标签:file plt pd filter 可视化 student import 数据 pandas
普通柱状图
''' 普通柱状图 ''' import pandas as pd import matplotlib.pyplot as plt file = '/tmp/Students2.xlsx' student = pd.read_excel(file) student_filter = student.sort_values(by='Number',ascending=False) print(student_filter) plt.bar(student_filter.Field,student_filter.Number,color='orange') plt.xticks(student_filter.Field,rotation='90') plt.xlabel('Field') plt.ylabel('Number') plt.title('International student by field',fontsize='16') plt.tight_layout() plt.show() ''' 原生方法 ''' # student_filter.plot.bar(x='Field',y='Number',color='orange',title='International student by field') # plt.show()
分组柱状图
''' 分组柱状图 ''' import pandas as pd import matplotlib.pyplot as plt file = '/tmp/Students3.xlsx' student = pd.read_excel(file) student_filter = student.sort_values(by='2017',ascending=False) print(student_filter) # plt.bar(student_filter.Field,[2017,2016],color=['orange','red']) # plt.show() student_filter.plot.bar('Field',['2016','2017'],color=['orange','red']) plt.title('International Students by Field',fontsize=16) plt.xlabel('Field',fontweight='bold') plt.ylabel('Number',fontweight='bold') ax = plt.gca() ax.set_xticklabels(student_filter['Field'],rotation=40,ha='right') plt.gcf().subplots_adjust(left=0.2,bottom=0.42) plt.show()
叠加柱状图-横向叠加柱状图
''' 叠加柱状图 横向叠加柱状图 ''' import pandas as pd import matplotlib.pyplot as plt file = '/tmp/Users.xlsx' users = pd.read_excel(file) users['Total'] = users['Oct'] + users['Nov'] + users['Dec'] users.sort_values(by='Total',inplace=True,ascending=False) print(users) users.plot.bar(x='Name',y=['Oct','Nov','Dec'],stacked=True) # 水平方向叠加 # users.plot.barh(x='Name',y=['Oct','Nov','Dec'],stacked=True) plt.tight_layout() plt.show()
饼状图
''' 饼状图 ''' import pandas as pd import matplotlib.pyplot as plt file = '/tmp/Students.xlsx' # 要显示的列为主键列 students = pd.read_excel(file,index_col='From') print(students) # 按照2017列排序 students['2017'].plot.pie(fontsize=8,counterclock=False,startangle=-270) plt.title('Source of International Students',fontsize=16,fontweight='bold') plt.ylabel('2017',fontsize=12,fontweight='bold') plt.show()
曲线图-叠加曲线图
''' 曲线图 叠加曲线图 ''' import pandas as pd import matplotlib.pyplot as plt file = '/tmp/Orders.xlsx' weeks = pd.read_excel(file,index_col='Week') print(weeks) # 曲线图 # weeks.plot(y=['Accessories', 'Bikes', 'Clothing', 'Components']) weeks.plot.area(y=['Accessories', 'Bikes', 'Clothing', 'Components']) plt.title('Sales Trends',fontsize=16,fontweight='bold') plt.xticks(weeks.index,fontsize=8) plt.show()
密度图-离散图-直方图
''' 密度图 离散图 直方图 ''' import pandas as pd import matplotlib.pyplot as plt pd.options.display.max_columns = 999 file = '/tmp/home_data.xlsx' homes = pd.read_excel(file) print(homes.head()) # 密度图 # homes.plot.scatter(x='sqft_living',y='price') # 离散图 # homes.sqft_living.plot.kde() # 直方图 homes.price.plot.hist(bins=200) plt.xticks(range(0,max(homes.price),100000),fontsize=8,rotation=90) # homes.sqft_living.plot.hist(bins=100) # plt.xticks(range(0,max(homes.sqft_living),500),fontsize=8,rotation=90) plt.show() # 神奇的相关性 # print(homes.corr())
标签:file,plt,pd,filter,可视化,student,import,数据,pandas 来源: https://www.cnblogs.com/soymilk2019/p/13862948.html
本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享; 2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关; 3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关; 4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除; 5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。