Transform dataframe Python

Question

I have data in one format (summing up events' occurrences by UUID, events in separate rows) but would need it in a different format (events' occurrences in separate columns for each event). How to, by using Python, transform it from:

UUid	Event	sum
B32E7AA5B11A40AE	page XY opened	121
B32E7AA5B11A40AE	shopcart filled	92
B32E7AA5B11A40AE	purchase made	23
2AEFB3AEA64140BE	page XY opened	221
2AEFB3AEA64140BE	shopcart filled	21
A0DFAACDEB4D40C1	page XY opened	1345
A0DFAACDEB4D40C1	shopcart filled	1323
A0DFAACDEB4D40C1	purchase made	1321

To this:

UUid	page XY opened	shopcart filled	purchase made
B32E7AA5B11A40AE	121	92	23
2AEFB3AEA64140BE	221	23	0
A0DFAACDEB4D40C1	1345	1323	1321

I don't know where to start.

use [pivot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html) — Tranbi, Feb 23 '23 at 14:08
Create a new DF with the columns you want. Iterate your original df and add the data for one UID to your new df. — SickerDude43, Feb 23 '23 at 14:10

score 2 · Accepted Answer · answered Feb 23 '23 at 14:11

Try using pivot() and fillna()

df_pivot = df.pivot(index='UUid', columns='Event', values='sum').fillna(0)
df_pivot = df_pivot.reset_index()

df_pivot

Output:

	UUid	page XY opened	purchase made	shopcart filled
0	2AEFB3AEA64140BE	221	0	21
1	A0DFAACDEB4D40C1	1345	1321	1323
2	B32E7AA5B11A40AE	121	23	92

score 1 · Answer 2 · answered Feb 23 '23 at 14:11

1

df = pd.pivot_table(df, values='sum', index='UUid', columns='Event', fill_value=0)

df = df.reset_index()

answered Feb 23 '23 at 14:11

user21273596

11
1

Transform dataframe Python

2 Answers2