Pandas: Convert DataFrame Column Values Into New Dataframe Indices and Columns

Question

I have a dataframe that looks like this:

a  b  c
0  1  10
1  2  10
2  2  20
3  3  30
4  1  40
4  3  10

The dataframe above as default (0,1,2,3,4...) indices. I would like to convert it into a dataframe that looks like this:

    1     2     3
0   10    0     0
1   0     10    0
2   0     20    0
3   0     0     30
4   40    0     10

Where column 'a' in the first dataframe becomes the index in the second dataframe, the values of 'b' become the column names and the values of c are copied over, with 0 or NaN filling missing values. The original dataset is large and will result in a very sparse second dataframe. I then intend to add this dataframe to a much larger one, which is straightforward.

Can anyone advise the best way to achieve this please?

Answer 1

You can use the pivot method for this.

See the docs: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-pivoting-dataframe-objects

An example:

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'a':[0,1,2,3,4,4], 'b':[1,2,2,3,1,3], 'c':[10,10,20,3
0,40,10]})

In [3]: df
Out[3]:
   a  b   c
0  0  1  10
1  1  2  10
2  2  2  20
3  3  3  30
4  4  1  40
5  4  3  10

In [4]: df.pivot(index='a', columns='b', values='c')
Out[4]:
b   1   2   3
a
0  10 NaN NaN
1 NaN  10 NaN
2 NaN  20 NaN
3 NaN NaN  30
4  40 NaN  10

If you want zeros instead of NaN's as in your example, you can use fillna:

In [5]: df.pivot(index='a', columns='b', values='c').fillna(0)
Out[5]:
b   1   2   3
a
0  10   0   0
1   0  10   0
2   0  20   0
3   0   0  30
4  40   0  10