Pandas 解析列中的 json 并扩展到数据框中的新行 [英] Pandas parse json in column and expand to new rows in dataframe
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问题描述
我有一个包含(记录格式)json 字符串的数据框,如下所示:
I have a dataframe containing (record formatted) json strings as follows:
In[9]: pd.DataFrame( {'col1': ['A','B'], 'col2': ['[{"t":"05:15","v":"20.0"}, {"t":"05:20","v":"25.0"}]',
'[{"t":"05:15","v":"10.0"}, {"t":"05:20","v":"15.0"}]']})
Out[9]:
col1 col2
0 A [{"t":"05:15","v":"20.0"}, {"t":"05:20","v":"2...
1 B [{"t":"05:15","v":"10.0"}, {"t":"05:20","v":"1...
我想提取 json 并为每条记录向数据帧添加一个新行:
I would like to extract the json and for each record add a new row to the dataframe:
co1 t v
0 A 05:15:00 20
1 A 05:20:00 25
2 B 05:15:00 10
3 B 05:20:00 15
我一直在试验以下代码:
I've been experimenting with the following code:
def json_to_df(x):
df2 = pd.read_json(x.col2)
return df2
df.apply(json_to_df, axis=1)
但结果数据帧被分配为元组,而不是创建新行.有什么建议吗?
but the resulting dataframes are assigned as tuples, rather than creating new rows. Any advice?
推荐答案
好的,从上面 hellpanderrr 的回答中得到一点启发,我想出了以下几点:
Ok, taking a little inspiration from hellpanderrr's answer above, I came up with the following:
In [92]:
pd.DataFrame( {'X': ['A','B'], 'Y': ['fdsfds','fdsfds'], 'json': ['[{"t":"05:15","v":"20.0"}, {"t":"05:20","v":"25.0"}]',
'[{"t":"05:15","v":"10.0"}, {"t":"05:20","v":"15.0"}]']},)
Out[92]:
X Y json
0 A fdsfds [{"t":"05:15","v":"20.0"}, {"t":"05:20","v":"2...
1 B fdsfds [{"t":"05:15","v":"10.0"}, {"t":"05:20","v":"1...
In [93]:
dfs = []
def json_to_df(row, json_col):
json_df = pd.read_json(row[json_col])
dfs.append(json_df.assign(**row.drop(json_col)))
_.apply(json_to_df, axis=1, json_col='json')
pd.concat(dfs)
Out[93]:
t v X Y
0 05:15 20 A fdsfds
1 05:20 25 A fdsfds
0 05:15 10 B fdsfds
1 05:20 15 B fdsfds
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