用Python分析BOSS直聘的薪资数据，年后找工作有方向了

发表于

前面已经分享了Python相关的面试题，具体可以看这里

万字长文Python面试题，年后找工作就靠这了

今天我们来看看招聘网站上，关于Python的工作，薪资状况是怎样的呢！

数据来源

数据来源于BOSS直聘，说实话，现在的招聘网站，做的比较好的还是BOSS直聘，其相关的数据、报告等都是比较有代表性的。今天我们就来看看相关的数据吧！

数据获取

BOSS直聘上有这么一个接口，可以很好的获取当前不同岗位，不同城市的薪资水平

www.zhipin.com/wapi/zpboss…

可以很方便的获取比较详细的薪资数据

1
2
3
4
5
6
7
8
Python复制代码import requests

headers = {'accept': 'application/json, text/plain, */*',
          'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36'}
querystring = {"positionId":"100109","industryId":"0","cityId":"0","companySize":"0","financingStage":"0","experienceCode":"0"}
job_statics_url = 'https://www.zhipin.com/wapi/zpboss/h5/marketpay/statistics.json'

job_statics_data = requests.get(job_statics_url, params=querystring, headers=headers)

这样，就可以获取到我们想要的 json 数据了

下面我们就可以简单的来分析下相关的薪资数据了

数据分析

薪资分位值

在我们获取到的数据当中，就有分位值的数据，可以方便的获取

1
2
Python复制代码job_statics_data_json = job_staticis_data.json()
job_statics_data_json['zpData']['salaryByPoints']

接下来就可以整理横纵坐标轴了

1
2
3
4
5
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByPoints']:
    statics_x.append(i['name'] + '\n' + i['title'])
    statics_y.append(i['salary'])

下面开始作图

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
Python复制代码import pyecharts.options as opts
from pyecharts.charts import Line, Bar, Pie, Calendar, WordCloud
from pyecharts.commons.utils import JsCode
from pyecharts.globals import SymbolType

x_data = statics_x
y_data = statics_y

background_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)

c_line = (
    Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="薪资",
        y_axis=y_data,
        is_smooth=True,
        is_symbol_show=True,
        symbol="circle",
        symbol_size=6,
        linestyle_opts=opts.LineStyleOpts(color="#fff"),
        label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
        itemstyle_opts=opts.ItemStyleOpts(
            color="red", border_color="#fff", border_width=3
        ),
        tooltip_opts=opts.TooltipOpts(is_show=False),
        areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="收入分位",
            pos_bottom="5%",
            pos_left="center",
            title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
        ),
        xaxis_opts=opts.AxisOpts(
            type_="category",
            boundary_gap=False,
            axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
            axisline_opts=opts.AxisLineOpts(is_show=False),
            axistick_opts=opts.AxisTickOpts(
                is_show=True,
                length=25,
                linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
            ),
            splitline_opts=opts.SplitLineOpts(
                is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
            ),
        ),
        yaxis_opts=opts.AxisOpts(
            type_="value",
            position="right",
            axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
            ),
            axistick_opts=opts.AxisTickOpts(
                is_show=True,
                length=15,
                linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
            ),
            splitline_opts=opts.SplitLineOpts(
                is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
            ),
        ),
        legend_opts=opts.LegendOpts(is_show=False),
    )
)

可以得到一个还不错的折线图

可以看到，业内Python的薪资水平，大部分应该都处于1万左右，这个薪资水平其实并不太高，看来纯的Python岗位并不太吃香，要想获得更高的薪资，还是需要有更多的技能傍身！

薪资区间分布

下面再来看看薪资的分布情况

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByDistributed']:
    statics_y.append(i['percent'])
    statics_x.append(i['salaryRange'])

def bar_chart(x, y) -> Bar:
    background_color_js = (
        "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
        "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
    )
    c = (
        Bar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
        #Bar()
        .add_xaxis(x)
        # .add_xaxis(searchcount.index.tolist()[:10])
        .reversal_axis()
        .add_yaxis("", y, 
                   label_opts=opts.LabelOpts(position='inside', formatter="{c}%"),
                  color='plum', category_gap="60%"
                  )
        .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30, formatter="{value}%"),
                                                 axisline_opts=opts.AxisLineOpts(is_show=False),),
                        yaxis_opts=opts.AxisOpts(
                            axislabel_opts=opts.LabelOpts(is_show=True),
                        axisline_opts=opts.AxisLineOpts(is_show=False),
                        axistick_opts=opts.AxisTickOpts(
                        is_show=True,
                        length=25,
                        linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
                    ),)
                        )
        .set_series_opts(
            itemstyle_opts={
            "normal": {
                "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                    offset: 0,
                    color: 'rgba(255,100,97,.5)'
                }, {
                    offset: 1,
                    color: 'rgba(221,160,221)'
                }], false)"""),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": 'rgb(0, 160, 221)',
            }}
        )
    )
    return c

来看看薪资分布情况

可以看到，15K以上的薪资还是占了16%以上，而占比最大的薪资区间则是7-9K

工作年限薪资分布

下面我们继续来看看薪资水平和工作年限之间的关系

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByWorkExp']:
    statics_y.append(i['percent'])
    statics_x.append(i['workExp'] + ':' + str(i['aveSalary']))

background_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
c = (
    Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
    .add(
        "",
        list(zip(statics_x, statics_y)),
        radius=["40%", "55%"],
        label_opts=opts.LabelOpts(
            position="outside",
            formatter="{a|job}{abg|}\n{hr|}\n {b|{b}: }{per|{d}%}  ",
            background_color="#eee",
            border_color="#aaa",
            border_width=1,
            border_radius=4,
            rich={
                "a": {"color": "#999", "lineHeight": 22, "align": "center"},
                "abg": {
                    "backgroundColor": "#e3e3e3",
                    "width": "100%",
                    "align": "right",
                    "height": 22,
                    "borderRadius": [4, 4, 0, 0],
                },
                "hr": {
                    "borderColor": "#aaa",
                    "width": "100%",
                    "borderWidth": 0.5,
                    "height": 0,
                },
                "b": {"fontSize": 16, "lineHeight": 33},
                "per": {
                    "color": "#eee",
                    "backgroundColor": "#334455",
                    "padding": [2, 4],
                    "borderRadius": 2,
                },
            },
        ),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title=""))
)

可以看到，下面的图片还是比较直观的

1-3年的应聘者还是最多的，占比达到了50%+，这个经验段，确实是职场的主力军了！

任职年龄分布

职场的年龄也是一个热点话题，35+岁的程序员们，总是一言难尽啊

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByAge']:
    statics_x.append(i['ageRange'])
    statics_y.append(i['people'])

def bar_chart_age(x, y) -> Bar:
    background_color_js = (
        "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
        "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
    )
    c = (
        Bar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
        #Bar()
        .add_xaxis(x)
        # .add_xaxis(searchcount.index.tolist()[:10])
        # .reversal_axis()
        .add_yaxis("", y, 
                   label_opts=opts.LabelOpts(position='inside', formatter="{c}"),
                  color='plum', category_gap="60%"
                  )
        .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30, formatter="{value}"),
                                                 axisline_opts=opts.AxisLineOpts(is_show=False),),
                        yaxis_opts=opts.AxisOpts(
                            axislabel_opts=opts.LabelOpts(is_show=True),
                        axisline_opts=opts.AxisLineOpts(is_show=False),
                        axistick_opts=opts.AxisTickOpts(
                        is_show=True,
                        length=25,
                        linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
                    ),)
                        )
        .set_series_opts(
            itemstyle_opts={
            "normal": {
                "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                    offset: 0,
                    color: 'rgba(255,100,97,.5)'
                }, {
                    offset: 1,
                    color: 'rgba(221,160,221)'
                }], false)"""),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": 'rgb(0, 160, 221)',
            }}
        )
    )
    return c

数据很能说明问题

可以看到，35岁以下的占据了绝大多数，可想而知，35+的程序员生存状况是多么的糟糕！

月薪环比变化

我们通过每个月的薪资变化，来看看哪个月找工作比较有机会获得更高的薪资呢

1
2
3
4
5
6
7
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByMonth']:
    statics_x.append(i['year'] + '-' + i['month'])
    statics_y.append(i['monthAveSalary'])
x_data = statics_x
y_data = statics_y

每月薪资变化

可以看到，去年2月份的薪资水平是最高的，之后一路下滑，再之后就基本趋于稳定了，7-8K这个平均水平

薪资城市分布

通过Pycharts画地图还是蛮方便的

1
2
3
4
5
6
7
8
9
10
11
12
13
14
Python复制代码statics_x = []
statics_y = []
for i in job_statics_data_json['zpData']['salaryByCity']:
    if i['cityList']:
        statics_x.append(i['cityList'][0]['cityAveMonthSalary'])
    statics_y.append(i['provinceName'])
c = (
    Map()
    .add("全国薪资", [list(z) for z in zip(statics_y, statics_x)], "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title=""),
        visualmap_opts=opts.VisualMapOpts(max_=15000, min_=6000),
    )
)

全国薪资分布

好了，今天的分享就到这里了，希望对大家有所帮助！
【萝卜大杂烩】公众号回复“boss”获取完整代码

本文转载自: 掘金

开发者博客 – 和开发相关的 这里全都有

0%