<artifactId>AuctionWebsiteM</artifactId>
<packaging>war</packaging>
<name>AuctionWebsiteM</name>
<description>An auction website</description>
<version>1.0</version>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>1.5.2.RELEASE</version>
</parent>
<properties>
<java.version>1.8</java.version>
</properties>
<dependencies>
<!-- Web -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!-- Web with Tomcat + Embed -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-tomcat</artifactId>
<scope>provided</scope>
</dependency>
<!-- JSTL -->
<dependency>
<groupId>javax.servlet</groupId>
<artifactId>jstl</artifactId>
</dependency>
<!-- Need this to compile JSP -->
<dependency>
<groupId>org.apache.tomcat.embed</groupId>
<artifactId>tomcat-embed-jasper</artifactId>
<scope>provided</scope>
</dependency>
<!-- Need this to compile JSP -->
<dependency>
<groupId>org.eclipse.jdt.core.compiler</groupId>
<artifactId>ecj</artifactId>
<version>4.6.1</version>
<scope>provided</scope>
</dependency>
<!-- Optional, for bootstrap -->
<dependency>
<groupId>org.webjars</groupId>
<artifactId>bootstrap</artifactId>
<version>3.3.7</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-jdbc</artifactId>
</dependency>
<dependency>
<groupId>org.hibernate</groupId>
<artifactId>hibernate-entitymanager</artifactId>
<version>5.2.9.Final</version>
</dependency>
<dependency>
<groupId>org.springframework.data</groupId>
<artifactId>spring-data-commons</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.data</groupId>
<artifactId>spring-data-jpa</artifactId>
<type>jar</type>
</dependency>
<dependency>
<groupId>unknown.binary</groupId>
<artifactId>mysql-connector-java-5.1.23-bin</artifactId>
<version>SNAPSHOT</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- Package as an executable jar/war -->
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>unknown-jars-temp-repo</id>
<name>A temporary repository created by NetBeans for libraries and jars it could not identify. Please replace the dependencies in this repository with correct ones and delete this repository.</name>
<url>file:${project.basedir}/lib</url>
</repository>
</repositories>
需要将b列中的每个键映射到文件中的值（示例NSE_FO_BHP_1703与文件中的NSE_FO_BHP_1703_ *匹配） 如果文件中的键包含LONG作为子字符串，则在列d中减去值 如果文件中的键包含SHORT作为子字符串，则在列d
中添加值 >>> print(df)
out_0 out_1 b c e d
0 out_GFD_994 out_GFD_Part2 NSE_FO_BHP_1703 -27022 361.45 -13200
1 out_GFD_994 out_GFD_Part2 NSE_FO_BHP_1704 30132 399.98 13200
2 out_GFD_994 out_GFD_Part2 NSE_FO_MQG_1703 -145173 1177.97 -11200
3 out_GFD_994 out_GFD_Part2 NSE_FO_MQG_1704 158747 1180.55 12300
4 out_GFD_994 out_GFD_Part2 NSE_FO_TLS_1703 23316 1247.50 3200
5 out_GFD_994 out_GFD_Part2 NSE_FO_TLS_1704 -19333 1253.62 -3200
更改NSE_FO_BHP_1703_MAXLONGPOS = 200000 changes to 213200。分别更改NSE_FO_BHP_1703_MAXSHORTPOS to 186800。
这将针对df的b列中的每个键完成。
MAXLONGEXPOSURE and MAXSHORTEXPOSURE to 263200 and 236800答案 0 :(得分：1)
我认为如果所有数据都成对，我可以使用 - *LONG*和*SHORT*：
from pandas.compat import StringIO
temp=u"""
NSE_FO_BHP_1703_MAXLONGPOS = 200000
NSE_FO_BHP_1703_MAXSHORTPOS = 200000
NSE_FO_BHP_1703_MAXLONGEXPOSURE = 250000
NSE_FO_BHP_1703_MAXSHORTEXPOSURE = 250000
NSE_FO_BHP_1704_MAXLONGPOS = 200000
NSE_FO_BHP_1704_MAXSHORTPOS = 200000
NSE_FO_BHP_1704_MAXLONGEXPOSURE = 250000
NSE_FO_BHP_1704_MAXSHORTEXPOSURE = 250000
NSE_FO_TLS_1703_MAXLONGPOS = 100000
NSE_FO_TLS_1703_MAXSHORTPOS = 100000
NSE_FO_TLS_1703_MAXLONGEXPOSURE = 200000
NSE_FO_TLS_1703_MAXSHORTEXPOSURE = 200000
NSE_FO_TLS_1704_MAXLONGPOS = 100000
NSE_FO_TLS_1704_MAXSHORTPOS = 100000
NSE_FO_TLS_1704_MAXLONGEXPOSURE = 200000
NSE_FO_TLS_1704_MAXSHORTEXPOSURE = 200000
NSE_FO_MQG_1703_MAXLONGPOS = 300000
NSE_FO_MQG_1703_MAXSHORTPOS = 300000
NSE_FO_MQG_1703_MAXLONGEXPOSURE = 400000
NSE_FO_MQG_1703_MAXSHORTEXPOSURE = 400000
NSE_FO_DEF_1704_MAXLONGPOS = 300000
NSE_FO_MQG_1704_MAXSHORTPOS = 300000
NSE_FO_MQG_1704_MAXLONGEXPOSURE = 400000
NSE_FO_MQG_1704_MAXSHORTEXPOSURE = 400000"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df2 = pd.read_csv(StringIO(temp), sep="\s+=\s+", engine='python', names=['A','B'])
#create dict for map
d = df1.set_index('b')['d'].to_dict()
#split by last _ and map
a = df2.A.str.rsplit('_', n=1).str[0].map(d)
#if LONG substract, else add
df2['C'] = np.where(df2.A.str.contains('LONG'), df2.B - a, df2.B + a)
print (df2)
A B C
0 NSE_FO_BHP_1703_MAXLONGPOS 200000 213200.0
1 NSE_FO_BHP_1703_MAXSHORTPOS 200000 186800.0
2 NSE_FO_BHP_1703_MAXLONGEXPOSURE 250000 263200.0
3 NSE_FO_BHP_1703_MAXSHORTEXPOSURE 250000 236800.0
4 NSE_FO_BHP_1704_MAXLONGPOS 200000 186800.0
5 NSE_FO_BHP_1704_MAXSHORTPOS 200000 213200.0
6 NSE_FO_BHP_1704_MAXLONGEXPOSURE 250000 236800.0
7 NSE_FO_BHP_1704_MAXSHORTEXPOSURE 250000 263200.0
8 NSE_FO_TLS_1703_MAXLONGPOS 100000 96800.0
9 NSE_FO_TLS_1703_MAXSHORTPOS 100000 103200.0
10 NSE_FO_TLS_1703_MAXLONGEXPOSURE 200000 196800.0
11 NSE_FO_TLS_1703_MAXSHORTEXPOSURE 200000 203200.0
12 NSE_FO_TLS_1704_MAXLONGPOS 100000 103200.0
13 NSE_FO_TLS_1704_MAXSHORTPOS 100000 96800.0
14 NSE_FO_TLS_1704_MAXLONGEXPOSURE 200000 203200.0
15 NSE_FO_TLS_1704_MAXSHORTEXPOSURE 200000 196800.0
16 NSE_FO_MQG_1703_MAXLONGPOS 300000 311200.0
17 NSE_FO_MQG_1703_MAXSHORTPOS 300000 288800.0
18 NSE_FO_MQG_1703_MAXLONGEXPOSURE 400000 411200.0
19 NSE_FO_MQG_1703_MAXSHORTEXPOSURE 400000 388800.0
20 NSE_FO_DEF_1704_MAXLONGPOS 300000 NaN <-no data in df1 - map return NaN
21 NSE_FO_MQG_1704_MAXSHORTPOS 300000 312300.0
22 NSE_FO_MQG_1704_MAXLONGEXPOSURE 400000 387700.0
23 NSE_FO_MQG_1704_MAXSHORTEXPOSURE 400000 412300.0