val selectedValue = timeData .selectExpr(cols: _*) .na.drop()优化代码

可以尝试以下几个优化措施：

1. 使用别名代替 `cols: _*`，这样可以减少代码长度并提高可读性，例如：

val cols = Seq("col1", "col2", "col3")
val selectedValue = timeData.selectExpr(cols.map(c => s"$c as $c"): _*).na.drop()

2. 使用 `dropDuplicates` 去重，如果数据中存在重复的行，可以使用 `dropDuplicates` 去重，例如：

val selectedValue = timeData.selectExpr(cols: _*).na.drop().dropDuplicates()

3. 使用 `filter` 过滤数据，如果只需要满足某个条件的数据，可以使用 `filter` 过滤数据，例如：

val selectedValue = timeData.selectExpr(cols: _*).na.drop().filter($"col1" > 0 && $"col2" < 100)

这样可以减少不必要的计算。

相关问题

def iu_status_filter(i, data, num): val_last = data.iloc[i-1]['iu34Status01'] val_now = data.iloc[i]['iu34Status01'] if val_last == 0 and val_now > 0: if (data.iloc[i:i+num]['iu34Status01'] == 2).all(): return True elif (data.iloc[i:i+num]['iu34Status01'] == 1).all(): return True else: # prev_val = data.iloc[i]['iu34Status01'] for index in range(i+1, i+num): curr_val = data.iloc[index]['iu34Status01'] if curr_val != val_now and curr_val <= val_now: return False val_now = curr_val return True else: return False

这是一个函数，可以根据给定的数据和数字，过滤出符合一定条件的数据。具体来说，当给定数据的第i行的iu34Status01值从0变为非0时，会检查接下来的num行数据的iu34Status01值是否都为2或都为1，如果都是，则返回True；否则，会检查接下来的num行数据的iu34Status01值是否都等于当前值或者更大，如果是，则返回True；否则，返回False。

为每句代码做注释：for class_name in class_names: current_class_data_path = os.path.join(src_data_folder, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name) val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name) test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name) train_stop_flag = current_data_length * train_scale val_stop_flag = current_data_length * (train_scale + val_scale) current_idx = 0 train_num = 0 val_num = 0 test_num = 0 for i in current_data_index_list: src_img_path = os.path.join(current_class_data_path, current_all_data[i]) if current_idx <= train_stop_flag: copy2(src_img_path, train_folder) train_num = train_num + 1 elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag): copy2(src_img_path, val_folder) val_num = val_num + 1 else: copy2(src_img_path, test_folder) # print("{}复制到了{}".format(src_img_path, test_folder)) test_num = test_num + 1 current_idx = current_idx + 1

# 循环遍历每个类别的文件夹
for class_name in class_names:
# 拼接当前类别的数据路径
current_class_data_path = os.path.join(src_data_folder, class_name)
# 获取当前类别的所有数据文件名
current_all_data = os.listdir(current_class_data_path)
# 获取当前类别的数据数量
current_data_length = len(current_all_data)
# 生成当前类别数据的索引列表
current_data_index_list = list(range(current_data_length))
# 随机打乱当前类别数据的索引列表
random.shuffle(current_data_index_list)

# 拼接训练集、验证集、测试集的路径
train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)
val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name)
test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)

# 计算训练集、验证集、测试集在当前类别中的截止点
train_stop_flag = current_data_length * train_scale
val_stop_flag = current_data_length * (train_scale + val_scale)

# 初始化当前类别的数据索引、训练集数量、验证集数量、测试集数量
current_idx = 0
train_num = 0
val_num = 0
test_num = 0

# 循环遍历当前类别的数据索引列表，将数据复制到对应的训练集、验证集、测试集文件夹中
for i in current_data_index_list:
src_img_path = os.path.join(current_class_data_path, current_all_data[i])
# 如果当前索引在训练集截止点之前，则将数据复制到训练集
if current_idx <= train_stop_flag:
copy2(src_img_path, train_folder)
train_num = train_num + 1
# 如果当前索引在训练集截止点和验证集截止点之间，则将数据复制到验证集
elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag):
copy2(src_img_path, val_folder)
val_num = val_num + 1
# 如果当前索引在验证集截止点之后，则将数据复制到测试集
else:
copy2(src_img_path, test_folder)
# 打印当前数据的拷贝信息
# print("{}复制到了{}".format(src_img_path, test_folder))
test_num = test_num + 1

# 更新当前类别的数据索引和数量统计信息
current_idx = current_idx + 1