解读代码： data.append({ "file_path": file_path, "text_content": text_content, "year_label": year_label, "emotion_label": emotion_label })

这段代码是将一些数据以字典的形式添加到一个名为`data`的列表中。

其中，`file_path`、`text_content`、`year_label`和`emotion_label`是字典的键，它们分别对应着文件路径、文本内容、年份标签和情感标签。这些键的值来自于之前的变量或数据。

通过使用`data.append()`方法，将一个新的字典作为元素添加到`data`列表中。这个字典包含了上述四个键和对应的值。

可以将这段代码看作是将一条数据记录添加到`data`列表中，每个记录都有文件路径、文本内容、年份标签和情感标签这四个属性。通过重复执行这段代码，可以逐步构建一个具有多条数据记录的列表。

相关问题

优化以下代码，# 构建特征矩阵和标签向量 X = [] y = data['Rating'] for index, row in data.iterrows(): features = [] # 添加运行时长区间评分 if pd.notna(row['RunTime']): category1 = pd.cut([row['RunTime']], bins=bins1, labels=labels1)[0] if category1 in avg_runtime_ratings: features.append(avg_runtime_ratings[category1]) else: features.append(0) else: features.append(0) # 添加年份区间评分 if pd.notna(row['year']): category2 = pd.cut([row['year']], bins=bins2, labels=labels2)[0] if category2 in avg_year_ratings: features.append(avg_year_ratings[category2]) else: features.append(0) else: features.append(0) # 添加导演评分 if row.Director in avg_director_ratings: features.append(avg_director_ratings[row.Director]) else: features.append(0) # 添加编剧评分 if row.Writer in avg_writer_ratings: features.append(avg_writer_ratings[row.Writer]) else: features.append(0) # 添加主演评分 casts = row.TopTwoCasts.split(',') if len(casts) == 1: cast = casts[0] if cast in avg_casts_ratings: features.append(avg_casts_ratings[cast]) else: features.append(0) features.extend([0, 0]) else: cast_1, cast_2 = casts if cast_1 in avg_casts_ratings: features.append(avg_casts_ratings[cast_1] * 0.6) else: features.append(0) if cast_2 in avg_casts_ratings: features.append(avg_casts_ratings[cast_2] * 0.4) else: features.append(0) # 添加类型评分 genres = row.Genres.split(',') if len(genres) == 1: genre = genres[0] if genre in avg_genres_ratings: features.append(avg_genres_ratings[genre]) else: features.append(0) features.extend([0, 0]) elif len(genres) == 2: genre_1, genre_2 = genres if genre_1 in avg_genres_ratings: features.append(avg_genres_ratings[genre_1] * 0.6) else: features.append(0) if genre_2 in avg_genres_ratings: features.append(avg_genres_ratings[genre_2] * 0.4) else: features.append(0) features.append(0) else: genre_1, genre_2, genre_3 = genres if genre_1 in avg_genres_ratings: features.append(avg_genres_ratings[genre_1] * 0.4) else: features.append(0) if genre_2 in avg_genres_ratings: features.append(avg_genres_ratings[genre_2] * 0.3) else: features.append(0) if genre_3 in avg_genres_ratings: features.append(avg_genres_ratings[genre_3] * 0.3) else: features.append(0) X.append(features) X = pd.DataFrame(X)

可以优化的地方有：

1. 使用向量化操作代替迭代操作，可以显著提高代码效率；
2. 缩短代码长度和提高可读性，可以通过使用函数和列表推导式等技巧实现。

以下是优化后的代码：

import numpy as np

def get_feature(row, avg_runtime_ratings, avg_year_ratings, avg_director_ratings, avg_writer_ratings, avg_casts_ratings, avg_genres_ratings):
    features = []
    
    # 添加运行时长区间评分
    runtime = row.get('RunTime', np.nan)
    category1 = pd.cut([runtime], bins=bins1, labels=labels1)[0] if pd.notna(runtime) else None
    if category1 in avg_runtime_ratings:
        features.append(avg_runtime_ratings[category1])
    else:
        features.append(0)
        
    # 添加年份区间评分
    year = row.get('year', np.nan)
    category2 = pd.cut([year], bins=bins2, labels=labels2)[0] if pd.notna(year) else None
    if category2 in avg_year_ratings:
        features.append(avg_year_ratings[category2])
    else:
        features.append(0)
        
    # 添加导演评分
    director = row.get('Director', None)
    features.append(avg_director_ratings.get(director, 0))
    
    # 添加编剧评分
    writer = row.get('Writer', None)
    features.append(avg_writer_ratings.get(writer, 0))
    
    # 添加主演评分
    casts = row.get('TopTwoCasts', '').split(',')
    if len(casts) == 1:
        cast = casts[0]
        features.append(avg_casts_ratings.get(cast, 0))
        features.extend([0, 0])
    else:
        cast_1, cast_2 = casts
        features.append(avg_casts_ratings.get(cast_1, 0) * 0.6)
        features.append(avg_casts_ratings.get(cast_2, 0) * 0.4)
        
    # 添加类型评分
    genres = row.get('Genres', '').split(',')
    if len(genres) == 1:
        genre = genres[0]
        features.append(avg_genres_ratings.get(genre, 0))
        features.extend([0, 0])
    elif len(genres) == 2:
        genre_1, genre_2 = genres
        features.append(avg_genres_ratings.get(genre_1, 0) * 0.6)
        features.append(avg_genres_ratings.get(genre_2, 0) * 0.4)
        features.append(0)
    else:
        genre_1, genre_2, genre_3 = genres
        features.append(avg_genres_ratings.get(genre_1, 0) * 0.4)
        features.append(avg_genres_ratings.get(genre_2, 0) * 0.3)
        features.append(avg_genres_ratings.get(genre_3, 0) * 0.3)
        
    return features

X = data.apply(lambda row: get_feature(row, avg_runtime_ratings, avg_year_ratings, avg_director_ratings, avg_writer_ratings, avg_casts_ratings, avg_genres_ratings), axis=1)
X = pd.DataFrame(X.tolist())

优化使得每检索50张照片，输出一次已检索文件数：import os from PIL import Image from multiprocessing import Pool def is_black_image(file_path): with Image.open(file_path) as img: pixels = list(img.getdata()) if all(sum(pixel) == 0 for pixel in pixels): return True else: return False def delete_black_images(file_path): if is_black_image(file_path): os.remove(file_path) print('{} has del'.format(file_path)) def batch_delete_black_images(folder_path): file_list = [] for f in os.listdir(folder_path): file_path = os.path.join(folder_path, f) if os.path.isfile(file_path): file_list.append(file_path) with Pool() as p: p.map(delete_black_images, file_list) if __name__ == '__main__': batch_delete_black_images(r'F:\H18\Dfinal640.tif\8-2')

为了每检索50张照片输出一次已检索文件数，您可以在 `batch_delete_black_images` 函数中添加一个计数器来实现。具体地，您可以在函数中添加一个变量 `count`，每删除一张黑色图片时，将计数器加1，并且当计数器能够被50整除时，打印当前已经检索的文件数。下面是修改后的代码：

import os
from PIL import Image
from multiprocessing import Pool

def is_black_image(file_path):
    with Image.open(file_path) as img:
        pixels = list(img.getdata())
        if all(sum(pixel) == 0 for pixel in pixels):
            return True
        else:
            return False

def delete_black_images(file_path):
    if is_black_image(file_path):
        os.remove(file_path)
        print('{} has del'.format(file_path))
        return True
    return False

def batch_delete_black_images(folder_path):
    file_list = []
    count = 0
    for f in os.listdir(folder_path):
        file_path = os.path.join(folder_path, f)
        if os.path.isfile(file_path):
            file_list.append(file_path)
    with Pool() as p:
        results = p.map(delete_black_images, file_list)
        for result in results:
            if result:
                count += 1
                if count % 50 == 0:
                    print('已检索 {} 个文件'.format(count))

if __name__ == '__main__':
    batch_delete_black_images(r'F:\H18\Dfinal640.tif\8-2')

注意，为了避免多个进程同时打印计数器的值，我们在每50次删除操作后只打印计数器的值，而不是实时打印。