import pandas as pd

# Read the data from a CSV file (replace 'your_file.csv' with your actual file path)
df = pd.read_csv('your_file.csv')

# Convert 'Date' column to datetime
df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y')

# Create a new DataFrame with minimum daily vaccinations for each country
min_daily_vaccinations = df.groupby('Country')['Daily_Vaccinations'].min().reset_index()
min_daily_vaccinations.columns = ['Country', 'Min_Daily_Vaccinations']

# Merge the original DataFrame with the minimum daily vaccinations DataFrame
df = pd.merge(df, min_daily_vaccinations, on='Country', how='left')

# Fill missing values in 'Daily_Vaccinations' with the minimum daily vaccinations
df['Daily_Vaccinations'] = df['Daily_Vaccinations'].fillna(df['Min_Daily_Vaccinations'])

# Drop the 'Min_Daily_Vaccinations' column if you don't need it
df = df.drop(columns=['Min_Daily_Vaccinations'])

# Fill remaining missing values with 0
df['Daily_Vaccinations'] = df['Daily_Vaccinations'].fillna(0)

# Print the final DataFrame
print(df)