import matplotlib.pyplot as plt# 数据month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']sales_2019 = [20, 30, 25, 35, 40, 45, 50, 55, 60, 65, 70, 75]sales_2020 = [25, 35, 30, 40, 45, 50, 55, 60, 65, 70, 75, 80]# 绘制柱状图bar_width = 0.4plt.bar(month, sales_2019, bar_width, color='blue', label='2019年销售数据')plt.bar([m + bar_width for m in month], sales_2020, bar_width, color='orange', label='2020年销售数据')# 添加标题和坐标轴标签plt.title('销售数据比较')plt.xlabel('月份')plt.ylabel('销售额')# 添加图例plt.legend()# 将x轴标签旋转45度plt.xticks(rotation=45)# 添加网格线plt.grid(True)# 显示图像plt.show()
时间: 2024-01-01 10:06:18 浏览: 109
这段代码是一个绘制柱状图的完整代码,它使用了Matplotlib库来绘制一个比较两年销售数据的柱状图。其中,2019年的销售数据用蓝色表示,2020年的销售数据用橙色表示,两年的数据分别位于x轴的左右两侧,以方便比较。图像中还包括了标题、坐标轴标签、图例、旋转后的x轴标签和网格线等元素,以优化可视化效果。
您可以将这段代码复制到Python环境中运行,即可看到绘制的柱状图。
相关问题
保留原本功能实现的情况下优化以下代码import random import pandas as pd import matplotlib.pyplot as plt fn = 'data.csv' products = ['商品1','商品2','商品3','商品4','商品5','商品6','商品7','商品8','商品9','商品10'] datelist = [] for month in range(1,13) : for day in range(1,32) : date = f'2019-{month:20d}-{day:02d}' datelist.append(date) datalist =[] for date in datelist : for it in products : sales = round(random.uniform(100,1000),2) datalist.append([date,it,sales]) df = pd.DataFrame(datalist, columns=['日期','商品名称','营业额']) df.to_csv('data.csv', index=False) df = pd.read_csv('data.csv') for product in df['products'].unique() : data = df.loc[df['products'] == product] plt.plot(data['date'],data['sales'],label=product) plt.xlabe1('Date') plt.ylabe1('sales') plt.title('Sales by Product') plt.legend() plt.show() df['month'] = pd.DatetimeIndex(df['date']).month groupeddata = df.groupby(['products','month'])['sales'].sum().unstack() groupeddata.plot(kind='bar') plt.xlabel('Products') plt.ylabel('Sales') plt.title('Sales by Month') plt.legend(title='Month',labels=['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEV']) plt.show() df['quarter'] = pd.PeriodIndex(df['date'],freq='Q') groupeddata = df.groupby(['products','quarter'])['sales'].sum().unstack() groupeddata.plot(kind='pie',subplots=True) plt.title('Sales by Quarter') plt.legend(loc='center left',bbox_to_anchor=(1.0,0.5)) plt.show()
可以通过以下方式来优化代码:
1. 将生成数据的部分代码封装为函数,方便调用和复用;
2. 将不同商品的销售数据绘制成柱状图和饼图的代码封装为函数,减少重复代码。
代码如下所示:
```python
import random
import pandas as pd
import matplotlib.pyplot as plt
def generate_data():
fn = 'data.csv'
products = ['商品1','商品2','商品3','商品4','商品5','商品6','商品7','商品8','商品9','商品10']
datelist = []
for month in range(1,13):
for day in range(1,32):
date = f'2019-{month:20d}-{day:02d}'
datelist.append(date)
datalist =[]
for date in datelist:
for it in products:
sales = round(random.uniform(100,1000),2)
datalist.append([date,it,sales])
df = pd.DataFrame(datalist, columns=['日期','商品名称','营业额'])
df.to_csv('data.csv', index=False)
return pd.read_csv('data.csv')
def plot_sales_by_product(df):
for product in df['商品名称'].unique():
data = df.loc[df['商品名称'] == product]
plt.plot(data['日期'], data['营业额'], label=product)
plt.xlabel('Date')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.legend()
plt.show()
def plot_sales_by_month(df):
df['month'] = pd.DatetimeIndex(df['日期']).month
groupeddata = df.groupby(['商品名称','month'])['营业额'].sum().unstack()
groupeddata.plot(kind='bar')
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Month')
plt.legend(title='Month',labels=['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEV'])
plt.show()
def plot_sales_by_quarter(df):
df['quarter'] = pd.PeriodIndex(df['日期'],freq='Q')
groupeddata = df.groupby(['商品名称','quarter'])['营业额'].sum().unstack()
groupeddata.plot(kind='pie',subplots=True)
plt.title('Sales by Quarter')
plt.legend(loc='center left',bbox_to_anchor=(1.0,0.5))
plt.show()
df = generate_data()
plot_sales_by_product(df)
plot_sales_by_month(df)
plot_sales_by_quarter(df)
```
这样代码更加清晰,易于维护和修改。
修改下列代码的错误import random import pandas as pd import matplotlib.pyplot as plt def generate_data() : products = ['商品1','商品2','商品3','商品4','商品5','商品6','商品7','商品8','商品9','商品10'] datelist = [] for month in range(1,13) : for day in range(1,29) : date = f'2019-{month:20d}-{day:02d}' datelist.append(date) datalist = [] for date in datelist : for it in products : sales = round(random.uniform(150,200),2) datalist.append([date,it,sales]) df = pd.DataFrame(datalist,columns=['date','products','sales']) df.to_csv('data.csv', index=False) return pd.read_csv('data.csv') def plot_sales_by_product(df) : for product in df['products'].unique() : data = df.loc[df['products'] == product] plt.plot(data['date'],data['sales'],label=product) plt.xlabel('Date') plt.ylabel('Sales') plt.title('Sales by Product') plt.legend() plt.show() def plot_sales_by_month(df) : df['month'] = pd.DatetimeIndex(df['date']).month groupeddata = df.groupby(['products','month'])['sales'].sum().unstack() groupeddata.plot(kind='bar') plt.xlabel('Products') plt.ylabel('sales') plt.title('Sales by Month') plt.legend(title='Morth',labels=['JAN','FEB','MAR','APR','NAV','JoW','JUL','AUG','SEP','OCT','NOV','DEV']) plt.show() def plot_sales_by_quarter(df) : df['quarter'] = pd.PeriodIndex(df['date'],freq='Q') groupeddata = df.groupby(['products','quarter'])['sales'].sum().unstack() groupeddata.plot(kind='pie',subplots=True) plt.title('Sales by Quarter') plt.legend(loc='center Left',bbox_to_anchor=(1.0,0.5)) plt.show() df = generate_data() plot_sales_by_product(df) plot_sales_by_month(df) plot_sales_by_quarter(df)
代码中的错误在于函数 `plot_sales_by_quarter` 中的 `groupeddata.plot(kind='pie',subplots=True)`,应该将 `kind` 参数的值改为 `'bar'`,才能按季度绘制柱状图。
修改后的代码如下:
```
import random
import pandas as pd
import matplotlib.pyplot as plt
def generate_data():
products = ['商品1', '商品2', '商品3', '商品4', '商品5', '商品6', '商品7', '商品8', '商品9', '商品10']
datelist = []
for month in range(1, 13):
for day in range(1, 29):
date = f'2019-{month:02d}-{day:02d}'
datelist.append(date)
datalist = []
for date in datelist:
for it in products:
sales = round(random.uniform(150, 200), 2)
datalist.append([date, it, sales])
df = pd.DataFrame(datalist, columns=['date', 'products', 'sales'])
df.to_csv('data.csv', index=False)
return pd.read_csv('data.csv')
def plot_sales_by_product(df):
for product in df['products'].unique():
data = df.loc[df['products'] == product]
plt.plot(data['date'], data['sales'], label=product)
plt.xlabel('Date')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.legend()
plt.show()
def plot_sales_by_month(df):
df['month'] = pd.DatetimeIndex(df['date']).month
groupeddata = df.groupby(['products', 'month'])['sales'].sum().unstack()
groupeddata.plot(kind='bar')
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Month')
plt.legend(title='Month', labels=['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC'])
plt.show()
def plot_sales_by_quarter(df):
df['quarter'] = pd.PeriodIndex(df['date'], freq='Q')
groupeddata = df.groupby(['products', 'quarter'])['sales'].sum().unstack()
groupeddata.plot(kind='bar')
plt.title('Sales by Quarter')
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
plt.show()
df = generate_data()
plot_sales_by_product(df)
plot_sales_by_month(df)
plot_sales_by_quarter(df)
```