Python: Plotting percentage in seaborn bar plot

Question

For a dataframe

import pandas as pd
df=pd.DataFrame({'group':list("AADABCBCCCD"),'Values':[1,0,1,0,1,0,0,1,0,1,0]})

I am trying to plot a barplot showing percentage of times A, B, C, D takes zero (or one).

I have a round about way which works but I am thinking there has to be more straight forward way

tempdf=df.groupby(['group','Values']).Values.count().unstack().fillna(0)
tempdf['total']=df['group'].value_counts()
tempdf['percent']=tempdf[0]/tempdf['total']*100

tempdf.reset_index(inplace=True)
print tempdf

sns.barplot(x='group',y='percent',data=tempdf)

If it were plotting just the mean value, I could simply do sns.barplot on df dataframe than tempdf. I am not sure how to do it elegantly if I am interested in plotting percentages.

Thanks,

Answer 1

You can use Pandas in conjunction with seaborn to make this easier:

import pandas as pd
import seaborn as sns

df = sns.load_dataset("tips")
x, y, hue = "day", "proportion", "sex"
hue_order = ["Male", "Female"]

(df[x]
 .groupby(df[hue])
 .value_counts(normalize=True)
 .rename(y)
 .reset_index()
 .pipe((sns.barplot, "data"), x=x, y=y, hue=hue))

Answer 2

You can use the library Dexplot, which has the ability to return relative frequencies for categorical variables. It has a similar API to Seaborn. Pass the column you would like to get the relative frequency for to the agg parameter. If you would like to subdivide this by another column, do so with the hue parameter. The following returns raw counts.

import dexplot as dxp
dxp.aggplot(agg='group', data=df, hue='Values')

To get the relative frequencies, set the normalize parameter to the column you want to normalize over. Use 'all' to normalize over the overall total count.

dxp.aggplot(agg='group', data=df, hue='Values', normalize='group')

Normalizing over the 'Values' column would produce the following graph, where the total of all the '0' bars are 1.

dxp.aggplot(agg='group', data=df, hue='Values', normalize='Values')

Answer 3

You could use your own function in sns.barplot estimator, as from docs:

estimator : callable that maps vector -> scalar, optional
Statistical function to estimate within each categorical bin.

For you case you could define function as lambda:

sns.barplot(x='group', y='Values', data=df, estimator=lambda x: sum(x==0)*100.0/len(x))