How to create multiple line graph using seaborn and find rate? - pandas

Question

I need help to create a multiple line graph using below DataFrame
num user_id first_result second_result result date point1 point2 point3 point4
0 0 1480R clear clear pass 9/19/2016 clear consider clear consider
1 1 419M consider consider fail 5/18/2016 consider consider clear clear
2 2 416N consider consider fail 11/15/2016 consider consider consider consider
3 3 1913I consider consider fail 11/25/2016 consider consider consider clear
4 4 1938T clear clear pass 8/1/2016 clear consider clear clear
5 5 1530C clear clear pass 6/22/2016 clear clear consider clear
6 6 1075L consider consider fail 9/13/2016 consider consider clear consider
7 7 1466N consider clear fail 6/21/2016 consider clear clear consider
8 8 662V consider consider fail 11/1/2016 consider consider clear consider
9 9 1187Y consider consider fail 9/13/2016 consider consider clear clear
10 10 138T consider consider fail 9/19/2016 consider clear consider consider
11 11 1461Z consider clear fail 7/18/2016 consider consider clear consider
12 12 807N consider clear fail 8/16/2016 consider consider clear clear
13 13 416Y consider consider fail 10/2/2016 consider clear clear clear
14 14 638A consider clear fail 6/21/2016 consider clear consider clear
data file linke data.xlsx or data as dict
data = {'num': {0: 0,
1: 1,
2: 2,
3: 3,
4: 4,
5: 5,
6: 6,
7: 7,
8: 8,
9: 9,
10: 10,
11: 11,
12: 12,
13: 13,
14: 14},
'user_id': {0: '1480R',
1: '419M',
2: '416N',
3: '1913I',
4: '1938T',
5: '1530C',
6: '1075L',
7: '1466N',
8: '662V',
9: '1187Y',
10: '138T',
11: '1461Z',
12: '807N',
13: '416Y',
14: '638A'},
'first_result': {0: 'clear',
1: 'consider',
2: 'consider',
3: 'consider',
4: 'clear',
5: 'clear',
6: 'consider',
7: 'consider',
8: 'consider',
9: 'consider',
10: 'consider',
11: 'consider',
12: 'consider',
13: 'consider',
14: 'consider'},
'second_result': {0: 'clear',
1: 'consider',
2: 'consider',
3: 'consider',
4: 'clear',
5: 'clear',
6: 'consider',
7: 'clear',
8: 'consider',
9: 'consider',
10: 'consider',
11: 'clear',
12: 'clear',
13: 'consider',
14: 'clear'},
'result': {0: 'pass',
1: 'fail',
2: 'fail',
3: 'fail',
4: 'pass',
5: 'pass',
6: 'fail',
7: 'fail',
8: 'fail',
9: 'fail',
10: 'fail',
11: 'fail',
12: 'fail',
13: 'fail',
14: 'fail'},
'date': {0: '9/19/2016',
1: '5/18/2016',
2: '11/15/2016',
3: '11/25/2016',
4: '8/1/2016',
5: '6/22/2016',
6: '9/13/2016',
7: '6/21/2016',
8: '11/1/2016',
9: '9/13/2016',
10: '9/19/2016',
11: '7/18/2016',
12: '8/16/2016',
13: '10/2/2016',
14: '6/21/2016'},
'point1': {0: 'clear',
1: 'consider',
2: 'consider',
3: 'consider',
4: 'clear',
5: 'clear',
6: 'consider',
7: 'consider',
8: 'consider',
9: 'consider',
10: 'consider',
11: 'consider',
12: 'consider',
13: 'consider',
14: 'consider'},
'point2': {0: 'consider',
1: 'consider',
2: 'consider',
3: 'consider',
4: 'consider',
5: 'clear',
6: 'consider',
7: 'clear',
8: 'consider',
9: 'consider',
10: 'clear',
11: 'consider',
12: 'consider',
13: 'clear',
14: 'clear'},
'point3': {0: 'clear',
1: 'clear',
2: 'consider',
3: 'consider',
4: 'clear',
5: 'consider',
6: 'clear',
7: 'clear',
8: 'clear',
9: 'clear',
10: 'consider',
11: 'clear',
12: 'clear',
13: 'clear',
14: 'consider'},
'point4': {0: 'consider',
1: 'clear',
2: 'consider',
3: 'clear',
4: 'clear',
5: 'clear',
6: 'consider',
7: 'consider',
8: 'consider',
9: 'clear',
10: 'consider',
11: 'consider',
12: 'clear',
13: 'clear',
14: 'clear'}
}
I need to create a bar graph and a line graph, I have created the bar graph using point1 where x = consider, clear and y = count of consider and clear
but I have no idea how to create a line graph by this scenario
x = date
y = pass rate (%)
Pass Rate is a number of clear/(consider + clear)
graph the rate for first_result, second_result, result all on the same graph
and the graph should look like below
please comment or answer how can I do it. if I can get an idea of grouping dates and getting the ratio then also great.

Answer 1

Here's my idea how to do it:
# first convert all `clear`, `consider` to 1,0
tmp_df = df[['first_result', 'second_result']].apply(lambda x: x.eq('clear').astype(int))
# convert `pass`, `fail` to 1,0
tmp_df['result'] = df.result.eq('pass').astype(int)
# copy the date
tmp_df['date'] = df['date']
# groupby and compute mean, i.e. number_pass/total_count
tmp_df = tmp_df.groupby('date').mean()
tmp_df.plot()
Output for this dataset