当你使用pandas时，你可以通过创建一个pivot table，同时使用grouper来聚合每小时的值：

import pandas as pd

data = [['9:32', 'System'], ['9:32', 'Class'], ['9:32', 'System'], ['9:32', 'System'], ['9:32', 'System'], ['9:32', 'Class'], ['9:32', 'System'], ['9:32', 'Class'], ['10:32', 'System'], ['10:32', 'System'], ['10:32', 'Class'], ['11:22', 'Class'], ['11:22', 'System'], ['11:22', 'System'], ['11:32', 'System'], ['11:32', 'Class'], ['11:32', 'Class'], ['12:32', 'System'], ['12:32', 'Class']]

df = pd.DataFrame(data, columns=['Time','Component'])
df['Time'] = pd.to_datetime(df['Time']) # convert Time to datetime object 
df.pivot_table(index=pd.Grouper(key = 'Time', freq = 'H'), columns='Component', aggfunc=len, fill_value=0).plot(kind='bar')

字符串
结果：

的数据
如果你想在图中绘制图表：

import plotly.graph_objects as go

df2 = df.pivot_table(index=pd.Grouper(key = 'Time', freq = 'H'), columns='Component', aggfunc=len, fill_value=0).plot(kind='bar')

fig = go.Figure(data=[
    go.Bar(name='Class', x=df2.index, y = df2.Class),
    go.Bar(name='System', x=df2.index, y = df2.System)
])

fig.update_layout(barmode='group')
fig.show()

型
结果：

在使用pd.pivot_table处理数据结构之后，我会使用px.bar。您提供的数据集对您的挑战没有多大意义，因为您需要更多的唯一时间戳来显示您想要的内容，所以我在您的源代码中添加了一些数据点。

一些核心步骤（完整代码在最后）：

# data munging using pandas
dfp = pd.pivot_table(df,index=pd.Grouper(key='Time', freq='H'),
                     columns='Component',
                     aggfunc=len,
                     fill_value=0)

# plotly express figure
fig = px.bar(dfp, x=dfp.index, y=['Class', 'System'])
fig.update_layout(barmode='group')

字符串

Plot：

的数据

完整代码：

# imports
import plotly.express as px
import pandas as pd

# data
df = pd.DataFrame({'Time': {0: '9:32',
                          1: '9:32',
                          2: '9:32',
                          3: '9:32',
                          4: '9:32',
                          5: '9:32',
                          6: '9:32',
                          7: '9:32',
                          8: '13:32',
                          9: '13:32',
                          10: '13:32',
                          11: '17:22',
                          12: '17:22',
                          13: '17:22',
                          14: '17:32',
                          15: '19:32',
                          16: '19:32',
                          17: '19:32',
                          18: '19:32'},
                         'Component': {0: 'System',
                          1: 'Class',
                          2: 'System',
                          3: 'System',
                          4: 'System',
                          5: 'Class',
                          6: 'System',
                          7: 'Class',
                          8: 'System',
                          9: 'System',
                          10: 'Class',
                          11: 'Class',
                          12: 'System',
                          13: 'System',
                          14: 'System',
                          15: 'Class',
                          16: 'Class',
                          17: 'System',
                          18: 'Class'}})

# data munging us pd.pivot_table
df['Time'] = pd.to_datetime(df['Time'])
dfp = pd.pivot_table(df, index=pd.Grouper(key='Time', freq='H'), columns='Component', aggfunc=len, fill_value=0)

# plotly
fig = px.bar(dfp, x=dfp.index, y=['Class', 'System'])
fig.update_layout(barmode='group')
fig.show()

型

感谢所有的建议，我从你们这里挑选了几行代码，类似于下面的代码，以实现我正在寻找的东西。下面是使用Plotly。

import plotly.express as px
df=series
#df.set_index('Time', inplace=True)
Component_count = df['Component'].resample('s').count()
Time_Component_count = pd.DataFrame({'Time': Component_count.index, 'Component Count': Component_count.values})

fig1 = px.histogram(Time_Component_count, x='Time', y='Component Count', histfunc='sum', title='Histogram Chart')
fig1.show(renderer="iframe_connected")

字符串

的数据

import matplotlib.pyplot as plt

df.set_index('Time', inplace=True)
Component_count = df['Component'].resample('H').count()
Time_Component_count = pd.DataFrame({'Time': Component_count.index, 'Component Count': Complonent_count.values})

plt.hist(x = Time_Component_count['Time'], y = Time_Component_count['Component Count'])
plt.show()

字符串