add new columns to your data for new labels generated in k-Means andAgglomerative methods. Name the new data file, df_Quiz.

To add new columns to your data for new labels generated in k-Means and Agglomerative methods, you can follow the steps below, assuming you're using Python with libraries such as pandas and scikit-learn:

1. Load Your Data

Make sure to load your existing DataFrame using pandas.

import pandas as pd

# Load your existing data
df = pd.read_csv('your_data_file.csv')

2. Apply k-Means Clustering

Use the k-Means algorithm to generate new labels.

from sklearn.cluster import KMeans

# Assuming you want to use 3 clusters
kmeans = KMeans(n_clusters=3, random_state=42)
df['kmeans_labels'] = kmeans.fit_predict(df[['feature1', 'feature2']])  # replace with your features

3. Apply Agglomerative Clustering

Use the Agglomerative method to generate new labels.

from sklearn.cluster import AgglomerativeClustering

agglo = AgglomerativeClustering(n_clusters=3)  # adjust the number of clusters as needed
df['agglo_labels'] = agglo.fit_predict(df[['feature1', 'feature2']])  # replace with your features

4. Save the New DataFrame

Now, save the DataFrame with the new labels into a new file named df_Quiz.

df.to_csv('df_Quiz.csv', index=False)

Final Answer

The new file df_Quiz.csv will now contain your original data along with the new columns for k-Means and Agglomerative clustering labels.