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Asked: 2024-09-27T13:17:32+05:30 In:

How can I calculate the pairwise Cohen’s kappa statistic for the rows in a pandas DataFrame? I have a dataset where each row represents a different rater’s ratings, and I need to assess the agreement between these raters. What would be the best approach to implement this in Python using pandas?

I’ve been diving into a project that’s all about analyzing ratings from multiple raters, and I hit a bit of a snag. I’ve got this dataset in a pandas DataFrame, where each row corresponds to a different rater’s scores on a set of items. The ratings are categorical and I want to measure the degree of agreement between these raters. I’ve heard about Cohen’s kappa being a good way to do this, but I’m not sure how to tackle it for each pair of raters in my DataFrame.

So here’s the issue: my DataFrame is structured in a way that each column represents a different item being rated, and each row is a different rater. For instance, let’s say I have four raters rating three items, which would look something like this:

“`
Rater 1: [1, 2, 3]
Rater 2: [1, 2, 2]
Rater 3: [2, 2, 3]
Rater 4: [1, 3, 3]
“`

Now, I want to calculate the pairwise Cohen’s kappa statistic for all possible pairs of raters to see how consistent their ratings are. The idea is to create a square matrix where the row and column indices are the raters and the values are the Cohen’s kappa statistics for their ratings.

I know there’s a function in `statsmodels` or maybe `sklearn` that computes Cohen’s kappa, but it seems tricky to apply it across all rows in my DataFrame to get every pair’s agreement score.

Has anyone worked on something similar or could you share how I might approach this? Like, should I loop through the DataFrame, or is there a more efficient way to pair the rating columns together? Also, any tips on handling cases where the ratings might not match up completely, as in missing values or discrepancies in categories?

I’m all ears for any guidance or code snippets that could help me out with this. Thanks!

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    2 Answers

    1. To calculate pairwise Cohen’s kappa for raters’ categorical ratings in a pandas DataFrame, you can utilize the `cohen_kappa_score` function from the `sklearn.metrics` module. First, iterate through all possible pairs of raters. You can employ the `itertools.combinations` function to generate these pairs efficiently. For each pair, you’ll extract the corresponding columns for the two raters and ensure that you handle any missing values by utilizing pandas’ `dropna()` method. After aligning the ratings, you can compute the kappa score and store the result in a square matrix where both the rows and columns correspond to the raters.

      Here’s a code snippet that illustrates this approach. Assume your DataFrame is called `df`:

      import pandas as pd
from sklearn.metrics import cohen_kappa_score
from itertools import combinations

# Example DataFrame
data = {'Rater 1': [1, 2, 3], 'Rater 2': [1, 2, 2], 'Rater 3': [2, 2, 3], 'Rater 4': [1, 3, 3]}
df = pd.DataFrame(data)

# Initialize a square matrix for kappa scores
raters = df.columns
kappa_matrix = pd.DataFrame(index=raters, columns=raters)

# Iterate through pairs of raters
for rater1, rater2 in combinations(raters, 2):
    # Drop missing values
    aligned_ratings = df[[rater1, rater2]].dropna()
    
    # Calculate Cohen's kappa
    if not aligned_ratings.empty:
        kappa = cohen_kappa_score(aligned_ratings[rater1], aligned_ratings[rater2])
    else:
        kappa = None  # or some indicator of no agreement
    
    # Fill the matrix
    kappa_matrix.at[rater1, rater2] = kappa
    kappa_matrix.at[rater2, rater1] = kappa  # Symmetric matrix

print(kappa_matrix)

    2. Calculating Cohen’s Kappa for Rater Agreement

      So, you’ve got a DataFrame with raters and their scores, and you want to see how much they agree using Cohen’s kappa. That’s a great idea! Here’s a simple way to get started:

      Step-by-Step Approach

      1. Install Required Libraries: If you haven’t yet, make sure you have `pandas`, `numpy`, and `scikit-learn` installed: 
        pip install pandas numpy scikit-learn
      2. Import Libraries: At the top of your script, you’ll want to import these libraries: 
        import pandas as pd
from sklearn.metrics import cohen_kappa_score
      3. Prepare Your Data: Make sure your DataFrame is set up correctly. Each rater’s scores should be in rows and items should be in columns.
      4. Calculate Kappa: Now the fun part! You can loop through the raters to compute kappa for each pair. Here’s an example code snippet:
      
# Example DataFrame
data = {
    'Item 1': [1, 1, 2, 1],
    'Item 2': [2, 2, 2, 3],
    'Item 3': [3, 2, 3, 3]
}
df = pd.DataFrame(data)

# Getting the number of raters
raters = df.index.tolist()
kappa_matrix = pd.DataFrame(index=raters, columns=raters)

# Calculating pairwise Cohen's Kappa
for i in range(len(raters)):
    for j in range(i + 1, len(raters)):
        kappa = cohen_kappa_score(df.iloc[i], df.iloc[j])
        kappa_matrix.iloc[i, j] = kappa
        kappa_matrix.iloc[j, i] = kappa  # Since it's symmetric

print(kappa_matrix)
    3. Handle Missing Values: If your DataFrame might have missing ratings, you can drop those rows or fill them with a method of your choice (like the mode or mean).
    4. Consider Categories: Ensure that the categories are the same for all raters. You might need to standardize them or address mismatches before calculating kappa.

      5. Final Thoughts

      This approach should give you a good start on analyzing your ratings. Just remember to double-check your DataFrame to ensure everything lines up right. Happy coding!

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