images = [img for img in os.listdir(data_dir) if img.lower().endswith('________')]for i, old_name in enumerate(images):

new_name = '{}.jpg'.format(old_name.___________('.')[0])

os.____________(os.path.join(data_dir, old_name), os.path.join(data_dir, new_name))

images = [img for img in os.listdir(data_dir) if img.lower().endswith('__________')]

new_name = '{:04d}.jpg'.format(_____)

os.______________(os.path.join(data_dir, old_name), os.path.join(data_dir, new_name))

print(f'Renamed: {old_name} -> {new_name}')

xmin = ____________________________

ymin = ____________________________

xmax =____________________________

ymax = ___________________________

objects.append({

'filename': os.path.splitext(os.path.basename(xml_file))[0], # 提取文件名（不包括扩展名）

'name': obj_name,

'xmin': _______,

'ymin': _______,

'xmax': ______,

'ymax': ______,

})

# 遍历数据集文件夹中的所有文件

# 如果文件是以.xml结尾，则认为是需要解析的XML文件。

data = pd.________(A超市的销售数据.csv)

# 打印数据集的前几行

print(data.___________)

unique_categories_count = data[大类名称].______________()

category_sales_sum = data._____________(大类名称)[销售金额].__________()

sorted_category_sales = category_sales_sum._______________(ascending=_________)

max_sales_category = sorted_category_sales._______________[0]

sub_category_sales_sum = sub_data.__________(小类名称)[销售金额].______()

top_15_sub_categories = sub_category_sales_sum.______________

plt.figure(figsize=(12, 6))

top_15_sub_categories.______________(kind=__________)

data = pd._____(A地区的天气数据.csv)

print(data._____)

data['日期'] = pd._____(data['日期'])

data_2009 = data[_____ == 2009]

monthly_pressure_diff = data_2009._____('月份')['压力差']._____.sort_values(ascending=_____)

print(monthly_pressure_diff)