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The following is a visual analysis of data such as students' sentence formation and performance

1: import module

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['simhei']
plt.rcParams['font.serif'] = ['simhei']
 
import warnings
warnings.filterwarnings('ignore')

2: Get the data and print the first four lines

from matplotlib.font_manager import FontProperties
myfont=FontProperties(fname=r'C:\Windows\Fonts\SimHei.ttf',size=12)
sns.set(font=myfont.get_name())
df = pd.read_csv('.\data\StudentPerformance.csv')
df.head(4)

 The corresponding meaning of the attribute list is as follows

Gender

Nationality

Place of Birth

Stageid school level

Gradeid

Sectionid class

Topic subjects

semester semester

ralation child family education director

raisedhands The number of times students raised their hands in class

announcementviews The number of times students viewed online courseware

discussion The number of times students participated in classroom discussions

parentanswersurvey Did the parents fill out the school's questionnaire

parentsschoolsatisfaction Parents' satisfaction with the school

studentabsencedays student absence days

3: Data visualization analysis

Next, modify the table column name to Chinese

df.rename(columns={'gender':'性别','NationalITy':'国籍','PlaceofBirth':'出生地',
                   'StageID':'学段','GradeID':'年级','SectionID':'班级','Topic':'科目',
                  'Semester':'学期','Relation':'监管人','raisedhands':'举手次数',
                  'VisITedResources':'浏览课件次数','AnnouncementsView':'浏览公告次数',
                  'Discussion':'讨论次数','ParentAnsweringSurvey':'父母问卷',
                  'ParentschoolSatisfaction':'家长满意度','StudentAbsenceDays':'缺勤次数',
                   'Class':'成绩'},inplace=True)
df.replace({'lowerlevel':'小学','MiddleSchool':'中学','HighSchool':'高中'},inplace=True)
df.columns

 Display the values ​​of term and period

Then modify the data

df.replace({'lowerlevel':'小学','MiddleSchool':'中学','HighSchool':'高中'},inplace=True)
df['性别'].replace({'M':'男','F':'女'},inplace=True)
df['学期'].replace({'S':'春季','F':'秋季'},inplace=True)
df.head(4)

 View vacancies

df.isnull().sum()

View data statistics

 Then draw a histogram of counts by grade

sns.countplot(x = '成绩', order = ['L', 'M', 'H'], data = df, linewidth=2,edgecolor=sns.color_palette("dark",4))

 Then draw a histogram of counts by gender

sns.countplot(x = '性别', order = ['女', '男'],data = df)

 Draw a histogram of counts by subject

sns.set_style('whitegrid')
sns.set(rc={'figure.figsize':(16,8)},font=myfont.get_name(),font_scale=1.5)
sns.countplot(x = '科目', data = df)

 Draw a histogram of counts of different grades by subject

Plot a histogram of counts by gender and grade

sns.countplot(x = '性别', hue = '成绩',data = df, order = ['女', '男'], hue_order = ['L', 'M', 'H'])

View grade distribution ratio by class

sns.countplot(x = '班级', hue='成绩', data=df, hue_order = ['L','M','H'])
# 从这里可以看出虽然每个班人数较少，但是没有那个班优秀的人数的比例比较突出，这个特征可以删除

 Analyze the correlation between 4 performance and grades

# 了解四个课堂和课后表现与成绩的相关性
fig, axes = plt.subplots(2,2,figsize=(14,10))
sns.barplot(x='成绩', y='浏览课件次数',data=df,order=['L','M','H'],ax=axes[0,0])
sns.barplot(x='成绩', y='浏览公告次数',data=df,order=['L','M','H'],ax=axes[0,1])
sns.barplot(x='成绩', y='举手次数',data=df,order=['L','M','H'],ax=axes[1,0])
sns.barplot(x='成绩', y='讨论次数',data=df,order=['L','M','H'],ax=axes[1,1])
# 在sns.barplot中，默认的计算方式为计算平均值

 Analyze the discussion of students with different grades

# 了解举手次数与成绩之间的相关性
sns.set(rc={'figure.figsize':(8,6)},font=myfont.get_name(),font_scale=1.5)
sns.boxplot(x='成绩',y='讨论次数',data=df,order=['L','M','H'])

 Analyze the correlation between the number of hands raised and the number of discussions participated

# 了解四个课堂后量化表现之间的相关性
# fig,axes = plt.subplots(2,1,figsize=(10,10))
sns.regplot(x='举手次数',y='讨论次数',order =4,data=df)
# sns.regplot(x='浏览公告次数',y='浏览课件次数',order=4,data=df,ax=axes[1])   ,ax=axes[0]

 Analyze the correlation between the number of times of browsing courseware, the number of times of raising hands, the number of times of browsing announcements, and the number of discussions

# Correlation Matrix 相关性矩阵
corr = df[['浏览课件次数','举手次数','浏览公告次数','讨论次数']].corr()
corr         

 Finally, visualize the correlation matrix with a heat map

# Correlation Matrix Visualization 相关性可视化
sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns)

 It's not easy to create and find it helpful, please like, follow and collect~~~