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Pandas+pyecharts 𞓜 40000+ pieces of postgraduate entrance examination information data visualization

編輯：Python

Postgraduate entrance examination data set ： Dataset Download
Run the tool ：jupyter notebook

Preface

The blog analyzes the information data of postgraduate entrance examination and enrollment in major universities , have a look ：

The largest number of students enrolled 10 What are the big universities
The highest professional score 、 Lowest score distribution
Several major majors TOP20 What are the schools

I hope it will help my friends , If you have any questions or need to improve, you can leave a message in the comment area .

Libraries involved ：
Pandas — Data processing
Pyecharts — Data visualization

Visualization part ：
Histogram — Bar
Pictogram — PictorialBar
Clouds of words — stylecloud
Combined components — Grid

1. The import module

!pip install stylecloud

import re
import stylecloud
from PIL import Image
import numpy as np
import pandas as pd
from collections import Counter
from pyecharts.charts import Line,PictorialBar,Bar,Grid
from pyecharts import options as opts
from pyecharts.globals import ThemeType
from pyecharts.globals import SymbolType
from pyecharts.commons.utils import JsCode

2.Pandas Data processing

2.1 Reading data

df = pd.read_csv('/home/mw/input/202201106373/ Postgraduate entrance examination information .csv')
df.head(10)

2.2 Look at the index 、 Data types and memory information

df.info()

You can see , School name 、 The name of the Department 、 Some data are missing in columns such as specialty code

2.3 Remove the blank lines

df.dropna(inplace=True)

2.4 Screening 2020 Postgraduate entrance examination information

df_2020 = df[df[' year '] == 2020]
df_2020.info()

You can see , No missing data

2.5 View rows with duplicate Columns

df_2020.duplicated().sum()

2.6 View rows with duplicate Columns

df_2020.duplicated(subset=[" School name "," Professional code "]).sum()

2.7 Format processing of head office and branch

df_2020 = df_2020[~(df_2020[' Total score ']=='-')]
df_2020[' Total score '] = df_2020[' Total score '].astype('int')

df_2020.info()

3. Pyecharts Data visualization

3.1 The largest number of students enrolled 10 Big colleges and Universities

df_major_10 = df_2020.groupby(' School name ')[' Major name '].count().sort_values(ascending = False)[:10].to_frame(' Number ')
df_major_10 = df_major_10 = df_major_10.sort_values(by=' Number ').sort_values(by=' Number ')
schs = list(df_major_10.index)
sch_icons = {

' Wuhan University ': 'image://https://www.shanghairanking.cn/_uni/logo/46182017.png',
' Jilin University ': 'image://https://www.shanghairanking.cn/_uni/logo/76557044.png',
' Xiamen University ': 'image://https://www.shanghairanking.cn/_uni/logo/14008229.png',
' Southwest University ': 'image://https://www.shanghairanking.cn/_uni/logo/68012227.png',
' Peking University, ': 'image://https://www.shanghairanking.cn/_uni/logo/86350223.png',
' Sichuan University ': 'image://https://www.shanghairanking.cn/_uni/logo/75651370.png',
' Shandong University ': 'image://https://www.shanghairanking.cn/_uni/logo/97189370.png',
' Fudan University ': 'image://https://www.shanghairanking.cn/_uni/logo/28312850.png',
' Yunnan University ': 'image://https://www.shanghairanking.cn/_uni/logo/31586909.png',
' Nankai University ': 'image://https://www.shanghairanking.cn/_uni/logo/44629152.png'
}

icons = []
for sch in schs:
icons.append(dict(name=sch, value=1, symbol=sch_icons[sch]))
p1 = (
PictorialBar()
.add_xaxis(schs)
.add_yaxis("",
icons,
label_opts=opts.LabelOpts(is_show=False),
category_gap='40%',
symbol_pos='start',
symbol_size=60,
is_symbol_clip=False,
itemstyle_opts={
"normal": {

'shadowBlur': 10,
'shadowColor': 'rgba(0, 0, 0, 0.5)',
'shadowOffsetX': 10,
'shadowOffsetY': 10,}
})
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
is_show=True,
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(font_size=20,color='#ed1941',font_weight=700,margin=20),
splitline_opts=opts.SplitLineOpts(is_show=False,
linestyle_opts=opts.LineStyleOpts(type_='dashed')),
axisline_opts=opts.AxisLineOpts(is_show=False,
linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))
),
)
.reversal_axis()
)
b1 = (
Bar()
.add_xaxis(schs)
.add_yaxis('', df_major_10[' Number '].values.tolist(), category_gap='40%')
.set_series_opts(
label_opts=opts.LabelOpts(
position="insideLeft",
vertical_align='middle',
horizontal_align='top',
font_size=18,
font_weight='bold',
formatter=' {c} '),
itemstyle_opts={

'opacity': 0.9,
'shadowBlur': 10,
'shadowOffsetX': 10,
'shadowOffsetY': 10,
'shadowColor': 'rgba(0, 0, 0, 0.5)',
'barBorderRadius': [30, 30, 30, 30],
'color':'red'
}
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
is_scale=True,
type_="value",
name_location="middle",
position='top',
name_textstyle_opts=opts.TextStyleOpts(font_size=14, font_weight='bold',),
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),),
title_opts=opts.TitleOpts(title=' The top ten schools in terms of enrollment ',
title_textstyle_opts=opts.TextStyleOpts(color="blue",font_size=30),
pos_top='2%',pos_left = 'center'),
)
.reversal_axis()
)
grid = Grid(init_opts=opts.InitOpts(theme='light', width='1000px', height='800px'))
grid.add(p1, is_control_axis_index=False, grid_opts=opts.GridOpts(pos_left='15%', pos_right='80%', pos_top='10%'))
grid.add(b1, is_control_axis_index=False, grid_opts=opts.GridOpts(pos_left='23%', pos_right='10%', pos_top='10%'))
grid.render_notebook()

3.2 The highest score for some majors 、 Lowest score distribution

df_tmp = df_2020[df_2020[" Major name "].str.contains(' Computer science and technology | business administration | accounting | Management science and engineering | Finance | law | Materials science and engineering | Public administration | Mechanical engineering | mathematics ')]
df_major = df_tmp.groupby(' Major name ')[' Total score '].agg([np.mean, np.max,np.min])
df_major = df_major.sort_values(by=['mean'],ascending=False)[:20]
df_major['amin'] = df_major['amin']*(-1)

bar = (
Bar()
.add_xaxis(df_major.index.tolist())
.add_yaxis(' The highest ',
df_major['amax'].tolist()[::-1],
z_level=1,
stack='1',
category_gap='50%',
tooltip_opts=opts.TooltipOpts(is_show=False),
label_opts=opts.LabelOpts(position='insideLeft', formatter='{c} branch '),
itemstyle_opts={
"normal": {

"barBorderRadius": [30, 30, 30, 30],
'shadowBlur': 10,
'shadowColor': 'rgba(120, 36, 50, 0.5)',
'shadowOffsetY': 5,
'color':'#a61e4d',
}
}
)
.add_yaxis(' Lowest score ',
df_major['amin'].tolist()[::-1],
z_level=1,
stack='1',
category_gap='50%',
tooltip_opts=opts.TooltipOpts(is_show=False),
label_opts=opts.LabelOpts(position='insideRight',
formatter=JsCode(
"""function(params) { if (params.value && params.value < 0) { return -params.value + ' branch '; } }"""
),
),
itemstyle_opts={
"normal": {

"barBorderRadius": [30, 30, 30, 30],
'shadowBlur': 10,
'shadowColor': 'rgba(120, 36, 50, 0.5)',
'shadowOffsetY': 5,
'color':'#009ad6',
}
}
)
.set_global_opts(title_opts=opts.TitleOpts(title=' The highest and lowest scores of some majors ',pos_top='1%',pos_left='40%',
title_textstyle_opts=opts.TextStyleOpts(font_size=20,color='#fff000')),
legend_opts=opts.LegendOpts(is_show=True, pos_top='4%',pos_left='45%'),
datazoom_opts=opts.DataZoomOpts(type_='inside',
range_start=10, # Set the start stop position ,50%-100%
range_end=100,
orient='vertical'),
xaxis_opts=opts.AxisOpts(is_show=False, max_=500),
yaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(color='#00c6d7', font_size=12, font_weight='bold')),
)
).reversal_axis()
grid = (
Grid(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='800px'))
.add(bar, grid_opts=opts.GridOpts(pos_top='8%', pos_left='18%', pos_right='5%'))
)
grid.render_notebook()

3.3 Some majors have the highest requirements for the total score of postgraduate entrance examination 20 Big colleges and Universities

3.3.1 Computer science and technology TOP20 School

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0, [{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#ed1941'}], false)"""
range_colors=['#fff5f0','#fee0d2','#fcbba1','#fc9272','#fb6a4a','#ef3b2c','#cb181d','#99000d']

df_computer = df_2020.loc[df_2020[' Major name ']==' Computer science and technology ',:]
df_computer = df_computer.sort_values(by=' Total score ',ascending=False)[:20]
df_computer[' Total score '] = df_computer[' Total score '].astype('int')
df_computer.style.bar(subset=[' Total score '],color='#ed1941', vmin=300, vmax=360)

x_data = df_computer[' School name '].values.tolist()
y_data = df_computer[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=360,
min_=300,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Computer science and technology TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=400,
min_=200,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.2 Management science and engineering TOP20 School

df_management = df_2020.loc[df_2020[' Major name '].str.contains(' Management science and engineering '),:]
df_management = df_management.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_management[' School name '].values.tolist()
y_data = df_management[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=405,
min_=360,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Management science and engineering TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=410,
min_=360,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.3 accounting profession TOP20 School

df_accountant = df_2020.loc[df_2020[' Major name '].str.contains(' accounting '),:]
df_accountant = df_accountant.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_accountant[' School name '].values.tolist()
y_data = df_accountant[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=400,
min_=350,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' accounting profession TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=400,
min_=350,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.4 Business Administration TOP20 School

df_mba = df_2020.loc[df_2020[' Major name '].str.contains(' business administration '),:]
df_mba = df_mba.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_mba[' School name '].values.tolist()
y_data = df_mba[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=390,
min_=340,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Business Administration TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=390,
min_=340,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.5 Law major TOP20 School

df_law = df_2020.loc[df_2020[' Major name '].str.contains(' law '),:]
df_law = df_law.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_law[' School name '].values.tolist()
y_data = df_law[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=380,
min_=350,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Law major TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=380,
min_=350,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.6 Finance TOP20 School

df_finance = df_2020.loc[df_2020[' Major name '].str.contains(' Finance '),:]
df_finance = df_finance.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_mba[' School name '].values.tolist()
y_data = df_mba[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=390,
min_=340,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Finance TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=390,
min_=350,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.7 Materials science and Engineering TOP20 School

df_materials = df_2020.loc[df_2020[' Major name '].str.contains(' Materials science and engineering '),:]
df_materials = df_materials.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_materials[' School name '].values.tolist()
y_data = df_materials[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=340,
min_=290,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Materials science and Engineering TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=340,
min_=290,
range_color=range_colors
),
)
)
bar2.render_notebook()

3.3.8 Major in mechanical engineering TOP20 School

df_mechanical = df_2020.loc[df_2020[' Major name '].str.contains(' Mechanical engineering '),:]
df_mechanical = df_mechanical.sort_values(by=' Total score ',ascending=False)[:20]
x_data = df_mechanical[' School name '].values.tolist()
y_data = df_mechanical[' Total score '].values.tolist()
bar2 = (
Bar(init_opts=opts.InitOpts(theme='chalk',width='1000px', height='800px'))
.add_xaxis(x_data)
.add_yaxis('', y_data,
category_gap='30%',
itemstyle_opts={

'normal': {

'shadowColor': 'rgba(0, 0, 0, .5)',
'shadowBlur': 5,
'shadowOffsetY': 2,
'shadowOffsetX': 2,
'borderColor': '#fff'
}
}
)
.set_series_opts(label_opts=opts.LabelOpts(font_weight='bold',font_size=12, color='#66d9e8' ))
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,font_size=12,font_weight='bold', color="#41b6c4",margin=10),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(is_show=False,
max_=360,
min_=290,
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)
),
title_opts=opts.TitleOpts(
title=' Major in mechanical engineering TOP20 School ',
pos_left='center',
pos_top='4%',
title_textstyle_opts=opts.TextStyleOpts(color='#fec44f', font_size=24, font_weight='bold')
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
max_=360,
min_=290,
range_color=range_colors
),
)
)
bar2.render_notebook()

4 School name 、 Professional name word cloud

4.1 School name word cloud

schools = df_2020[' School name '].values.tolist()
pic_name = ' School name .png'
stylecloud.gen_stylecloud(
text=' '.join(schools),
font_path=r'/home/mw/input/202201106373/STXINWEI.TTF',
palette='cartocolors.qualitative.Bold_5',
max_font_size=100,
icon_name='fas fa-graduation-cap',
background_color='#212529',
output_name=pic_name,
)
Image.open(pic_name)

4.2 Professional name word cloud

major = df_2020[' Major name '].values.tolist()
pic_name = ' Major name .png'
stylecloud.gen_stylecloud(
text=' '.join(major),
font_path=r'/home/mw/input/202201106373/STXINWEI.TTF',
palette='cartocolors.qualitative.Bold_5',
max_font_size=100,
icon_name='fas fa-book-open',
background_color='#212529',
output_name=pic_name,
)
Image.open(pic_name)