import pandas as pd
import numpy as np

data = {
    'Branch': ['A', 'B', 'B', 'C', 'D', 'E', 'F', 'G'],
    'Sales': [10000, 15000, 15000, 20000, None, 17000, None, 20000],
    'Manager': ['Raj', 'Simran', 'Simran', 'Kunal', np.nan, 'Arjun', 'Kunal', 'Raj'],
    'Region': ['East', 'West', 'West', np.nan, 'North', 'South', 'North', np.nan]
}

df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

print("\nMissing Values in Each Column:")
print(df.isnull().sum())

print("\nRows where Sales is Missing:")
print(df[df['Sales'].isnull()])

# Replace missing Sales with mean
mean_sales = df['Sales'].mean()
df['Sales'] = df['Sales'].fillna(mean_sales)

# Replace missing Manager with 'Unknown'
df['Manager'] = df['Manager'].fillna('Unknown')

# Drop rows with missing Region
df.dropna(subset=['Region'], inplace=True)

print("\nCleaned DataFrame:")
print(df)

# Check for duplicate rows
duplicates = df.duplicated()
print("\nDuplicate Rows:")
print(duplicates)

# Count of duplicates
print("\nTotal Duplicate Rows:", duplicates.sum())

# Drop duplicates keeping last occurrence
df = df.drop_duplicates(keep='last')

# Drop duplicates based on Branch and Manager
df_unique = df.drop_duplicates(subset=['Branch', 'Manager'])

# Rows remaining after cleaning
print("\nTotal Rows After Cleaning:", len(df_unique))

# Column with most missing values before cleaning
initial_missing = pd.DataFrame(data).isnull().sum()
most_missing_col = initial_missing.idxmax()
print("Column with Most Missing Values:", most_missing_col)

# Average Sales after filling missing values
print("Average Sales After Cleaning:", round(df['Sales'].mean(), 2))