如何從嵌套的 for 循環構建 Pandas 數據框

Question

我正在使用谷歌雲視頻智能 API，我正在嘗試將結果放入 Pandas 數據幀中。 API的輸出類是repeatcompositecontainer。 所以，我的想法是在 API 函數中使用的 for 循環中構建一個數據框。

這是 API 函數處理結果的方式：

    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')

在這篇 Stack Overflow 文章的幫助下，我創建了一個空列表並附加了結果，以便稍后將其轉換為 Pandas 數據框，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')

當我只嘗試最后一個 for 循環時，它給了我一個很好的結構化數據框，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

但是，當我將相同的邏輯添加到 for 循環時，它會給出一個扭曲的數據框，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

我希望是否有辦法修復它並獲得如下數據框：

>>> 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

我接下來可以嘗試什么？

Answer 1

更改您的代碼，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))
        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')

總之：您在每個子循環中添加行列表。 您只想添加該行一次。