CODEWITHSUNDEEP
pythonpython-3.xdataframematplotlibboxplot
Noobcoder
Noobcoder

Reputation: 396

multiple boxplots, side by side, using matplotlib from a dataframe

I'm trying to plot 60+ boxplots side by side from a dataframe and I was wondering if someone could suggest some possible solutions.

At the moment I have df_new, a dataframe with 66 columns, which I'm using to plot boxplots. The easiest way I found to plot the boxplots was to use the boxplot package inside pandas:

boxplot = df_new.boxplot(column=x, figsize = (100,50))

This gives me a very very tiny chart with illegible axis which I cannot seem to change the font size for, so I'm trying to do this natively in matplotlib but I cannot think of an efficient way of doing it. I'm trying to avoid creating 66 separate boxplots using something like:

fig, ax = plt.subplots(nrows = 1,
                       ncols = 66, 
                       figsize = (10,5),
                       sharex = True)
ax[0,0].boxplot(#insert parameters here)

I actually do not not how to get the data from df_new.describe() into the boxplot function, so any tips on this would be greatly appreciated! The documentation is confusing. Not sure what x vectors should be.

Ideally I'd like to just give the boxplot function the dataframe and for it to automatically create all the boxplots by working out all the quartiles, column separations etc on the fly - is this even possible?

Thanks!

Upvotes: 1

Views: 1333

Answers (1)

Zephyr
Zephyr

Reputation: 12496

I tried to replace the boxplot with a ridge plot, which takes up less space because:

  1. it requires half of the width
  2. you can partially overlap the ridges
  3. it develops vertically, so you can scroll down all the plot

I took the code from the seaborn documentation and adapted it a little bit in order to have 60 different ridges, normally distributed; here the code:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import itertools
sns.set(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# # Create the data
n = 20
x = list(np.random.randn(1, 60)[0])
g = [item[0] + item[1] for item in list(itertools.product(list('ABCDEFGHIJ'), list('123456')))]
df = pd.DataFrame({'x': n*x,
                   'g': n*g})

# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw=.2)
g.map(plt.axhline, y=0, lw=2, clip_on=False)


# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .2, label, fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)


g.map(label, "x")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)

plt.show()

This is the result I get:

enter image description here

I don't know if it will be good for your needs, in any case keep in mind that keeping so many distributions next to each other will always require a lot of space (and a very big screen). Maybe you could try dividing the distrubutions into smaller groups and plotting them a little at a time?

Upvotes: 1

Related Questions