pandas 分组和可视化

Question

I have to do some analysis using Python3 and pandas with a dataset which is shown as a toy example-

data
'''
    location importance    agent  count
0     London        Low  chatbot      2
1        NYC     Medium  chatbot      1
2     London       High    human      3
3     London        Low    human      4
4        NYC       High    human      1
5        NYC     Medium  chatbot      2
6  Melbourne        Low  chatbot      3
7  Melbourne        Low    human      4
8  Melbourne       High    human      5
9        NYC       High  chatbot      5
'''

My aim is to group the location and then count the number of Low, Medium and/or High 'importance' column for each location. So far, the code I have come up with is-

data.groupby(['location', 'importance']).aggregate(np.size)
'''
                      agent  count
location  importance              
London    High            1      1
          Low             2      2
Melbourne High            1      1
          Low             2      2
NYC       High            2      2
          Medium          2      2
'''

This grouping and count aggregation contains index as the grouping objects-

data.groupby(['location', 'importance']).aggregate(np.size).index

I don't know how to proceed next? Also, how can I visualize this?

Help?

Answer 1

I think you need DataFrame.pivot_table , added aggfunc=sum for aggregate if duplicates and then use DataFrame.plot :

df = data.pivot_table(index='location', columns='importance', values='count', aggfunc='sum')

df.plot()

If need counts of pairs location with importance use crosstab :

df = pd.crosstab(data['location'], data['importance'])

df.plot()