0

我正在尝试将带有字典值列表的熊猫数据框中的列拆分并转换为新列。使用将 Pandas 列中的字典/列表拆分为单独的列作为参考，事情似乎失败了，因为其中一些值是 NaN。当遇到这些行时，会引发错误，无法迭代浮点数，如果我fillna使用 None 错误将更改为str相关错误。

我试图首先使用：

df.explode('freshness_grades')

df_new = pd.concat([df_new.drop('freshness_grades', axis=1), pd.DataFrame(df_new['freshness_grades'].tolist())], axis=1)

我这样做是为了本质上将字典列表更改为字典。

    _id  freshness_grades
0   57ea8d0d9c624c035f96f45e    [{'creation_date': '2019-04-20T06:02:02.865000+00:00', 'end_date': '2015-07-23T18:43:00+00:00', 'grade': 'A', 'start_date': '2015-03-05T01:54:47+00:00'}, {'creation_date': '2019-04-20T06:02:02.865000+00:00', 'end_date': '2015-08-22T18:43:00+00:00', 'grade': 'B', 'start_date': '2015-07-23T18:43:00+00:00'}, {'creation_date': '2019-04-20T06:02:02.865000+00:00', 'end_date': '2015-10-21T18:43:00+00:00', 'grade': 'C', 'start_date': '2015-08-22T18:43:00+00:00'}, {'creation_date': '2019-04-20T06:02:02.865000+00:00', 'end_date': '2016-02-02T12:12:00+00:00', 'grade': 'D', 'start_date': '2015-10-21T18:43:00+00:00'}, {'creation_date': '2019-04-20T06:02:02.865000+00:00', 'end_date': '2016-07-22T18:43:00+00:00', 'grade': 'E', 'start_date': '2016-02-02T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:02.865000+00:00', 'grade': 'F', 'start_date': '2016-07-22T18:43:00+00:00'}]
1   57ea8d0e9c624c035f96f460    [{'creation_date': '2019-06-25T10:54:40.387000+00:00', 'end_date': '2015-07-20T14:04:00+00:00', 'grade': 'A', 'start_date': '2015-07-14T08:48:49+00:00'}, {'creation_date': '2019-06-25T10:54:40.387000+00:00', 'end_date': '2015-08-19T14:04:00+00:00', 'grade': 'B', 'start_date': '2015-07-20T14:04:00+00:00'}, {'creation_date': '2019-06-25T10:54:40.387000+00:00', 'end_date': '2015-10-18T14:04:00+00:00', 'grade': 'C', 'start_date': '2015-08-19T14:04:00+00:00'}, {'creation_date': '2019-06-25T10:54:40.387000+00:00', 'end_date': '2016-02-02T12:12:00+00:00', 'grade': 'D', 'start_date': '2015-10-18T14:04:00+00:00'}, {'creation_date': '2019-06-25T10:54:40.387000+00:00', 'end_date': '2016-07-19T14:04:00+00:00', 'grade': 'E', 'start_date': '2016-02-02T12:12:00+00:00'}, {'creation_date': '2019-06-25T10:54:40.387000+00:00', 'grade': 'F', 'start_date': '2016-07-19T14:04:00+00:00'}]
2   57ea8d0e9c624c035f96f462    [{'creation_date': '2019-04-20T06:02:03.600000+00:00', 'end_date': '2015-09-29T09:46:00+00:00', 'grade': 'A', 'start_date': '2015-07-27T15:21:32+00:00'}, {'creation_date': '2019-04-20T06:02:03.600000+00:00', 'end_date': '2015-10-29T09:46:00+00:00', 'grade': 'B', 'start_date': '2015-09-29T09:46:00+00:00'}, {'creation_date': '2019-04-20T06:02:03.600000+00:00', 'end_date': '2015-12-04T12:12:00+00:00', 'grade': 'C', 'start_date': '2015-10-29T09:46:00+00:00'}, {'creation_date': '2019-04-20T06:02:03.600000+00:00', 'end_date': '2016-02-02T12:12:00+00:00', 'grade': 'D', 'start_date': '2015-12-04T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:03.600000+00:00', 'end_date': '2016-09-28T09:46:00+00:00', 'grade': 'E', 'start_date': '2016-02-02T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:03.600000+00:00', 'grade': 'F', 'start_date': '2016-09-28T09:46:00+00:00'}]
3   57ea8d0f9c624c035f96f466    [{'creation_date': '2019-04-20T06:02:04.305000+00:00', 'end_date': '2015-09-29T09:46:00+00:00', 'grade': 'A', 'start_date': '2015-09-09T13:20:14+00:00'}, {'creation_date': '2019-04-20T06:02:04.305000+00:00', 'end_date': '2015-10-29T09:46:00+00:00', 'grade': 'B', 'start_date': '2015-09-29T09:46:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.305000+00:00', 'end_date': '2015-12-04T12:12:00+00:00', 'grade': 'C', 'start_date': '2015-10-29T09:46:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.305000+00:00', 'end_date': '2016-02-02T12:12:00+00:00', 'grade': 'D', 'start_date': '2015-12-04T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.305000+00:00', 'end_date': '2016-09-28T09:46:00+00:00', 'grade': 'E', 'start_date': '2016-02-02T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.305000+00:00', 'grade': 'F', 'start_date': '2016-09-28T09:46:00+00:00'}]
4   57ea8d109c624c035f96f468    [{'creation_date': '2019-04-20T06:02:04.673000+00:00', 'end_date': '2015-11-04T12:12:00+00:00', 'grade': 'A', 'start_date': '2015-10-30T07:43:46+00:00'}, {'creation_date': '2019-04-20T06:02:04.673000+00:00', 'end_date': '2015-11-11T12:12:00+00:00', 'grade': 'B', 'start_date': '2015-11-04T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.673000+00:00', 'end_date': '2015-12-04T12:12:00+00:00', 'grade': 'C', 'start_date': '2015-11-11T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.673000+00:00', 'end_date': '2016-02-02T12:12:00+00:00', 'grade': 'D', 'start_date': '2015-12-04T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.673000+00:00', 'end_date': '2016-11-03T12:12:00+00:00', 'grade': 'E', 'start_date': '2016-02-02T12:12:00+00:00'}, {'creation_date': '2019-04-20T06:02:04.673000+00:00', 'grade': 'F', 'start_date': '2016-11-03T12:12:00+00:00'}]
5   5f1eb63dbed8bd4f99e2a280    NaN

以 ehf 第一行为例，我希望实现：

   _id                        creation_date                     end_date                   grade     start_date
0 57ea8d0d9c624c035f96f45e    2019-04-20T06:02:02.865000+00:00  2015-07-23T18:43:00+00:0    A        2015-03-05T01:54:47+00:00
0 57ea8d0d9c624c035f96f45e    2019-04-20T06:02:02.865000+00:00  2015-08-22T18:43:00+00:00   B        2015-07-23T18:43:00+00:00
...

我从 开始explode，这一步非常有效。

但是，我没有尝试使用reset_index(). 它pd.concat()是失败的，我认为它要么与 要么NaN列表中实际上有多个字典有关。例如，在explode()ie之后{}, {}, {}

4

1 回答 1

0 
#  explode the list
df = df.explode('freshness_grades').reset_index(drop=True)

# now fill the NaN with an empty dict
df.freshness_grades = df.freshness_grades.fillna({i: {} for i in df.index})

# then normalize the column
df = df.join(pd.json_normalize(df.freshness_grades))

# drop the column
df.drop(columns=['freshness_grades'], inplace=True)

输出

                        _id                     creation_date                   end_date grade                 start_date
0  57ea8d0d9c624c035f96f45e  2019-04-20T06:02:02.865000+00:00  2015-07-23T18:43:00+00:00     A  2015-03-05T01:54:47+00:00
1  57ea8d0d9c624c035f96f45e  2019-04-20T06:02:02.865000+00:00  2015-08-22T18:43:00+00:00     B  2015-07-23T18:43:00+00:00
2  57ea8d0d9c624c035f96f45e  2019-04-20T06:02:02.865000+00:00  2015-10-21T18:43:00+00:00     C  2015-08-22T18:43:00+00:00
3  57ea8d0d9c624c035f96f45e  2019-04-20T06:02:02.865000+00:00  2016-02-02T12:12:00+00:00     D  2015-10-21T18:43:00+00:00
4  57ea8d0d9c624c035f96f45e  2019-04-20T06:02:02.865000+00:00  2016-07-22T18:43:00+00:00     E  2016-02-02T12:12:00+00:00
于 2020-09-13T21:41:42.267 回答