import pandas as pd
catering_sale=(r'D:\sjfx\catering_sale.xls')
data=pd.read_excel(catering_sale,index_col='日期')
print(data.describe())
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
plt.figure()
p=data.boxplot(return_type='dict')
x=p['fliers'][0].get_xdata()
y=p['fliers'][0].get_ydata()
y.sort()
for i in range(len(x)):
if i>0:
plt.annotate(y[i],xy=(x[i],y[i]),xytext=(x[i]+0.05-0.8/(y[i]-y[i-1]),y[i]))
else:
plt.annotate(y[i],xy=(x[i],y[i]),xytext=(x[i]+0.08,y[i]))
plt.title("箱型图3146")
plt.show()
# 代码3-3 捞起生鱼片的季度销售情况
import pandas as pd
import numpy as np
catering_sale = (r'D:\sjfx\catering_fish_congee.xls') # 餐饮数据
data = pd.read_excel(catering_sale,names=['date','sale']) # 读取数据，指定“日期”列为索引
bins = [0,500,1000,1500,2000,2500,3000,3500,4000]
labels = ['[0,500)','[500,1000)','[1000,1500)','[1500,2000)',
'[2000,2500)','[2500,3000)','[3000,3500)','[3500,4000)']
data['sale分层'] = pd.cut(data.sale, bins, labels=labels)
aggResult = data.groupby('sale分层').agg({'sale':'count'})
pAggResult = round(aggResult/aggResult.sum(), 2, ) * 100
import matplotlib.pyplot as plt
plt.figure(figsize=(10,6)) # 设置图框大小尺寸
pAggResult['sale'].plot(kind='bar',width=0.8,fontsize=10) # 绘制频率直方图
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.title('季度销售额频率分布直方图3146',fontsize=20)
catering_dish_profit= (r'D:\sjfx\catering_dish_profit.xls')
data=pd.read_excel(catering_dish_profit)
x=data['盈利']
labels=data['菜品名']
plt.figure(figsize=(8,6))
plt.pie(x,labels=labels)
plt.rcParams['font.sans-serif']='SimHei'
plt.axis('equal')
plt.title('菜品销售量饼图3146',fontsize=20)
plt.show()
x=data['菜品名']
y=data['盈利']
plt.figure(figsize=(8,4))
plt.bar(x,y)
plt.rcParams['font.sans-serif']='SimHei'
plt.xlabel('菜品')
plt.ylabel('销量')
plt.title('菜品销售量分布（条形图）3146')
plt.show()
dish_sale= (r'D:\sjfx\dish_sale.xls')
data=pd.read_excel(dish_sale)
plt.figure(figsize=(8,4))
plt.plot(data['月份'],data['A部门'],color='green',label='A部门',marker='o')
plt.plot(data['月份'],data['B部门'],color='red',label='B部门',marker='s')
plt.plot(data['月份'],data['C部门'],color='skyblue',label='C部门',marker='x')
plt.legend()
plt.ylabel('销售额（万元）')
plt.title('部门销售额比较3146',fontsize=20)
plt.show()
dish_sale_b= (r'D:\sjfx\dish_sale_b.xls')
data=pd.read_excel(dish_sale_b)
plt.figure(figsize=(8,4))
plt.plot(data['月份'],data['2012年'],color='green',label='2012年',marker='o')
plt.plot(data['月份'],data['2013年'],color='red',label='2013年',marker='s')
plt.plot(data['月份'],data['2014年'],color='skyblue',label='2014年',marker='x')
plt.legend()
plt.ylabel('销售额（万元）')
plt.title('各年份销售额比较3146',fontsize=20)
plt.show()
import numpy as np
x=np.linspace(0,2*np.pi,25,endpoint=True)
s=np.sin(x)
plt.figure()
plt.plot(x,s,'b-*')
plt.xlabel("x")
plt.ylabel("y")
plt.title("y=sin（x）3146")
plt.legend("sin(x)")
plt.show()
import matplotlib.pyplot as plt
years = [2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
turnovers = [0.5, 9.36, 52, 191, 350, 571, 912, 1027, 1682, 2135, 2684]
plt.figure()
plt.scatter(years, turnovers, c='orange', s=50, label='legend')
plt.xticks(range(2008, 2020, 3))
plt.yticks(range(0, 3200, 800))
plt.xlabel("Year", fontdict={'size': 16})
plt.ylabel("number", fontdict={'size': 16})
plt.title("Title3146", fontdict={'size': 20})
plt.legend(loc='best')
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
df_normal=pd.read_csv(r'D:\sjfx\user.csv')
plt.figure(figsize=(8,4))
plt.plot(df_normal["Date"],df_normal["Eletricity"])
plt.xlabel("日期")
x_major_locator=plt.MultipleLocator(7)
ax=plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
plt.ylabel("每日电量")
plt.title("3146，正常用户电量趋势")
plt.rcParams['font.sans-serif']=['SimHei']
plt.show()
df_steal=pd.read_csv(r'D:\sjfx\Steal user.csv')
plt.figure(figsize=(10,9))
plt.plot(df_steal["Date"],df_steal["Eletricity"])
plt.xlabel("日期")
plt.ylabel("日期")
x_major_locator=plt.MultipleLocator(7)
ax=plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
plt.title("3146，窃电用户电量趋势")
plt.rcParams['font.sans-serif']=['SimHei']
plt.show()
import pandas as pd
dish_profit=(r'D:\sjfx\catering_dish_profit.xls')
data=pd.read_excel(dish_profit,index_col='菜品名')
data=data['盈利'].copy()
data.sort_values(ascending=False)
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
plt.figure()
data.plot(kind='bar')
plt.ylabel('盈利（元）')
p=1.0*data.cumsum()/data.sum()
p.plot(color='pink',secondary_y=True,style='-o',linewidth=2)
plt.annotate(format(p[6],'.4%'),xy=(6,p[6]),xytext=(6*0.9,p[6]*0.9),
arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('盈利（比例）')
plt.title('学号3146')
plt.show()
#餐饮销量数据相关性分析
import pandas as pd
catering_sale=(r'D:\sjfx\catering_sale_all.xls')
data=pd.read_excel(catering_sale,index_col='日期')
print(data.corr())
print(data.corr()['百合酱蒸凤爪'])
print(data['百合酱蒸凤爪'].corr(data['翡翠蒸香茜饺']))
import matplotlib.pyplot as plt
labels='Frogs','Hogs','Dogs','Logs'
sizes=[15,30,45,10]
colors=['yellowgreen','gold','lightskyblue','lightcoral']
explode=(0,0.1,0,0)
plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%',
shadow=True,startangle=90)
plt.axis('equal')
plt.title('3146')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
x=np.random.randn(1000)
plt.hist(x,10)
plt.title('3146')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
x=np.random.randn(1000)
D=pd.DataFrame([x,x+1]).T
D.plot(kind='box')
plt.title('3146')
plt.show()
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
import numpy as np
import pandas as pd
x=pd.Series(np.exp(np.arange(20)))
plt.figure(figsize=(8,9))
ax1=plt.subplot(2,1,1)
x.plot(label='原始数据图',legend=True)
ax1=plt.subplot(2,1,2)
x.plot(logy=True,label='对数数据图',legend=True)
plt.title('3146')
plt.show()
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
import numpy as np
import pandas as pd
error=np.random.randn(10)
y=pd.Series(np.sin(np.arange(10)))
y.plot(yerr=error)
plt.title('3146')
plt.show()
标签：数据分析,plot,plt,第三章,title,python,show,sale,data From： https://www.cnblogs.com/shizihao/p/17157940.html