How to create a Pandas dataframe from another column in a dataframe by splitting it?

Question

I have the following source dataframe

Person	Country	Is Rich?
0	US	Yes
1	India	No
2	India	Yes
3	US	Yes
4	US	Yes
5	India	No
6	US	No
7	India	No

I need to convert it another dataframe for plotting a bar graph like below for easily accessing data

Bar chart of economic status per country

Data frame to be created is like below.

Country	Rich	Poor
US	3	1
India	1	3

I am new to Pandas and Exploratory data science. Please help here

Answer 1

You can try pivot_table

df['Is Rich?'] = df['Is Rich?'].replace({'Yes': 'Rich', 'No': 'Poor'})
out = df.pivot_table(index='Country', columns='Is Rich?', values='Person', aggfunc='count')

print(out)

Is Rich?  Poor  Rich
Country
India        3     1
US           1     3

Answer 2

You could do:

converted = df.assign(Rich=df['Is Rich?'].eq('Yes')).eval('Poor = ~Rich').groupby('Country').agg({'Rich': 'sum', 'Poor': 'sum'})

print(converted)

         Rich  Poor
Country            
India       1     3
US          3     1

However, if you want to plot it as a barplot, the following format might work best with a plotting library like seaborn :

plot_df = converted.reset_index().melt(id_vars='Country', value_name='No. of people', var_name='Status')
print(plot_df)

  Country Status  No. of people
0   India   Rich              1
1      US   Rich              3
2   India   Poor              3
3      US   Poor              1

Then, with seaborn :

import seaborn as sns

sns.barplot(x='Country', hue='Status', y='No. of people', data=plot_df)

Resulting plot: