如何从嵌套的for循环构建pandas数据帧
如何从嵌套的for循环构建pandas数据帧
提问于 2019-07-19 08:14:57
我正在使用谷歌云视频智能API，并试图将结果放入熊猫数据帧中。该接口的输出类为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。
下面是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this Stack Overflow article的帮助下，我创建了一个空列表，并附加了结果，稍后将其转换为pandas数据帧，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，它给出了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但是，当我将相同的to逻辑添加到for循环中时，它会给出一个失真的数据帧，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我希望有一种方法可以修复它并获得数据帧，如下所示：
>>> 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下一步我可以尝试什么？
回答 1
Stack Overflow用户
回答已采纳
发布于 2019-07-19 08:32:24
如下所示更改您的代码：
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')总之:您在每个子循环中添加了行的列表。您只想添加一次行。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/57104050
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