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How to identify and remove extreme values and long tails from a distribution.How to consider the size of the sample and whether the law of large numbers may help improve the distribution of a sample.In this tutorial, you will discover the reasons why a Gaussian-like distribution may be distorted and techniques that you can use to make a data sample more normal.Īfter completing this tutorial, you will know: This is more likely if you are familiar with the process that generated the observations and you believe it to be a Gaussian process, or the distribution looks almost Gaussian, except for some distortion. It is possible that your data does not look Gaussian or fails a normality test, but can be transformed to make it fit a Gaussian distribution. Even if your data does not have a Gaussian distribution. This gives some incentive to use them if possible. If your data has a Gaussian distribution, the parametric methods are powerful and well understood. It may take some more time at the beginning, but with the advantage that you will not have to repeat the process when, for some reason, you need to modify the plots you generated with matplotlib (to add more data or modify the parameters of your analysis, just to name a few reasons).A large portion of the field of statistics is concerned with methods that assume a Gaussian distribution: the familiar bell curve. The basic operations are similar to what you would do in a vector graphics editor, but instead of using a mouse you will do some scripting (I am sure you love it as much as I do). It is written completely in Python and uses only standard libraries.
To this end, I will use a small Python package I wrote with this purpose svgutils.
Now, we would like to combine both plots into one figure and add some annotations (such as one-letter labels: A,B, etc.). You may try to open one of them in a text editor to find out what I mean. Not going too much into details, I will only say that SVG files are text files with special predefined tags (much alike HTML tags).
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You may download the scripts I will use in the example from github repository: anscombe.py and sigmoid_fit.py.Ī nice feature of matplotlib is that it allows to export figure to Scalable Vector Graphics (SVG) which is an open vector format understood by many applications (such as Inkscape, Adobe Illustrator or even web browsers). Here, I will describe an automatic workflow which completely resides on Python tools.įirst you need to create nice matplotlib-based plots you would like to compose your figure from. Therefore it makes sense to try and automate the process.
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This includes manual editing and arranging the figure, which is obviously time consuming. To make things worse you may need to repeat the process several times, when, for example, you want to include more data into the analysis. Import figures to vector-graphics editorĪs you probably see, the typical workflow is quite complicated.Therefore, post-processing of plots is usually done in some other vector graphicseditor such as inkscape or Adobe Illustrator. However, one limitation of matplotlib is that creating complex layouts can be at times complicated. Matplotlib is a decent Python library for creating publication-quality plots which offers a multitude of different plot types.
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