如何将 Python 中 DataFrame 中的行转换为字典
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How to convert rows in DataFrame in Python to dictionaries
提问by Vicky
For example, I have DataFrame now as
例如，我现在有 DataFrame 作为
id score1 score2 score3 score4 score5
1 0.000000 0.108659 0.000000 0.078597 1
2 0.053238 0.308253 0.286353 0.446433 1
3 0.000000 0.083979 0.808983 0.233052 1
I want to convert it as
我想将其转换为
id scoreDict
1 {'1': 0, '2': 0.1086, ...}
2 {...}
3 {...}
Anyway to do that?
无论如何要这样做？
Thanks in advance!
提前致谢！
采纳答案by Jianxun Li
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}]
回答by oldmonk
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
我认为下面的代码将为您提供您正在寻找的格式的数据框。它还允许您选择任何列作为索引
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...
回答by Adav
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.
对于像我这样遇到这个问题但希望执行以下操作的其他人：逐行创建一个 dict 以根据相邻列的值映射一列。
Here's our mapping table:
这是我们的映射表：
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:
在：
import pandas as pd
df_map.set_index('y')
df_map.transpose()
dict_y = df_map['Rating'].to_dict()
OUT:
出去：
{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'}
回答by alienzj
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}}
