![]() For instance, we can see that the most common flipper length is about 195 mm, but the distribution appears bimodal, so this one number does not represent the data well. This plot immediately affords a few insights about the flipper_length_mm variable. displot ( penguins, x = "flipper_length_mm" ) A histogram is a bar plot where the axis representing the data variable is divided into a set of discrete bins and the count of observations falling within each bin is shown using the height of the corresponding bar: This is the default approach in displot(), which uses the same underlying code as histplot(). Perhaps the most common approach to visualizing a distribution is the histogram. It is important to understand these factors so that you can choose the best approach for your particular aim. There are several different approaches to visualizing a distribution, and each has its relative advantages and drawbacks. They are grouped together within the figure-level displot(), jointplot(), and pairplot() functions. ![]() The axes-level functions are histplot(), kdeplot(), ecdfplot(), and rugplot(). ![]() The distributions module contains several functions designed to answer questions such as these. What range do the observations cover? What is their central tendency? Are they heavily skewed in one direction? Is there evidence for bimodality? Are there significant outliers? Do the answers to these questions vary across subsets defined by other variables? Techniques for distribution visualization can provide quick answers to many important questions. # you are iterating over day=i, so you only have 5 frames hereĪnim = animation.An early step in any effort to analyze or model data should be to understand how the variables are distributed. # print(i,col)# use this to understand "where" we are # for convenience: define a function which prepares the dataĬol = data.loc here, but you could also just use plt.Īx.set_title('Daily changes in pollution levels',fontsize=20) # print (data)# use this to better understand what is being plotted Very grateful for advice on this or any other obvious errors I suspect there are other errors but the biggest obstacle is the error message AttributeError: 'AxesSubplot' object has no attribute 'set_data' in reference to the initiation function when I attempt to save animation but I cannot find an alternative way of doing this. Hue = data.locĪnim = animation.FuncAnimation(fig, animate, init_func=init, frames=15, interval=20, blit=True)Īnim.save('sensors.mp4', writer = 'ffmpeg', fps = 5) ![]() Plt.title('Daily changes in pollution levels',fontsize=20) Lat = (random.sample(range(25, 35), 3))*5Ĭolours = ĭata = pd.DataFrame(list(zip(days, channel, long, lat, colour)), columns = ) # Suppose 3 fixed sensors, each with a result every day for 5 days I have subsequently attempted to use matplotlib scatter using x, y and facecolors as variables but with the same problem import pandas as pd My original goal was to use seaborn scatterplot with x and y fixed but with hue changing in each frame depending on the pollution level (see code below) but I seem to get a problem when setting initialisation functionĪttributeError: 'AxesSubplot' object has no attribute 'set_data' The second solution given gave me NameError: name 'xrange' is not defined This has proved challenging as seaborn and scatterplots seem to have different code structure to lines. The link identified as a duplicate covers the same topic but found the first option complex and hard to adjust to my own fairly simple needs. Most of the examples I have found online on SO and matplotlib documentation related to plotting animated line graphs rather than scatterplots. ![]() I am running into problems using 'set_data' in the initialisation and animation functions (In a more advanced case the same situation but with mobile sensors so coordinates will change and colours will change over time). In the first case the location (lat, long) of each sensor is fixed on the scatterplot but at different times of the day, or times of the year the colour will change depending upon the level of pollution. I am trying to plot the readings of several air pollution sensors over time. ![]()
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