populate a dense dataframe given a key-value dataframe - pandas

Question

I have a key-value dataframe:
pd.DataFrame(columns=['X','Y','val'],data= [['a','z',5],['b','g',3],['b','y',6],['e','r',9]])
> X Y val
0 a z 5
1 b g 3
2 b y 6
3 e r 9
Which I'd like to convert into a denser dataframe:
X z g y r
0 a 5 0 0 0
1 b 0 3 6 0
2 e 0 0 0 9
Before I resort to a pure-python I was wondering if there was a simple way to do this with pandas.

Answer 1

You can use get_dummies:
In [11]: dummies = pd.get_dummies(df['Y'])
In [12]: dummies
Out[12]:
g r y z
0 0 0 0 1
1 1 0 0 0
2 0 0 1 0
3 0 1 0 0
and then multiply by the val column:
In [13]: res = dummies.mul(df['val'], axis=0)
In [14]: res
Out[14]:
g r y z
0 0 0 0 5
1 3 0 0 0
2 0 0 6 0
3 0 9 0 0
To fix the index, you could just add the X as this index, you could first apply set_index:
In [21]: df1 = df.set_index('X', append=True)
In [22]: df1
Out[22]:
Y val
X
0 a z 5
1 b g 3
2 b y 6
3 e r 9
In [23]: dummies = pd.get_dummies(df['Y'])
In [24]: dummies.mul(df['val'], axis=0)
Out[24]:
g r y z
X
0 a 0 0 0 5
1 b 3 0 0 0
2 b 0 0 6 0
3 e 0 9 0 0
If you wanted to do this pivot (you can also use pivot_table):
In [31]: df.pivot('X', 'Y').fillna(0)
Out[31]:
val
Y g r y z
X
a 0 0 0 5
b 3 0 6 0
e 0 9 0 0
Perhaps you want to reset_index, to make X a column (I'm not sure whether than makes sense):
In [32]: df.pivot('X', 'Y').fillna(0).reset_index()
Out[32]:
X val
Y g r y z
0 a 0 0 0 5
1 b 3 0 6 0
2 e 0 9 0 0
For completeness, the pivot_table:
In [33]: df.pivot_table('val', 'X', 'Y', fill_value=0)
Out[33]:
Y g r y z
X
a 0 0 0 5
b 3 0 6 0
e 0 9 0 0
In [34]: df.pivot_table('val', 'X', 'Y', fill_value=0).reset_index()
Out[34]:
Y X g r y z
0 a 0 0 0 5
1 b 3 0 6 0
2 e 0 9 0 0
Note: the column name are named Y, after reseting the index, not sure if this makes sense (and easy to rectify via res.columns.name = None).

Answer 2

If you want something that feels more direct. Something akin to DataFrame.lookup but for np.put might make sense.
def lookup_index(self, row_labels, col_labels):
values = self.values
ridx = self.index.get_indexer(row_labels)
cidx = self.columns.get_indexer(col_labels)
if (ridx == -1).any():
raise ValueError('One or more row labels was not found')
if (cidx == -1).any():
raise ValueError('One or more column labels was not found')
flat_index = ridx * len(self.columns) + cidx
return flat_index
flat_index = lookup_index(df, vals.X, vals.Y)
np.put(df.values, flat_index, vals.val.values)
This assumes that df has the appropriate columns and index to hold the X/Y values. Here's an ipython notebook http://nbviewer.ipython.org/6454120