The noise can be random numbers between, for example, 0 and 1. You need to test a few values to determine the best balance for your particular use case.Ī good exercise to get a feel for this is to take the code example above and add some noise to the sine wave. The larger the window size, the greater the lags of the peaks and the troughs but the smoother the data. Running the above code produces the following plot in a new window: > plt.plot(x, average_y, 'r.-', label='Running average') > plt.plot(x, y, 'k.-', label='Original data') Now, we can plot the results using matplotlib: Here’s how to add NaNs to the start of the running average to ensure the list has the same length as the original data: To demonstrate this, we can create a sine wave and calculate a running average in Python like we have done earlier: This is important to keep in mind if you want to identify when a peak in the data has happened and what its magnitude is. The magnitude of the values is also different from the real data. Plotting a Running Average in matplotlibĪs a consequence of this method for smoothing data, the features (e.g., peaks or troughs) in a graph of a moving average lag the real features in the original data. A convenient way to do this is by inserting a NaN at the start of the list using list.insert().
If you want to compare the running average to the original data, you have to align them correctly. Notice the loop is over len(data) – window + 1, which means our smoothed data has only 9 data points. The index then gets advanced with a for loop, and we repeat. Then, we use NumPy to calculate the mean value. Here, we define a window size of 2 data points and use a list slice to get the subset of data we want to average. > for ind in range(len(data) – window + 1): To demonstrate this, let’s define some data and calculate a running average in Python in a for loop: We define a window, calculate an average in the window, slide the window by one data point, and repeat until we get to the end. This can be any number from 2 to n-1, where n is the number of data points in the time series. To generate a running average, we need to decide on a window size in which to calculate the average values. For more material that builds on top of that, take a look at this data science track. If you’re looking for an introduction to data science, we have a course that provides the foundational skills. Here’s what that graph looks like.This article is aimed at people with a bit of experience in data analysis. The columns can then use data labels to show the exact values for the audience. In this situation I suggest creating a graph that has a minimal measurement axis, just showing the lower and upper expected range as the minimum and maximum values. In this case, the segment of the column from 0% to 90% does not add any value for the audience. This equipment operates within a known tolerance and the real message is where the values are within that expected range. In the above example, the presenter and audience know that the expected values are between 90% and 100%.
When the values are close to each other and the expected values are within a certain range, then I think it is OK to not start the measurement axis at zero. The example above illustrates the exception I see in this rule. In most cases, starting the measurement axis at zero is the right approach because we want to accurately portray the data in a visual. The comparison is more accurate, but now the challenge is being able to distinguish the values in the different days because the heights of each column are so similar. In this example, the default chosen by PowerPoint makes the values for Day 2-5 look like they are double the value of Day 1, when that is not the case. This is especially true when there are no data labels and the audience is only comparing the height of the columns. This can lead to misinterpretation by the audience. What the programs seem to be trying to do is to make the difference in values easier to see by adjusting the starting value of the axis. The problem comes when the axis does not start at zero. It seems to depend on the values in the data. I haven’t been able to figure out how the programs select these values. For example, the vertical axis on a column graph is the measurement axis. By default, Excel and PowerPoint select the minimum and maximum values for the measurement axis on a graph. While I agree with them in most cases, I want to suggest an exception to this rule. If you read articles by data visualization experts on the topic of measurement axes that don’t start at zero, you will find one common theme: strong opposition to the idea.