Remove none values from dataframe - pandas

Question

I have a dataframe like :
Country Name Income Group
1 Norway High income
2 Switzerland Middle income
3 Qatar Low income
4 Luxembourg Low income
5 Macao High income
6 India Middle income
i need something like:
High income Middle income Low income
1 Norway Switzerland Qatar
2 Macao India Luxembourg
I have used pivot tables :
df= df.pivot(values='Country Name', index=None, columns='Income Group')
and i get something like :
High income Middle income Low income
1 Norway none none
2 none Switzerland none
.
.
.
Can someone suggest a better solution than pivot here so that i don't have to deal with none values?

Answer 1

The trick is to introduce a new column index whose values are groupby/cumcount values. cumcount returns a cumulative count -- thus numbering the items in each group:
df['index'] = df.groupby('Income Group').cumcount()
# Country Name Income Group index
# 1 Norway High income 0
# 2 Switzerland Middle income 0
# 3 Qatar Low income 0
# 4 Luxembourg Low income 1
# 5 Macao High income 1
# 6 India Middle income 1
Once you have the index column, the desired result can be obtained by pivoting:
import pandas as pd
df = pd.DataFrame({'Country Name': ['Norway', 'Switzerland', 'Qatar', 'Luxembourg', 'Macao', 'India'], 'Income Group': ['High income', 'Middle income', 'Low income', 'Low income', 'High income', 'Middle income']})
df['index'] = df.groupby('Income Group').cumcount() + 1
result = df.pivot(index='index', columns='Income Group', values='Country Name')
result.index.name = result.columns.name = None
print(result)
yields
High income Low income Middle income
1 Norway Qatar Switzerland
2 Macao Luxembourg India