Creating summary from stock transactions table - current code execution SLOW

Question

I have a table of stock transactions that looks like this...

The account number may be duplicated many times, also that account may have also ordered the same product multiple times.

+------------+------------+------------+--------+---------+--------------+
| SA_ACCOUNT | SA_TRDATE  | SA_TRVALUE | SA_QTY | SA_COST | SA_PRODUCT   |
+------------+------------+------------+--------+---------+--------------+
| CSU1       | 23/03/2017 | 21.01      | 1      | 30      | W100/18      |
| AAA1       | 12/07/2018 | 38.04      | 6      | 19.8    | GPLR03REC800 |
| BWR1       | 01/11/2018 | 0          | -1     | 0       | W562/20      |
| CNT1       | 01/11/2018 | -2.22      | -1     | -1.23   | RX613S/12    |
| GBH1       | 15/09/2017 | 0          | 1      | 0       | COR2         |
+------------+------------+------------+--------+---------+--------------+

I want to output a table that has each account as a row and ALL products as a column - with a total sales value for that customer and a total pcs for the customer.

Expected output (there would be a lot more columns than the example below):

+---------+----------+------------+---------------+-----------------+--------------+----------------+-----------+
| Account | MISC_PCS | MISC_VALUE | RX613S/12_PCS | RX613S/12_VALUE | R623S/12_PCS | R623S/12_VALUE | SP377_PCS |
+---------+----------+------------+---------------+-----------------+--------------+----------------+-----------+
| AGT1    | 25       | 32.65      | 2             | 5.26            | 0            | 0              | 0         |
| AHB1    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AHB2    | 0        | 0          | 0             | 0               | 2            | 1.25           | 0         |
| AJB1    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AJE2    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AJT4    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AJW1    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AK11    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
| AKS1    | 0        | 0          | 0             | 0               | 0            | 0              | 0         |
+---------+----------+------------+---------------+-----------------+--------------+----------------+-----------+

I've written the below code buts its terribly slow and although it works its unusable (i've got 300,000 rows to iterate through)

Can anyone offer a better solution?

my code:

acc=""
index_test = -1
test_df = pd.DataFrame()
#For every row in the dataframe, iterate through the list of genres and place a 1 into the corresponding column
for index, row in stock_tran_df.iterrows():
    if acc != row["SA_DACCNT"]:
        acc = row["SA_DACCNT"]
        print(acc)
        index_test += 1
        test_df.loc[index_test,"Account"] = acc
    try:
        test_df.loc[index_test,row["SA_PRODUCT"] + "_PCS"] = test_df.loc[index_test,row["SA_PRODUCT"] + "_PCS"]  + row["SA_QTY"]
        test_df.loc[index_test,row["SA_PRODUCT"] + "_VALUE"] = test_df.loc[index_test,row["SA_PRODUCT"] + "_VALUE"]  + row["SA_TRVALUE"]
    except:
        test_df.loc[index_test,row["SA_PRODUCT"] + "_PCS"] = row["SA_QTY"]
        test_df.loc[index_test,row["SA_PRODUCT"] + "_VALUE"] = row["SA_TRVALUE"]  


test_df.fillna(0,inplace=True)

Answer 1

Looks like what you're looking for is

pandas.pivot_table

function with parameter aggfunc=np.sum