Now, before you start rolling your eyes and muttering under your breath, let me explain what this fancy term means. Essentially, it’s a way to keep track of how often certain values occur within a dataset without having to constantly update a global counter or write data back and forth between memory and disk.

Here’s an example: say you have a large dataset with millions of records, each containing a numerical value. Instead of keeping track of the frequency of each value in a separate table (which would take up tons of space and be slow to update), you can use local memory to store histograms for each individual record.

So how does this work? Well, let’s say we have a dataset with three columns: ID, Value, and Frequency. We start by initializing an empty array called `histogram` that will hold our slow accumulation histograms. Then, as we iterate through each record in the dataset, we calculate the value of the current record (let’s call it `current_value`) and add 1 to the corresponding bin in the histogram for that value:

# Initialize empty array for slow accumulation histograms
histogram = [0] * (max(dataset['Value']) + 1) # Adding an extra bin with a value of 1 to ensure all values are represented

# Iterate through each record in the dataset
for index, row in dataset.iterrows():
    # Calculate the value of the current record
    current_value = int(row['Value'])
    # Increment the corresponding bin for the current value
    histogram[current_value] += 1

Now, whenever we need to know how often a certain value occurs within our dataset, all we have to do is look at the appropriate bin in `histogram`. And because this data is stored locally (in memory), it’s much faster and more efficient than constantly updating a global counter or writing data back and forth between memory and disk.

It might sound like a mouthful, but trust us when we say this is a powerful tool for working with large datasets without sacrificing speed or efficiency. Give it a try !