How to make a bar plot of non-numerical data in pandas

Question

Suppose I had this data:

>>> df = pd.DataFrame(data={"age": [11, 12, 11, 11, 13, 11, 12, 11],
                        "response": ["Yes", "No", "Yes", "Yes", "Yes", "No", "Yes", "Yes"]})
>>> df
    age response
0   11  Yes
1   12  No
2   11  Yes
3   11  Yes
4   13  Yes
5   11  No
6   12  Yes
7   11  Yes

I would like to make a bar plot that shows the yes or no responses aggregated by age. Would it be possible at all? I have tried hist and kind=bar, but neither was able to sort by age, instead graphing both age and response separately.

It would look like this:

  ^
4 |   o
3 |   o
2 |   o
1 |   ox      ox      o
0 .----------------------->
      11      12      13  

where o is "Yes", and x is "No".

Also, would it be possible to make the numbers grouped? If you had a range from 11 to 50, for instance, you might be able to put it in 5-year bins. Also, would it be possible to show percentages or counts on the axis or on the individual bar?

Answer 1

To generate a multiple bar plot, you would first need to group by age and response and then unstack the dataframe:

df=df.groupby(['age','response']).size()
df=df.unstack()
df.plot(kind='bar')

Here is the output plot:

Answer 2

To bin your data, take a look at pandas.cut() see docs. For categorical plots, I've found the seaborns package quite helpful - see the tutorial on categorical plots. Below an example for a plot of the yes/no counts for the bins you mention using a random sample:

df = pd.DataFrame(data={"age": randint(10, 50, 1000),
                    "response": [choice(['Yes', 'No']) for i in range(1000)]})

df['age_group'] = pd.cut(df.age, bins=[g for g in range(10, 51, 5)], include_lowest=True)
df.head()

   age response age_group
0   48      Yes  (45, 50]
1   31       No  (30, 35]
2   25      Yes  (20, 25]
3   29      Yes  (25, 30]
4   19      Yes  (15, 20]

import seaborn as sns
sns.countplot(y='response', hue='age_group', data=df, palette="Greens_d")