How To Get Values Above The Bars On A Seaborn Countplot Grouped Using "melt"

I have a seaborn count plot, but instead of colour bars I need the value above each bar. My input is pandas data frame. ax = sns.countplot(x='variable', hue='value', data=pd.melt(d

Solution 1:

Sometimes it's easier to not try to find ways to tweak seaborn, but rather to directly use matplotlib and build a chart up from scratch.

Here, we can assume to have a dataframe named counts that looks like

hue     c    m    w
class              
A203129B4011263
C      85203117

where the index are the positions along x axis and the columns are the different hues. In the following, groupedbarplot is a function to take such dataframe as input and plot the bars as groups, and in addition add a label to each one of them.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(42)

defgroupedbarplot(df, width=0.8, annotate="values", ax=None, **kw):
    ax = ax or plt.gca()
    n = len(df.columns)
    w = 1./n
    pos = (np.linspace(w/2., 1-w/2., n)-0.5)*width
    w *= width
    bars = []
    for col, x inzip(df.columns, pos):
        bars.append(ax.bar(np.arange(len(df))+x, df[col].values, width=w, **kw))
        for val, xi inzip(df[col].values, np.arange(len(df))+x):
            if annotate:
                txt = val if annotate == "values"else col
                ax.annotate(txt, xy=(xi, val), xytext=(0,2), 
                            textcoords="offset points",
                            ha="center", va="bottom")
    ax.set_xticks(np.arange(len(df)))
    ax.set_xticklabels(df.index)
    return bars


df = pd.DataFrame({"class" : np.random.choice(list("ABC"), size=700, p=[.1,.3,.6]),
                   "hue" : np.random.choice(["m", "w" ,"c"], size=700, p=[.5,.3,.2])})

counts = df.groupby(["class", "hue"]).size().unstack()

groupedbarplot(counts, annotate="col")
plt.show()

We could also label the values directly, groupedbarplot(counts, annotate="values")