How to convert vertical pandas table of 2 columns to horizontal table based on common ID value in python

Question

df1 = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
                    'bar': ['A', 'B', 'C', 'A', 'B', 'C']})

	foo	bar
0	one	A
1	one	B
2	one	C
3	two	A
4	two	B
5	two	C

I would like to convert this to

foo	val1	val2	val3
One	A	B	C
Two	A	B	C

And the code I tried is:

pd.pivot_table(df1,index='foo',aggfunc=['first'])

But the above code is returning only the first value

Answer 1

We can enumerate groups with groupby cumcount and use those as the pivot columns then add_prefix to the numerical values and reset_index to return the 'foo' values to the columns:

new_df = (
    df1.pivot_table(index='foo',
                    columns=df1.groupby('foo').cumcount() + 1,
                    values='bar', 
                    aggfunc='first')
        .add_prefix('val')
        .reset_index()
)

   foo val1 val2 val3
0  one    A    B    C
1  two    A    B    C

---

See how df1.groupby('foo').cumcount() + 1 makes the columns:

   foo  columns
0  one        1  # First instance of "one"
1  one        2  # Second instance of "one"
2  one        3  # Third instance of "one"
3  two        1
4  two        2
5  two        3

Code to generate the above DataFrame:

demo_df = pd.DataFrame({
    'foo': df1['foo'],
    'columns': df1.groupby('foo').cumcount() + 1
})

Answer 2

Try:

df1.groupby([df1.groupby('foo').cumcount() + 1,
             'foo']).first()['bar'].unstack(0).add_prefix('val').reset_index()

Output:

   foo val1 val2 val3
0  one    A    B    C
1  two    A    B    C

Answer 3

Another solution:

x = df1.pivot("foo", "bar", "bar")
x.columns = [f"var{i}" for i in range(1, len(x.columns) + 1)]
x = x.reset_index()
print(x)

Prints:

   foo var1 var2 var3
0  one    A    B    C
1  two    A    B    C