拉勾网-深圳福利待遇词云

字体安装好了，数据也有了，做一个词语，看下招聘的hr承诺给大家都是什么样的福利
安装jieba

pip install jieba

安装 wordcloud

pip install wordcloud

下面上代码

import pandas as pd
data=pd.read_csv("data_df.csv",index_col=0)
data.info()
'''
<class 'pandas.core.frame.DataFrame'>
Index: 450 entries, Java to java高级工程师
Data columns (total 12 columns):
1     450 non-null object
2     450 non-null object
3     450 non-null object
4     450 non-null object
5     450 non-null object
6     450 non-null object
7     450 non-null object
8     450 non-null object
9     399 non-null object
10    450 non-null object
11    435 non-null object
12    415 non-null object
dtypes: object(12)
memory usage: 55.7+ KB
从打印的数据来看提取出来的数据列索引为9，11，12都有缺失
'''

因为要选第11列内容作为词云的数据源，可以对11列内容做缺失补偿，具体代码如下

from sklearn.impute import SimpleImputer
#众数填补缺失值
mode=SimpleImputer(strategy='most_frequent')
mode=mode.fit_transform(treatment)
mode[:20]
data.loc[:,'11']=mode
data.info()
'''
9     399 non-null object
10    450 non-null object
11    450 non-null object
12    415 non-null object
从index=11的查询出来的个数可以判定此列已经被众数填补缺失值
'''

下面收集数据写入到切片

temp=[]
for item in data['11']:items=item.split("|")    for each in items:temp.append(each)
temp
#打印temp数据结果
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统计每个词出现的频率排序取前20个作为云图词汇的代表

counts = {}  #创建字典,统计公司福利对应的个数
for word in temp:counts[word]= counts.get(word,0)+1
items = list(counts.items())
items.sort(key=lambda x:x[1], reverse =True) #降序排序
wordlist=list()
for i in range(20):word,count = items[i]print("{0:<10}{1:<5}".format(word,count)) #输出前N个词频的词语wordlist.append(word)    #把词语word放进一个列表
wl=' '.join(wordlist)
wl
#20个热词排序
'''
带薪年假      255
岗位晋升      174
节日礼物      171
技能培训      166
绩效奖金      138
年底双薪      97
五险一金      79
定期体检      74
股票期权      66
扁平管理      59
年度旅游      46
弹性工作      43
专项奖金      36
领导好       21
交通补助      18
年终分红      18
免费班车      17
通讯津贴      16
管理规范      14
午餐补助      12
'带薪年假 岗位晋升 节日礼物 技能培训 绩效奖金 年底双薪 五险一金 定期体检 股票期权 扁平管理 年度旅游 弹性工作 专项奖金 领导好 交通补助 年终分红 免费班车 通讯津贴 管理规范 午餐补助'
'''

生成词条

from wordcloud import WordCloud
import matplotlib.pyplot as plt
wc = WordCloud(background_color = "black", #背景颜色max_words=100,              #最大词语数目font_path = r'/usr/share/fonts/simsun.ttc',   #调用font里的simsun.tff字体，需要提前安装height=2400,                #设置高度width=1600,                 #设置宽度max_font_size=1000,         #最大字体号random_state=1000,          #设置随机生成状态，即有多少种配色方案)
myword = wc.generate(wl)
plt.imshow(myword)
plt.axis("off")
plt.show()
wc.to_file('1.jpg')

上面图太小，服务器里把保存的图片保存到本地，这个图够大了

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