I have a data set which is in wide format like this
Index Country Variable 2000 2001 2002 2003 2004 2005
0 Argentina var1 12 15 18
Instead of melt, you can use a combination of stack and unstack:
(df.set_index(['Country', 'Variable'])
.rename_axis(['Year'], axis=1)
.stack()
.unstack('Variable')
.reset_index())
Variable Country Year var1 var2
0 Argentina 2000 12 1
1 Argentina 2001 15 3
2 Argentina 2002 18 2
3 Argentina 2003 17 5
4 Argentina 2004 23 7
5 Argentina 2005 29 5
6 Brazil 2000 20 0
7 Brazil 2001 23 1
8 Brazil 2002 25 2
9 Brazil 2003 29 2
10 Brazil 2004 31 3
11 Brazil 2005 32 3
Using melt
then unstack
for var1, var2, etc...
(df1.melt(id_vars=['Country','Variable'],var_name='Year')
.set_index(['Country','Year','Variable'])
.squeeze()
.unstack()
.reset_index())
Output:
Variable Country Year var1 var2
0 Argentina 2000 12 1
1 Argentina 2001 15 3
2 Argentina 2002 18 2
3 Argentina 2003 17 5
4 Argentina 2004 23 7
5 Argentina 2005 29 5
6 Brazil 2000 20 0
7 Brazil 2001 23 1
8 Brazil 2002 25 2
9 Brazil 2003 29 2
10 Brazil 2004 31 3
11 Brazil 2005 32 3
Using pivot
then stack
:
(df1.pivot(index='Country',columns='Variable')
.stack(0)
.rename_axis(['Country','Year'])
.reset_index())
Output:
Variable Country Year var1 var2
0 Argentina 2000 12 1
1 Argentina 2001 15 3
2 Argentina 2002 18 2
3 Argentina 2003 17 5
4 Argentina 2004 23 7
5 Argentina 2005 29 5
6 Brazil 2000 20 0
7 Brazil 2001 23 1
8 Brazil 2002 25 2
9 Brazil 2003 29 2
10 Brazil 2004 31 3
11 Brazil 2005 32 3
Using set_index
, stack
, and unstack
:
(df.set_index(['Country', 'Variable'])
.rename_axis(['Year'], axis=1)
.stack()
.unstack('Variable')
.reset_index())
Output:
Variable Country Year var1 var2
0 Argentina 2000 12 1
1 Argentina 2001 15 3
2 Argentina 2002 18 2
3 Argentina 2003 17 5
4 Argentina 2004 23 7
5 Argentina 2005 29 5
6 Brazil 2000 20 0
7 Brazil 2001 23 1
8 Brazil 2002 25 2
9 Brazil 2003 29 2
10 Brazil 2004 31 3
11 Brazil 2005 32 3
numpy
years = df.drop(['Country', 'Variable'], 1)
y = years.values
m = y.shape[1]
c = df.Country.values
v = df.Variable.values
f0, u0 = pd.factorize(df.Country.values)
f1, u1 = pd.factorize(df.Variable.values)
w = np.empty((u1.size, u0.size, m), dtype=y.dtype)
w[f1, f0] = y
results = pd.DataFrame(dict(
Country=u0.repeat(m),
Year=np.tile(years.columns.values, u0.size),
)).join(pd.DataFrame(w.reshape(-1, m * u1.size).T, columns=u1))
results
Country Year var1 var2
0 Argentina 2000 12 1
1 Argentina 2001 15 3
2 Argentina 2002 18 2
3 Argentina 2003 17 5
4 Argentina 2004 23 7
5 Argentina 2005 29 5
6 Brazil 2000 20 0
7 Brazil 2001 23 1
8 Brazil 2002 25 2
9 Brazil 2003 29 2
10 Brazil 2004 31 3
11 Brazil 2005 32 3