Скачать или смотреть Load Data from Multiple CSV Files into a Nested Dictionary in Python

Learn how to merge multiple CSV files into a well-structured `nested dictionary` format in Python, efficiently organizing data by IDs and dates.
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How to Load Data from Multiple CSV Files into a Nested Dictionary

If you've ever worked with CSV files in Python, especially when dealing with daily data, you know how tedious it can become to merge everything into a single, usable format. In this post, we will tackle the problem of loading multiple CSV files, each identified by a date in their filename, and transforming them into a nested dictionary structure based on unique IDs.

The Problem

Imagine you have several CSV files named with dates like 03-09-2020.csv, each containing a series of records similar to the following format:

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Your goal is to consolidate all this data into one master dictionary where each unique ID acts as a key, and contains nested dictionaries of the corresponding name, date, and values.

For instance, for id=3, the desired output would look something like this:

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Let’s break down how to accomplish this with Python step by step.

The Solution

Step 1: Import Libraries

First, you'll want to import the necessary library to handle file operations:

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Step 2: List Files in Directory

Use the following code snippet to list all CSV files in your specified directory:

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Step 3: Read and Parse CSV Files

Now, let's initialize some data structures for our processing:

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Next, you will loop through each file, read its content, and parse the information. Here's how it can be done:

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Step 4: Final Output

After processing all the CSV files, data_dict will now contain all the structured information you need. You can check your final output:

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Conclusion

By following the above steps, you can efficiently load data from multiple CSV files into a nested dictionary in Python. This structure not only organizes your data neatly but also makes it flexible for further data analysis or processing tasks.

Using a nested dictionary can greatly enhance how you handle complex datasets, especially with time-series or categorized information. With this guide, you are now equipped to tackle similar problems in your projects. Happy coding!