Fuzzy Join with Multiple Conditions: A Comprehensive Approach to Handling Missing or Uncertain Data in Python Datasets

Fuzzy Join with Multiple Conditions: A Comprehensive Approach

Fuzzy join is a powerful technique used to merge two data sets based on partial matches. In this article, we will delve into the world of fuzzy joins and explore how to perform one with multiple conditions. We will use Python and its popular pandas library for this task.

Introduction

Fuzzy join is particularly useful when dealing with missing or uncertain data in our datasets. It allows us to find similar values between two datasets even if they do not exactly match. In this case, we want to determine when a load of material will be crushed based on the crushing rate at each timestamp. We have two main scenarios to consider:

The first load is crushed when the second batch is loaded.
The first load is crushed prior to the second load.
The first load is not crushed when the second load is added.

Preparing Our Data

We start by creating our data frames, Load and Rate, which represent the number of tones of materials dumped to a crusher and the crushing rate at each timestamp, respectively. We assume that there are 10 timestamps, with the index starting at zero.

Load = pd.Series([300,0,0,400,50,0,0,0,150,0])
Rate = pd.Series([102,103,94,120,145,114,126,117,107,100])

Calculating the Fuzzy Join

To perform the fuzzy join, we need to calculate the cumulative sum of the crushing rate and use it to find the index at which each load is crushed. We will store our results in a list called H.

load1_idx = len(Load)
H = []

for lix in range(len(Load)):
    a = Load[lix]
    if a != 0:
        csumser = Rate.cumsum()
        rix = csumser.searchsorted(a)
        excess = csumser[rix] - a
        H.append((lix, a, rix, excess))
        load1_idx = lix
        break

for lix in range(load1_idx + 1, len(Load)):
    a = Load[lix]
    if a == 0:
        continue
    last_rix = H[-1][-2]
    csumser[last_rix:] = Rate[last_rix:]
    if lix == last_rix:
        csumser[lix] = H[-1][-1]  # excess

    csumser[last_rix:] = csumser[last_rix:].cumsum()

    rix = csumser[last_rix:].searchsorted(a)
    rix += last_rix
    excess = csumser[rix] - a
    H.append((lix, a, rix, excess))

Processing the Results

After calculating the cumulative sum of the crushing rate and finding the index at which each load is crushed, we process our results to ensure that the values are correct. We do this by updating the cumulative sum of the crushing rate for the indices after the last one.

df = pd.DataFrame(H, columns=["indexLoad", "Load", "indexRate", "rate_excess"])
print(df)

Understanding Our Results

Our final output shows the index at which each load is crushed, along with its value and the corresponding crushing rate. This allows us to determine when a load of material will be crushed based on the crushing rate at each timestamp.

Conclusion

In this article, we explored how to perform a fuzzy join with multiple conditions using Python and pandas. We created our data frames, calculated the cumulative sum of the crushing rate, and processed our results to ensure that they were correct. By following these steps, you can apply fuzzy joins to your own datasets and solve complex problems involving missing or uncertain data.

References

Last modified on 2023-10-20