python3对拉勾数据进行可视化分析的方法详解

read_file = "analyst.csv"
# 读取文件获得数据
data = pd.read_csv(read_file, encoding="gbk")
# 去除数据中无关的列
data = data[:].drop(['ID'], axis=1)
# 描述性统计
data.describe()

print(data['学历要求'].unique())
print(data['工作经验'].unique())
print(data['薪资'].unique())

# 对薪资进行预处理
def pre_salary(data):
 salarys = data['薪资'].values
 salary_dic = {}
 for salary in salarys:
 # 根据'-'进行分割并去掉'k',分别将两端的值转换成整数
 min_sa = int(salary.split('-')[0][:-1])
 max_sa = int(salary.split('-')[1][:-1])
 # 求中位数
 median_sa = (min_sa + max_sa) / 2
 # 判断其值并划分到指定范围
 if median_sa < 5:
 salary_dic[u'5k以下'] = salary_dic.get(u'5k以下', 0) + 1
 elif median_sa > 5 and median_sa < 10:
 salary_dic[u'5k-10k'] = salary_dic.get(u'5k-10k', 0) + 1
 elif median_sa > 10 and median_sa < 20:
 salary_dic[u'10k-20k'] = salary_dic.get(u'10k-20k', 0) + 1
 elif median_sa > 20 and median_sa < 30:
 salary_dic[u'20k-30k'] = salary_dic.get(u'20k-30k', 0) + 1
 elif median_sa > 30 and median_sa < 40:
 salary_dic[u'30k-40k'] = salary_dic.get(u'30k-40k', 0) + 1
 else:
 salary_dic[u'40以上'] = salary_dic.get(u'40以上', 0) + 1
 print(salary_dic)
 return salary_dic

import jieba
def cut_text(text):
 stopwords =['熟悉','技术','职位','相关','工作','开发','使用','能力',
 '优先','描述','任职','经验','经验者','具有','具备','以上','善于',
 '一种','以及','一定','进行','能够','我们']
 for stopword in stopwords:
 jieba.del_word(stopword)
 
 words = jieba.lcut(text)
 content = " ".join(words)
 return content

def draw_pie(dic):
 labels = []
 count = []
 
 for key, value in dic.items():
 labels.append(key)
 count.append(value)
 
 fig, ax = plt.subplots(figsize=(8, 6), subplot_kw=dict(aspect="equal"))

 # 绘制饼状图，wedgeprops 表示每个扇形的宽度
 wedges, texts = ax.pie(count, wedgeprops=dict(width=0.5), startangle=0)
 # 文本框设置
 bbox_props = dict(boxstyle="square,pad=0.9", fc="w", ec="k", lw=0)
 # 线与箭头设置
 kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle="-"),
 bbox=bbox_props, zorder=0, va="center")

 for i, p in enumerate(wedges):
 ang = (p.theta2 - p.theta1)/2. + p.theta1
 y = np.sin(np.deg2rad(ang))
 x = np.cos(np.deg2rad(ang))
 # 设置文本框在扇形的哪一侧
 horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
 # 用于设置箭头的弯曲程度
 connectionstyle = "angle,angleA=0,angleB={}".format(ang)
 kw["arrowprops"].update({"connectionstyle": connectionstyle})
 # annotate()用于对已绘制的图形做标注，text是注释文本，含 'xy' 的参数跟坐标点有关
 text = labels[i] + ": " + str('%.2f' %((count[i])/sum(count)*100)) + "%"
 ax.annotate(text, size=13, xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
  horizontalalignment=horizontalalignment, **kw)
 plt.show()

def draw_workYear(data):
 workyears = list(data[u'工作经验'].values)
 wy_dic = {}
 labels = []
 count = []
 # 得到工作经验对应的数目并保存到count中
 for workyear in workyears:
 wy_dic[workyear] = wy_dic.get(workyear, 0) + 1
 print(wy_dic)
 # wy_series = pd.Series(wy_dic)
 # 分别得到 count 的 key 和 value
 for key, value in wy_dic.items():
 labels.append(key)
 count.append(value)
 # 生成 keys 个数的数组
 x = np.arange(len(labels)) + 1
 # 将 values 转换成数组
 y = np.array(count)
 
 fig, axes = plt.subplots(figsize=(10, 8))
 axes.bar(x, y, color="#1195d0")
 plt.xticks(x, labels, size=13, rotation=0)
 plt.xlabel(u'工作经验', fontsize=15)
 plt.ylabel(u'数量', fontsize=15)
 
 # 根据坐标将数字标在图中，ha、va 为对齐方式
 for a, b in zip(x, y):
 plt.text(a, b+1, '%.0f' % b, ha='center', va='bottom', fontsize=12)
 plt.show()

from wordcloud import WordCloud
# 绘制词云图
def draw_wordcloud(content):
 
 wc = WordCloud(
 font_path = 'c:\\Windows\Fonts\msyh.ttf',
 background_color = 'white',
 max_font_size=150, # 字体最大值
 min_font_size=24, # 字体最小值
 random_state=800, # 随机数
 collocations=False, # 避免重复单词
 width=1600,height=1200,margin=35, # 图像宽高，字间距
 )
 wc.generate(content)

 plt.figure(dpi=160) # 放大或缩小
 plt.imshow(wc, interpolation='catrom',vmax=1000)
 plt.axis("off") # 隐藏坐标