How to convert rows in DataFrame in Python to dictionaries

Solution 1:

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}]

Solution 2:

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'}

Solution 3:

df = pd.DataFrame({'col1': [1, 2],
                   'col2': [0.5, 0.75]},
                   index=['row1', 'row2'])
df
      col1  col2
row1    1   0.50
row2    2   0.75

df.to_dict(orient='index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}