34

I have some data, based on which I am trying to build a countplot in seaborn. So I do something like this:

data = np.hstack((np.random.normal(10, 5, 10000), np.random.normal(30, 8, 10000))).astype(int)
plot_ = sns.countplot(data)

and get my countplot:

enter image description here

The problem is that ticks on the x-axis are too dense (which makes them useless). I tried to decrease the density with plot_.xticks=np.arange(0, 40, 10) but it didn't help.

Also is there a way to make the plot in one color?

Archie
  • 2,247
  • 1
  • 18
  • 35
Salvador Dali
  • 214,103
  • 147
  • 703
  • 753
  • 2
    Don't use `countplot` for this application. Use, e.g. `distplot`, or just `plt.hist`. – mwaskom Aug 15 '16 at 01:57
  • You can increase the `figsize` of the plots to fill the entire window. Then, you get the `xticks` not being cluttered. Inorder to keep the same colors throughout the `countplot`, you can do `sns.countplot(data, color='r', saturation=1)` – Nickil Maveli Aug 15 '16 at 11:41

4 Answers4

34

Tick frequency

There seem to be multiple issues here:

    1. You are using the = operator while using plt.xticks. You should use a function call instead (but not here; read point 2 first)!
    1. seaborn's countplot returns an axes-object, not a figure
      • you need to use the axes-level approach of changing x-ticks (which is not plt.xticks())

Try this:

for ind, label in enumerate(plot_.get_xticklabels()):
    if ind % 10 == 0:  # every 10th label is kept
        label.set_visible(True)
    else:
        label.set_visible(False)

Colors

I think the data-setup is not optimal here for this type of plot. Seaborn will interpret each unique value as new category and introduce a new color. If i'm right, the number of colors / and x-ticks equals the number of np.unique(data).

Compare your data to seaborn's examples (which are all based on data which can be imported to check).

I also think working with seaborn is much easier using pandas dataframes (and not numpy arrays; i often prepare my data in a wrong way and subset-selection needs preprocessing; dataframes offer more). I think most of seaborn's examples use this data-input.

sascha
  • 32,238
  • 6
  • 68
  • 110
11

even though this has been answered a while ago, adding another perhaps simpler alternative that is more flexible.

you can use an matplotlib axis tick locator to control which ticks will be shown.

in this example you can use LinearLocator to achieve the same thing:

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.ticker as ticker

data  = np.hstack((np.random.normal(10, 5, 10000), np.random.normal(30, 8, 10000))).astype(int)
plot_ = sns.countplot(data)
plot_.xaxis.set_major_locator(ticker.LinearLocator(10))
Nitzan Volman
  • 1,809
  • 3
  • 17
  • 31
8

Since you have tagged matplotlib, one solution different from setting the ticks visible True/False is to plot every nth label as following

fig = plt.figure(); np.random.seed(123)

data = np.hstack((np.random.normal(10, 5, 10000), np.random.normal(30, 8, 10000))).astype(int)
plot_ = sns.countplot(data)

fig.canvas.draw()
new_ticks = [i.get_text() for i in plot_.get_xticklabels()]
plt.xticks(range(0, len(new_ticks), 10), new_ticks[::10])

enter image description here

Sheldore
  • 37,862
  • 7
  • 57
  • 71
6

As a slight modification of the accepted answer, we typically select labels based on their value (and not index), e.g. to display only values which are divisible by 10, this would work:

for label in plot_.get_xticklabels():
    if np.int(label.get_text()) % 10 == 0:  
        label.set_visible(True)
    else:
        label.set_visible(False)
mloning
  • 825
  • 7
  • 18