您可以手动创建子图：

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
for name, df in csv.groupby('shop'):
    df.plot(x='timestamp', y='sales', label=name, ax=ax)
ax.set_title('Sales')
plt.show()

[海运替代品（native Pandas.Dataframe.plot answer）]
这是作为备用“* 答案 *”发布的;为了清楚起见而不是将它们混为一谈。
Seaborn根据时间戳（格式为天）绘制每个商店的销售额（由色调指定）。

## import seaborn
import seaborn as sns
## data formater
import matplotlib.dates as mdates

## plot timestamp on horizontal (formated to days), sales on vertical
## with hue set to shop, seaborn plots sales per shop
ax = sns.lineplot(data=df_csv, x='timestamp', y='sales', hue='shop')

## set datetime to days. Ensure this is set AFTER setting ax
ax.xaxis.set_major_locator(locator=mdates.DayLocator())

使用ax关键字绘图。df_csv.groupby('shop').plot(x='timestamp', ax=plt.gca())
工作代码如下。

## load libraries
import pandas as pd
import matplotlib.pyplot as plt

## load dataset
df_csv = pd.read_csv('datasets/SO_shop_timestamp_sale.csv')

## check dataset
df_csv.head(3)
df_csv.describe()
df_csv.shape

## ensure data type
df_csv.timestamp = pd.to_datetime(df_csv.timestamp)
df_csv.sales = pd.to_numeric(df_csv.sales)

## Pandas plot of sales against timestamp grouped by shop, using `ax` keyword to subplot.
df_csv.groupby('shop').plot(x='timestamp', ax=plt.gca())

## Pandas plot of timestamp and sales grouped by shop, use `ax` keyword to plot on combined axes.
df_csv.groupby('shop').plot(x='timestamp', kind='kde', ax=plt.gca())