Plotly is a powerful Python graphing library, but integrating multiple data sources can sometimes cause your graph to break or behave unpredictably. If you’ve noticed “weird jumps,” blank plots, or lines that don’t connect as expected when combining datasets—for example, tracking theme park ride wait times from several CSVs—the underlying cause is often subtle issues in how Plotly interprets your time and categorical data. The good news is, with targeted data preparation and a few smart tweaks to your Plotly code, you can fix these problems and get reliable, clear visualizations—even with complex, asynchronous data streams.
Short answer: Plotly graphs often break with multiple data sources because of inconsistent datetime handling, data ordering, gaps in x-values, or large numbers of interactive subplots. To fix this, ensure all time or category axes are consistently converted to datetime objects, sort your combined dataframe by the x-axis variable, handle missing values or gaps using rangebreaks or connectgaps settings, and manage system resource limits when displaying many interactive plots.
Let’s break down why this happens, and walk through concrete solutions using real-world scenarios, as discussed across Stack Overflow and the Plotly Community.
Understanding the Root Causes
When you pool data from multiple sources—say, separate CSV files for each ride or time period—your dataframe amalgamates records that might not align perfectly. This is especially true for time series data, where different data sources might have slightly different sampling intervals or missing values. As noted on stackoverflow.com, “the weird jumps only happen when a sample is not taken at the same time every day,” which can make lines shoot off unexpectedly or sections of the plot appear blank.
A major culprit is how Plotly treats your x-axis. If your time stamps are stored as strings (like "18:41"), Plotly may interpret them as categorical rather than continuous data. This can wreak havoc when you split or color data by some grouping, such as ride name. One Stack Overflow contributor reproduced the issue, observing, “Plotly does not seem to like these ‘HH:MM’ time strings too much. I can reproduce your issue easily with ‘problematic times’ such as 18:41, which gets placed at the very end of the x-axes.” As a result, lines may connect points in the wrong order, or some traces might vanish altogether.
The same kinds of issues can arise with financial data, as seen in questions about plotting stock prices only during market hours. Plotly’s default behavior is to fill in every possible time slot on the x-axis, even if your data is missing those intervals, leading to unwanted gaps or misleading connections.
How to Fix Plotly Graphs with Multiple Data Sources
1. Always Convert Time Strings to Datetime Objects
This is the most crucial step. If your data’s time column is just a string, Plotly will treat it as a category, not a timeline. Convert your time columns using pandas’ pd.to_datetime function before plotting. For example:
self.frame['last_updated'] = pd.to_datetime(self.frame['last_updated'], format='%H:%M')
This ensures that Plotly recognizes the x-axis as a true timeline and arranges your points in the correct order, even when merging data from different sources or with slightly irregular intervals. According to stackoverflow.com, this “prevents plotly from treating this as categorical data,” and helps keep lines from going “crazy.”
2. Explicitly Sort Your Data by the X-Axis Variable
After concatenating data from multiple sources, always sort your dataframe by the variable you intend to use on the x-axis. For time series, this is usually your datetime column:
self.frame = self.frame.sort_values(by="last_updated", ignore_index=True)
This step ensures that regardless of the order in which data arrives or is read from files, your plot will show a coherent progression.
3. Handle Missing Data and Gaps with Rangebreaks or Connectgaps
When your data has gaps—such as missing time intervals or weekends in financial data—Plotly will by default leave blank spaces or connect lines across the gap, which can be visually misleading. To control this, use x-axis rangebreaks or the connectgaps parameter:
- Use rangebreaks for discontinuous axes. For example, to hide weekends or missing trading hours, you can specify which periods to skip:
fig.update_xaxes(rangebreaks=[ dict(bounds=["sat", "mon"]), # hide weekends dict(bounds=[16, 9.5], pattern="hour"), # hide non-trading hours ])
This technique is well-documented on stackoverflow.com, where users have shown how to “remove gaps for outside trading hours and weekends” using rangebreaks, resulting in a much cleaner and more accurate visualization.
- Use connectgaps=False to prevent lines from jumping across missing data:
fig.update_traces(connectgaps=False)
As noted by nicolaskruchten on stackoverflow.com, this will make Plotly “not connect gaps in plotly express line graph.” However, this only works if there are actual NaN values in your data for the gaps—otherwise, Plotly won’t recognize the missing intervals. If your data for each ride or group doesn’t include all possible timestamps, you may need to “generate the missing dates for all the colors,” as user2974951 suggests, so that gaps are registered correctly.
4. Flatten Arrays and Check Data Shapes
When using Plotly within Dash or when combining multiple sources, sometimes the plot appears blank even though the data is present. This can happen if your y-values are multidimensional arrays instead of flat vectors. As stackoverflow.com points out, “Your y-coordinates have the wrong format. I suggest you flatten them in your scatter creation.” Use y=y_data.flatten() to ensure compatibility.
5. Manage System Limits When Displaying Many Interactive Plots
If you’re building dashboards with many subplots or interactive graphs, you may encounter technical or resource limitations, especially in environments like Jupyter Notebook. For instance, users have reported that “when I plot more than 9 graphs, the plots go blank starting from the 1st one,” a memory or buffer issue (stackoverflow.com). The solution can involve changing the renderer (for example, to SVG for static plots), or increasing Jupyter’s max_buffer_size in your configuration file:
c.NotebookApp.max_buffer_size = 18000000000
Alternatively, consider using JupyterDash, which is “pretty awesome and should handle your use case easily,” according to community advice.
6. Use Category Type Axes with Caution
In some cases, especially when plotting categorical or irregular time data, you can force the x-axis to be treated as categories:
fig.update_layout(xaxis={'type':'category'})
However, as highlighted on stackoverflow.com, this can lead to side effects: tick marks will be evenly spaced, regardless of the actual time intervals between your data points. It works well for some use cases, but can distort timelines if not used carefully.
7. Data Cleaning: Remove Out-of-Bounds or Irrelevant Data
Before plotting, make sure to remove any data points outside the desired time intervals (such as before market open or after close). This will prevent Plotly from drawing lines across irrelevant periods, reducing visual noise and confusion, as recommended by Hamzah Al-Qadasi on stackoverflow.com.
Key Takeaways and Real-World Examples
Let’s recap with specific, checkable details from the sources:
- Plotly treats time strings like "18:41" as categories, not times, unless explicitly converted (stackoverflow.com). - Sorting your dataframe by the datetime column is critical after merging different data sources (stackoverflow.com). - Use pd.to_datetime to convert time strings, as in self.frame['last_updated'] = pd.to_datetime(self.frame['last_updated'], format = '%H:%M') (stackoverflow.com). - Use rangebreaks to remove gaps for weekends or non-trading hours, e.g., dict(bounds=["sat", "mon"]) (stackoverflow.com). - connectgaps=False prevents lines from jumping across NaNs, but you may need to generate these missing values for each color/group (stackoverflow.com). - For many interactive subplots, increase Jupyter’s max_buffer_size or switch to JupyterDash to avoid blank plots (stackoverflow.com). - Flatten multidimensional arrays for y-values in Dash to ensure plots render (stackoverflow.com).
If you follow these steps, your Plotly graphs should display smoothly and accurately, even as you combine multiple, asynchronous data sources. The key is to always treat your x-axis data (especially time series) with precision: convert, sort, fill or mark missing values, and configure your plot to handle real-world data irregularities. This approach is echoed again and again by Plotly experts and Stack Overflow contributors, ensuring that “lines graph from two different data sources” can be both robust and insightful (community.plotly.com).
In summary, the solution to Plotly graphs breaking with multiple data sources lies in careful preprocessing—especially datetime handling—plus thoughtful configuration of Plotly’s axes and trace settings. With these tools, you can tame even the messiest multi-source data into clear, compelling visualizations.