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The "dots" at the end of the boxplot represent outliers. There are a number of different rules for determining if a point is an outlier, but the method that R and ggplot use is the "1.5 rule". If a data point is: less than Q1 - 1.5*IQR. greater than Q3 + 1.5*IQR. then that point is classed as an "outlier". The whiskers are defined as: upper ...
The box extends from the lower to upper quartile values of the data, with a line at the median. i.e. a quarter of the input data values is below the box, a quarter of the data lies in each part of the box, and the remaining quarter lies above the box. Question B: whis : float, sequence, or string (default = 1.5)
I'm trying to use ggplot2 / geom_boxplot to produce a boxplot where the whiskers are defined as the 5 and 95th percentile instead of 0.25 - 1.5 IQR / 0.75 + IQR and outliers from those new whiskers are plotted as usual. I can see that the geom_boxplot aesthetics include ymax / ymin, but it's not clear to me how I put values in here.
25. From ?boxplot. range: this determines how far the plot whiskers extend out from the box. If ‘range’ is positive, the whiskers extend to the most extreme data point which is no more than ‘range’ times the interquartile range from the box. A value of zero causes the whiskers to extend to the data extremes. So set range=3.
The whisker of the box plot has the following possible definitions: the minimum and maximum of all of the data [1] the lowest datum still within 1.5 IQR of the lower quartile, and the highest datum still within 1.5 IQR of the upper quartile. one standard deviation above and below the mean of the data. the 9th percentile and the 91st percentile.
15. Pandas just wraps the boxplot function from matplotlib. The matplotlib docs have the definition of the whiskers in detail: whis : float, sequence, or string (default = 1.5) As a float, determines the reach of the whiskers to the beyond the first and third quartiles. In other words, where IQR is the interquartile range (Q3-Q1), the upper ...
Solution using only matplotlib, just because: # start the plot: 2 rows, because we want the boxplot on the first row # and the hist on the second fig, ax = plt.subplots( 2, figsize=(7, 5), sharex=True, gridspec_kw={"height_ratios": (.3, .7)} # the boxplot gets 30% of the vertical space ) # the boxplot ax[0].boxplot(data, vert=False) # removing borders ax[0].spines['top'].set_visible(False) ax ...
The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be “outliers” using a method that is a function of the inter-quartile range. I would really like to know what method they use.
I understand that the ends of whiskers in matplotlib's box plot function extend to max value below 75% + 1.5 IQR and minimum value above 25% - 1.5 IQR. I would like to change it to represent max and minimum values of the data or the 5th and 95th quartile of the data.
Putting @nicolaskruchten comments in an answer: The labelled "upper fence" and "lower fence" in the plotly barplot (and violin plot) hover text point at the ends of the whiskers. The whiskers extend to the point closest to, but within, 1.5 * IQR where IQR is the interquartile range, 75th percentile - 25th percentile. answered Feb 12 at 19:11.