问题

I can help you with the translation. Here is the translated content:

我有一个pandas数据框，其中有两列是嵌套列，包含小数值：df.tail(1).to_dict('list') 会得到这样的数据：

{'nested_col1': [array([{'key1': 'CO', 'key2': Decimal('8.940000000')}], dtype=object)],
 'nested_col2': [array([{'key3': 'CO', 'key4': 'P14', 'key5': Decimal('8.940000000'), 'key6': None}], dtype=object)]}

我试图通过以下方式展开数据框：

df = (df.drop(cols, axis=1)
        .join(pd.concat(
            [pd.json_normalize(df[x].explode(), errors='ignore').applymap(
                lambda x: str(x) if isinstance(x, (int, float)) else x).add_prefix(f'{x}.') for x in
             cols],
            axis=1)))

但在某些情况下，我得到以下错误：

Traceback (most recent call last):
  File "data_load.py.py", line 365, in <module>
    df = prepare_data(data, transaction_id, cohort_no)
  File "data_load.py.py", line 274, in prepare_data
    df = flatten_dataframe(cols_to_explode, df)
  File "data_load.py.py", line 204, in flatten_dataframe
    df1 = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
  File "data_load.py.py", line 204, in <listcomp>
    df1 = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
  File "/project1/venv/lib/python3.6/site-packages/pandas/io/json/_normalize.py", line 270, in _json_normalize
    if any([isinstance(x, dict) for x in y.values()] for y in data):
  File "/project1/venv/lib/python3.6/site-packages/pandas/io/json/_normalize.py", line 270, in <genexpr>
    if any([isinstance(x, dict) for x in y.values()] for y in data):
AttributeError: 'float' object has no attribute 'values'
failed to run commands: exit status 1

我还有什么遗漏的地方吗，或者有没有更好的方法来做同样的事情？

预期输出应该是：

nested_col1.key1,nested_col1.key2 nested_col2.key3 ... 这样

--- 

If you need further assistance, feel free to ask.

<details>
<summary>英文:</summary>

I have a pandas dataframe where 2 columns are nested column having decimal value: `df.tail(1).to_dict(&#39;list&#39;)` gives this kind of data

 

     {&#39;nested_col1&#39;: [array([{&#39;key1&#39;: &#39;CO&#39;, &#39;key2&#39;: Decimal(&#39;8.940000000&#39;)}],
      dtype=object)], &#39;nested_col2&#39;: [array([{&#39;key3&#39;: &#39;CO&#39;, &#39;key4&#39;: &#39;P14&#39;, &#39;key5&#39;: Decimal(&#39;8.940000000&#39;), &#39;key6&#39;: None}],
      dtype=object)]}

I am trying to explode the dataframe with this:

    df = (df.drop(cols, axis=1)
            .join(pd.concat(
                [pd.json_normalize(df[x].explode(), errors=&#39;ignore&#39;).applymap(
                    lambda x: str(x) if isinstance(x, (int, float)) else x).add_prefix(f&#39;{x}.&#39;) for x in
                 cols],
                axis=1)))

With this I am getting below error in some cases:

        Traceback (most recent call last):
      File &quot;data_load.py.py&quot;, line 365, in &lt;module&gt;
        df = prepare_data(data, transaction_id, cohort_no)
      File &quot;data_load.py.py&quot;, line 274, in prepare_data
        df = flatten_dataframe(cols_to_explode, df)
      File &quot;data_load.py.py&quot;, line 204, in flatten_dataframe
        df1 = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
      File &quot;data_load.py.py&quot;, line 204, in &lt;listcomp&gt;
        df1 = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
      File &quot;/project1/venv/lib/python3.6/site-packages/pandas/io/json/_normalize.py&quot;, line 270, in _json_normalize
        if any([isinstance(x, dict) for x in y.values()] for y in data):
      File &quot;/project1/venv/lib/python3.6/site-packages/pandas/io/json/_normalize.py&quot;, line 270, in &lt;genexpr&gt;
        if any([isinstance(x, dict) for x in y.values()] for y in data):
    AttributeError: &#39;float&#39; object has no attribute &#39;values&#39;
    failed to run commands: exit status 1

anything still I am missing here or any better way to do the same?

Expected Output should be:

    nested_col1.key1,nested_col1.key2 nested_col2.key3 ... like this

        


</details>


# 答案1
**得分**: 1

```plaintext
似乎每个嵌套列中只有一个元素：

``` out = pd.concat([pd.json_normalize(df[x][0]).add_prefix(f'{x}.') 
                     for x in cols], axis=1)
out = out.apply(pd.to_numeric, errors='coerce').fillna(out)
print(out)

# 输出
  nested_col1.key1  nested_col1.key2 nested_col2.key3 nested_col2.key4  nested_col2.key5 nested_col2.key6
0               CO              8.94               CO              P14              8.94             None

注意：如果每个嵌套列表中有多个记录，可以用.explode()来替代[0]。

<details>
<summary>英文:</summary>

It seems there is only one element for each nested column:

out = pd.concat([pd.json_normalize(df[x][0]).add_prefix(f'{x}.')
for x in cols], axis=1)
out = out.apply(pd.to_numeric, errors='coerce').fillna(out)
print(out)

Output

nested_col1.key1 nested_col1.key2 nested_col2.key3 nested_col2.key4 nested_col2.key5 nested_col2.key6
0 CO 8.94 CO P14 8.94 None

Note: you can replace `[0]` by `.explode()` if you have more than one record in each nested list.

</details>



# 答案2
**得分**: 1

以下是代码的翻译部分：

你可以使用 [`json_normalize`](https://pandas.pydata.org/docs/reference/api/pandas.json_normalize.html) 和 [`concat`](https://pandas.pydata.org/docs/reference/api/pandas.concat.html)：

```python
cols = ['nested_col1', 'nested_col2']

out = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
                keys=cols, axis=1)
out.columns = out.columns.map('.'.join)

输出结果：

  nested_col1.key1 nested_col1.key2 nested_col2.key3 nested_col2.key4 nested_col2.key5 nested_col2.key6
0               CO      8.940000000               CO              P14      8.940000000             None

cols = ['nested_col1', 'nested_col2']

out = pd.concat([pd.json_normalize(df[c].explode()) for c in cols],
                keys=cols, axis=1)
out.columns = out.columns.map('.'.join)

  nested_col1.key1 nested_col1.key2 nested_col2.key3 nested_col2.key4 nested_col2.key5 nested_col2.key6
0               CO      8.940000000               CO              P14      8.940000000             None