java數據處理框架-Joinery 使用(類似於python中的pandas)

cankao :https://blog.csdn.net/weixin_44112790/article/details/95387314

git doc:

然后是DataFrame的手冊，可以在里面查找更多的方法，其實都和pandas的差不多。
http://cardillo.github.io/joinery/v1.9/api/reference/joinery/DataFrame.html
接着是GitHub地址，有興趣的可以研究研究源碼
https://github.com/cardillo/joinery

依賴:

<dependency>
      <groupId>joinery</groupId>
      <artifactId>joinery-dataframe</artifactId>
      <version>1.9</version>
    </dependency>

如果需要處理csv的話，還得添加一個依賴:

<dependency>
        <groupId>org.apache.poi</groupId>
        <artifactId>poi</artifactId>
        <version>3.17</version>
</dependency>

 

簡單使用:

 

@Test
        public  void testDataFrame() 
        {
         //創建
         DataFrame<Object> df = new DataFrame<>("name", "value");
         //添加數據
         df.append(Arrays.asList("xiaoming", 1));
         df.append(Arrays.asList("lily", 2));
         df.append(Arrays.asList("tom", 3));
         df.append(Arrays.asList("sea", 3));
         
         List<Object> col = df.col("name");
         System.err.println(col);
         System.err.println("******");
         //行數
         System.out.println(df.length());
         //空表判斷
         System.out.println(df.isEmpty());
         //多列合並成一列進行輸出
         System.out.println(df.flatten());
         //計算常用統計量
         System.out.println(df.mean().col("value"));
         System.out.println(df.median().col("value"));
         System.out.println(df.max().col("value"));
         System.out.println(df.min().col("value"));
         System.out.println(df.var().col("value"));
         // 以下演示如何獲取每一格的數據
         Set<Object> indexs = df.index();
         Set<Object> columns = df.columns();
         for(Object index:indexs)
         {
            for(Object column:columns)
            {
                System.out.print(df.get(index, column));
                System.out.print("\t");
            }
            System.out.println();
         }
         //保存為csv文件
         try {
//            df.writeCsv("./test.csv");
            df.writeXls("./test.xls");
//            df.readXls(file)
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

 

 

 

 

 

 

 

 

 

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<!-- https://mvnrepository.com/artifact/com.github.chen0040/java-data-frame -->
<dependency>
    <groupId>com.github.chen0040</groupId>
    <artifactId>java-data-frame</artifactId>
    <version>1.0.11</version>
</dependency>