如何在 Python 中将 DataFrame 中的行转换为字典

小开

最佳答案

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}]

小开

I think the below code will give you the data frame in the format you are looking for. Also it allows you to choose any column as an index

import pandas as pd


#IMPORT YOUR DATA
#Any other way to import data can also be used. I saved it in .csv file
df=pd.read_csv('dftestid.csv')
print("INITIAL DATAFRAME")
print(df)
print()


#Convert Data Frame to Dictionary (set_index method allows any column to be used as index)
df2dict=df.set_index('id').transpose().to_dict(orient='dict')




#Convert Dictionary to List with 'score' replaced
dicttolist=[[k,{int(k1.replace('score','')):v1 for k1,v1 in v.items()}] for k,v in df2dict.items()]


#"Create the new DataFrame"


df2=pd.DataFrame(dicttolist,columns=['id', 'scoreDict'])
print("NEW DATAFRAME")
print(df2)




OUT:
INITIAL DATAFRAME
id    score1    score2    score3    score4  score5
0   1  0.000000  0.108659  0.000000  0.078597       1
1   2  0.053238  0.308253  0.286353  0.446433       1
2   3  0.000000  0.083979  0.808983  0.233052       1


NEW DATAFRAME
id                                          scoreDict
0   1  {1: 0.0, 2: 0.108659, 3: 0.0, 4: 0.078597, 5: ...
1   2  {1: 0.053238, 2: 0.308253, 3: 0.286353, 4: 0.4...
2   3  {1: 0.0, 2: 0.083979, 3: 0.808983, 4: 0.233052...

小开

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'}

小开

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}}