Reshaping data with .melt()

Joining Data with pandas

Aaren Stubberfield

Instructor

Wide versus long data

Wide Format Data in wide format more

Long Format Data in long format

What does the .melt() method do?

The melt method will allow us to unpivot our dataset

Dataset in wide format

This table is called social_fin

  financial      company   2019      2018      2017      2016    
0 total_revenue  twitter   3459329   3042359   2443299   2529619 
1 gross_profit   twitter   2322288   2077362   1582057   1597379 
2 net_income     twitter   1465659   1205596   -108063   -456873 
3 total_revenue  facebook  70697000  55838000  40653000  27638000
4 gross_profit   facebook  57927000  46483000  35199000  23849000
5 net_income     facebook  18485000  22112000  15934000  10217000

Example of .melt()

social_fin_tall = social_fin.melt(id_vars=['financial','company'])
print(social_fin_tall.head(10))

  financial      company   variable  value   
0 total_revenue  twitter   2019      3459329 
1 gross_profit   twitter   2019      2322288 
2 net_income     twitter   2019      1465659 
3 total_revenue  facebook  2019      70697000
4 gross_profit   facebook  2019      57927000
5 net_income     facebook  2019      18485000
6 total_revenue  twitter   2018      3042359 
7 gross_profit   twitter   2018      2077362 
8 net_income     twitter   2018      1205596 
9 total_revenue  facebook  2018      55838000

Melting with value_vars

social_fin_tall = social_fin.melt(id_vars=['financial','company'], 
                                  value_vars=['2018','2017'])
print(social_fin_tall.head(9))

  financial      company   variable  value   
0 total_revenue  twitter   2018      3042359 
1 gross_profit   twitter   2018      2077362 
2 net_income     twitter   2018      1205596 
3 total_revenue  facebook  2018      55838000
4 gross_profit   facebook  2018      46483000
5 net_income     facebook  2018      22112000
6 total_revenue  twitter   2017      2443299 
7 gross_profit   twitter   2017      1582057 
8 net_income     twitter   2017      -108063

Melting with column names

social_fin_tall = social_fin.melt(id_vars=['financial','company'], 
                                  value_vars=['2018','2017'],
                                  var_name='year', value_name='dollars')
print(social_fin_tall.head(8))

  financial      company   year  dollars 
0 total_revenue  twitter   2018  3042359 
1 gross_profit   twitter   2018  2077362 
2 net_income     twitter   2018  1205596 
3 total_revenue  facebook  2018  55838000
4 gross_profit   facebook  2018  46483000
5 net_income     facebook  2018  22112000
6 total_revenue  twitter   2017  2443299 
7 gross_profit   twitter   2017  1582057

Let's practice!

Joining Data with pandas