Counting column values based on values in other columns for Pandas dataframes

Question

I'm trying to count the number of each category of storm for each unique x and y combination. For example. My dataframe looks like:

x   y  year  Category
1   1  1988     3
2   1  1977     1
2   1  1999     2
3   2  1990     4

I want to create a dataframe that looks like:

x   y   Category 1   Category 2   Category 3  Category 4
1   1        0           0            1           0
2   1        1           1            0           0
3   2        0           0            0           1

I have tried various combinations of .groupby() and .count(), but I am still not getting the desired result. The closet thing I could get is:

df[['x','y','Category']].groupby(['Category']).count()

However, the result counts for all x and y, not the unique pairs:

Cat       x           y     
1       3773         3773
2       1230         1230
3       604          604
4       266          266
5       50           50
NA      27620        27620
TS      16884        16884

Does anyone know how to do a count operation on one column based on the uniqueness of two other columns in a dataframe?

Answer 1

pivot_table sounds like what you want. A bit of a hack is to add a column of 1's to use to count. This allows pivot_table to add 1 for each occurrence of a particular x-y and Category combination. You will set this new column as your value parameter in pivot_table and the aggfunc paraemter to np.sum. You'll probably want to set fill_value to 0 as well:

df['count'] = 1
result = df.pivot_table(
    index=['x', 'y'], columns='Category', values='count',
    fill_value=0, aggfunc=np.sum
)

result:

Category  1  2  3  4
x y                 
1 1       0  0  1  0
2 1       1  1  0  0
3 2       0  0  0  1

---

If you're interested in keeping x and y as columns and having the other column names as Category X, you can rename the columns and use reset_index:

result.columns = [f'Category {x}' for x in result.columns]
result = a.reset_index()