我想基于package classwork7_2;
import java.util.*;
import java.io.*;
public class ClassWork7_2 {
public static void main(String[] args)throws IOException {
Scanner s = new Scanner(System.in);
int[] numbers = fileToArray();
Arrays.sort(numbers);
System.out.print("Enter a number in the file: ");
int numb = s.nextInt();
int i = Arrays.binarySearch(numbers, numb);
if(i < 0){
while(i < 0){
System.out.print("Number is not in file\n");
System.out.print("Enter number in the file: ");
s.nextInt();
}
}
else if(i >= 0){
while(i >= 0){
System.out.print("This number is in the file\n");
System.out.print("Enter number in the file: ");
s.nextInt();
}
}
}
public static int[] fileToArray() throws IOException{
Scanner s = new Scanner(System.in);
int[] array = new int[7];
System.out.print("Enter name of file: ");
String filename = s.nextLine();
File f = new File(filename);
Scanner inputFile = new Scanner(f);
int i = 0;
while(inputFile.hasNext()){
array[i] = inputFile.nextInt();
i++;
}
inputFile.close();
return array;
}
}
中的查找值使用map函数更新df1中的值。查找列为df2
ISIN_CUSIP_CODE我的地图函数未返回df1 = [('ISIN_CUSIP_CODE', ['US68323ABL70', '9128284D9', '912828W89', 'CA135087J470','CA135087J470','912796QP7','US20030NCM11','US912810SD19','XS1851277969',]),
('Product', ['GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', 'GOVT', '', '', '',]),
]
df1 = pd.DataFrame.from_items(df1)
print(df1)
df2 = [('ISIN_CUSIP_CODE', ['US20030NCM11', 'US912810SD19', 'XS1851277969', 'XS1391086987', 'CA064151BL66', 'CA13595ZZ661', ]),
('Product_MRD', ['CORP', 'GOVT', 'CORP', 'CORP','CORP','CORP',]),
]
df2 = pd.DataFrame.from_items(df2)
print(df2)
df1
ISIN_CUSIP_CODE Product
0 US68323ABL70 GOVT
1 9128284D9 GOVT
2 912828W89 GOVT
3 CA135087J470 GOVT
4 CA135087J470 GOVT
5 912796QP7 GOVT
6 US20030NCM11
7 US912810SD19
8 XS1851277969
df2
ISIN_CUSIP_CODE Product_MRD
0 US20030NCM11 CORP
1 US912810SD19 GOVT
2 XS1851277969 CORP
3 XS1391086987 CORP
4 CA064151BL66 CORP
5 CA13595ZZ661 CORP
中的查找值
df2答案 0 :(得分：2)
纯熊猫解决方案：
pd.concat([df1,df2.rename(columns = {'Product_MRD':'Product'})]).drop_duplicates(['ISIN_CUSIP_CODE'],keep='last').sort_values('ISIN_CUSIP_CODE')
不需要额外的库
答案 1 :(得分：1)
这是使用局部的简单解决方案。
from functools import partial
def lookup(row, lookup_df):
try:
return lookup_df[lookup_df.ISIN_CUSIP_CODE == row['ISIN_CUSIP_CODE']].Product_MRD.values[0]
except:
return row['Product']
df1['ProductLooked'] = df1.apply(partial(lookup, lookup_df=df2), axis=1)