Seaborn: countplot() with frequencies

Question

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 follo

Answer 1

I got it to work using core matplotlib's bar plot. I didn't have your data obviously, but adapting it to yours should be straight forward.

Approach

I used matplotlib's twin axis and plotted the data as bars on the second Axes object. The rest ist just some fiddeling around to get the ticks right and make annotations.

Hope this helps.

Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns

tot = np.random.rand( 1 ) * 100
data = np.random.rand( 1, 12 )
data = data / sum(data,1) * tot

df = pd.DataFrame( data )
palette = sns.husl_palette(9, s=0.7 )

### Left Axis
# Plot nothing here, autmatically scales to second axis.

fig, ax1 = plt.subplots()
ax1.set_ylim( [0,100] )

# Remove grid lines.
ax1.grid( False )
# Set ticks and add percentage sign.
ax1.yaxis.set_ticks( np.arange(0,101,10) )
fmt = '%.0f%%'
yticks = matplotlib.ticker.FormatStrFormatter( fmt )
ax1.yaxis.set_major_formatter( yticks )

### Right Axis
# Plot data as bars.
x = np.arange(0,9,1)
ax2 = ax1.twinx()
rects = ax2.bar( x-0.4, np.asarray(df.loc[0,3:]), width=0.8 )

# Set ticks on x-axis and remove grid lines.
ax2.set_xlim( [-0.5,8.5] )
ax2.xaxis.set_ticks( x )
ax2.xaxis.grid( False )

# Set ticks on y-axis in 10% steps.
ax2.set_ylim( [0,tot] )
ax2.yaxis.set_ticks( np.linspace( 0, tot, 11 ) )

# Add labels and change colors.
for i,r in enumerate(rects):
    h = r.get_height()
    r.set_color( palette[ i % len(palette) ] )
    ax2.text( r.get_x() + r.get_width()/2.0, \
              h + 0.01*tot,                  \
              r'%d%%'%int(100*h/tot), ha = 'center' )