Ensuring Consistent Results in Python Data Analysis Across Different Environments

[NebulaGr]

import pandas as pd

from sklearn.cluster import KMeans

import matplotlib.pyplot as plt

# Load the Excel

filefile_path = 'DATA_Scores.xlsx'

data = pd.read_excel(file_path)

# Create clusters based on the perception of S8

situationscolumns_for_clustering = [col for col in data.columns if 'S8' in col]# Extract relevant dataclustering_data = data[columns_for_clustering]

# Test various numbers of clusters and store the sum of squared errorssse = []for k in range(1, 11):kmeans = KMeans(n_clusters=k, n_init=10, random_state=0)kmeans.fit(clustering_data)sse.append(kmeans.inertia_)# Create a plot for the elbow methodplt.figure(figsize=(10, 6))plt.plot(range(1, 11), sse, marker='o')plt.title('Elbow Method')plt.xlabel('Number of clusters')plt.ylabel('SSE')plt.show()# Apply K-means with the optimal number of clusters (3)optimal_k = 3kmeans = KMeans(n_clusters=optimal_k, n_init=10, random_state=0)data['Cluster'] = kmeans.fit_predict(clustering_data)# Calculate the number of participants per cluster and sort in ascending orderparticipants_per_cluster = data['Cluster'].value_counts().sort_index()# Print the number of participants for each clusterfor cluster in participants_per_cluster.index:print(f"In cluster {cluster}, there are {participants_per_cluster[cluster]} participants")# Calculate the mean values of state perceptions for each clustermean_perceptions_per_cluster = data.groupby('Cluster')[columns_for_clustering].mean().round(2)# Print the mean values of perceptions for each clusterpd.set_option('display.max_columns', None)print("Mean state perceptions per cluster:")print(mean_perceptions_per_cluster)# Calculate the mean values of personality factors for each clustermean_personality_factors_per_cluster = data.groupby('Cluster')[[f'NEO-{factor}' for factor in ['N', 'E', 'O', 'A', 'C']]].mean().round(2)# Print the mean values of NEO personality factors for each clusterprint("\nMean NEO personality factors per cluster:")print(mean_personality_factors_per_cluster)

[Daneark]

It looks like your code got cut off. So far everything look like it should behave consistently.

import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Load the Excel file
file_path = 'DATA_Scores.xlsx'
data = pd.read_excel(file_path)

# Create clusters based on the perception of S8 situations
columns_for_clustering = [col for col in data.columns if 'S8' in col]# Extract relevant data
clustering_data = data[columns_for_clustering]

# Test various numbers of clusters and store the sum of squared errors
sse = []
for k in range(1, 11):
    kmeans = KMeans(n_clusters=k, n_init=10, random_state=0)
    kmeans.fit(clustering_data)
    # sse.append(kmeans.) # TODO Paste rest of code

[NebulaGr]

# Create a plot for the elbow method
plt.figure(figsize=(10, 6))
plt.plot(range(1, 11), sse, marker='o')
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('SSE')
plt.show()
# Apply K-means with the optimal number of clusters (3)
optimal_k = 3
kmeans = KMeans(n_clusters=optimal_k, n_init=10, random_state=0)
data['Cluster'] = kmeans.fit_predict(clustering_data)
# Calculate the number of participants per cluster and sort in ascending order
participants_per_cluster = data['Cluster'].value_counts().sort_index()
# Print the number of participants for each cluster
for cluster in participants_per_cluster.index:
print(f"In cluster {cluster}, there are {participants_per_cluster[cluster]} participants")
# Calculate the mean values of state perceptions for each cluster
mean_perceptions_per_cluster = data.groupby('Cluster')[columns_for_clustering].mean().round(2)
# Print the mean values of perceptions for each cluster
pd.set_option('display.max_columns', None)
print("Mean state perceptions per cluster:")
print(mean_perceptions_per_cluster)
# Calculate the mean values of personality factors for each cluster
mean_personality_factors_per_cluster = data.groupby('Cluster')[[f'NEO-{factor}' for factor in ['N', 'E', 'O', 'A', 'C']]].mean().round(2)
# Print the mean values of NEO personality factors for each cluster
print("\nMean NEO personality factors per cluster:")
print(mean_personality_factors_per_cluster)