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Q1 import pandas as pd # Load the dataset (if not already loaded) df = pd.read_csv('/path/to/your/file.csv') # Convert 'Order Date' to datetime format df['Order Date'] = pd.to_datetime(df['Order Date'], format='%d/%m/%Y') # Filter sales data for the year 2018 sales_2018 = df[df['Order Date'].dt.year == 2018] # Display all sales data for 2018 sales_2018_data = sales_2018[['Order Date', 'Sales']] print(sales_2018_data) Q2 import pandas as pd # Load the dataset (if not already loaded) df = pd.read_csv('/path/to/your/file.csv') # Find the product with the highest sales top_product = df.loc[df['Sales'].idxmax()] # Display the product name and sales print("Product with highest sales:") print("Product Name:", top_product['Product Name']) print("Sales:", top_product['Sales']) Q3 import pandas as pd # Load the dataset (if not already loaded) df = pd.read_csv('/path/to/your/file.csv') # Count the frequency of each country country_counts = df['Country'].value_counts() # Display the frequency of countries print(country_counts) Q4 import pandas as pd import matplotlib.pyplot as plt # Load the dataset (if not already loaded) df = pd.read_csv('/path/to/your/file.csv') # Convert 'Order Date' to datetime format df['Order Date'] = pd.to_datetime(df['Order Date'], format='%d/%m/%Y') # Extract year and month from 'Order Date' df['Year'] = df['Order Date'].dt.year df['Month'] = df['Order Date'].dt.month # Group by year and month, then sum the sales monthly_sales = df.groupby(['Year', 'Month'])['Sales'].sum().reset_index() # Pivot the data to make months as columns monthly_sales_pivot = monthly_sales.pivot(index='Year', columns='Month', values='Sales') # Plotting the data plt.figure(figsize=(10, 6)) monthly_sales_pivot.plot(kind='bar', stacked=True, figsize=(12, 6)) plt.title('Total Sales by Month for Each Year') plt.xlabel('Year') plt.ylabel('Total Sales') plt.xticks(rotation=45) plt.legend(title='Month', labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']) plt.tight_layout() plt.show() Q5 import pandas as pd import matplotlib.pyplot as plt # Load the dataset (if not already loaded) df = pd.read_csv('/path/to/your/file.csv') # Convert 'Order Date' to datetime format df['Order Date'] = pd.to_datetime(df['Order Date'], format='%d/%m/%Y') # Extract year from 'Order Date' df['Year'] = df['Order Date'].dt.year # Find the highest sales for each year highest_sales_per_year = df.groupby('Year').apply(lambda x: x.loc[x['Sales'].idxmax()]) # Plotting the scatter plot plt.figure(figsize=(10, 6)) plt.scatter(highest_sales_per_year['Year'], highest_sales_per_year['Sales'], color='red') # Add labels and title plt.title('Highest Sales of Each Year') plt.xlabel('Year') plt.ylabel('Highest Sales') # Show the plot plt.tight_layout() plt.show()
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