Visualizing Business Data
As a startup founder, you need to quickly understand how your business is performing. Data visualization is a powerful way to turn raw numbers into insights you can act on right away. By creating simple charts and graphs, you can easily spot trends in user growth, compare sales across products, or track the effectiveness of marketing campaigns. For example, you might visualize weekly user signups to see if your growth strategies are working, or compare revenue from different product lines to identify your best performers.
123456789101112import matplotlib.pyplot as plt # Hardcoded data for weekly user signups weeks = ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5"] signups = [120, 150, 170, 160, 200] plt.plot(weeks, signups, marker="o") plt.title("Weekly User Signups") plt.xlabel("Week") plt.ylabel("Number of Signups") plt.grid(True) plt.show()
In this code, you use the matplotlib library to create a line chart showing user signups over five weeks. The plt.plot() function draws the line by connecting the data points for each week. Adding marker="o" places a circle at each data point for clarity. The plt.title() function gives your chart a clear heading, while plt.xlabel() and plt.ylabel() label the x-axis and y-axis so anyone can quickly understand what the chart shows. Finally, plt.grid(True) adds a background grid to make the trends easier to see, and plt.show() displays the chart window.
1234567891011import matplotlib.pyplot as plt # Hardcoded sales data for different products products = ["Basic", "Pro", "Enterprise"] sales = [3200, 5400, 4100] plt.bar(products, sales, color=["#4CAF50", "#2196F3", "#FFC107"]) plt.title("Sales by Product") plt.xlabel("Product") plt.ylabel("Sales ($)") plt.show()
1. What is the main advantage of visualizing business data?
2. Which matplotlib function is used to create a line chart?
3. How can visualizations help founders make better decisions?
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As a startup founder, you need to quickly understand how your business is performing. Data visualization is a powerful way to turn raw numbers into insights you can act on right away. By creating simple charts and graphs, you can easily spot trends in user growth, compare sales across products, or track the effectiveness of marketing campaigns. For example, you might visualize weekly user signups to see if your growth strategies are working, or compare revenue from different product lines to identify your best performers.
123456789101112import matplotlib.pyplot as plt # Hardcoded data for weekly user signups weeks = ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5"] signups = [120, 150, 170, 160, 200] plt.plot(weeks, signups, marker="o") plt.title("Weekly User Signups") plt.xlabel("Week") plt.ylabel("Number of Signups") plt.grid(True) plt.show()
In this code, you use the matplotlib library to create a line chart showing user signups over five weeks. The plt.plot() function draws the line by connecting the data points for each week. Adding marker="o" places a circle at each data point for clarity. The plt.title() function gives your chart a clear heading, while plt.xlabel() and plt.ylabel() label the x-axis and y-axis so anyone can quickly understand what the chart shows. Finally, plt.grid(True) adds a background grid to make the trends easier to see, and plt.show() displays the chart window.
1234567891011import matplotlib.pyplot as plt # Hardcoded sales data for different products products = ["Basic", "Pro", "Enterprise"] sales = [3200, 5400, 4100] plt.bar(products, sales, color=["#4CAF50", "#2196F3", "#FFC107"]) plt.title("Sales by Product") plt.xlabel("Product") plt.ylabel("Sales ($)") plt.show()
1. What is the main advantage of visualizing business data?
2. Which matplotlib function is used to create a line chart?
3. How can visualizations help founders make better decisions?
Tak for dine kommentarer!
