As you suggest, the simplest method might be a for-loop to initialize the non-empty values. Alternatively, you can use pivot() or numpy advanced indexing. All options are shown below.
The only tricky thing is ensuring that your dataframe result will have the complete set of rows and columns, as explained in the update below.
text = """\
row,column,value
1,A 32
1,F 16
2,B 234
2,C 1345
2,E 13
2,G 999
3,D 5332
4,B 12
"""
from io import StringIO
import numpy as np
import pandas as pd
# Load your data and convert the column letters to integers.
# Note: Your exapmple data is delimited with both spaces and commas,
# which is why we need a custom 'sep' argument here.
entries = pd.read_csv(StringIO(text), sep='[ ,]+')
entries['icol'] = entries['column'].map(lambda c: ord(c) - ord('A'))
# Construct an empty DataFrame with the appropriate index and columns.
rows = range(1, 1 + entries['row'].max())
columns = [chr(ord('A') + i) for i in range(1 + entries['icol'].max())]
df = pd.DataFrame(index=rows, columns=columns)
##
## Three ways to populate the dataframe:
##
# Option 1: Iterate in a for-loop
for e in entries.itertuples():
df.loc[e.row, e.column] = e.value
# Option 2: Use pivot() or unstack()
df = df.fillna(entries.pivot('row', 'column', 'value'))
# Option 3: Use numpy indexing to overwrite the underlying array:
irows = entries['row'].values - 1
icols = entries['icol'].values
df.values[irows, icols] = entries['value'].values
Result:
A B C D E F G
1 32 NaN NaN NaN NaN 16 NaN
2 NaN 234 1345 NaN 13 NaN 999
3 NaN NaN NaN 5332 NaN NaN NaN
4 NaN 12 NaN NaN NaN NaN NaN
Update:
Late in the day, it occurred to me that this can be solved via pivot() (or unstack(), as suggested by @piterbarg). I've now included that option above.
In fact, it's tempting to just use pivot() without pre-initializing the DataFrame. HOWEVER, there's an important caveat to that approach: If any particular row or column value remains completely unused in your original entries data, then those rows will remain completely omitted from the final table. That is, if no entry uses row 3, your final table would only contain rows 1,2,4. Likewise, if your data contains no data for columns C,E,G (for example), then you would end up with columns A,B,D,F.
If you want to be sure that your rows use contiguous index values and your columns use a contiguous sequence of letters, then pivot() or unstack() is not enough. You must first initialize the indexes of your dataframe as shown above.
