import org.apache.spark.{SparkConf, SparkContext}/** * Created by loushsh on 2017/10/9. */object WordCount { def main(args:Array[String]): Unit ={ val conf=new SparkConf() val sc=new SparkContext(conf) val line= sc.textFile(args(0)) val count=line.fl

Excel中COUNTIFS函数统计词频个数出现次数   在Excel中经常需要实现如下需求:在某一列单元格中有不同的词语,有些词语相同,有的不同(如图1所示).需要统计Excel表格中每个词语出现的个数,即相当于统计词频出现次数. 图1. Excel表格统计个数 解决方法:采用COUNTIFS函数. COUNTIFS 函数语法及格式:COUNTIFS(criteria_range1, criteria1, [criteria_range2, criteria2]…)其中,criteria_ra

语料 text = """My fellow citizens: I stand here today humbled by the task before us, grateful for the trust you've bestowed, mindful of the sacrifices borne by our ancestors. I thank President Bush for his service to our nation -- (applause)

原始数据: 程序: #统计词频 library(wordcloud) # F:/master2017/ch4/weibo170.cut.txt text <- readLines("F:/master2017/ch4/weibo170.cut.txt") txtList <- lapply(txt, strsplit," ") txtChar <- unlist(txtList) txtChar <- gsub(pattern = "

 solr7实现搜索框的自动提示并统计词频 1:用solr 的suggest组件,统计词频相对麻烦. 2:用TermsComponent,自带词频统计功能. Terms组件提供访问索引项的字段和每个词相匹配的文档数量,类似于关系型数据库的like模糊查询(keywords like "手机%"),然后统计数量返回给前端,但这样有一个问题.如果该字段非词性的.精确性和效率性不高. solr中TermsComponent组件完美的解决了这么一个方案,能够统计指定搜索域中所有词的信息.类似于

刚刚在写文章时360浏览器崩溃了,结果内容还是找回来了,感谢博客园的自动保存功能!!! ------------恢复内容开始------------ 最近在学习Python,自己写了一个小程序,可以从指定的路径中读取文本文档,并统计其中各单词出现的个数并打印 import os #此方法用于创建文件夹及文件 def createFile(fileName,content,filePath=r'd:/PythonExercise/'): # 创建文件夹 os.mkdir(filePath) ful

Write a bash script to calculate the frequency of each word in a text file words.txt. For simplicity sake, you may assume: words.txt contains only lowercase characters and space ' ' characters. Each word must consist of lowercase characters only. Wor

1. 词频统计: import jieba txt = open("threekingdoms3.txt", "r", encoding='utf-8').read() words = jieba.lcut(txt) counts = {} for word in words: if len(word) == 1: continue else: counts[word] = counts.get(word,0) + 1 items = list(counts.ite

示例一:统计所有单词出现的次数 1.在本地创建文件并上传到hdfs中 #vin data.txt //将文件上传到hadoop的根目录下 #hdfs dfs -put data.txt / 2.在spark中,创建一个RDD并读取文件 %spark var data = sc.textFile("/data.txt") data.collect 3.将读取到的文本使用flatMap方法(数据流映射)组合split方法拆分为单个单词 //注意:split("")引号中

这里的图片可以拖拽到一个新页面查看原图!!!! 这里有两个表,需要查询总成绩和排名 Sql语句: select ST.name,SE.Chinese,SE.Math,SE.English, ( SE.Chinese + SE.Math + SE.English ) as '总成绩', ROW_NUMBER()over(order by ( SE.Chinese + SE.Math +SE.English ) desc) as '排名' from Student ST left join Scor

一.对中国十九大报告做词频分析 import jieba txt = open("中国十九大报告.txt.txt","r",encoding="utf-8").read() words = jieba.lcut(txt) counts = {} for word in words: if len(word)==1: continue else: counts[word] = counts.get(word,0)+1 items = list(co

http://www.myexception.cn/sql/2004512.html http://blog.csdn.net/ssw_1990/article/details/52220466 http://www.tuicool.com/articles/uIRZFv http://mt.sohu.com/20160514/n449468405.shtml http://blog.csdn.net/asongoficeandfire/article/details/21490101 http

package com.uniclick.dapa.dstest; import java.io.IOException; import java.net.URI; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import

arr = [1,2,3,4,5,6,4,5,2,3,6,8,9,6,5,3,6,2,4]dic={}for item in arr: if item in dic.keys(): dic[item]+=1 else: dic[item]=1dic= sorted(dic.items(), key=lambda d:d[1], reverse = True)

import string path = 'waldnn' with open(path,'r') as text: words = [raw_word.strip(string.punctuation).lower() for raw_word in text.read().split()] words_index = set(words) counts_dict = {index:words.count(index) for index in words_index} for word in

<dependency> <groupId>com.janeluo</groupId> <artifactId>ikanalyzer</artifactId> <version>2012_u6</version> </dependency> 首先引入 ikanalyzer相关jar包 /** * @Description: * @Author: lizhang * @CreateDate: 2018/7/31

# -*- coding: utf-8 -*- #!/usr/bin/env python import re f = open("C:\\Users\\陶敏\\Documents\\Pyscript\\test.txt") str = f.read() li = re.split(r'[, ;.\n\t]',str) for i in li: if(len(i))==0: li.remove(i) res_world = [] res_count = [] for i in li:

import nltk Freq_dist_nltk = nltk.FreqDist(list) for k,y in Freq_dist_nltk: print str(k),str(y)

>>> from collections import Counter>>> Counter(['apple','red','apple','red','red','pear'])Counter({'red': 3, 'apple': 2, 'pear': 1})

1.准备文件并设置编码格式为UTF-8并上传Linux 2.新建一个Java Project 3.导入jar 4.编写Map()和Reduce() 5.将代码输出成jar 6.在linux中启动hdfs 7.修改两个配置文件 8.在linux中启动yarn 9.运行mapReduce 10.查看运行结果 1. 准备文件并设置编码格式为UTF-8并上传Linux 1)设置编码:首先打开文件点击左上角 文件(F) 点击另存为并将编码(E)设置为UTF-8 然后保存(S)替换的原来的文件 2)用工具将

#!/bin/bash if [ $# -ne 1 ]; then echo "Usage:$0 filename"; exit -1 fi filename=$1 egrep -o "\b[[:alpha:]]+\b" $filename | awk '{count[$0]++}END{printf("%-14s%s\n","Word","Count");for(ind in count){printf(