如何从嵌套循环构建熊猫数据帧

Question

我正在使用谷歌云视频智能API，我正在尝试将结果放入一个数据框中。API的输出类是repeatedcompositecontainer。因此，我的想法是在API函数中使用的for循环中构建一个数据帧。

以下是API函数处理结果的方式：

    segment_labels = result.annotation_results[0].segment_label_annotations
    for i, segment_label in enumerate(segment_labels):
        print('Video label description: {}'.format(
            segment_label.entity.description))
            
        for category_entity in segment_label.category_entities:
            print('\tLabel category description: {}'.format(
                category_entity.description))

        for i, segment in enumerate(segment_label.segments):
            start_time = (segment.segment.start_time_offset.seconds +
                          segment.segment.start_time_offset.nanos / 1e9)
            end_time = (segment.segment.end_time_offset.seconds +
                        segment.segment.end_time_offset.nanos / 1e9)
            positions = '{}s to {}s'.format(start_time, end_time)
            confidence = segment.confidence
            print('\tSegment {}: {}'.format(i, positions))
            print('\tConfidence: {}'.format(confidence))
        print('\n')

在这篇Stack Overflow文章的帮助下，我创建了一个空列表，并将结果附加到后面的数据框中，如下所示：

    df = []
    
    # Process video/segment level label annotations
    segment_labels = result.annotation_results[0].segment_label_annotations
    for i, segment_label in enumerate(segment_labels):
        print('Video label description: {}'.format(
            segment_label.entity.description))
            
        for category_entity in segment_label.category_entities:
            print('\tLabel category description: {}'.format(
                category_entity.description))
            df.append({'Description': category_entity.description})

        for i, segment in enumerate(segment_label.segments):
            start_time = (segment.segment.start_time_offset.seconds +
                          segment.segment.start_time_offset.nanos / 1e9)
            end_time = (segment.segment.end_time_offset.seconds +
                        segment.segment.end_time_offset.nanos / 1e9)
            positions = '{}s to {}s'.format(start_time, end_time)
            confidence = segment.confidence
            df.append({'Confidence': segment.confidence, 'Start': start_time, 'End': end_time})
            print('\tSegment {}: {}'.format(i, positions))
            print('\tConfidence: {}'.format(confidence))
        print('\n')

当我只尝试最后一个for循环时，它给了我一个很好的结构化数据框架，如下所示

>>> frame = pd.DataFrame(df)
>>> frame
Confidence         End  Start
  0.704168  599.682416    0.0
  0.737053  599.682416    0.0
  0.832496  599.682416    0.0
  0.427637  599.682416    0.0
  0.518693  599.682416    0.0

然而，当我将相同的逻辑添加到for循环中时，它给出了一个扭曲的数据帧，如下所示

>>> frame = pd.DataFrame(df)
>>> frame
Confidence    Description         End  Start
       NaN     technology         NaN    NaN
  0.741133            NaN  599.682416    0.0
       NaN       keyboard         NaN    NaN
  0.328138            NaN  599.682416    0.0
       NaN         person         NaN    NaN
  0.436333            NaN  599.682416    0.0
       NaN         person         NaN    NaN

我希望如果有一种方法来修复它，并得到一个数据帧如下：

>>> frame = pd.DataFrame(df)
>>> frame
Confidence  Description    End        Start
  0.741133  technology   599.682416    0.0
  0.328138  keyboard     599.682416    0.0
  0.436333  person       599.682416    0.0

接下来我可以尝试什么？

Answer 1

按以下方式更改代码：

    df = []

    # Process video/segment level label annotations
    segment_labels = result.annotation_results[0].segment_label_annotations
    for i, segment_label in enumerate(segment_labels):
        print('Video label description: {}'.format(
            segment_label.entity.description))
        label_row = {} # Create a dictionary for the label
        for category_entity in segment_label.category_entities:
            print('\tLabel category description: {}'.format(
                category_entity.description))
            # Add the description
            label_row['Description'] = category_entity.description

        for i, segment in enumerate(segment_label.segments):
            start_time = (segment.segment.start_time_offset.seconds +
                          segment.segment.start_time_offset.nanos / 1e9)
            end_time = (segment.segment.end_time_offset.seconds +
                        segment.segment.end_time_offset.nanos / 1e9)
            positions = '{}s to {}s'.format(start_time, end_time)
            confidence = segment.confidence
            row_segment_info = {'Confidence': segment.confidence, 'Start': start_time, 'End': end_time})
            # Add the segment info for this row
            label_row.update(row_segment_info)
            df.append(label_row) # Now add the row
            print('\tSegment {}: {}'.format(i, positions))
            print('\tConfidence: {}'.format(confidence))
        print('\n')

总而言之：您在每个子循环中添加了行列表。您只想添加该行一次。