将速度从 zip 提高到 json
Improving speed from zip to json
我有一些财务数据组织为 zip 文件。自 2007 年以来每个交易日一个 zip 文件。每个文件包含 150'000 到 550'000 records/rows 和 20 columns/features 左右。我需要按照使用逻辑重新组织数据：
/DataFolder/{Year}/{Column1}/{Column2}/{filename}.json
为清楚起见，Column1 是 Ticker 列，Column2 是 ExpiryDate 列。
此外，对于单个文件中的每个 ticker-expiryDate 对，我想填充一个新列（0 或 1），这是按货币行使价（给定 StockPrice 列中的值，我得到最接近行使价列的值股票价格）。
然后我将数据（对于每个 ticker-expiryDate 对）作为 json 文件写入我的硬盘。
最初我打算采用一些“蛮力”方法，即将 zip 文件作为数据帧加载，并对代码和到期日期进行一些排序。显然这不是最优的。处理一个 zip 文件大约需要 12 分钟。
然后我尝试使用 pandas groupby 函数生成的组来改进我的代码。它确实将每个 zip 文件的性能提高到略低于 10 分钟。但仍然考虑到我有近 3'000 个文件要处理我需要大量改进...
我使用一个函数来获得给定股票价格的最接近行使价：
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
我对每个 zip 文件使用以下代码：
def process_directory(files2proc):
#files2proc list of files to process
for filename in files2proc:
df_zip = pd.read_csv(filename)
df_grouped = df_zip.groupby(['ticker', 'expirDate'])
for name, group in df_grouped:
expYear = str(name[1])[-4:]
exp_fmt = str(pd.to_datetime(name[1]).date()).replace('-', '')
temp_path = '/Volumes/DataFolder/{}/{}/{}/{}/'.format(name[0][0], name[0], expYear, exp_fmt)
dir_path = pathlib.Path(temp_path)
dir_path.mkdir(parents=True, exist_ok=True)
df_temp = df_grouped.get_group(name).reset_index(drop=True)
strikes = df_temp['strike'].unique()
stkprice = df_temp['stkPx'].iloc[0]
atm_strike = find_nearest(strikes, stkprice)
df_temp['atTheMoney'] = df_temp['strike'] == atm_strike
file_path = temp_path + '_' + name[0] + '_' + exp_fmt + '.json'
if not os.path.exists(file_path):
df_temp.to_json(file_path, orient="records")
else:
df_orig = pd.read_json(file_path, orient="records")
df_calls_up = df_orig.append(df_temp)
df_calls_up.to_json(file_path, orient="records")
我担心 .reset_index 和 .iloc 语句很耗时。如何绕过对 find_nearest 的调用以提高速度？如果没有，是否有一种向量化的方式来填充平价列？
更一般地说，我认为我创建了太多对整体速度没有帮助的临时数据帧 (?)
编辑：zip 仅包含一个 csv 文件。我在下面复制了 csv 示例的一部分：
ticker stkPx expirDate strike (other columns) trade_date
AAPL 34.3 1/20/2007 32.5 1/3/2007
AAPL 34.3 1/20/2007 35 1/3/2007
AAPL 34.3 1/20/2007 37.5 1/3/2007
AAPL 34.3 2/17/2007 30 1/3/2007
AAPL 34.3 2/17/2007 35 1/3/2007
AAPL 34.3 2/17/2007 40 1/3/2007
(...)
我需要将此文件重新组织成几个 json 文件，如下所示：
Datafolder/A/AAPL/2007/20070120/_AAPL_20070120.json
Datafolder/A/AAPL/2007/20070120/_AAPL_20070217.json
使用这种格式：
Datafolder/{TickerInitial}/{Ticker}/{year of expirDate}/{expirDate as yyyymmdd}/{ticker}{expirDate as yyyymmdd}.json
每个 json 文件都应该有一个名为 atTheMoney 的额外列（位于原始 zip 文件列的顶部）以标记 at the money 选项，即 _AAPL_20070120.json:
ticker stkPx expirDate strike (other columns) trade_date atTheMoney
AAPL 34.3 1/20/2007 32.5 1/3/2007 False
AAPL 34.3 1/20/2007 35 1/3/2007 True
AAPL 34.3 1/20/2007 37.5 1/3/2007 False
和 _AAPL_20070217.json:
ticker stkPx expirDate strike (other columns) trade_date atTheMoney
AAPL 34.3 2/17/2007 30 1/3/2007 False
AAPL 34.3 2/17/2007 35 1/3/2007 True
AAPL 34.3 2/17/2007 40 1/3/2007 False
货币标志对应于给定 stkPx 列值的最接近行使价列值（即行使价 35 是 34.3 最接近 [32.5, 35, 37.5] 或 [30, 35, 40] 的值）
Edit2：这是我修改后的 JonSG 版本：
def process_file(zip_path, ifilename):
stage_1 = collections.defaultdict(list)
with zipfile.ZipFile(zip_path) as zf:
with zf.open(ifilename + ".csv", "r") as file_in:
reader = csv.reader(TextIOWrapper(file_in, 'utf-8'))
colnames = {v: i for i, v in enumerate(next(reader))}
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
stage_1[key].append(row)
for key, value in stage_1.items():
min_strike_delta = float("inf")
for row in value:
row.append(abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']])))
if min_strike_delta > row[-1]:
min_strike_delta = row[-1]
value = [
{
**{cname: row[cvalue] for cname, cvalue in colnames.items()},
**{"atTheMoney": row[-1] == min_strike_delta}
}
for row in value
]
ticker, expirDate = key.split("_")
exp_fmt = str(pd.to_datetime(expirDate).date()).replace('-', '')
temp_path = f"/Volumes/DataCenter/{ticker[0]}/{ticker}/{exp_fmt[:4]}/{exp_fmt}/"
filename = f"{temp_path}_{ticker}_{exp_fmt}.json"
dir_path = pathlib.Path(temp_path)
dir_path.mkdir(parents=True, exist_ok=True)
with open(filename, "a+") as file_out:
for row in value:
file_out.write(json.dumps(row) + ",\n")
我想我可能会考虑以更手动的方式执行此操作。
从我们的模块开始：
import collections
import csv
import json
那么我们可以：
## ---------------------
## Gather the rows of data by key
## ---------------------
stage_1 = collections.defaultdict(list)
with open("strike.csv", "r") as file_in:
reader = csv.reader(file_in)
colnames = {v:i for i,v in enumerate(next(reader))}
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
stage_1[key].append(row)
## ---------------------
现在我们有一个字典，其中包含您的分组 ID 的键和给定键的各个行的值。
现在，对于上述字典中的每个键，我们将确定该键所有行的 atTheMoney 状态，然后写出一个 csv。
## ---------------------
## For each key find the minimum strike delta and update the values
## based on it. then write to output file
## ---------------------
for key, value in stage_1.items():
## --------------------
## find the min_strike_delta
## --------------------
min_strike_delta = float("inf")
for row in value:
row.append(abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']])))
if min_strike_delta > row[-1]:
min_strike_delta = row[-1]
## --------------------
## --------------------
## Cast value to a list of dictionaries rather than a list of lists.
## This is a little heavier so let's just do it part by part rather than as part of stage 1
## --------------------
value = [
{
**{cname: row[cvalue] for cname, cvalue in colnames.items()},
**{"atTheMoney": row[-1] == min_strike_delta}
}
for row in value
]
## --------------------
## --------------------
## Create a result file name.
## This is just a simple one for testing and yours is more complicated
## --------------------
ticker, expirDate = key.split("_")
filename = f"{ticker[0]}_{ticker}_{expirDate.replace('/', '')}.json"
## --------------------
## --------------------
## Overwrite the list to output file.
## --------------------
#with open(filename, "w", newline="") as file_out:
# json.dump(value, file_out)
## --------------------
## --------------------
## To append rows to a prior file
## You probably don't want an actual json array
## you probably want rows of json records...
## --------------------
with open(filename, "a+") as file_out:
for row in value:
file_out.write(json.dumps(row) + ",\n")
## --------------------
## ---------------------
使用您的测试数据，这会生成几个文件：
A_AAPL_1202007.json:
[
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "32.5", "trade_date": "1/3/2007", "atTheMoney": false},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "37.5", "trade_date": "1/3/2007", "atTheMoney": false}
]
A_AAPL_2172007.json
[
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "30", "trade_date": "1/3/2007", "atTheMoney": false},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "40", "trade_date": "1/3/2007", "atTheMoney": false}
]
希望这比你现在拥有的更快。
因为我们知道数据是按键聚类的，所以我们可以利用这一事实来减少我们需要做的工作量。我们现在只需要读取一次数据。
import csv
import json
## ---------------------
## Funtion to handle writing data.
## this way we don't repeate ourselves
## ---------------------
def append_data(key, rows, min_strike_delta, colnames):
if not rows:
return
ticker, expirDate = key.split("_")
filename = f"{ticker[0]}_{ticker}_{expirDate.replace('/', '')}.json"
with open(filename, "a+") as file_out:
for row in rows:
row[-1] = row[-1] == min_strike_delta
row_json = {cname: row[cvalue] for cname, cvalue in colnames.items()}
file_out.write(json.dumps(row_json) + "\n")
## ---------------------
## ---------------------
## Let's use the fact that rows are already grouped by key to our advantage:
## ---------------------
with open("strike.csv", "r") as file_in:
reader = csv.reader(file_in)
colnames = {v:i for i,v in enumerate(next(reader) + ["atTheMoney"])}
current_key = ""
current_rows = []
current_min_strike_delta = float("inf")
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
## ---------------------
## If the keys are different try to write out the prior batch of rows
## ---------------------
if key != current_key:
append_data(current_key, current_rows, current_min_strike_delta, colnames)
current_key = key
current_rows = []
current_min_strike_delta = float("inf")
## ---------------------
strike_delta = abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']]))
current_min_strike_delta = min(current_min_strike_delta, strike_delta)
row.append(strike_delta)
current_rows.append(row)
## ---------------------
## If there are any remaining rows write them out
## ---------------------
append_data(current_key, current_rows, current_min_strike_delta, colnames)
## ---------------------
当运行时，它应该给我们两个文件：
A_AAPL_1202007.json:
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "32.5", "trade_date": "1/3/2007", "atTheMoney": false}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "37.5", "trade_date": "1/3/2007", "atTheMoney": false}
A_AAPL_2172007.json:
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "30", "trade_date": "1/3/2007", "atTheMoney": false}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "40", "trade_date": "1/3/2007", "atTheMoney": false}
此时，速度变慢可能是由于反复打开文件并寻找文件末尾进行追加。
我有一些财务数据组织为 zip 文件。自 2007 年以来每个交易日一个 zip 文件。每个文件包含 150'000 到 550'000 records/rows 和 20 columns/features 左右。我需要按照使用逻辑重新组织数据： /DataFolder/{Year}/{Column1}/{Column2}/{filename}.json
为清楚起见，Column1 是 Ticker 列，Column2 是 ExpiryDate 列。 此外，对于单个文件中的每个 ticker-expiryDate 对，我想填充一个新列（0 或 1），这是按货币行使价（给定 StockPrice 列中的值，我得到最接近行使价列的值股票价格）。
然后我将数据（对于每个 ticker-expiryDate 对）作为 json 文件写入我的硬盘。
最初我打算采用一些“蛮力”方法，即将 zip 文件作为数据帧加载，并对代码和到期日期进行一些排序。显然这不是最优的。处理一个 zip 文件大约需要 12 分钟。
然后我尝试使用 pandas groupby 函数生成的组来改进我的代码。它确实将每个 zip 文件的性能提高到略低于 10 分钟。但仍然考虑到我有近 3'000 个文件要处理我需要大量改进...
我使用一个函数来获得给定股票价格的最接近行使价：
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
我对每个 zip 文件使用以下代码：
def process_directory(files2proc):
#files2proc list of files to process
for filename in files2proc:
df_zip = pd.read_csv(filename)
df_grouped = df_zip.groupby(['ticker', 'expirDate'])
for name, group in df_grouped:
expYear = str(name[1])[-4:]
exp_fmt = str(pd.to_datetime(name[1]).date()).replace('-', '')
temp_path = '/Volumes/DataFolder/{}/{}/{}/{}/'.format(name[0][0], name[0], expYear, exp_fmt)
dir_path = pathlib.Path(temp_path)
dir_path.mkdir(parents=True, exist_ok=True)
df_temp = df_grouped.get_group(name).reset_index(drop=True)
strikes = df_temp['strike'].unique()
stkprice = df_temp['stkPx'].iloc[0]
atm_strike = find_nearest(strikes, stkprice)
df_temp['atTheMoney'] = df_temp['strike'] == atm_strike
file_path = temp_path + '_' + name[0] + '_' + exp_fmt + '.json'
if not os.path.exists(file_path):
df_temp.to_json(file_path, orient="records")
else:
df_orig = pd.read_json(file_path, orient="records")
df_calls_up = df_orig.append(df_temp)
df_calls_up.to_json(file_path, orient="records")
我担心 .reset_index 和 .iloc 语句很耗时。如何绕过对 find_nearest 的调用以提高速度？如果没有，是否有一种向量化的方式来填充平价列？ 更一般地说，我认为我创建了太多对整体速度没有帮助的临时数据帧 (?)
编辑：zip 仅包含一个 csv 文件。我在下面复制了 csv 示例的一部分：
ticker stkPx expirDate strike (other columns) trade_date
AAPL 34.3 1/20/2007 32.5 1/3/2007
AAPL 34.3 1/20/2007 35 1/3/2007
AAPL 34.3 1/20/2007 37.5 1/3/2007
AAPL 34.3 2/17/2007 30 1/3/2007
AAPL 34.3 2/17/2007 35 1/3/2007
AAPL 34.3 2/17/2007 40 1/3/2007
(...)
我需要将此文件重新组织成几个 json 文件，如下所示： Datafolder/A/AAPL/2007/20070120/_AAPL_20070120.json Datafolder/A/AAPL/2007/20070120/_AAPL_20070217.json
使用这种格式： Datafolder/{TickerInitial}/{Ticker}/{year of expirDate}/{expirDate as yyyymmdd}/{ticker}{expirDate as yyyymmdd}.json
每个 json 文件都应该有一个名为 atTheMoney 的额外列（位于原始 zip 文件列的顶部）以标记 at the money 选项，即 _AAPL_20070120.json:
ticker stkPx expirDate strike (other columns) trade_date atTheMoney
AAPL 34.3 1/20/2007 32.5 1/3/2007 False
AAPL 34.3 1/20/2007 35 1/3/2007 True
AAPL 34.3 1/20/2007 37.5 1/3/2007 False
和 _AAPL_20070217.json:
ticker stkPx expirDate strike (other columns) trade_date atTheMoney
AAPL 34.3 2/17/2007 30 1/3/2007 False
AAPL 34.3 2/17/2007 35 1/3/2007 True
AAPL 34.3 2/17/2007 40 1/3/2007 False
货币标志对应于给定 stkPx 列值的最接近行使价列值（即行使价 35 是 34.3 最接近 [32.5, 35, 37.5] 或 [30, 35, 40] 的值）
Edit2：这是我修改后的 JonSG 版本：
def process_file(zip_path, ifilename):
stage_1 = collections.defaultdict(list)
with zipfile.ZipFile(zip_path) as zf:
with zf.open(ifilename + ".csv", "r") as file_in:
reader = csv.reader(TextIOWrapper(file_in, 'utf-8'))
colnames = {v: i for i, v in enumerate(next(reader))}
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
stage_1[key].append(row)
for key, value in stage_1.items():
min_strike_delta = float("inf")
for row in value:
row.append(abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']])))
if min_strike_delta > row[-1]:
min_strike_delta = row[-1]
value = [
{
**{cname: row[cvalue] for cname, cvalue in colnames.items()},
**{"atTheMoney": row[-1] == min_strike_delta}
}
for row in value
]
ticker, expirDate = key.split("_")
exp_fmt = str(pd.to_datetime(expirDate).date()).replace('-', '')
temp_path = f"/Volumes/DataCenter/{ticker[0]}/{ticker}/{exp_fmt[:4]}/{exp_fmt}/"
filename = f"{temp_path}_{ticker}_{exp_fmt}.json"
dir_path = pathlib.Path(temp_path)
dir_path.mkdir(parents=True, exist_ok=True)
with open(filename, "a+") as file_out:
for row in value:
file_out.write(json.dumps(row) + ",\n")
我想我可能会考虑以更手动的方式执行此操作。
从我们的模块开始：
import collections
import csv
import json
那么我们可以：
## ---------------------
## Gather the rows of data by key
## ---------------------
stage_1 = collections.defaultdict(list)
with open("strike.csv", "r") as file_in:
reader = csv.reader(file_in)
colnames = {v:i for i,v in enumerate(next(reader))}
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
stage_1[key].append(row)
## ---------------------
现在我们有一个字典，其中包含您的分组 ID 的键和给定键的各个行的值。
现在，对于上述字典中的每个键，我们将确定该键所有行的 atTheMoney 状态，然后写出一个 csv。
## ---------------------
## For each key find the minimum strike delta and update the values
## based on it. then write to output file
## ---------------------
for key, value in stage_1.items():
## --------------------
## find the min_strike_delta
## --------------------
min_strike_delta = float("inf")
for row in value:
row.append(abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']])))
if min_strike_delta > row[-1]:
min_strike_delta = row[-1]
## --------------------
## --------------------
## Cast value to a list of dictionaries rather than a list of lists.
## This is a little heavier so let's just do it part by part rather than as part of stage 1
## --------------------
value = [
{
**{cname: row[cvalue] for cname, cvalue in colnames.items()},
**{"atTheMoney": row[-1] == min_strike_delta}
}
for row in value
]
## --------------------
## --------------------
## Create a result file name.
## This is just a simple one for testing and yours is more complicated
## --------------------
ticker, expirDate = key.split("_")
filename = f"{ticker[0]}_{ticker}_{expirDate.replace('/', '')}.json"
## --------------------
## --------------------
## Overwrite the list to output file.
## --------------------
#with open(filename, "w", newline="") as file_out:
# json.dump(value, file_out)
## --------------------
## --------------------
## To append rows to a prior file
## You probably don't want an actual json array
## you probably want rows of json records...
## --------------------
with open(filename, "a+") as file_out:
for row in value:
file_out.write(json.dumps(row) + ",\n")
## --------------------
## ---------------------
使用您的测试数据，这会生成几个文件：
A_AAPL_1202007.json:
[
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "32.5", "trade_date": "1/3/2007", "atTheMoney": false},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "37.5", "trade_date": "1/3/2007", "atTheMoney": false}
]
A_AAPL_2172007.json
[
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "30", "trade_date": "1/3/2007", "atTheMoney": false},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true},
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "40", "trade_date": "1/3/2007", "atTheMoney": false}
]
希望这比你现在拥有的更快。
因为我们知道数据是按键聚类的，所以我们可以利用这一事实来减少我们需要做的工作量。我们现在只需要读取一次数据。
import csv
import json
## ---------------------
## Funtion to handle writing data.
## this way we don't repeate ourselves
## ---------------------
def append_data(key, rows, min_strike_delta, colnames):
if not rows:
return
ticker, expirDate = key.split("_")
filename = f"{ticker[0]}_{ticker}_{expirDate.replace('/', '')}.json"
with open(filename, "a+") as file_out:
for row in rows:
row[-1] = row[-1] == min_strike_delta
row_json = {cname: row[cvalue] for cname, cvalue in colnames.items()}
file_out.write(json.dumps(row_json) + "\n")
## ---------------------
## ---------------------
## Let's use the fact that rows are already grouped by key to our advantage:
## ---------------------
with open("strike.csv", "r") as file_in:
reader = csv.reader(file_in)
colnames = {v:i for i,v in enumerate(next(reader) + ["atTheMoney"])}
current_key = ""
current_rows = []
current_min_strike_delta = float("inf")
for row in reader:
key = f"{row[colnames['ticker']]}_{row[colnames['expirDate']]}"
## ---------------------
## If the keys are different try to write out the prior batch of rows
## ---------------------
if key != current_key:
append_data(current_key, current_rows, current_min_strike_delta, colnames)
current_key = key
current_rows = []
current_min_strike_delta = float("inf")
## ---------------------
strike_delta = abs(float(row[colnames['stkPx']]) - float(row[colnames['strike']]))
current_min_strike_delta = min(current_min_strike_delta, strike_delta)
row.append(strike_delta)
current_rows.append(row)
## ---------------------
## If there are any remaining rows write them out
## ---------------------
append_data(current_key, current_rows, current_min_strike_delta, colnames)
## ---------------------
当运行时，它应该给我们两个文件：
A_AAPL_1202007.json:
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "32.5", "trade_date": "1/3/2007", "atTheMoney": false}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "1/20/2007", "strike": "37.5", "trade_date": "1/3/2007", "atTheMoney": false}
A_AAPL_2172007.json:
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "30", "trade_date": "1/3/2007", "atTheMoney": false}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "35", "trade_date": "1/3/2007", "atTheMoney": true}
{"ticker": "AAPL", "stkPx": "34.3", "expirDate": "2/17/2007", "strike": "40", "trade_date": "1/3/2007", "atTheMoney": false}
此时，速度变慢可能是由于反复打开文件并寻找文件末尾进行追加。