代码(Python、Java)实现分组求和

测试数据(xx.txt)：

a 1
a 2
b 3
c 4
d 5
c 6
b 7
a 8
x 9

一、SQL 分组求和

SELECT key, SUM(val)
FROM kv
GROUP BY key

二、Python 编码实现分组求和(pip install pandas)

import re
 
import pandas as pd
from matplotlib import pyplot as plt
 
lines = []
with open(r"pandas_group_sum.txt") as f:
    lines = f.read().splitlines()
print(len(lines))
 
 
p = re.compile(r'(?P\S*)\s*(?P\S*)')
 
kvs = []
for line in lines:
    m = p.match(line)
    if m:
        # print(line)
        kvs.append((m.group("key"), int(m.group("val"))))
 
 
df = pd.DataFrame(kvs, columns=["key", "val"])
# print(df.dtypes)
 
# 注：先创建空的`DataFrame`，再追加数据，该方式已过时。
# df = pd.DataFrame(columns=["key", "val"])
# for kv in kvs:
#     df = df.append({"key":kv[0], "val": int(float(kv[1]))}, ignore_index=True)
 
 
# 设置 `pandas` 打印展示效果
# 显示最大列数，None 为全部
pd.set_option('display.max_columns', None)
# 显示最大行数，None 为全部
pd.set_option('display.max_rows', None)
# 显示每列字段最大宽度
pd.set_option('max_colwidth', None)
# 显示行宽度
pd.set_option('display.width', None)
 
# 分组求和
df = df.groupby(["key"], as_index=True).sum().sort_values("val", ascending=False)
 
# 结果打印
print(df)
 
# 结果输出到 excel
# df.to_excel("结果.xlsx")
 
# 结果绘图
# 设置中文
plt.rcParams['font.sans-serif'] = ['SimHei']
# 坐标轴负号显示不正常及不能正常显示中文
plt.rcParams['axes.unicode_minus'] = False
# 默认为折线图
df.plot(kind='bar', title="统计图", figsize=(12, 8), legend=True, fontsize=18)
 
plt.show()

三、Java 编码实现分组求和 (import sh.joinery:joinery-dataframe 库)

① `pom.xml` 引入相关依赖：

<dependency>
	<groupId>sh.joinerygroupId>
	<artifactId>joinery-dataframeartifactId>
	<version>1.10version>
dependency>
 
<dependency>
	<groupId>com.xeiam.xchartgroupId>
	<artifactId>xchartartifactId>
	<version>2.3.1version>
dependency>
 
<dependency>
	<groupId>org.apache.poigroupId>
	<artifactId>poiartifactId>
	<version>4.1.2version>
dependency>

 ② 代码实现

package org.kwok.joinery;
 
import java.nio.charset.Charset;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.List;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
 
import joinery.DataFrame;
 
/**
 * 使用 sh.joinery:joinery-dataframe 库进行分组求和
 * 类似 Python pandas 库
 * @author Kwok
 * 2022-11-06
 */
public class Test_Joinery {
 
	public static void main(String[] args) throws Exception {
		
		System.out.println(Paths.get(Test_Joinery.class.getResource("Test_Joinery.txt").toURI()));
		
		List lines = Files.readAllLines(Paths.get(Test_Joinery.class.getResource("Test_Joinery.txt").toURI()), Charset.defaultCharset());
		DataFrame