如何将包含嵌套列表的字典列表转换为 pandas df
How to transform a list of dictionaries, containing nested lists into a pandas df
我有一个听写列表:
list_of_dicts = [{'name': 'a', 'counts': [{'dog': 2}]},
{'name': 'b', 'counts': [{'cat': 1}, {'capibara': 5}, {'whale': 10}]},
{'name': 'c', 'counts': [{'horse':1}, {'cat': 1}]]
我想将其转换为 pandas 数据框,如下所示:
Name
Animal
Frequency
a
dog
2
b
cat
1
b
capibara
5
b
whale
10
c
horse
1
c
cat
1
在目前的代码中,我尝试规范化它:
from pandas import json_normalize
df = json_normalize(list_of_dicts, 'counts')
但我觉得我走错了方向。另外,如果我做一个简单的 df = pd.DataFrame(list_of_dicts)
,它会导致每个字典列表都是一个单行值,这是不需要的。
- 必须使用
pandas.json_normalize
的record_path
和meta
参数。
- 这些列将是动物,它们被堆叠成一个列。
import pandas as pd
# test data
list_of_dicts = [{'name': 'a', 'counts': [{'dog': 2}]}, {'name': 'b', 'counts': [{'cat': 1}, {'capibara': 5}, {'whale': 10}]}, {'name': 'c', 'counts': [{'horse':1}, {'cat': 1}]}]
# load and transform the dataframe
pd.json_normalize(list_of_dicts, 'counts', 'name').set_index('name').stack().reset_index().rename(columns={'level_1': 'Animal', 0: 'Frequency'})
# display(df)
name Animal Frequency
0 a dog 2.0
1 b cat 1.0
2 b capibara 5.0
3 b whale 10.0
4 c horse 1.0
5 c cat 1.0
尝试 json_normalize
和 melt
:
(pd.json_normalize(list_of_dicts, record_path='counts', meta='name')
.melt('name', var_name='Animal', value_name='Frequency')
.dropna()
)
输出:
name Animal Frequency
0 a dog 2.0
7 b cat 1.0
11 c cat 1.0
14 b capibara 5.0
21 b whale 10.0
28 c horse 1.0
试试这个?
>>> pd.json_normalize(list_of_dicts, 'counts').melt().dropna()
您也可以使用 df.explode
with df.apply
:
In [50]: df = pd.DataFrame(list_of_dicts).explode('counts')
In [74]: df.counts = df.counts.apply(lambda x: list(x.items())[0])
In [77]: df[['Animal', 'Frequency']] = pd.DataFrame(df['counts'].tolist(), index=df.index)
In [79]: df.drop('counts', 1, inplace=True)
In [80]: df
Out[80]:
name Animal Frequency
0 a dog 2
1 b cat 1
1 b capibara 5
1 b whale 10
2 c horse 1
2 c cat 1
我有一个听写列表:
list_of_dicts = [{'name': 'a', 'counts': [{'dog': 2}]},
{'name': 'b', 'counts': [{'cat': 1}, {'capibara': 5}, {'whale': 10}]},
{'name': 'c', 'counts': [{'horse':1}, {'cat': 1}]]
我想将其转换为 pandas 数据框,如下所示:
Name | Animal | Frequency |
---|---|---|
a | dog | 2 |
b | cat | 1 |
b | capibara | 5 |
b | whale | 10 |
c | horse | 1 |
c | cat | 1 |
在目前的代码中,我尝试规范化它:
from pandas import json_normalize
df = json_normalize(list_of_dicts, 'counts')
但我觉得我走错了方向。另外,如果我做一个简单的 df = pd.DataFrame(list_of_dicts)
,它会导致每个字典列表都是一个单行值,这是不需要的。
- 必须使用
pandas.json_normalize
的record_path
和meta
参数。 - 这些列将是动物,它们被堆叠成一个列。
import pandas as pd
# test data
list_of_dicts = [{'name': 'a', 'counts': [{'dog': 2}]}, {'name': 'b', 'counts': [{'cat': 1}, {'capibara': 5}, {'whale': 10}]}, {'name': 'c', 'counts': [{'horse':1}, {'cat': 1}]}]
# load and transform the dataframe
pd.json_normalize(list_of_dicts, 'counts', 'name').set_index('name').stack().reset_index().rename(columns={'level_1': 'Animal', 0: 'Frequency'})
# display(df)
name Animal Frequency
0 a dog 2.0
1 b cat 1.0
2 b capibara 5.0
3 b whale 10.0
4 c horse 1.0
5 c cat 1.0
尝试 json_normalize
和 melt
:
(pd.json_normalize(list_of_dicts, record_path='counts', meta='name')
.melt('name', var_name='Animal', value_name='Frequency')
.dropna()
)
输出:
name Animal Frequency
0 a dog 2.0
7 b cat 1.0
11 c cat 1.0
14 b capibara 5.0
21 b whale 10.0
28 c horse 1.0
试试这个?
>>> pd.json_normalize(list_of_dicts, 'counts').melt().dropna()
您也可以使用 df.explode
with df.apply
:
In [50]: df = pd.DataFrame(list_of_dicts).explode('counts')
In [74]: df.counts = df.counts.apply(lambda x: list(x.items())[0])
In [77]: df[['Animal', 'Frequency']] = pd.DataFrame(df['counts'].tolist(), index=df.index)
In [79]: df.drop('counts', 1, inplace=True)
In [80]: df
Out[80]:
name Animal Frequency
0 a dog 2
1 b cat 1
1 b capibara 5
1 b whale 10
2 c horse 1
2 c cat 1