I have this pandas dataframe from a query:
| name | event |
----------------------------
| name_1 | event_1 |
| name_1 | event_2 |
| name_2 | event_1 |
I need to convert the column event to numerical, or something to look like this:
| name | event_1 | event_2 |
-------------------------------
| name_1 | 1 | 0 |
| name_1 | 0 | 1 |
| name_2 | 1 | 0 |
In the software rapidminer, i can do this with an operator "nominal to numerical", so i assume that in python convert the type of the column should be effective, but i can be mistaken.
In the final, the idea is make a sum on the columns value with same name and have as result a table that should look like this:
| name | event_1 | event_2 |
-------------------------------
| name_1 | 1 | 1 |
| name_2 | 1 | 0 |
There is a function that returns what a expected?
important: i can't do a simple count of the events because i do not know them, and the events is different for the users
EDIT: well thanks guys, i can see there is multiple ways to do this, can you guys say which one of these is the most pythonic way?
Some ways of doing it
1)
In [366]: pd.crosstab(df.name, df.event)
Out[366]:
event event_1 event_2
name
name_1 1 1
name_2 1 0
2)
In [367]: df.groupby(['name', 'event']).size().unstack(fill_value=0)
Out[367]:
event event_1 event_2
name
name_1 1 1
name_2 1 0
3)
In [368]: df.pivot_table(index='name', columns='event', aggfunc=len, fill_value=0)
Out[368]:
event event_1 event_2
name
name_1 1 1
name_2 1 0
4)
In [369]: df.assign(v=1).pivot(index='name', columns='event', values='v').fillna(0)
Out[369]:
event event_1 event_2
name
name_1 1.0 1.0
name_2 1.0 0.0