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1. `DDBDailyFactor.py` => `DDBFactor.py`

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2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据
3. 其他一些优化
main
Guofu Li 2 years ago
parent 621b38dce3
commit 95de99046a
4 changed files with 1692 additions and 94 deletions
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3
DDBDailyFactor.py → DDBFactor.py
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@ -10,6 +10,9 @@ from DDBLoader import DDBLoader
def load_ddb_table(hft_tbl_name):
"""
这是一个用来简化载入分区表过程的语法糖，但似乎需要预先调用这个函数的场景并不多，简化效果不是很明显。
"""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):

599
DDBLoader.py
Unescape View File

@ -2,6 +2,8 @@ import importlib
import gzip
import pickle
import functools
import abc
import warnings
from pprint import pprint
from pathlib import Path
@ -11,6 +13,8 @@ from multiprocessing import Pool
import numpy as np
import pandas as pd
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
import dolphindb as ddb
import dolphindb.settings as keys
@ -20,84 +24,465 @@ import sqlalchemy as sa
import ProtoBuffEntitys
def make_stock_daily_df(blob, type_name, stock_id):
class DDBLoader(object):
"""
用于做多进程录入ddb的函数
- 放了几个公用的配置字段，包括：
1. SQL-Server的链接参数
2. DolphinDB的链接参数
- 放了几个@abstractmethod在里面，不过如果不需要使用多态特性，那应该用处不大：
1. create_ddb_database
2. create_ddb_partition_table
"""
blob = gzip.decompress(blob)
dataArray = eval(f"ProtoBuffEntitys.{type_name}Message_pb2.{type_name}Array()")
dataArray.ParseFromString(blob)
data_dict_list = [
{field.name : val for field, val in entry.ListFields()}
for entry in dataArray.dataArray
mssql_config = {
'host' : '192.168.1.7',
'username' : 'sa',
'password' : 'passw0rd!'
}
ddb_config = {
'host' : '192.168.1.167',
'username' : 'admin',
'password' : '123456'
}
def __init__(self):
self.mssql_engine = sa.create_engine(
"mssql+pyodbc://{username}:{password}@{host}/master?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
connect_args = {
"TrustServerCertificate": "yes"
}, echo=False
)
self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
@abc.abstractmethod
def create_ddb_database(self, *args, **kwargs):
"""
创建database函数，需要被子类具体实现。
"""
return
@abc.abstractmethod
def create_ddb_partition_table(self, *args, **kwargs):
"""
创建分区表函数，需要被子类具体实现。
"""
return
@staticmethod
def tscode_to_windcode(series):
return series.apply(lambda x : x[2:] + '.' + x[:2])
@staticmethod
def make_symbol(series):
return series.astype('int32').astype('str')\
.apply(str.zfill, args=(6,))\
.apply(lambda code : \
code + '.SH' if code[0] == '6' \
else code + '.SZ')
@staticmethod
def make_date(series):
# 特别是对于分红表，如果某些关键日期还未公布，则会填充0，导致日期解析失败
series.loc[series == 0] = np.nan
return pd.to_datetime(
series.astype(str), format='%Y%m%d')
@staticmethod
def make_nparray(series):
return series.apply(lambda x : np.array(x))
@staticmethod
def make_time(series):
s_hr = series // 10000000 * 3600000
s_min = series % 10000000 // 100000 * 60000
s_sec = series % 100000 // 1000
s_ms = series % 1000
return pd.to_timedelta(s_hr + s_min + s_sec + s_ms, unit='ms')
class DDBPITLoader(DDBLoader):
ddb_path = "dfs://pit_stock_ts"
ddb_dbname = "ddb_pit_stock_ts"
num_code_partition = 50
table_name_mapping = {
#'CBS_AFTER_ADJ' : 'bs_common_adj',
#'CBS_BEFORE_ADJ' : 'bs_common_ori',
#'CCFS_AFTER_ADJ' : 'cfs_common_adj',
#'CCFS_BEFORE_ADJ' : 'cfs_common_ori',
#'CIS_AFTER_ADJ' : 'is_common_adj',
#'CIS_BEFORE_ADJ' : 'is_common_ori',
'DIV_WIND' : 'divident',
#'EP_WIND' : 'earnings_preannouncement',
#'PEE_WIND' : 'preliminary_earnings_estimate'
}
meta_col_config = {
'WIND_CODE' : ('code', 'SYMBOL'),
# mssql表中不需要记录的meta字段，在这里直接设置为None
'IntCode' : None,
'ACTUAL_ANN_DT' : None,
'ReportPeriod' : ('report_period', 'DATE'),
'AppearInPeriod' : ('appear_in_period', 'DATE'),
'AppearAtDate' : ('appear_at_date', 'DATE')
}
date_col_set = {
'report_period',
'appear_in_period',
'appear_at_date',
'ReportPeriod',
'AppearInPeriod',
'AppearAtDate',
'EQY_RECORD_DT',
'EX_DT',
'DVD_PAYOUT_DT',
'S_DIV_PRELANDATE',
'S_DIV_SMTGDATE',
'DVD_ANN_DT',
'S_DIV_PREANNDT'
}
ddb_type_mapping = {
'float' : 'DOUBLE',
'int' : 'INT',
'text' : 'STRING',
'varchar' : 'STRING',
'str' : 'STRING'
}
# 基本面数据库现在存放在91服务器之上
mssql_config = {
'host' : '192.168.1.91',
'username' : 'sa',
'password' : 'xn.123',
'dbname' : 'tr_statement'
}
def __init__(self):
super().__init__()
# 重新设定mssql_engine对象，此时我们需要使用基本面数据库
self.mssql_engine = sa.create_engine(
"mssql+pyodbc://{username}:{password}@{host}/{dbname}?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
connect_args = {
"TrustServerCertificate": "yes"
}, echo=False
)
def create_ddb_database(self):
self.ddb_sess.run("""
{dbName} = database(
directory = '{dbPath}',
partitionType = HASH,
partitionScheme = [SYMBOL, {num_code_partition}],
engine = 'TSDB'
)
""".format(
dbName = self.ddb_dbname,
dbPath = self.ddb_path,
num_code_partition = self.num_code_partition
))
def _make_col_config(self, mssql_table_name):
"""
Return:
mssql_col_name_list, ddb_col_name_list, ddb_col_type_list
"""
with self.mssql_engine.connect() as conn:
col_sp_list = list(conn.execute(f"exec sp_columns {mssql_table_name}").fetchall())
mssql_col_name_list, ddb_col_name_list, ddb_col_type_list = \
[], [], []
for col_sp in col_sp_list:
_col_name = col_sp[3]
_col_type = col_sp[5]
# 对于meta字段，需要根据meta配置表来进行处理
if _col_name in self.meta_col_config:
# 跳过mssql表中 不需要记录的meta字段
if self.meta_col_config[_col_name] is None:
continue
# 字段名和字段类型都要进行映射
mssql_col_name_list.append(_col_name)
ddb_col_name_list.append(self.meta_col_config[_col_name][0])
ddb_col_type_list.append(self.meta_col_config[_col_name][1])
# 对于非meta字段，仅需要检查是否是float类型，对于float类型设置类型为DOUBLE
else:
# 需要之后被转换成DATE的字段，一般在原表中为为INT类型
if _col_name in self.date_col_set:
mssql_col_name_list.append(_col_name)
ddb_col_name_list.append(_col_name)
ddb_col_type_list.append('DATE')
# 按照对照表进行类型转换
elif _col_type in self.ddb_type_mapping:
mssql_col_name_list.append(_col_name)
ddb_col_name_list.append(_col_name)
ddb_col_type_list.append(self.ddb_type_mapping[_col_type])
# 对照表中没有的字段类型，就不加入到字段列表中了
else:
print(f"!**Unrecognized type '{_col_type}' for column {_col_name}, will skip.")
return mssql_col_name_list, ddb_col_name_list, ddb_col_type_list
def create_ddb_partition_table(self, mssql_table_name):
"""创建分区表"""
memory_table_name = mssql_table_name
partition_table_name = self.table_name_mapping[mssql_table_name]
mssql_col_name_list, ddb_col_name_list, ddb_col_type_list = \
self._make_col_config(mssql_table_name)
# 根据是否
if 'appear_in_period' in ddb_col_name_list:
compress_methods = """{
'report_period' : 'delta',
'appear_in_period' : 'delta',
'appear_at_date' : 'delta'
}"""
else:
compress_methods = """{
'report_period' : 'delta',
'appear_at_date' : 'delta'
}"""
# 因为已经根据`appear_in_period`分列了调整前和调整后，因此不需要对它再进行排序了
sort_columns = "`code`report_period`appear_at_date"
# 1. 先创建内存表，内存表中设定好列名和列类型
# 2. 然后根据内存表创建分区表，设定分区列等信息
self.ddb_sess.run("""
{memory_table_name} = table(
{capacity}:0,
{column_name_list},
[{column_type_list}]
);
if (existsTable("{ddb_path}", "{partition_table_name}")) {{
dropTable({ddb_dbname}, "{partition_table_name}");
}}
{partition_table_name} = createPartitionedTable(
dbHandle = {ddb_dbname},
table = {memory_table_name},
tableName = "{partition_table_name}",
partitionColumns = 'code',
compressMethods = {compress_methods},
sortColumns = {sort_columns}
);
""".format(
ddb_dbname = self.ddb_dbname,
ddb_path = self.ddb_path,
memory_table_name = memory_table_name,
partition_table_name = partition_table_name,
capacity = 10,
column_name_list = '`' + '`'.join(ddb_col_name_list),
column_type_list = ','.join(ddb_col_type_list),
compress_methods = compress_methods.replace('\n', '').replace(' ', ''),
sort_columns = sort_columns
))
print('-' * 80)
print(f"Did create parition table <{partition_table_name}>:")
pprint(self.ddb_sess.run(f"schema({partition_table_name});"))
return partition_table_name, mssql_col_name_list
def create_ddb_partition_tables(self):
for mssql_table_name in self.table_name_mapping:
self.create_ddb_partition_table(mssql_table_name)
def _dump_pit_to_ddb(self, mssql_table_name):
print('Will work on table', mssql_table_name)
# 返回的`mssql_col_name_list`可以用来对SQL-Server获取的dataframe进行列过滤
partition_table_name, mssql_col_name_list = \
self.create_ddb_partition_table(mssql_table_name)
with self.mssql_engine.connect() as conn:
stat = f"select distinct [WIND_CODE] from {mssql_table_name}"
stock_id_list = list(conn.execute(stat).fetchall())
with tqdm(stock_id_list) as pbar:
for (stock_id,) in pbar:
pbar.set_description(f"Will work on {stock_id}")
#pbar.set_description(f"Will fetch all data of {stock_id} from SQL Server")
stat = """
select * from {mssql_table_name}
where WIND_CODE='{stock_id}' and AppearAtDate>0
""".format(
mssql_table_name = mssql_table_name,
stock_id = stock_id
)
row_list = list(conn.execute(stat).fetchall())
num_rows = len(row_list)
# 因为对AppearAtDate做了过滤，所以有可能得到一个空的数据集
if num_rows == 0:
print(f"Will skip {stock_id} due to empty result.")
continue
#pbar.set_description(f"Will work on dumping job on {stock_id} of len {num_rows}")
# 这里需要对select语句获取的所有列进行一次过滤，以保证和partition table中的列一致
df = pd.DataFrame(row_list)[mssql_col_name_list]
# 需要把部分字段的int字段类型转换成DATE字段类型
for df_col in df.columns:
if df_col in self.date_col_set:
df[df_col] = DDBLoader.make_date(df[df_col])
# 因为在做数据库View的时候已经做过一轮转换了，所以这里就不需要再次转换了
#df['WIND_CODE'] = DDBLoader.tscode_to_windcode(df['WIND_CODE'])
self.ddb_sess.upload({mssql_table_name : df})
self.ddb_sess.run(f"{partition_table_name}.tableInsert({mssql_table_name})")
def dump_pit_to_ddb(self):
for mssql_table_name in self.table_name_mapping:
self._dump_pit_to_ddb(mssql_table_name)
class DDBDailyLoader(DDBLoader):
ddb_path = "dfs://daily_stock_ts"
ddb_dbname = "db_daily_stock_ts"
daily_kline_cols = [
'code', 'm_nDate',
'open', 'high', 'low', 'close', 'vol',
'amount', 'cjbs', 'yclose',
'PctChg', 'IsZt', 'IsDt', 'IsST', 'IsGoDelist',
'FloatShares', 'MarketValues',
'factor'
]
array_type_list = [
field.name
for field, val in dataArray.dataArray[0].ListFields()
if isinstance(field.default_value, list)
daily_kline_col_types = [
'SYMBOL', 'DATE',
'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE',
'DOUBLE', 'INT', 'DOUBLE',
'DOUBLE', 'INT', 'INT', 'INT', 'INT',
'DOUBLE', 'DOUBLE',
'DOUBLE'
]
#pprint(array_type_list)
df = pd.DataFrame(data_dict_list)
#df['code'] = make_symbol(df['code'])
df['code'] = stock_id
df['m_nDate'] = make_date(df['m_nDate'])
df['m_nTime'] = df['m_nDate'] + make_time(df['m_nTime'])
for field_name in array_type_list:
df[field_name] = make_nparray(df[field_name])
#print(f"Did create ddb table for dataframe of shape {df.shape}")
# self.make_table_skeleton(type_name, df.shape[0])
return df
def create_ddb_database(self):
# TODO: daily数据库已经在DDBDailyFactor中被创建了
# 后续可以迁移过来，不过现在就暂时先不管了
pass
def dump_stock_daily_to_ddb(row, type_name, stock_id):
"""
用于做多进程录入ddb的函数
"""
df_table_name = type_name
df = make_stock_daily_df(row[2], type_name, stock_id)
def load_ddb_database(self):
self.ddb_sess.run("""
{dbName} = database(directory='{dbPath}')
""".format(
dbName = self.ddb_dbname,
dbPath = self.ddb_path
))
print('Did load database from', self.ddb_path)
ddb_sess = ddb.session(DDBLoader.ddb_config['host'], 8848)
ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
ddb_sess.upload({df_table_name : df})
ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
dbPath = DDBLoader.ddb_path,
partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
df_table_name = df_table_name
))
def create_ddb_partition_table(self, memory_table_name, partition_table_name):
# TODO: 现在只做一个日频行情数据表，今后可能考虑把基本面数据也迁移过来
# 由于日频行情数据的表结构相对简单，所以直接把表结构写在这里代码里即可
# 搬迁数据的时候需要考虑按照逐个股票来搬迁，以免造成对内存的巨大压力
self.ddb_sess.run("""
// 确保删除原表
if (existsTable("{ddb_daily_path}", "{partition_table_name}")) {{
dropTable({ddb_daily_dbname}, "{partition_table_name}");
}}
// 然后根据内存表的结构，创建持久化的分区表
{partition_table_name} = {ddb_daily_dbname}.createPartitionedTable(
table = {memory_table_name},
tableName = "{partition_table_name}",
partitionColumns = `code,
sortColumns = `code`m_nDate,
compressMethods = {{m_nDate:"delta"}}
);
""".format(
ddb_daily_path = self.ddb_path,
ddb_daily_dbname = self.ddb_dbname,
memory_table_name = memory_table_name,
partition_table_name = partition_table_name,
))
def make_symbol(series):
return series.astype('int32').astype('str')\
.apply(str.zfill, args=(6,))\
.apply(lambda code : \
code + '.SH' if code[0] == '6' \
else code + '.SZ')
def create_ddb_memory_table(self, memory_table_name, capacity):
self.ddb_sess.run("""
// 先创建一个空的内存表用来表征结构，如果无需插入数据，capacity可以设为10
{memory_table_name} = table({capacity}:0, {col_names}, [{col_types}]);
""".format(
memory_table_name = memory_table_name,
capacity = capacity,
col_names = '`' + '`'.join(self.daily_kline_cols),
col_types = ', '.join(self.daily_kline_col_types)
))
def make_date(series):
return pd.to_datetime(
series.astype(str), format='%Y%m%d')
def dump_daily_kline_to_ddb(self):
# 先创建一个分区表，然后再逐个股票的数据插入
# 1. 需要额外控制在插入第一个股票数据的时候创建分区表比较麻烦
# 2. python程序中的dataframe直接上传到dolphindb内存表，不需要考虑内存表字段类型，分区表中设置好即可
def make_nparray(series):
return series.apply(lambda x : np.array(x))
memory_table_name = 'daily_kline_mem'
partition_table_name = 'daily_kline'
self.create_ddb_memory_table(memory_table_name, 10)
print('Did create ddb memory table.')
pprint(self.ddb_sess.run(f"schema({memory_table_name})"))
self.create_ddb_partition_table(memory_table_name, partition_table_name)
print('Did create ddb partition table.')
pprint(self.ddb_sess.run(f"schema({partition_table_name})"))
def make_time(series):
s_hr = series // 10000000 * 3600000
s_min = series % 10000000 // 100000 * 60000
s_sec = series % 100000 // 1000
s_ms = series % 1000
return pd.to_timedelta(s_hr + s_min + s_sec + s_ms, unit='ms')
with self.mssql_engine.connect() as conn:
stat = "select distinct [StockID] from [StockDaily].dbo.[DailyKLine]"
stock_id_list = list(conn.execute(stat).fetchall())
with tqdm(stock_id_list) as pbar:
for (stock_id,) in pbar:
pbar.set_description(f"Will work on {stock_id}")
#pbar.set_description(f"Will fetch all data of {stock_id} from SQL Server")
stat = """
select * from [StockDaily].dbo.[DailyKLine]
where StockID='{stock_id}'
""".format(
stock_id = stock_id
)
row_list = list(conn.execute(stat).fetchall())
num_rows = len(row_list)
#pbar.set_description(f"Will work on dumping job on {stock_id} of len {num_rows}")
df = pd.DataFrame(row_list)
df['date'] = DDBLoader.make_date(df['date'])
df['StockID'] = DDBLoader.tscode_to_windcode(df['StockID'])
self.ddb_sess.upload({memory_table_name : df})
#print('Did upload dataframe to ddb.')
#pprint(self.ddb_sess.run(f"schema({memory_table_name})"))
#break
self.ddb_sess.run(f"{partition_table_name}.tableInsert({memory_table_name})")
class DDBLoader(object):
class DDBHFTLoader(DDBLoader):
"""
0. 从sql-server中读取calendar数据，并创建成员变量df_calendar，df_calendar可以保存在本地pickle作为缓存
|- `def make_calendar_df(self) -> df_calendar`
@ -162,18 +547,6 @@ class DDBLoader(object):
13 : 'INT',
}
mssql_config = {
'host' : '192.168.1.7',
'username' : 'sa',
'password' : 'passw0rd!'
}
ddb_config = {
'host' : '192.168.1.7',
'username' : 'admin',
'password' : '123456'
}
# this value may be used by factor makers, which may loop through code partitions
num_code_partition = 50
@ -182,18 +555,6 @@ class DDBLoader(object):
ddb_dump_journal_fname = 'ddb_dump_journal.csv'
def __init__(self):
self.mssql_engine = sa.create_engine(
"mssql+pyodbc://{username}:{password}@{host}/master?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
connect_args = {
"TrustServerCertificate": "yes"
}, echo=False
)
self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
def init_ddb_database(self, df_calendar):
"""
1. 创建ddb_database
@ -490,6 +851,7 @@ class DDBLoader(object):
print("Will create new Pool object, but this is not encourage for large batch work.")
pool = Pool(self.num_worker)
# 在单个股票内部，对不同日期进行并行处理，对内存使用较为友好，不需要同时载入多个股票海量的全历史数据
with tqdm(total=num_rows, leave=False) as sub_pbar:
for _ in pool.imap_unordered(
functools.partial(
@ -505,18 +867,85 @@ class DDBLoader(object):
self.dump_journal_writer.flush()
@staticmethod
def make_stock_daily_df(blob, type_name, stock_id):
"""
用于做多进程录入ddb的函数
"""
blob = gzip.decompress(blob)
dataArray = eval(f"ProtoBuffEntitys.{type_name}Message_pb2.{type_name}Array()")
dataArray.ParseFromString(blob)
data_dict_list = [
{field.name : val for field, val in entry.ListFields()}
for entry in dataArray.dataArray
]
array_type_list = [
field.name
for field, val in dataArray.dataArray[0].ListFields()
if isinstance(field.default_value, list)
]
#pprint(array_type_list)
df = pd.DataFrame(data_dict_list)
#df['code'] = make_symbol(df['code'])
df['code'] = stock_id
df['m_nDate'] = make_date(df['m_nDate'])
df['m_nTime'] = df['m_nDate'] + make_time(df['m_nTime'])
for field_name in array_type_list:
df[field_name] = make_nparray(df[field_name])
#print(f"Did create ddb table for dataframe of shape {df.shape}")
# self.make_table_skeleton(type_name, df.shape[0])
return df
@staticmethod
def dump_stock_daily_to_ddb(row, type_name, stock_id):
"""
用于做多进程录入ddb的函数
"""
df_table_name = type_name
df = make_stock_daily_df(row[2], type_name, stock_id)
ddb_sess = ddb.session(DDBLoader.ddb_config['host'], 8848)
ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
ddb_sess.upload({df_table_name : df})
ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
dbPath = DDBLoader.ddb_path,
partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
df_table_name = df_table_name
))
def main():
loader = DDBLoader()
df_calendar = loader.make_calendar_df()
loader.init_ddb_database(df_calendar)
print('Did finish init_ddb_database')
# PIT基本面数据
loader = DDBPITLoader()
loader.create_ddb_database()
#loader.create_ddb_partition_tables()
loader.dump_pit_to_ddb()
# 日频行情数据
#loader = DDBDailyLoader()
#loader.load_ddb_database()
#loader.dump_daily_kline_to_ddb()
# 高频数据
#df_calendar = loader.make_calendar_df()
#loader.init_ddb_database(df_calendar)
#print('Did finish init_ddb_database')
#loader.load_ddb_database()
#print('Did load ddb database')
loader.init_ddb_table_data(df_calendar)
print('Did finish init_table_data')
#loader.init_ddb_table_data(df_calendar)
#print('Did finish init_table_data')
if __name__ == '__main__':

33
ddb.ipynb
Unescape View File

@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 66,
"id": "5d0f471e-682e-43cc-abdb-7e52f3bbd707",
"metadata": {},
"outputs": [],
@ -23,30 +23,30 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 67,
"id": "ed17fd0b-9b36-47e4-9ab6-11459a3621fb",
"metadata": {},
"outputs": [],
"source": [
"# backup(backup_path, sql_obj, force, parallel)\n",
"code = \"\"\"\n",
" backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"KLinePartitioned\")>, false, true)\n",
" backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"OrderPartitioned\")>, true, true)\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 68,
"id": "8b7dae3d-aef1-4c50-92b2-460d4fea0a96",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
"114350"
]
},
"execution_count": 10,
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
@ -55,6 +55,27 @@
"sess.run(code)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "bf3bc38e-74cb-4549-bdca-6a3d2a601488",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2287.0"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"114350 / 50"
]
},
{
"cell_type": "code",
"execution_count": 6,

1151
mssql.ipynb
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