data.txt) that contains multiple rows of records. The issue you encountered is that, upon executing your code, only the first row was inserted into the SQLite database. For instance, your expected output consists of multiple rows like this:

[[See Video to Reveal this Text or Code Snippet]]

However, what actually ended up in your database was just the first entry:

[[See Video to Reveal this Text or Code Snippet]]

After examining your code, it appears that you were iterating over the column names instead of the actual rows of data in the DataFrame, which is why only one record was inserted. Additionally, inconsistent spacing in your data further complicated the reading process.

Solution: Step-by-Step Guide

Step 1: Adjusting the DataFrame Read Function

To successfully read and insert all rows from your data, you can modify your pd.read_csv() function call to handle the whitespace issues and specify column names properly. Here's how to do it:

[[See Video to Reveal this Text or Code Snippet]]

Key Points:

header=None: This argument ensures that the first row in your file is not treated as a header, which solves the problem of losing your first data row.

names=['object_id', 'object_type']: This explicitly defines the names of the columns in your DataFrame.

skipinitialspace=True: This option automatically handles inconsistent spacing, allowing you to disregard extra spaces or tabs in your data fields.

Step 2: Iterating Over Rows Correctly

Once you've read the data properly, the next step is to loop through the rows correctly. Use the itertuples() method, which provides a way to iterate over DataFrame rows as named tuples. Here’s how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

Explanation:

data.itertuples(index=False): This iterates over DataFrame rows without including the index, thus allowing access to the data you need in each row.

Step 3: Committing Changes Efficiently

To enhance performance, only commit once after all insert operations have been executed instead of committing after each row. This reduces database write overhead:

[[See Video to Reveal this Text or Code Snippet]]

Complete Example Code

With the above explanations integrated, here's how the complete code should look:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

There you have it! By adjusting your reading method and ensuring that you are iterating over the correct structure, you can successfully insert all rows from your Pandas DataFrame into your SQLite database. Remember to keep an eye on your data formats and whitespace issues, as they can lead to similar hurdles in the future. Happy coding!