至此，职位详细信息的获取及保存的工作已经完成，来看一下此时的main函数：
def main(city, keyword, region, pages):
‘’’
主函数
‘’’
csv_filename = ‘zl_’ + city + ‘_’ + keyword + ‘.csv’
txt_filename = ‘zl_’ + city + ‘_’ + keyword + ‘.txt’
headers = [‘job’, ‘years’, ‘education’, ‘salary’, ‘company’, ‘scale’, ‘job_url’]
write_csv_headers(csv_filename, headers)
for i in range(pages):
‘’’
获取该页中所有职位信息，写入csv文件
‘’’
job_dict = {}
html = get_one_page(city, keyword, region, i)
items = parse_one_page(html)
for item in items:
html = get_detail_page(item.get(‘job_url’))
job_detail = get_job_detail(html)
job_dict[‘job’] = item.get(‘job’)
job_dict[‘years’] = job_detail.get(‘years’)
job_dict[‘education’] = job_detail.get(‘education’)
job_dict[‘salary’] = item.get(‘salary’)
job_dict[‘company’] = item.get(‘company’)
job_dict[‘scale’] = job_detail.get(‘scale’)
job_dict[‘job_url’] = item.get(‘job_url’)
# 对数据进行清洗，将标点符号等对词频统计造成影响的因素剔除
pattern = re.compile(r’[一-龥]+')
filterdata = re.findall(pattern, job_detail.get(‘requirement’))
write_txt_file(txt_filename, ‘’.join(filterdata))
write_csv_rows(csv_filename, headers, job_dict)
## 4、数据分析
本节内容为此版本的重点。
### 4.1 工资统计
我们对各个阶段工资的占比进行统计，分析该行业的薪资分布水平。前面我们已经把数据保存到csv文件里了，接下来要读取`salary`列：
def read_csv_column(path, column):
‘’’
读取一列
‘’’
with open(path, ‘r’, encoding=‘gb18030’, newline=‘’) as f:
reader = csv.reader(f)
return [row[column] for row in reader]
main函数里添加
print(read_csv_column(csv_filename, 3))
#下面为打印结果
[‘salary’, ‘7000’, ‘5000’, ‘25000’, ‘12500’, ‘25000’, ‘20000’, ‘32500’, ‘20000’, ‘15000’, ‘9000’, ‘5000’, ‘5000’, ‘12500’, ‘24000’, ‘15000’, ‘18000’, ‘25000’, ‘20000’, ‘0’, ‘20000’, ‘12500’, ‘17500’, ‘17500’, ‘20000’, ‘11500’, ‘25000’, ‘12500’, ‘17500’, ‘25000’, ‘22500’, ‘22500’, ‘25000’, ‘17500’, ‘7000’, ‘25000’, ‘3000’, ‘22500’, ‘15000’, ‘25000’, ‘20000’, ‘22500’, ‘15000’, ‘15000’, ‘25000’, ‘17500’, ‘22500’, ‘10500’, ‘20000’, ‘17500’, ‘22500’, ‘17500’, ‘25000’, ‘20000’, ‘11500’, ‘11250’, ‘12500’, ‘14000’, ‘12500’, ‘17500’, ‘15000’]
从结果可以看出，除了第一项，其他的都为平均工资，但是此时的工资为字符串，为了方便统计，我们将其转换成整形:
salaries = []
sal = read_csv_column(csv_filename, 3)
# 撇除第一项，并转换成整形，生成新的列表
for i in range(len(sal) - 1):
# 工资为’0’的表示招聘上写的是’面议’,不做统计
if not sal[i] == ‘0’:
salaries.append(int(sal[i + 1]))
print(salaries)
下面为打印结果
[7000, 5000, 25000, 12500, 25000, 20000, 32500, 20000, 15000, 9000, 5000, 5000, 12500, 24000, 15000, 18000, 25000, 20000, 0, 20000, 12500, 20000, 11500, 17500, 25000, 12500, 17500, 25000, 25000, 22500, 22500, 17500, 17500, 7000, 25000, 3000, 22500, 15000, 25000, 20000, 22500, 15000, 22500, 10500, 20000, 15000, 17500, 17500, 25000, 17500, 22500, 25000, 12500, 20000, 11250, 11500, 14000, 12500, 15000, 17500]
我们用直方图进行展示：
plt.hist(salaries, bins=10 ,)
plt.show()
生成效果图如下：
![](https://img-blog.csdn.net/20180425203353549?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3podXNvbmd6aXll/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve