pythonpandasmatplotlibseaborncountplot

countplot() with frequencies

I have a Pandas DataFrame with a column called "AXLES", which can take an integer value between 3-12. I am trying to use Seaborn's countplot() option to achieve the following plot:

  1. left y axis shows the frequencies of these values occurring in the data. The axis extends are [0%-100%], tick marks at every 10%.
  2. right y axis shows the actual counts, values correspond to tick marks determined by the left y axis (marked at every 10%.)
  3. x axis shows the categories for the bar plots [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
  4. Annotation on top of the bars show the actual percentage of that category.

The following code gives me the plot below, with actual counts, but I could not find a way to convert them into frequencies. I can get the frequencies using df.AXLES.value_counts()/len(df.index) but I am not sure about how to plug this information into Seaborn's countplot().

I also found a workaround for the annotations, but I am not sure if that is the best implementation.

Any help would be appreciated!

Thanks

plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')

for p in ax.patches:
        ax.annotate('%{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))

enter image description here

EDIT:

I got closer to what I need with the following code, using Pandas' bar plot, ditching Seaborn. Feels like I'm using so many workarounds, and there has to be an easier way to do it. The issues with this approach:

enter image description here

Solution

  • You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move the axis labels

    You can then set the ticks using the matplotlib.ticker module, specifically ticker.MultipleLocator and ticker.LinearLocator.

    As for your annotations, you can get the x and y locations for all 4 corners of the bar with patch.get_bbox().get_points(). This, along with setting the horizontal and vertical alignment correctly, means you don't need to add any arbitrary offsets to the annotation location.

    Finally, you need to turn the grid off for the twinned axis, to prevent grid lines showing up on top of the bars (ax2.grid(None))

    Here is a working script:

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

# Some random data
dfWIM = pd.DataFrame({'AXLES': np.random.normal(8, 2, 5000).astype(int)})
ncount = len(dfWIM)

plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')

# Make twin axis
ax2=ax.twinx()

# Switch so count axis is on right, frequency on left
ax2.yaxis.tick_left()
ax.yaxis.tick_right()

# Also switch the labels over
ax.yaxis.set_label_position('right')
ax2.yaxis.set_label_position('left')

ax2.set_ylabel('Frequency [%]')

for p in ax.patches:
    x=p.get_bbox().get_points()[:,0]
    y=p.get_bbox().get_points()[1,1]
    ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y), 
            ha='center', va='bottom') # set the alignment of the text

# Use a LinearLocator to ensure the correct number of ticks
ax.yaxis.set_major_locator(ticker.LinearLocator(11))

# Fix the frequency range to 0-100
ax2.set_ylim(0,100)
ax.set_ylim(0,ncount)

# And use a MultipleLocator to ensure a tick spacing of 10
ax2.yaxis.set_major_locator(ticker.MultipleLocator(10))

# Need to turn the grid on ax2 off, otherwise the gridlines end up on top of the bars
ax2.grid(None)

plt.savefig('snscounter.pdf')

    enter image description here