How to convert rows in DataFrame in Python to dictionaries
import pandas as pd
# your df
# =========================
print(df)
id score1 score2 score3 score4 score5
0 1 0.0000 0.1087 0.0000 0.0786 1
1 2 0.0532 0.3083 0.2864 0.4464 1
2 3 0.0000 0.0840 0.8090 0.2331 1
# to_dict
# =========================
df.to_dict(orient='records')
Out[318]:
[{'id': 1.0,
'score1': 0.0,
'score2': 0.10865899999999999,
'score3': 0.0,
'score4': 0.078597,
'score5': 1.0},
{'id': 2.0,
'score1': 0.053238000000000001,
'score2': 0.308253,
'score3': 0.28635300000000002,
'score4': 0.44643299999999997,
'score5': 1.0},
{'id': 3.0,
'score1': 0.0,
'score2': 0.083978999999999998,
'score3': 0.80898300000000001,
'score4': 0.23305200000000001,
'score5': 1.0}]
For others like me coming to this question but looking to do the following: Create a dict row by row to map a column based of the value of the adjacent column.
Here's our mapping table:
Rating y
0 AAA 19
1 AA1 18
2 AA2 17
3 AA3 16
4 A1 15
5 A2 14
6 A3 13
...
19 D 0
IN:
import pandas as pd
df_map.set_index('y')
dict_y = df_map['Rating'].to_dict()
OUT:
{19: 'AAA',
18: 'AA1',
17: 'AA2',
16: 'AA3',
15: 'A1',
14: 'A2',
13: 'A3',
12: 'BBB1',
11: 'BBB2',
10: 'BBB3',
9: 'BB1',
8: 'BB2',
7: 'BB3',
6: 'B1',
5: 'B2',
4: 'B3',
3: 'CCC1',
2: 'CCC2',
1: 'D'}