2023年3月1日 08:42:02go评论87阅读模式

英文:

Python flatten a dictionary column

问题

以下是翻译好的部分：

原始数据框：

Python中展开（扁平化）字典列

df['addresses'][0]

[{'addressLine1': '124 Main Street',
  'addressLine2': '',
  'addressLine3': '',
  'city': 'Portland',
  'region': 'ME',
  'postalCode': '04019',
  'country': 'USA'}]

test = pd.json_normalize(result['addresses'][0])
test

Python中展开（扁平化）字典列

到目前为止，一切都正常，但当我使用该函数并将其应用于整个列时，生成的数据框如下所示。

test = pd.json_normalize(result['addresses'])
test

Python中展开（扁平化）字典列

以下是一些列数据：

[[{'addressLine1': '124 Main Street',
   'addressLine2': '',
   'addressLine3': '',
   'city': 'Portland',
   'region': 'ME',
   'postalCode': '04019',
   'country': 'USA'}],
  ...
  ...
]

英文:

It should be a simple line of code using pd.json_normalize function but it's working only with a single string and it's not batch processing my whole column

Orginial dataframe

df[&#39;addresses&#39;][0]

[{&#39;addressLine1&#39;: &#39;124 Main Street&#39;,
  &#39;addressLine2&#39;: &#39;&#39;,
  &#39;addressLine3&#39;: &#39;&#39;,
  &#39;city&#39;: &#39;Portland&#39;,
  &#39;region&#39;: &#39;ME&#39;,
  &#39;postalCode&#39;: &#39;04019&#39;,
  &#39;country&#39;: &#39;USA&#39;}]

test = pd.json_normalize(result[&#39;addresses&#39;][0])
test

Everything up to this point works, but when I use the function and apply to the whole column, the resulting dataframe turned out to look like this.

test = pd.json_normalize(result[&#39;addresses&#39;])
test

Here are some column data:

[[{&#39;addressLine1&#39;: &#39;124 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;1234 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Chattanooga&#39;,
   &#39;region&#39;: &#39;TN&#39;,
   &#39;postalCode&#39;: &#39;37402&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;1684151 Chair Street&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Notaplace&#39;,
   &#39;region&#39;: &#39;AL&#39;,
   &#39;postalCode&#39;: &#39;48835&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;136 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;22118&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;123452 HoneyDo LN&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;123 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;Apt 2B&#39;,
   &#39;addressLine3&#39;: &#39;Building B&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;123 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;Apt 2B&#39;,
   &#39;addressLine3&#39;: &#39;Building B&#39;,
   &#39;city&#39;: &#39;New York City&#39;,
   &#39;region&#39;: &#39;NY&#39;,
   &#39;postalCode&#39;: &#39;10001&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;123 Main Street&#39;,
   &#39;addressLine2&#39;: &#39;Apt 2B&#39;,
   &#39;addressLine3&#39;: &#39;Building B&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;4578 Shiver Me Timbers Road&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;Portland&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}],
 [{&#39;addressLine1&#39;: &#39;124 Main ST&#39;,
   &#39;addressLine2&#39;: &#39;&#39;,
   &#39;addressLine3&#39;: &#39;&#39;,
   &#39;city&#39;: &#39;PORTLAND&#39;,
   &#39;region&#39;: &#39;ME&#39;,
   &#39;postalCode&#39;: &#39;04019&#39;,
   &#39;country&#39;: &#39;USA&#39;}]]

答案1

得分: 1

如果我理解您的意思，您可以使用以下示例将您的数据帧 df 转换为包含 dict 数据的形式：

df = pd.concat([df, df.pop('addresses').str[0].apply(pd.Series)], axis=1)
print(df)

打印结果：

                  addressLine1 addressLine2 addressLine3           city region postalCode country
0              124 Main Street                                 Portland     ME      04019     USA
1             1234 Main Street                              Chattanooga     TN      37402     USA
2         1684151 Chair Street                                Notaplace     AL      48835     USA
3              136 Main Street                                 Portland     ME      22118     USA
4            123452 HoneyDo LN                                 Portland     ME      04019     USA
5              123 Main Street       Apt 2B   Building B       Portland     ME      04019     USA
6              123 Main Street       Apt 2B   Building B  New York City     NY      10001     USA
7              123 Main Street       Apt 2B   Building B       Portland     ME      04019     USA
8  4578 Shiver Me Timbers Road                                 Portland     ME      04019     USA
9                  124 Main ST                                 PORTLAND     ME      04019     USA

英文:

If I understand you correctly, you can transform your dataframe df with dict data with following example:

df = pd.concat([df, df.pop(&#39;addresses&#39;).str[0].apply(pd.Series)], axis=1)
print(df)

Prints:

                  addressLine1 addressLine2 addressLine3           city region postalCode country
0              124 Main Street                                 Portland     ME      04019     USA
1             1234 Main Street                              Chattanooga     TN      37402     USA
2         1684151 Chair Street                                Notaplace     AL      48835     USA
3              136 Main Street                                 Portland     ME      22118     USA
4            123452 HoneyDo LN                                 Portland     ME      04019     USA
5              123 Main Street       Apt 2B   Building B       Portland     ME      04019     USA
6              123 Main Street       Apt 2B   Building B  New York City     NY      10001     USA
7              123 Main Street       Apt 2B   Building B       Portland     ME      04019     USA
8  4578 Shiver Me Timbers Road                                 Portland     ME      04019     USA
9                  124 Main ST                                 PORTLAND     ME      04019     USA

答案2

得分: 1

以下是您要翻译的内容：

"It seems your list has one-element lists as elements.

Lets say your list is address_list then you get the first element in that list and then use json_normalize

pd.json_normalize([e[0] for e in address_list])

If the test data that you posted is actually a column then just use:

pd.json_normalize(result[&quot;addresses&quot;].str[0])

Or if you have other columns in addition to addresses in your result dataframe:

pd.concat(
    [result.drop(column=&quot;addresses&quot;), pd.json_normalize(result[&quot;addresses&quot;].str[0])],
    axis=1
)"

英文:

It seems your list has one-element lists as elements.

Lets say your list is address_list then you get the first element in that list and then use json_normalize

pd.json_normalize([e[0] for e in address_list])

If the test data that you posted is actually a column then just use:

pd.json_normalize(result[&quot;addresses&quot;].str[0])

Or if you have other columns in addition to addresses in your result dataframe:

pd.concat(
    [result.drop(column=&quot;addresses&quot;), pd.json_normalize(result[&quot;addresses&quot;].str[0])],
    axis=1
)

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Python中展开（扁平化）字典列

问题

答案1

答案2

使用pandas以递增方式添加列名。

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