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The question is pretty simple but I'm unclear if the solution is. I have basketball player stats that I want to visualize over time and sometimes I want to visualize it across multiple seasons (years). They don't play year-round so there are generally about 6 months where there is simply no data.
If I don't care necessarily that the dates on the x-axis are scaled accurately, is there a good way to visualize these multiple seasons without a) having massive gaps in the graph and b) interpolating a physically long line filler?
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I am trying to find the correlation between the dominant color of an image and how well it does on social media. I have found the dominant color in an image and have converted the rgb value I received into a categorical value such as red or blue. The problem is I now have have over 130 unique values which is too much to use for machine learning. I am wondering if there is any other way to achieve my goal?
Perhaps I can get rid of the color name, and create three columns for R,G,B and predict whether a higher value in a column leads to a higher correlation among social media success?
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I recently read this article, where someone maps words to colors in books and produces visualizations. Does anything dictionary exist that maps words to colors in python? I suppose words that are colors like 'yellow' would be fairly easy to identify, but I am wondering if there is an existing way to map words like 'sunshine' to yellow or 'smoke' to gray.
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I want to produce a flow field map in Python which should be perfectly readable for people with colorblindness. Just like the image on the following page (created with GMT):
My data is in netcdf format, the flow vectors are in u and v components and I am plotting it on a meshgrid(lon, lat) with plt.quiver.
The problem with quiver is that you just can control the colors ond not the style of arrows.
I have a workaround with windbarbs (plt.barbs), however it would be much better to include custom arrows for specific data values.
Is there a way in python to include different arrow styles?
Or is there a possibility to include arrow styles for specific data ranges?
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right now I'm looking for an sklearn method that does something like:
arr = [13,15,41,45,90,100]
print(KMeans.num_clusters(arr))
Outputs 3
You can use mean-shift clustering. It does not require number of clusters beforehand. However, the drawback of mean shift is that it is not very efficient compared to the k-means. Since your example array is only 1 dimensional it should not be a problem. If you are going to use mean-shift with 2 or more dimensional data, be careful with the curse of dimensionality.
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I am doing a dissertation on data visualization of land prices in Tanzania.
My intendants want me to use a GIS software to map the data on a map and use python code to visualize it.
My question is how can i use python to draw the maps ?
If you want to perform data visualisation, you can use the python library called matplotlib
To perform the mapping of data on geographical maps using matplotlib, here are the examples which helps you to get started.
I hope this helps you.