将pandas数据框单元格中的字典解析为新的行单元格(新的列) [英] parsing a dictionary in a pandas dataframe cell into new row cells (new columns)

问题描述

我有一个Pandas Dataframe，其中包含一列，该列包含包含key:value对字典的单元格，如下所示:

I have a Pandas Dataframe that contains one column containing cells containing a dictionary of key:value pairs, like this:

{"name":"Test Thorton","company":"Test Group","address":"10850 Test #325\r\n","city":"Test City","state_province":"CA","postal_code":"95670","country":"USA","email_address":"[e-mail]","phone_number":"999-888-3333","equipment_description":"I'm a big red truck\r\n\r\nRSN# 0000","response_desired":"week","response_method":"email"}

我正在尝试解析字典，因此生成的数据框为每个键包含一个新列，并用每一列的结果值填充行，如下所示:

I'm trying to parse the dictionary, so the resulting Dataframe contains a new column for each key and the row is populated with the resulting values for each column, like this:

//Before

1  2  3  4  5
a  b  c  d  {6:y, 7:v}

//After

1  2  3  4  5           6  7
a  b  c  d  {6:y, 7:v}  y  v

建议表示赞赏.

推荐答案

考虑df

df = pd.DataFrame([
        ['a', 'b', 'c', 'd', dict(F='y', G='v')],
        ['a', 'b', 'c', 'd', dict(F='y', G='v')],
    ], columns=list('ABCDE'))

df

   A  B  C  D                     E
0  a  b  c  d  {'F': 'y', 'G': 'v'}
1  a  b  c  d  {'F': 'y', 'G': 'v'}

---

选项1
使用pd.Series.apply，在适当位置分配新列

---

Option 1
Use pd.Series.apply, assign new columns in place

df.E.apply(pd.Series)

   F  G
0  y  v
1  y  v

这样分配

df[['F', 'G']] = df.E.apply(pd.Series)
df.drop('E', axis=1)

   A  B  C  D  F  G
0  a  b  c  d  y  v
1  a  b  c  d  y  v

---

选项2
使用pd.DataFrame.assign方法流水线化整个过程

---

Option 2
Pipeline the whole thing using the pd.DataFrame.assign method

df.drop('E', 1).assign(**pd.DataFrame(df.E.values.tolist()))

   A  B  C  D  F  G
0  a  b  c  d  y  v
1  a  b  c  d  y  v

这篇关于将pandas数据框单元格中的字典解析为新的行单元格(新的列)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！