从 pandas 专栏解开字典 [英] Unpack dictionary from Pandas Column

问题描述

我有一个数据框，其中的一列作为字典.我想将其解压缩为多列(即代码，金额为以下Raw列格式的单独列).以下代码曾经与pandas v0.22(现在是(0.23))一起工作，给出了索引错误:

I have a dataframe that has one of the columns as a dictionary. I want to unpack it into multiple columns (i.e. code, amount are separate columns in the below Raw column format). The following code used to work with pandas v0.22, now (0.23) giving an index error:

pd.DataFrame.from_records(df.col_name.fillna(pd.Series([{'code':'not applicable'}], index=df.index)).values.tolist())

ValueError: Length of passed values is 1, index implies x

我搜索了google/stack溢出了几个小时，以前提供的其他解决方案都无法正常工作了.

I searched google/stack overflow for hours and none of the other solutions previously presented work anymore.

原始列格式:

     dict_codes
0   {'code': 'xx', 'amount': '10.00',...
1   {'code': 'yy', 'amount': '20.00'...
2   {'code': 'bb', 'amount': '30.00'...
3   {'code': 'aa', 'amount': '40.00'...
10  {'code': 'zz', 'amount': '50.00'...
11                            NaN
12                            NaN
13                            NaN

有人有什么建议吗?

谢谢

推荐答案

设置

df = pd.DataFrame(dict(
    codes=[
        {'amount': 12, 'code': 'a'},
        {'amount': 19, 'code': 'x'},
        {'amount': 37, 'code': 'm'},
        np.nan,
        np.nan,
        np.nan,
    ]
))

df

                         codes
0  {'amount': 12, 'code': 'a'}
1  {'amount': 19, 'code': 'x'}
2  {'amount': 37, 'code': 'm'}
3                          NaN
4                          NaN
5                          NaN

---

apply与pd.Series

请务必先dropna

---

apply with pd.Series

Make sure to dropna first

df.codes.dropna().apply(pd.Series)

   amount code
0      12    a
1      19    x
2      37    m

---

df.drop('codes', 1).assign(**df.codes.dropna().apply(pd.Series))

   amount code
0    12.0    a
1    19.0    x
2    37.0    m
3     NaN  NaN
4     NaN  NaN
5     NaN  NaN

---

tolist和from_records

相同的想法，但跳过apply

---

tolist and from_records

Same idea but skip the apply

pd.DataFrame.from_records(df.codes.dropna().tolist())

   amount code
0      12    a
1      19    x
2      37    m

---

df.drop('codes', 1).assign(**pd.DataFrame.from_records(df.codes.dropna().tolist()))

   amount code
0    12.0    a
1    19.0    x
2    37.0    m
3     NaN  NaN
4     NaN  NaN
5     NaN  NaN

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