2023年3月7日 21:23:10go评论68阅读模式

英文:

Extract information from Google Doc into Pandas DF

问题

我正在寻找一种方法从我的私人Google文档中提取信息并将它们放入DF（数据框）以执行一些简单的分析。

我已连接到Google文档API，并能够读取文档，但我正在努力提取仅相关信息并按正确顺序排列。

我的文档如下所示：

我需要的输出如下所示：

我可以创建单个项目的正则表达式，但如何将它们分组到像上面那样的表中，考虑到单个日期的段落数和标签数可能会有所变化？

谢谢！

这是我从API收到的内容：

{'title': 'test',
 'body': {'content': [{'endIndex': 1,
    'sectionBreak': {'sectionStyle': {'columnSeparatorStyle': 'NONE',
      'contentDirection': 'LEFT_TO_RIGHT',
      'sectionType': 'CONTINUOUS'}}},
   {'startIndex': 1,
    'endIndex': 11,
    'paragraph': {'elements': [{'startIndex': 1,
       'endIndex': 11,
       'textRun': {'content': 'Doc Title\n',
        'textStyle': {'bold': True,
         'fontSize': {'magnitude': 13, 'unit': 'PT'}}}}],
     'paragraphStyle': {'namedStyleType': 'NORMAL_TEXT',
      'alignment': 'CENTER',
      'direction': 'LEFT_TO_RIGHT'}}},
   {'startIndex': 11,
    'endIndex': 12,
    'paragraph': {'elements': [{'startIndex': 11,
       'endIndex': 12,
       'textRun': {'content': '\n',
        'textStyle': {'bold': True,
         'fontSize': {'magnitude': 13, 'unit': 'PT'}}}}],
     'paragraphStyle': {'namedStyleType': 'NORMAL_TEXT',
      'alignment': 'CENTER',
      'direction': 'LEFT_TO_RIGHT'}}},
   ...
   ...

请注意，以上只是给出的一部分内容，其中包含了文档的结构和示例文本段。如果您需要对文档的特定部分进行进一步的翻译或解释，请提供详细的信息。

英文:

I'm looking for a way to extract information from my private Google Doc and put them into DF to perform some simple analysis.

I'm connected to the Google Doc API and able to read the document but I'm struggling with extracting only relevant information and in correct order.

This is how my doc looks like:

And this is the output I need to have:

I would be able to create separate regexes for single items but how to group it into table like above, given that number of paragraphs for single date can vary (same for tags)?

Thanks

This is what I received from API:

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&#39;body&#39;: {&#39;content&#39;: [{&#39;endIndex&#39;: 1,
&#39;sectionBreak&#39;: {&#39;sectionStyle&#39;: {&#39;columnSeparatorStyle&#39;: &#39;NONE&#39;,
&#39;contentDirection&#39;: &#39;LEFT_TO_RIGHT&#39;,
&#39;sectionType&#39;: &#39;CONTINUOUS&#39;}}},
{&#39;startIndex&#39;: 1,
&#39;endIndex&#39;: 11,
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&#39;textStyle&#39;: {&#39;bold&#39;: True,
&#39;fontSize&#39;: {&#39;magnitude&#39;: 13, &#39;unit&#39;: &#39;PT&#39;}}}}],
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答案1

得分: 3

假设 d 是由Google API返回的字典，您可以使用以下代码：

import itertools

# 提取内容数据
content = itertools.chain(*[c['paragraph']['elements']
                           for c in d['body']['content']
                           if 'paragraph' in c])

# 提取期望数据
df = (pd.json_normalize(content)['textRun.content']
    .str.strip().loc[lambda x: x.str.len() > 0]
    .str.extract('^(?P<Date>\d+\.\d+\.\d+)|(?P<Paragraph>\d+\.)|(?P<Tags>#.*)')
    .dropna(how='all'))

# 过滤数据框
out = (df.ffill().loc[df['Tags'].notna()]
     .assign(Tags=lambda x: x['Tags'].str.split(',\s*'))
     .reset_index(drop=True))

输出：

>>> out
        Date Paragraph                   Tags
0  7.03.2023        1.         [#tag1, #tag2]
1  7.03.2023        2.  [#tag1, #tag2, #tag3]
2  8.03.2023        1.         [#tag1, #tag2]
3  8.03.2023        2.  [#tag1, #tag2, #tag3]
4  8.03.2023        3.                [#tag1]

英文:

Suppose d the dictionary returned by Google API, you can use:

import itertools
# Extract content data
content = itertools.chain(*[c[&#39;paragraph&#39;][&#39;elements&#39;]
for c in d[&#39;body&#39;][&#39;content&#39;]
if &#39;paragraph&#39; in c])
# Extract expected data
df = (pd.json_normalize(content)[&#39;textRun.content&#39;]
.str.strip().loc[lambda x: x.str.len() &gt; 0]
.str.extract(&#39;^(?P&lt;Date&gt;\d+\.\d+\.\d+)|(?P&lt;Paragraph&gt;\d+\.)|(?P&lt;Tags&gt;#.*)&#39;)
.dropna(how=&#39;all&#39;))
# Filter dataframe
out = (df.ffill().loc[df[&#39;Tags&#39;].notna()]
.assign(Tags=lambda x: x[&#39;Tags&#39;].str.split(&#39;,\s*&#39;))
.reset_index(drop=True))

Output:

&gt;&gt;&gt; out
Date Paragraph                   Tags
0  7.03.2023        1.         [#tag1, #tag2]
1  7.03.2023        2.  [#tag1, #tag2, #tag3]
2  8.03.2023        1.         [#tag1, #tag2]
3  8.03.2023        2.  [#tag1, #tag2, #tag3]
4  8.03.2023        3.                [#tag1]

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