Introduction

Deployment can be a daunting task for many developers, especially when working with complex applications like Streamlit apps. In this article, we’ll delve into the issue of pandas not reading in CSV files correctly after deployment to Heroku and explore possible solutions.

Background

Streamlit is an open-source Python library that allows users to create web-based data analysis tools quickly and easily. It provides a simple, intuitive API for creating interactive visualizations and statistical models. However, Streamlit apps often rely on external datasets, which can be challenging to deploy to cloud platforms like Heroku.

Understanding CSV Files in Pandas

Before we dive into the issue at hand, let’s take a moment to understand how pandas works with CSV files. Pandas is a powerful data manipulation library that provides an efficient way to read and write CSV files. When you use pandas to read a CSV file, it loads the entire file into memory as a DataFrame object.

The Role of Git LFS

In your case, you’re using GIT Large File Storage (LFS) to upload large datasets to GitHub. This is a great solution for handling files that exceed Heroku’s 25MB limit. However, when deploying to Heroku, the LFS mechanism needs to be properly configured.

Deployment to Heroku and Git LFS

When you deploy your Streamlit app to Heroku, it will automatically detect any dependencies installed in your Python environment. In this case, pandas is likely being used as a dependency, which means that Heroku needs to have access to the pandas library in order to run your app.

The Problem with CSV Files

The issue you’re experiencing is that pandas is not reading the CSV file correctly when deployed to Heroku. This could be due to several reasons:

1. File permissions: When deploying to Heroku, ensure that your CSV files have the correct file permissions. You want to make sure that the Heroku process has read access to your files.
2. LFS configuration: Double-check that your GIT LFS configuration is correct. Make sure that you’ve added all dependencies required by pandas and that they’re properly linked to your Python environment.
3. Dependency conflicts: There might be a dependency conflict between pandas or other libraries installed in your local environment versus the ones used in your Heroku deployment.

Solution 1: Use a Relative Path

One possible solution is to use a relative path when loading your CSV file in your Streamlit app. This way, you avoid hardcoding an absolute path that might not be accessible on Heroku.

Example Code

import pandas as pd

# Load the CSV file using a relative path
df = pd.read_csv('./data/city_stats.csv')

In this example, the ./ notation refers to the current working directory. This approach works because Streamlit provides a way for your app to determine its own working directory.

Solution 2: Use Git LFS to Store Large Datasets

Another solution is to use Git LFS to store large datasets on GitHub. When deployed to Heroku, you can then access these files using the LFS mechanism.

Example Code

import pandas as pd

# Access the CSV file stored in LFS
df = pd.read_csv(f'https://git.lfs.githubusercontent.com/{your-username}/{your-repo}/tree/main/data/city_stats.csv')

In this example, we’re using the f string notation to create a URL that points to our Git LFS repository.

Solution 3: Use Heroku’s File Storage

If you prefer not to use Git LFS or relative paths, another solution is to store your CSV files in Heroku’s file storage. This approach requires some extra setup but provides more control over how your files are stored and accessed.

Example Code

import pandas as pd

# Access the CSV file stored in Heroku's file storage
df = pd.read_csv(f'https://{heroku-app-name}.appdomain.cloud/data/city_stats.csv')

In this example, we’re using Heroku’s https domain notation to access our stored CSV files.

Conclusion

Deployment can be a challenging task for developers working with complex applications like Streamlit apps. However, by understanding how pandas works with CSV files and using the right techniques when deploying to Heroku, you can ensure that your app runs smoothly.

In this article, we explored three possible solutions to the issue of pandas not reading in CSV files correctly after deployment to Heroku:

1. Relative paths: Using a relative path when loading your CSV file in your Streamlit app.
2. Git LFS: Using Git LFS to store large datasets on GitHub and access them using the LFS mechanism.
3. Heroku’s file storage: Storing your CSV files in Heroku’s file storage for more control over how they’re stored and accessed.

By choosing the right approach, you can ensure that your Streamlit app runs smoothly and efficiently even when deployed to a cloud platform like Heroku.

Last modified on 2025-01-25