Change column values to column headers in pandas

Question

I have the following code, which takes the values in one column of a pandas dataframe and makes them the columns of a new data frame. The values in the first column of the dataframe become the index of the new dataframe.

In a sense, I want to turn an adjacency list into an adjacency matrix. Here's the code so far:

import pandas as pa
print "Original Data Frame"
# Create a dataframe
oldcols = {'col1':['a','a','b','b'], 'col2':['c','d','c','d'], 'col3':[1,2,3,4]}
a = pa.DataFrame(oldcols)
print a

# The columns of the new data frame will be the values in col2 of the original
newcols = list(set(oldcols['col2']))
rows = list(set(oldcols['col1']))

# Create the new data matrix
data = np.zeros((len(rows), len(newcols)))

# Iterate over each row and fill in the new matrix
for row in zip(a['col1'], a['col2'], a['col3']):
    rowindex = rows.index(row[0])
    colindex = newcols.index(row[1])
    data[rowindex][colindex] = row[2]

newf = pa.DataFrame(data)
newf.columns = newcols
newf.index = rows

print "New data frame"
print newf

This works for this particular instance:

Original Data Frame
  col1 col2  col3
0    a    c     1
1    a    d     2
2    b    c     3
3    b    d     4
New data frame
   c  d
a  1  2
b  3  4

It will fail if the values in col3 are not numbers. My question is, is there a more elegant/robust way of doing this?

Answer 1

This looks like a job for pivot:

import pandas as pd
oldcols = {'col1':['a','a','b','b'], 'col2':['c','d','c','d'], 'col3':[1,2,3,4]}
a = pd.DataFrame(oldcols)  

newf = a.pivot(index='col1', columns='col2')
print(newf)

yields

      col3   
col2     c  d
col1         
a        1  2
b        3  4

If you don't want a MultiIndex column, you can drop the col3 using:

newf.columns = newf.columns.droplevel(0)

which would then yield

col2  c  d
col1      
a     1  2
b     3  4