How To Make A Graph in Excel?

Creating a graph in Excel is simpler than you might think. Here's how you can do it in just five steps:

Want it even easier? Use Excel's Copilot AI to create graphs with simple commands like, "Make a line chart for revenue trends." Copilot can also refine your chart on request.

Automate Updates with Latenode

Tired of manual updates? Connect Excel to tools like CRMs or databases with Latenode. It automates data syncing, so your graphs stay accurate and up-to-date - no extra effort required.

Graphs make data easy to understand. Whether you do it manually or with AI, Excel has you covered.

Excel Charts and Graphs Tutorial

How to Make a Graph in Excel (Manual Steps)

Creating a graph in Excel is straightforward: organize your data, select it, choose a chart type, and customize the appearance. Here's a step-by-step guide to help you get started.

Step 1: Input Your Data

Begin by arranging your data in columns with clear headers in the first row. For instance, if you're tracking monthly sales, list the months in column A (e.g., January, February, March) and the corresponding sales figures in column B (e.g., $15,000, $18,500, $22,300).

Make sure your data follows US formatting conventions: use commas for thousands, periods for decimals, and the MM/DD/YYYY format for dates (e.g., 01/15/2025). Avoid leaving blank rows or columns within your data range, as these can interfere with Excel's processing.

Step 2: Select Your Data

Highlight the entire data range, including the headers. These headers will be used by Excel to label your chart correctly. If you need to select non-adjacent cells, hold down the Ctrl key (or Command on a Mac) while making your selections.

For more intricate selections, use the Name Box located next to the formula bar. Simply type the cell references (e.g., A2, A9:D9, A12, A19:D19) and press Enter. This method is especially useful when working with data spread across different parts of your spreadsheet.

Once your data is selected, you're ready to choose a graph type.

Step 3: Choose a Graph Type

Navigate to the Insert tab and look under the Charts section. Select a graph type that best suits your data. For example:

Each chart type offers several subtypes. Hover over the options to see a preview of how your data will look before making your choice.

Step 4: Adjust Your Data

After inserting the chart, you might need to tweak the data it displays. Right-click the chart and select "Select Data" to open a menu where you can add or remove data series, modify category labels, or switch rows and columns if Excel didn't interpret your data correctly.

This feature is also handy for excluding specific data points or adding new series that weren't part of the original selection.

Step 5: Format Your Graph

Now it's time to make your chart visually appealing and easy to understand. Click on the chart to access the Chart Tools tabs, where you can customize various elements:

You can also adjust the colors and styles. Click on any data series and use the formatting options to refine the look. Aim for a balance between aesthetics and readability - avoid overly flashy effects that might distract from the data's message.

How to Make an Excel Graph with Copilot

Microsoft's AI assistant in Excel makes it easy to create professional charts using straightforward text commands, eliminating the need to navigate through multiple menus.

Creating Graphs with Text Commands

To get started with Copilot for chart creation, ensure your workbook is saved on OneDrive or SharePoint. Open the Copilot chat pane by selecting "Copilot" from the ribbon or clicking on a cell and then choosing the Copilot icon.

The key to success lies in providing clear and detailed instructions. Instead of a vague command like "make a chart", specify exactly what you want to visualize. For instance:

Once you submit your prompt, Copilot can insert the chart into a new sheet, keeping your original data intact. If you have an existing chart image you'd like to analyze or replicate, you can use the Windows Snipping Tool to capture it. Copy the image to your clipboard, then paste it into the Copilot compose box with Ctrl+V, along with a descriptive prompt for guidance.

Next, explore how to refine these AI-generated charts for a polished final product.

Editing Copilot-Generated Graphs

Although Copilot supports over a dozen chart types, it doesn't yet include every option available in Excel. Fortunately, you can refine the charts it generates by using follow-up prompts or manual adjustments. For example, you might say, "Add data labels to each point," or "Change the color scheme to match my branding."

You can also enhance Copilot's work by combining its output with Excel's traditional Chart Tools tabs. This allows you to fine-tune elements like fonts, colors, and spacing to better suit your presentation or report.

For the best results, make sure your data is well-organized with clear headers and consistent formatting. This ensures that Copilot can interpret your instructions accurately and deliver the chart you need.

Common Types of Excel Graphs and When to Use Them

Once you’ve mastered creating a basic chart, the next step is selecting the graph type that best aligns with your analysis. The right choice can make the difference between clarity and confusion. As Kelly L. Williams aptly puts it:

Data visualization transforms raw data into graphical representations, making complex financial information more accessible and understandable.

Column and Bar Graphs

Column and bar charts are excellent tools for comparing categories or tracking changes over time. The difference between the two lies in their orientation: column charts feature vertical bars, while bar charts use horizontal bars.

These charts are particularly effective for comparing multiple data series. For example, a clustered column chart can help you evaluate quarterly sales performance across various product lines, clearly showing which products excelled in each quarter. Column charts are especially useful when the order of categories matters. However, if you’re working with a large number of data points over time, a line chart might be a better fit.

Line and Scatter Graphs

Line graphs are ideal for illustrating trends and patterns over time, especially when changes between data points are relatively small [7]. They’re often used to track metrics like stock prices, website traffic, or temperature changes across a year, providing a clear view of gradual shifts in large datasets.

Scatter plots, on the other hand, focus on relationships between two variables. Unlike line charts, scatter plots don’t connect data points in sequence. Instead, they use an X-Y axis to position each data point, making it easier to spot patterns, clusters, or outliers.

For example, a line chart works well for showing how your monthly revenue has changed over two years. But if you want to explore whether there’s a connection between your advertising spend and lead generation, a scatter plot is the way to go.

Pie Charts

Pie charts are best for visualizing how individual parts contribute to a whole, particularly when dealing with percentages or proportions [6][7]. They’re perfect for scenarios like budget allocation across departments or market share distribution among competitors.

That said, pie charts have their limitations. They work best with data that’s clearly proportional and should be avoided when dealing with more than five categories, as the visualization can quickly become cluttered. Similarly, if one section of the pie dominates the others, the chart loses its effectiveness [4].

As NASA instructor Robert Frost explains:

Pie charts show a comparison of the part to the whole. Bar charts show a comparison of the part to the part.

This distinction underscores why bar charts often feel more intuitive - humans are better at comparing lengths than angles or areas [8].

Chart Type	Best Use Case	Avoid When
Column/Bar	Comparing categories, tracking changes over time	Axis labels are too long (use bar charts instead)
Line	Showing trends over time, especially with many data points	Comparing unrelated categories
Scatter	Identifying correlations between two variables	Tracking time-based trends
Pie	Highlighting parts of a whole or percentages	More than 5 categories or one dominant slice

Automating Data Input in Excel with Latenode

Keeping Excel graphs accurate and up-to-date is essential for making informed decisions.

Why Automate Data Input?

Manual data entry is prone to errors, which can undermine the reliability of your Excel graphs. Studies reveal that freight invoices, for instance, contain errors about 20% of the time, showcasing how common accuracy issues can be [1]. When you're manually copying and pasting data, the chances of introducing typos, missing entries, or outdated figures skyrocket - problems that can distort your visualizations and lead to poor decision-making.

Accurate data entry is essential for maintaining the integrity of information in Excel spreadsheets, enabling correct calculations and meaningful insights.

By automating data input, your graphs are powered by real-time, error-free data, transforming them into reliable tools for analysis and decision-making. Additionally, automation saves valuable time that can be redirected toward understanding and interpreting the data, rather than tediously managing it.

Latenode Automation Examples

Latenode simplifies Excel data management by connecting it to over 300 applications, enabling workflows that automatically update your spreadsheets. Here are some real-world examples of how businesses leverage these automations:

These examples illustrate how automation ensures your data remains accurate and up-to-date, allowing you to focus on analyzing trends and making decisions rather than wrestling with manual updates.

Benefits of Using Latenode

Automating Excel data input with Latenode offers more than just convenience - it fundamentally transforms the way you work with data.

Automate Excel tasks within Latenode workflows. Read, update, or create spreadsheets directly. Use Excel data to trigger actions in other apps, generate reports, or update databases. No manual data entry; improve accuracy and save time by connecting Excel to other systems via Latenode's visual interface.

Here’s how automation can elevate your workflow:

Latenode’s user-friendly visual interface makes it accessible even for users with minimal technical experience. With pre-built connectors and templates for common workflows [12], setting up integrations is straightforward. For instance, you can sync parsed data from tools like Airparser into Excel workbooks [2] or create dynamic dashboards by linking Excel with Airtable [3]. The visual workflow builder simplifies complex integrations, enabling you to design powerful data flows with ease.

Conclusion

Creating graphs in Excel is more straightforward than it might seem. By following a few basic steps - entering your data, selecting it, choosing the right chart type, and customizing its design - you can turn raw numbers into clear, impactful visuals. Whether you're tracking sales, analyzing survey data, or preparing quarterly reports, Excel's charting tools make it easy to present your information in a way that's both professional and easy to understand. But creating the chart is just the beginning; keeping your data up to date is just as important.

Manual data updates can be time-consuming and prone to mistakes, but automation offers a solution. By integrating Excel with your existing business tools using Latenode, your graphs can automatically pull real-time data from systems like CRMs, marketing platforms, or databases. This ensures your visualizations always reflect the latest information, saving you time and reducing errors. With automation, you can focus less on updating data and more on analyzing insights and making smarter decisions.

FAQs

What are the benefits of using Excel's Copilot AI to create graphs instead of doing it manually?

Using Excel's Copilot AI to create graphs streamlines the process, making it faster and more intuitive compared to traditional methods. Instead of manually selecting data and configuring chart settings, you can simply describe the type of graph you need using natural language. This approach eliminates much of the guesswork and effort, which is particularly useful for those who may not be well-versed in Excel's advanced features.

Another advantage lies in Copilot's ability to enhance data analysis. It can identify patterns and trends within your dataset, helping you generate visuals that are not only accurate but also insightful. Additionally, Copilot offers real-time suggestions and allows you to make quick adjustments, ensuring that your graphs are polished and professional. These capabilities enable users to create impactful, data-driven visuals with ease and efficiency.

How can Latenode automate data updates in Excel, and what are some practical use cases?

Streamlining Excel Data Management with Latenode

Latenode takes the hassle out of managing data in Excel by automating updates from multiple sources. Instead of manually entering information, it seamlessly connects Excel to various tools and platforms, ensuring your spreadsheets are consistently updated with precise and current data.

For instance, Latenode can integrate with web scraping tools or CRM systems. Imagine automatically syncing new leads from your sales database into Excel - no manual input required. This not only ensures real-time updates but also minimizes errors, allowing you to focus on analyzing and utilizing the data effectively.

By automating these workflows, Latenode empowers businesses to save valuable time, enhance data accuracy, and maintain consistency across their operations, enabling smarter and faster decision-making.

When should I use a line chart instead of a scatter plot in Excel?

Use a line chart to illustrate trends or changes over a period of time. By connecting data points, it effectively highlights patterns in continuous data. This makes it a great choice for visualizing sequences like monthly revenue, temperature fluctuations, or stock market performance.

On the other hand, a scatter plot is better suited for examining the relationship between two independent numerical variables. Examples include comparing height to weight or study hours to test results. Scatter plots are particularly useful for spotting correlations or understanding how data is distributed, without suggesting a continuous flow between points.

To summarize, line charts work best for showcasing trends and time-based sequences, while scatter plots excel at analyzing relationships or patterns between variables.