Posts

ML

 1. Write an Python program to perform data preprocessing on given data set. import pandas as pd # Load the dataset from the Excel file into a DataFrame file_path = 'C:\\Users\\datset_noise.xlsx' df = pd.read_excel(file_path, engine='openpyxl') print("Original DataFrame:") print(df.to_string(index=False)) # Step 2: Remove rows with missing values df_no_missing = df.dropna() print("\nDataFrame after removing rows with missing values:") print(df_no_missing.to_string(index=False)) print(f"\nTotal count after removing rows with missing values: {len(df_no_missing)}") # Store rows with missing values df_missing_rows = df[df.isna().any(axis=1)]  # Step 3: Remove duplicate rows df_no_duplicates = df_no_missing.drop_duplicates(subset=["Name","DOB","Age","Date of Joining"]) print("\nDataFrame after removing duplicate rows:") print(df_no_duplicates.to_string(index=False)) print(f"\nTotal count aft...
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Cloud

PG1 kubectl create deployment nginx --image=nginx:latest kubectl scale deployment nginx --replicas=3 Output kubectl get pods kubectl set image deployment/nginx nginx=nginx:1.21.0 Output kubectl rollout status deployment/nginx kubectl get pods -o wide kubectl rollout undo deployment/nginx output kubectl rollout status deployment/nginx PG2 kubectl create configmap my-config --from-literal=app_name=MyK8sApp --from-literal=version=1.0 output kubectl get configmap my-config -o yaml kubectl create secret generic my-secret --from-literal=username=admin --from-literal=password=SuperSecret123 output kubectl get secret my-secret -o yaml  kubectl get secret  kubectl get configmap   kubectl delete configmap my-config   kubectl delete secret my-secret PG3 nano pv.yaml apiVersion: v1 kind: PersistentVolume metadata:   name: my-pv spec:   capacity:     storage: 1Gi # Defines total storage capacity   accessModes:     - ReadWriteOnce # Single node...
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