I am trying to use the matplotlib boxplot to show the boxplot of a number of distributions. One group of distributions is on a very different scale than the other group. I have been trying to use twinx() to plot the second group but it overlaps with the other boxplots.
Is there a better method to add a different scale for specific data?
Related
When I was organizing my skewed distribution data to boxplot in python, it has a lot of outliers. I want to show only maximum & minimum outlier.
How can I make a code?
I don't want to remove my database. Just I want to show two outliers(Max, Min) in my graph image.
showfliers=False
or
plt.boxplot([data], showfliers=False)
try this ....
I currently have a dataframe called df with 18 columns, and I have plotted a histogram of each to check the distribution shape of each variable using the hist() function in pandas:
df.hist(figsize=(30,30))
What I now want to do is add a boxplot above each box plot so I can understand at a glance which variables contain outliers. I want the plot to look as follows:
I can plot the boxplot using the following code, but it displays all of the boxplots on a single plot:
df.boxplot(figsize=(30,30))
And I can add a group by, however, this isn't what I require. I just want each histogram in my df.hist plot to be overlayed with the boxplot derived from the same column of data. I suspect I could write a funciton to do this, but as the hist function seems quite intuitive, I suspect there is a straighforward way that I'm probably missing.
I have the recurrent issue of having matplotlib bar graphs containing too many categorical values in the X axis. Resize a figure automatically in matplotlib and Python matplotlib multiple bars does not make the trick because my x values are not x. I am having the idea of splitting the graph into two graphs when it get past a certain amount of data point in the graph. I cannot find anything about in the matplotlib document, nor anywhere.
Is there a matplotlib tool to do that? or i would need to write an algorithm that detects the length of the dataset?
Is there a way to draw a frequency distribution graph in python or R?
In R, using histograms, which show frequency on y axis vs some categorization on x-axis as in your example.
hist() function at the very least help you plot one vector (a set of values). ?hist for brief documentation, also search this site
how to plot two vectors side by side, similar to your posted example, an example is at http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/ , scroll down to Histogram and density plots with multiple groups
Is there a idiomatic way to plot the histogram of a feature for two classes?
In pandas, I basically want
df.feature[df.class == 0].hist()
df.feature[df.class == 1].hist()
To be in the same plot. I could do
df.feature.hist(by=df.class)
but that gives me two separate plots.
This seems to be a common task so I would imagine there to be an idiomatic way to do this. Of course I could manipulate the histograms manually to fit next to each other but usually pandas does that quite nicely.
Basically I want this matplotlib example in one line of pandas: http://matplotlib.org/examples/pylab_examples/barchart_demo.html
I thought I was missing something, but maybe it is not possible (yet).
How about df.groupby("class").feature.hist()? To see overlapping distributions you'll probably need to pass alpha=0.4 to hist(). Alternatively, I'd be tempted to use a kernel density estimate instead of a histogram with df.groupby("class").feature.plot(kind='kde').
As an example, I plotted the iris dataset's classes using:
iris.groupby("Name").PetalWidth.plot(kind='kde', ax=axs[1])
iris.groupby("Name").PetalWidth.hist(alpha=0.4, ax=axs[0])