前言
嗨嘍~大家好呀，這里是魔王吶
在前一章：讓我們用python來采集資料看看找作業都要會什么吧~
我們講了如何采集zhaopin網站資料，現在~
我們來把資料可視化，更好的查看在自己領域最需的技術是什么~
下面，我們直接上代碼~
代碼提供者：青燈教育-自游老師
代碼
import pandas as pd
from pyecharts.charts import *
from pyecharts import options as opts
import re
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode
# 讀取資料
df = pd.read_csv("招聘資料.csv")
df.head()
df.info()
df['薪資'].unique()
df['bottom']=df['薪資'].str.extract('^(\d+).*')
df['top']=df['薪資'].str.extract('^.*?-(\d+).*')
df['top'].fillna(df['bottom'],inplace=True)
df['commision_pct']=df['薪資'].str.extract('^.*?·(\d{2})薪')
df['commision_pct'].fillna(12,inplace=True)
df['commision_pct']=df['commision_pct'].astype('float64')
df['commision_pct']=df['commision_pct']/12
df.dropna(inplace=True)
df['bottom'] = df['bottom'].astype('int64')
df['top'] = df['top'].astype('int64')
df['平均薪資'] = (df['bottom']+df['top'])/2*df['commision_pct']
df['平均薪資'] = df['平均薪資'].astype('int64')
df.head()
df['薪資'] = df['薪資'].apply(lambda x:re.sub('.*千/月', '0.3-0.7萬/月', x))
df["薪資"].unique()
df['bottom'] = df['薪資'].str.extract('^(.*?)-.*?')
df['top'] = df['薪資'].str.extract('^.*?-(\d\.\d|\d)')
df.dropna(inplace=True)
df['bottom'] = df['bottom'].astype('float64')
df['top'] = df['top'].astype('float64')
df['平均薪資'] = (df['bottom']+df['top'])/2 * 10
df.head()
mean = df.groupby('學歷')['平均薪資'].mean().sort_values()
x = mean.index.tolist()
y = mean.values.tolist()
c = (
Bar()
.add_xaxis(x)
.add_yaxis(
"學歷",
y
)
.set_global_opts(title_opts=opts.TitleOpts(title="不同學歷的平均薪資"),datazoom_opts=opts.DataZoomOpts())
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()
color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
[{offset: 0, color: '#63e6be'}, {offset: 1, color: '#0b7285'}], false)"""
color_js1 = """new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#ed1941'
}, {
offset: 1,
color: '#009ad6'
}], false)"""
dq = df.groupby('城市')['職位'].count().to_frame('數量').sort_values(by='數量',ascending=False).reset_index()
x_data = https://www.cnblogs.com/Qqun261823976/archive/2022/07/19/dq['城市'].values.tolist()[:20]
y_data = https://www.cnblogs.com/Qqun261823976/archive/2022/07/19/dq['數量'].values.tolist()[:20]
b1 = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK,bg_color=JsCode(color_js1),width='1000px',height='600px'))
.add_xaxis(x_data)
.add_yaxis('',
y_data ,
category_gap="50%",
label_opts=opts.LabelOpts(
font_size=12,
color='yellow',
font_weight='bold',
font_family='monospace',
position='insideTop',
formatter = '{b}\n{c}'
),
)
.set_series_opts(
itemstyle_opts={
"normal": {
"color": JsCode(color_js),
"barBorderRadius": [15, 15, 0, 0],
"shadowColor": "rgb(0, 160, 221)",
}
}
)
.set_global_opts(
title_opts=opts.TitleOpts(title='招 聘 數 量 前 20 的 城 市 區 域',
title_textstyle_opts=opts.TextStyleOpts(color="yellow"),
pos_top='7%',pos_left = 'center'
),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
yaxis_opts=opts.AxisOpts(name="",
name_location='middle',
name_gap=40,
name_textstyle_opts=opts.TextStyleOpts(font_size=16)),
datazoom_opts=[opts.DataZoomOpts(range_start=1,range_end=50)]
)
)
b1.render_notebook()
boss = df['學歷'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
.add(
series_name="學歷需求占比",
data_pair=data_pair,
label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="學歷需求占比",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()
boss = df['經驗'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
.add(
series_name="經驗需求占比",
data_pair=data_pair,
label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="經驗需求占比",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()
boss = df['公司領域'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
.add(
series_name="公司領域占比",
data_pair=data_pair,
label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="公司領域占比",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
boss = df['經驗'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
Pie()
.add("", data_pair)
.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
.set_global_opts(title_opts=opts.TitleOpts(title="經驗要求占比"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)
c.render_notebook()
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
boss = df['經驗'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
Pie()
.add(
"",
data_pair,
radius=["40%", "55%"],
label_opts=opts.LabelOpts(
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {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="python招聘經驗要求"))
)
c.render_notebook()
gsly = df['公司領域'].value_counts()[:10]
x1 = gsly.index.tolist()
y1 = gsly.values.tolist()
c = (
Bar()
.add_xaxis(x1)
.add_yaxis(
"公司領域",
y1
)
.set_global_opts(title_opts=opts.TitleOpts(title="公司領域"),datazoom_opts=opts.DataZoomOpts())
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()
gsgm = df['公司規模'].value_counts()[1:10]
x2 = gsgm.index.tolist()
y2 = gsgm.values.tolist()
c = (
Bar()
.add_xaxis(x2)
.add_yaxis(
"公司規模",
y2
)
.set_global_opts(title_opts=opts.TitleOpts(title="公司規模"),datazoom_opts=opts.DataZoomOpts())
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()
import stylecloud
from PIL import Image
welfares = df['福利'].dropna(how='all').values.tolist()
welfares_list = []
for welfare in welfares:
welfares_list += welfare.split('，')
pic_name = '福利詞云.png'
stylecloud.gen_stylecloud(
text=' '.join(welfares_list),
font_path='msyh.ttc',
palette='cartocolors.qualitative.Bold_5',
max_font_size=100,
icon_name='fas fa-yen-sign',
background_color='#212529',
output_name=pic_name,
)
Image.open(pic_name)
效果（部分）
尾語
成功沒有快車道，幸福沒有高速路，
幸福是可以通過學習來獲得的，盡管它不是我們的母語，
——勵志語錄
本文章到這里就結束啦~感興趣的小伙伴可以復制代碼去試試哦
對啦！！記得三連哦~ ?? 另外，歡迎大家閱讀我往期的文章呀~
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標籤：其他