如何使用 Python Pandas 绘制堆叠事件持续时间（甘特图）
How to plot stacked event duration (Gantt Charts) using Python Pandas
我有一个 Pandas DataFrame，其中包含流量计开始测量流量的日期和站点退役的日期。我想生成一个以图形方式显示这些日期的图。这是我的 DataFrame 的示例：
import pandas as pd
data = {'index': [40623, 40637, 40666, 40697, 40728, 40735, 40742, 40773, 40796, 40819, 40823, 40845, 40867, 40887, 40945, 40964, 40990, 41040, 41091, 41100], 'StationId': ['UTAHDWQ-5932100', 'UTAHDWQ-5932230', 'UTAHDWQ-5932240', 'UTAHDWQ-5932250', 'UTAHDWQ-5932253', 'UTAHDWQ-5932254', 'UTAHDWQ-5932280', 'UTAHDWQ-5932290', 'UTAHDWQ-5932750', 'UTAHDWQ-5983753', 'UTAHDWQ-5983754', 'UTAHDWQ-5983755', 'UTAHDWQ-5983756', 'UTAHDWQ-5983757', 'UTAHDWQ-5983759', 'UTAHDWQ-5983760', 'UTAHDWQ-5983775', 'UTAHDWQ-5989066', 'UTAHDWQ-5996780', 'UTAHDWQ-5996800'], 'amin': ['1994-07-19 13:15:00', '2006-03-16 13:55:00', '1980-10-31 16:00:00', '1981-06-11 17:45:00', '2006-06-28 13:15:00', '2006-06-28 13:55:00', '1981-06-11 15:30:00', '1992-06-10 15:45:00', '2005-10-03 16:30:00', '2006-04-25 09:56:00', '2006-04-25 11:05:00', '2006-04-25 13:50:00', '2006-04-25 14:20:00', '2006-04-25 12:45:00', '2008-04-08 13:03:00', '2008-04-08 13:15:00', '2008-04-15 12:47:00', '2005-10-04 10:15:00', '1995-03-09 13:59:00', '1995-03-09 15:13:00'], 'amax': ['1998-06-30 14:51:00', '2007-01-24 12:55:00', '2007-07-31 11:35:00', '1990-08-01 08:30:00', '2007-01-24 13:35:00', '2007-01-24 14:05:00', '2006-08-22 16:00:00', '1998-06-30 11:33:00', '2005-10-22 15:00:00', '2006-04-25 10:00:00', '2008-04-08 12:16:00', '2008-04-08 09:10:00', '2008-04-08 09:30:00', '2008-04-08 11:27:00', '2008-04-08 13:05:00', '2008-04-08 13:23:00', '2009-04-07 13:15:00', '2005-10-05 11:40:00', '1996-03-14 10:40:00', '1996-03-14 11:05:00']}
df = pd.DataFrame(data)
df.set_index('index', inplace=True)
# display(df.head())
StationId amin amax
index
40623 UTAHDWQ-5932100 1994-07-19 13:15:00 1998-06-30 14:51:00
40637 UTAHDWQ-5932230 2006-03-16 13:55:00 2007-01-24 12:55:00
40666 UTAHDWQ-5932240 1980-10-31 16:00:00 2007-07-31 11:35:00
40697 UTAHDWQ-5932250 1981-06-11 17:45:00 1990-08-01 08:30:00
40728 UTAHDWQ-5932253 2006-06-28 13:15:00 2007-01-24 13:35:00
我想创建一个与此类似的图（请注意，我没有使用上述数据制作此图）：
绘图不必沿每行显示文本，只需在 y 轴上显示站点名称。
虽然这看起来像是 pandas 的小众应用，但我知道有几位科学家会从这种绘图功能中受益。
我能找到的最接近的答案在这里：
- How to plot stacked proportional graph?
- How to plot two columns of a pandas data frame using points?
- Matplotlib timelines
- Create Gantt Plot with python matplotlib
最后一个答案最符合我的需要。
虽然我更喜欢通过 Pandas 包装器来完成它，但我会开放并感谢直接的 matplotlib 解决方案。
虽然我不知道在 MatplotLib 中有什么方法可以做到这一点，但您可能想看看使用 D3 以您想要的方式可视化数据的选项，例如，使用这个库：
https://github.com/jiahuang/d3-timeline
如果您必须使用 Matplotlib 来完成，这里是一种完成方式：
Matplotlib timelines
- 我认为您正在尝试创建甘特图。
- This suggests using
hlines
- 在
matplotlib 3.4.2 中测试
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dt
# using df from the OP
# convert columns to a datetime dtype
df.amin = pd.to_datetime(df.amin)
df.amax = pd.to_datetime(df.amax)
fig, ax = plt.subplots(figsize=(8, 5))
ax = ax.xaxis_date()
ax = plt.hlines(df.index, dt.date2num(df.amin), dt.date2num(df.amax))
- 以下代码也有效
# using df from the OP
df.amin = pd.to_datetime(df.amin)
df.amax = pd.to_datetime(df.amax)
fig, ax = plt.subplots(figsize=(8, 5))
ax = plt.hlines(df.index, df.amin, df.amax)
也可以用水平条来做到这一点：broken_barh(xranges, yrange, **kwargs)
您可以使用 Bokeh（一个 python 库）制作甘特图，它非常漂亮。
这是我从推特上复制的代码。
http://nbviewer.jupyter.org/gist/quebbs/10416d9fb954020688f2
from bokeh.plotting import figure, show, output_notebook, output_file
from bokeh.models import ColumnDataSource, Range1d
from bokeh.models.tools import HoverTool
from datetime import datetime
from bokeh.charts import Bar
output_notebook()
#output_file('GanntChart.html') #use this to create a standalone html file to send to others
import pandas as ps
DF=ps.DataFrame(columns=['Item','Start','End','Color'])
Items=[
['Contract Review & Award','2015-7-22','2015-8-7','red'],
['Submit SOW','2015-8-10','2015-8-14','gray'],
['Initial Field Study','2015-8-17','2015-8-21','gray'],
['Topographic Procesing','2015-9-1','2016-6-1','gray'],
['Init. Hydrodynamic Modeling','2016-1-2','2016-3-15','gray'],
['Prepare Suitability Curves','2016-2-1','2016-3-1','gray'],
['Improvement Conceptual Designs','2016-5-1','2016-6-1','gray'],
['Retrieve Water Level Data','2016-8-15','2016-9-15','gray'],
['Finalize Hydrodynamic Models','2016-9-15','2016-10-15','gray'],
['Determine Passability','2016-9-15','2016-10-1','gray'],
['Finalize Improvement Concepts','2016-10-1','2016-10-31','gray'],
['Stakeholder Meeting','2016-10-20','2016-10-21','blue'],
['Completion of Project','2016-11-1','2016-11-30','red']
] #first items on bottom
for i,Dat in enumerate(Items[::-1]):
DF.loc[i]=Dat
#convert strings to datetime fields:
DF['Start_dt']=ps.to_datetime(DF.Start)
DF['End_dt']=ps.to_datetime(DF.End)
G=figure(title='Project Schedule',x_axis_type='datetime',width=800,height=400,y_range=DF.Item.tolist(),
x_range=Range1d(DF.Start_dt.min(),DF.End_dt.max()), tools='save')
hover=HoverTool(tooltips="Task: @Item<br>\
Start: @Start<br>\
End: @End")
G.add_tools(hover)
DF['ID']=DF.index+0.8
DF['ID1']=DF.index+1.2
CDS=ColumnDataSource(DF)
G.quad(left='Start_dt', right='End_dt', bottom='ID', top='ID1',source=CDS,color="Color")
#G.rect(,"Item",source=CDS)
show(G)
我有一个 Pandas DataFrame，其中包含流量计开始测量流量的日期和站点退役的日期。我想生成一个以图形方式显示这些日期的图。这是我的 DataFrame 的示例：
import pandas as pd
data = {'index': [40623, 40637, 40666, 40697, 40728, 40735, 40742, 40773, 40796, 40819, 40823, 40845, 40867, 40887, 40945, 40964, 40990, 41040, 41091, 41100], 'StationId': ['UTAHDWQ-5932100', 'UTAHDWQ-5932230', 'UTAHDWQ-5932240', 'UTAHDWQ-5932250', 'UTAHDWQ-5932253', 'UTAHDWQ-5932254', 'UTAHDWQ-5932280', 'UTAHDWQ-5932290', 'UTAHDWQ-5932750', 'UTAHDWQ-5983753', 'UTAHDWQ-5983754', 'UTAHDWQ-5983755', 'UTAHDWQ-5983756', 'UTAHDWQ-5983757', 'UTAHDWQ-5983759', 'UTAHDWQ-5983760', 'UTAHDWQ-5983775', 'UTAHDWQ-5989066', 'UTAHDWQ-5996780', 'UTAHDWQ-5996800'], 'amin': ['1994-07-19 13:15:00', '2006-03-16 13:55:00', '1980-10-31 16:00:00', '1981-06-11 17:45:00', '2006-06-28 13:15:00', '2006-06-28 13:55:00', '1981-06-11 15:30:00', '1992-06-10 15:45:00', '2005-10-03 16:30:00', '2006-04-25 09:56:00', '2006-04-25 11:05:00', '2006-04-25 13:50:00', '2006-04-25 14:20:00', '2006-04-25 12:45:00', '2008-04-08 13:03:00', '2008-04-08 13:15:00', '2008-04-15 12:47:00', '2005-10-04 10:15:00', '1995-03-09 13:59:00', '1995-03-09 15:13:00'], 'amax': ['1998-06-30 14:51:00', '2007-01-24 12:55:00', '2007-07-31 11:35:00', '1990-08-01 08:30:00', '2007-01-24 13:35:00', '2007-01-24 14:05:00', '2006-08-22 16:00:00', '1998-06-30 11:33:00', '2005-10-22 15:00:00', '2006-04-25 10:00:00', '2008-04-08 12:16:00', '2008-04-08 09:10:00', '2008-04-08 09:30:00', '2008-04-08 11:27:00', '2008-04-08 13:05:00', '2008-04-08 13:23:00', '2009-04-07 13:15:00', '2005-10-05 11:40:00', '1996-03-14 10:40:00', '1996-03-14 11:05:00']}
df = pd.DataFrame(data)
df.set_index('index', inplace=True)
# display(df.head())
StationId amin amax
index
40623 UTAHDWQ-5932100 1994-07-19 13:15:00 1998-06-30 14:51:00
40637 UTAHDWQ-5932230 2006-03-16 13:55:00 2007-01-24 12:55:00
40666 UTAHDWQ-5932240 1980-10-31 16:00:00 2007-07-31 11:35:00
40697 UTAHDWQ-5932250 1981-06-11 17:45:00 1990-08-01 08:30:00
40728 UTAHDWQ-5932253 2006-06-28 13:15:00 2007-01-24 13:35:00
我想创建一个与此类似的图（请注意，我没有使用上述数据制作此图）：
绘图不必沿每行显示文本，只需在 y 轴上显示站点名称。
虽然这看起来像是 pandas 的小众应用，但我知道有几位科学家会从这种绘图功能中受益。
我能找到的最接近的答案在这里：
- How to plot stacked proportional graph?
- How to plot two columns of a pandas data frame using points?
- Matplotlib timelines
- Create Gantt Plot with python matplotlib
最后一个答案最符合我的需要。
虽然我更喜欢通过 Pandas 包装器来完成它，但我会开放并感谢直接的 matplotlib 解决方案。
虽然我不知道在 MatplotLib 中有什么方法可以做到这一点，但您可能想看看使用 D3 以您想要的方式可视化数据的选项，例如，使用这个库：
https://github.com/jiahuang/d3-timeline
如果您必须使用 Matplotlib 来完成，这里是一种完成方式：
Matplotlib timelines
- 我认为您正在尝试创建甘特图。
- This suggests using
hlines - 在
matplotlib 3.4.2 中测试
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dt
# using df from the OP
# convert columns to a datetime dtype
df.amin = pd.to_datetime(df.amin)
df.amax = pd.to_datetime(df.amax)
fig, ax = plt.subplots(figsize=(8, 5))
ax = ax.xaxis_date()
ax = plt.hlines(df.index, dt.date2num(df.amin), dt.date2num(df.amax))
- 以下代码也有效
# using df from the OP
df.amin = pd.to_datetime(df.amin)
df.amax = pd.to_datetime(df.amax)
fig, ax = plt.subplots(figsize=(8, 5))
ax = plt.hlines(df.index, df.amin, df.amax)
也可以用水平条来做到这一点：broken_barh(xranges, yrange, **kwargs)
您可以使用 Bokeh（一个 python 库）制作甘特图，它非常漂亮。 这是我从推特上复制的代码。 http://nbviewer.jupyter.org/gist/quebbs/10416d9fb954020688f2
from bokeh.plotting import figure, show, output_notebook, output_file
from bokeh.models import ColumnDataSource, Range1d
from bokeh.models.tools import HoverTool
from datetime import datetime
from bokeh.charts import Bar
output_notebook()
#output_file('GanntChart.html') #use this to create a standalone html file to send to others
import pandas as ps
DF=ps.DataFrame(columns=['Item','Start','End','Color'])
Items=[
['Contract Review & Award','2015-7-22','2015-8-7','red'],
['Submit SOW','2015-8-10','2015-8-14','gray'],
['Initial Field Study','2015-8-17','2015-8-21','gray'],
['Topographic Procesing','2015-9-1','2016-6-1','gray'],
['Init. Hydrodynamic Modeling','2016-1-2','2016-3-15','gray'],
['Prepare Suitability Curves','2016-2-1','2016-3-1','gray'],
['Improvement Conceptual Designs','2016-5-1','2016-6-1','gray'],
['Retrieve Water Level Data','2016-8-15','2016-9-15','gray'],
['Finalize Hydrodynamic Models','2016-9-15','2016-10-15','gray'],
['Determine Passability','2016-9-15','2016-10-1','gray'],
['Finalize Improvement Concepts','2016-10-1','2016-10-31','gray'],
['Stakeholder Meeting','2016-10-20','2016-10-21','blue'],
['Completion of Project','2016-11-1','2016-11-30','red']
] #first items on bottom
for i,Dat in enumerate(Items[::-1]):
DF.loc[i]=Dat
#convert strings to datetime fields:
DF['Start_dt']=ps.to_datetime(DF.Start)
DF['End_dt']=ps.to_datetime(DF.End)
G=figure(title='Project Schedule',x_axis_type='datetime',width=800,height=400,y_range=DF.Item.tolist(),
x_range=Range1d(DF.Start_dt.min(),DF.End_dt.max()), tools='save')
hover=HoverTool(tooltips="Task: @Item<br>\
Start: @Start<br>\
End: @End")
G.add_tools(hover)
DF['ID']=DF.index+0.8
DF['ID1']=DF.index+1.2
CDS=ColumnDataSource(DF)
G.quad(left='Start_dt', right='End_dt', bottom='ID', top='ID1',source=CDS,color="Color")
#G.rect(,"Item",source=CDS)
show(G)