import pandas as pd

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

# 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'])

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

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

# Filter data for 1/6/2021 and calculate the total vaccinations
total_vaccinations_on_1_6_2021 = df[df['Date'] == '2021-01-06']['daily_vaccinations'].sum()

# Print the total vaccinations on 1/6/2021
print(total_vaccinations_on_1_6_2021)