How to Create a Hashmap from Dictionaries in Python

Discover how to efficiently create a `hashmap` from dictionaries in Python, using both loops and the pandas library for data aggregation.
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Creating a Hashmap from Dictionaries in Python

When working with data in Python, especially in finance or data science, you may find yourself needing to aggregate information from multiple dictionaries. For instance, suppose you have a series of trade data, and you want to group that data by date while summing up the total amounts for each date. This guide will walk you through how to achieve this step by step.

The Problem at Hand

Let's say you have the following dictionaries representing trade data:

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

From this data, you want to create a hashmap (or dictionary) that groups the amounts by dates, producing an output like this:

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

To achieve this, you will extract the date from the instrument_name field and sum up the amount for each unique date.

Solution Approaches

There are a couple of methods you can use to implement this solution: a traditional approach using loops and a data-centric approach using pandas. Let's explore both methods.

Method 1: Using a For Loop

The first method involves using a for loop along with a defaultdict from the collections module to accumulate the amounts.

Here’s how it works:

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

Explanation

Collection Initialization: We create a defaultdict which initializes the sum to 0 for any new date.

Loop Through Data: We iterate through each dictionary entry, extract the date from the instrument_name, and then sum the amount for that date.

Return Result: Eventually, we convert the defaultdict back to a regular dictionary for more standard use.

This method is efficient, with an average runtime of about 1.82 microseconds per loop.

Method 2: Using Pandas

If you are working with larger datasets or prefer to use the power of the pandas library, you can accomplish the same task in a more streamlined manner.

Here’s how:

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

Explanation

DataFrame Creation: The dictionaries are converted into a DataFrame df for easier data manipulation.

Date Extraction: A new column date is created by splitting the instrument_name.

Group and Sum: The DataFrame is grouped by date, and the amounts are summed.

Return Result: Finally, we convert the result back to a dictionary.

This method is slightly slower, averaging around 219 microseconds per loop, but it can handle larger datasets more gracefully.

Conclusion

Both methods effectively create a hashmap from your original data, allowing you to sum up amounts by date in Python. The choice between using a simple for loop or the pandas library largely depends on your dataset size and the complexity of your data manipulation needs.

The first solution (using a loop) is the fastest, while the second method (using pandas) provides a more convenient way to handle larger datasets. Choose the method that best suits your requirements and enjoy aggregating your data with ease!