[FIXED] Creating a function to standardize labels for each ID

Issue

I am attempting to create a function that standardizes a label column for a given ID for a given criteria.

I would like to standardize the label based on the most commonly used label for that ID, and if there is no common/majority label, then just take the first observation as the default standard.

The function I have so far is below:

def standardize_labels(df, id_col, label_col):
    # Function to find the most common label or the first one if there's a tie
    def most_common_label(group):
        labels = group[label_col].value_counts()
        # Check if the top two labels have the same count
        if len(labels) > 1 and labels.iloc[0] == labels.iloc[1]:
            return group[label_col].iloc[0]
        return labels.idxmax()

    # Group by the ID column and apply the most_common_label function
    common_labels = df.groupby(id_col).apply(most_common_label)

    # Map the IDs in the original DataFrame to their common labels
    df['standardized_label'] = df[id_col].map(common_labels)

    return df

It mostly works, however a quirk I've noticed with some where there is an shift in the trend in the labels, the labels then change per a given ID like this:

ID	raw_label	standardized_label
222	LA Metro	LA Metro
222	LA Metro	LA Metro
222	Los Angeles Metro	Los Angeles Metro
222	LA Metro	Los Angeles Metro
222	Los Angeles Metro	Los Angeles Metro

When instead the output I'm hoping for all the standardized_label to just be LA Metro since that is the majority label per that ID.

Solution

The code works as expected for me. However, you can use mode to make it easier to read. Also you can transform a function in groupby as well to assign directly to a column, which then would make your entire operation into a single line of code.

df['standardized_label'] = df.groupby('ID')['raw_label'].transform(lambda x: x.mode()[0])

Or you can use groupby.apply and map it as well. Anyway, the function would look like:

def standardize_labels(df, id_col, label_col):
    # Function to find the most common label or the first one if there's a tie
    def most_common_label(group):
        return group.mode()[0]

    # Group by the ID column and apply the most_common_label function
    common_labels = df.groupby(id_col)[label_col].apply(most_common_label)

    # Map the IDs in the original DataFrame to their common labels
    df['standardized_label'] = df[id_col].map(common_labels)

    return df

Since value_counts() works on a dataframe, we can use it directly without the groupby. So the function could be changed to the following. This is a refactoring of a function I wrote for a different question.

def standardize_labels(df, id_col, label_col):
    # Group by the ID column and apply the most_common_label function
    labels_counts = df.value_counts([id_col, label_col])
    dup_idx_msk = ~labels_counts.droplevel(label_col).index.duplicated()
    common_labels = labels_counts[dup_idx_msk]
    common_labels = common_labels.reset_index(level=1)[label_col]
    # Map the IDs in the original DataFrame to their common labels
    df['standardized_label'] = df[id_col].map(common_labels)
    return df

df = standardize_labels(df, 'ID', 'raw_label')

Answered By - cottontail