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average.py
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135 lines (104 loc) · 3.55 KB
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#! usr/bin/env python3
import re
total_x = None
total_y = []
total_city = []
for city_name,data in df.groupby(by="city"):
result = data.groupby(by=["language"])["avg_salary"].mean().sort_index()
plt.figure(figsize=(20,8),dpi=80)
_x = result.index
_y = result.values
plt.bar(_x,_y)
total_x = _x
total_y.append(_y)
total_city.append(city_name)
plt.xlabel("语言")
plt.ylabel("平均薪资")
plt.title("{}地区编程语言平均薪资".format(city_name))
plt.grid()
plt
#salary compare
plt.figure(figsize=(20,8),dpi=80)
interval = 6
ind = np.array(range(0,len(total_x) * interval,interval))
width = 1
for index in range(len(total_city)):
plt.bar(ind - (2 - index) * width + width/2,total_y[index],label=total_city[index],width=1)
plt.xticks(range(0,len(total_x) * interval,interval),total_x)
plt.xlabel("语言")
plt.ylabel("平均薪资")
plt.title("一线城市编程语言平均薪资")
plt.grid()
plt.legend()
plt
#compare
def position_advantage(data):
value = data.values
if not value:
return []
value = value[0]
if not value:
return []
value = re.sub(r"[.~]","",value)
return re.split(r'[,，； ;、+-]',value)
labels = list(set([i for row in df[["positionAdvantage"]].apply(position_advantage,axis=1).values for i in row if i]))
position_data = pd.DataFrame(np.zeros((df.shape[0],len(labels))).astype(int),columns=labels,index=df.index)
for label in labels:
position_data[label][df["positionAdvantage"].str.contains(label)] = 1
result = position_data.sum().sort_values(ascending=False)
size = result[:10].values
size = [row for row in size]
labels = result[:10].index
labels = [row for row in labels]
size.append(result.sum() - sum(size))
labels.append("其它")
explode = [0 for i in range(len(size))]
explode[0] = 0.1
plt.figure(figsize=(10,10),dpi=80)
plt.pie(size, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
plt.title("岗位优势百分比")
plt
#city compate
total_value = []
total_label = []
labels = [row for row in result[:10].index]
for index in range(len(total_city)):
city = total_city[index]
data = position_data[df["city"] == city]
total_size = data.sum().sum()
total_label.append(city)
total_value.append((data[labels].sum()/total_size*10000).values.tolist())
plt.figure(figsize=(20,8),dpi=80)
interval = 8
ind = np.array(range(0,len(labels) * interval,interval))
width = 1
for index in range(len(total_label)):
plt.bar(ind - (2 - index) * width + width/2,total_value[index],label=total_label[index],width=1)
plt.xticks(range(0,len(labels) * interval,interval),labels)
plt.xlabel("福利")
plt.ylabel("占比(*100)")
plt.title("岗位优势占比图")
plt.grid()
plt.legend()
plt
#工作经验要求占比
for city_name,data in df.groupby(by="city"):
result = data.groupby(by=["workYear"])["avg_salary"].count().sort_values()
plt.figure(figsize=(8,8),dpi=80)
_x = result.index
_y = result.values
plt.pie(_y, labels=_x, autopct='%1.1f%%',shadow=True, startangle=90)
plt.title("{}地区编程语言学历要求占比".format(city_name))
plt.grid()
plt
#学历要求占比
for city_name,data in df.groupby(by="city"):
result = data.groupby(by=["education"])["avg_salary"].count().sort_index()
plt.figure(figsize=(8,8),dpi=80)
_x = result.index
_y = result.values
plt.pie(_y, labels=_x, autopct='%1.1f%%',shadow=True, startangle=90)
plt.title("{}地区编程语言学历要求占比".format(city_name))
plt.grid()
plt