我在表格中有一个字段,用于存储以逗号分隔的多个标记,例如
tag1, tag2, tag3,tag4
对于波斯语版本,我需要用،
替换单个逗号
所以标签看起来像
tag1، tag2، tag3،tag4،
我在数据库中有超过三千条记录,标签与،
或,
一起存储,但我希望所有这些记录都与،
分开。
如何使用SQL查找并用,
替换所有،
。
我使用SQL Server作为我的数据库。
我们说表结构是
表BLOG
博客表格字段
ID int
Title nVarchar(300)
Description nVarchar(500)
Details nVarchar(max)
Date DateTime
Tags nVarchar(300)
Image varchar(60)
CategoryID int
Updated DateTime
Created DateTime
Published Boolean
答案 0 :(得分:1)
您是否尝试过使用import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import calendar
# frame
from get_year_deltas import get_year_deltas
from constant_short_rate import constant_short_rate
from market_environment import market_environment
from plot_option_stats import plot_option_stats
# simulation
from sn_random_numbers import sn_random_numbers
from simulation_class import simulation_class
from geometric_brownian_motion import geometric_brownian_motion
from jump_diffusion import jump_diffusion
from square_root_diffusion import square_root_diffusion
# valuation
from valuation_class import valuation_class
from valuation_mcs_european import valuation_mcs_european
from valuation_mcs_american import valuation_mcs_american
from derivatives_position import derivatives_position
from derivatives_portfolio import derivatives_portfolio
#import os
#path = os.getcwd()
url = 'http://www.stoxx.com/download/historical_values/h_vstoxx.txt'
vstoxx_index = pd.read_csv(url, index_col=0, header=2,parse_dates=True, dayfirst=True)
vstoxx_index = vstoxx_index[('2013/12/31' < vstoxx_index.index) & (vstoxx_index.index < '2014/4/1')]
vstoxx_futures = pd.read_excel('./vstoxx_march_2014.xlsx', 'vstoxx_futures')
del vstoxx_futures['A_SETTLEMENT_PRICE_SCALED']
del vstoxx_futures['A_CALL_PUT_FLAG']
del vstoxx_futures['A_EXERCISE_PRICE']
del vstoxx_futures['A_PRODUCT_ID']
columns = ['DATE', 'EXP_YEAR', 'EXP_MONTH', 'PRICE']
vstoxx_futures.columns = columns
def third_friday(date):
day = 21 - (calendar.weekday(date.year, date.month, 1) + 2) % 7
return dt.datetime(date.year, date.month, day)
set(vstoxx_futures['EXP_MONTH'])
third_fridays = {}
for month in set(vstoxx_futures['EXP_MONTH']):
third_fridays[month] = third_friday(dt.datetime(2014, month, 1))
#third_fridays
tf = lambda x: third_fridays[x]
vstoxx_futures['MATURITY'] = vstoxx_futures['EXP_MONTH'].apply(tf)
#vstoxx_futures.tail()
vstoxx_options = pd.read_excel('./vstoxx_march_2014.xlsx', 'vstoxx_options')
#vstoxx_options.info()
del vstoxx_options['A_SETTLEMENT_PRICE_SCALED']
del vstoxx_options['A_PRODUCT_ID']
columns = ['DATE', 'EXP_YEAR', 'EXP_MONTH', 'TYPE', 'STRIKE', 'PRICE']
vstoxx_options.columns = columns
vstoxx_options['MATURITY'] = vstoxx_options['EXP_MONTH'].apply(tf)
#vstoxx_options.head()
vstoxx_options['STRIKE'] = vstoxx_options['STRIKE'] / 100.0
save = False
if save is True:
import warnings
warnings.simplefilter('ignore')
h5 = pd.HDFStore('./vstoxx_march_2014.h5', complevel=9, complib='blosc')
h5['vstoxx_index'] = vstoxx_index
h5['vstoxx_futures'] = vstoxx_futures
h5['vstoxx_options'] = vstoxx_options
h5.close()
pricing_date = dt.datetime(2014, 3, 31)
# last trading day in March 2014
maturity = third_fridays[10]
# October maturity
initial_value = vstoxx_index['V2TX'][pricing_date]
# VSTOXX on pricing_date
forward = vstoxx_futures[(vstoxx_futures.DATE == pricing_date) & (vstoxx_futures.MATURITY == maturity)]['PRICE'].values[0]
tol = 0.20
option_selection = vstoxx_options[(vstoxx_options.DATE == pricing_date)
& (vstoxx_options.MATURITY == maturity)
& (vstoxx_options.TYPE == 'C')
& (vstoxx_options.STRIKE > (1 - tol) * forward)
& (vstoxx_options.STRIKE < (1 + tol) * forward)]
me_vstoxx = market_environment('me_vstoxx', pricing_date)
me_vstoxx.add_constant('initial_value', initial_value)
me_vstoxx.add_constant('final_date', maturity)
me_vstoxx.add_constant('currency', 'EUR')
me_vstoxx.add_constant('frequency', 'B')
me_vstoxx.add_constant('paths', 10000)
csr = constant_short_rate('csr', 0.01)
# somewhat arbitrarily chosen here
me_vstoxx.add_curve('discount_curve', csr)
# parameters to be calibrated later
me_vstoxx.add_constant('kappa', 1.0)
me_vstoxx.add_constant('theta', 1.2 * initial_value)
vol_est = vstoxx_index['V2TX'].std() * np.sqrt(len(vstoxx_index['V2TX']) / 252.0)
me_vstoxx.add_constant('volatility', vol_est)
# vol_est
vstoxx_model = square_root_diffusion('vstoxx_model', me_vstoxx)
me_vstoxx.add_constant('strike', forward)
me_vstoxx.add_constant('maturity', maturity)
payoff_func = 'np.maximum(maturity_value - strike, 0)'
vstoxx_eur_call = valuation_mcs_european('vstoxx_eur_call',vstoxx_model, me_vstoxx, payoff_func)
option_models = {}
for option in option_selection.index:
strike = option_selection['STRIKE'].ix[option]
me_vstoxx.add_constant('strike', strike)
option_models[option] = valuation_mcs_european( 'eur_call_%d' % strike, vstoxx_model, me_vstoxx, payoff_func )
def calculate_model_values(p0):
'''
Returns all relevant option values.
Parameters
p0 : tuple/list, tuple of kappa, theta, volatility
Returns
model_values : dict, dictionary with model values
'''
kappa, theta, volatility = p0
vstoxx_model.update(kappa=kappa,
theta=theta,
volatility=volatility)
model_values = {}
for option in option_models:
model_values[option] = option_models[option].present_value(fixed_seed=True)
return model_values
# calculate_model_values((0.5, 27.5, vol_est))
i = 0
def mean_squared_error(p0):
'''
Returns the mean-squared error given the model and market values.
Parameters
p0 : tuple/list, tuple of kappa, theta, volatility
Returns
MSE : float, mean-squared error
'''
global i
model_values = np.array(calculate_model_values(p0).values())
market_values = option_selection['PRICE'].values
option_diffs = model_values - market_values
MSE = np.sum(option_diffs ** 2) / len(option_diffs)
# vectorized MSE calculation
if i % 20 == 0:
if i == 0:
print( '%4s' % i, '%6s' % "kappa", '%6s' % "theta", '%6s —>' % "vola", '%6s' % "MSE")
print( '%4d' % i, '%6.3f' % p0[0], '%6.3f' % p0[1], '%6.3f —>' % p0[2], '%6.3f' % MSE )
i += 1
return MSE
mean_squared_error((0.5, 27.5, vol_est))
:
REPLACE()
查找并替换为Unicode字符