pandas 使用 python 和 matplotlib 的时间线条形图
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Timeline bar graph using python and matplotlib
提问by mrb28830
I am looking to draw a timeline bar graph using matplotlibthat will show the things a person did in one day. I am adding the code below,output and an expected output that i am looking for. Any library can be used, in my case the closet i could get to was using matplotlib. Any help would be greatly appreciated.
我正在寻找使用matplotlib绘制时间线条形图，该条形图将显示一个人一天所做的事情。我正在添加下面的代码、输出和我正在寻找的预期输出。可以使用任何库，在我的情况下，我可以使用的壁橱使用matplotlib。任何帮助将不胜感激。
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = [ (dt.datetime(2018, 7, 17, 0, 15), dt.datetime(2018, 7, 17, 0, 30), 'sleep'),
(dt.datetime(2018, 7, 17, 0, 30), dt.datetime(2018, 7, 17, 0, 45), 'eat'),
(dt.datetime(2018, 7, 17, 0, 45), dt.datetime(2018, 7, 17, 1, 0), 'work'),
(dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 30), 'sleep'),
(dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30), 'eat'),
(dt.datetime(2018, 7, 17, 1, 30), dt.datetime(2018, 7, 17, 1, 45), 'work')
]
rng=[]
for i in range(len(data)):
rng.append((data[i][0]).strftime('%H:%M'))
index={}
activity = []
for i in range(len(data)):
index[(data[i][2])]=[]
activity.append(data[i][2])
for i in range(len(index)):
for j in range(len(activity)):
if activity[j]==index.keys()[i]:
index[index.keys()[i]].append(15)
else:
index[index.keys()[i]].append(0)
data = list(index.values())
df = pd.DataFrame(data,index=list(index.keys()))
df.plot.barh(stacked=True, sharex=False)
plt.show()
My Output:
我的输出：
Using matplotlib this is what i was getting
使用 matplotlib 这就是我得到的
Expected Output:
预期输出：
I got this using google charts Timeline graph but I need this using python and the data used for generating both graphs is not exactly the same, I hope you get the point
我使用谷歌图表时间线图得到了这个，但我需要使用 python 并且用于生成两个图的数据并不完全相同，我希望你明白这一点
回答by ImportanceOfBeingErnest
You may create a PolyCollectionof "bars". For this you would need to convert your dates to numbers (matplotlib.dates.date2num).
您可以创建一个PolyCollection“条”。为此，您需要将日期转换为数字 ( matplotlib.dates.date2num)。
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.collections import PolyCollection
data = [ (dt.datetime(2018, 7, 17, 0, 15), dt.datetime(2018, 7, 17, 0, 30), 'sleep'),
(dt.datetime(2018, 7, 17, 0, 30), dt.datetime(2018, 7, 17, 0, 45), 'eat'),
(dt.datetime(2018, 7, 17, 0, 45), dt.datetime(2018, 7, 17, 1, 0), 'work'),
(dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 30), 'sleep'),
(dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30), 'eat'),
(dt.datetime(2018, 7, 17, 1, 30), dt.datetime(2018, 7, 17, 1, 45), 'work')
]
cats = {"sleep" : 1, "eat" : 2, "work" : 3}
colormapping = {"sleep" : "C0", "eat" : "C1", "work" : "C2"}
verts = []
colors = []
for d in data:
v = [(mdates.date2num(d[0]), cats[d[2]]-.4),
(mdates.date2num(d[0]), cats[d[2]]+.4),
(mdates.date2num(d[1]), cats[d[2]]+.4),
(mdates.date2num(d[1]), cats[d[2]]-.4),
(mdates.date2num(d[0]), cats[d[2]]-.4)]
verts.append(v)
colors.append(colormapping[d[2]])
bars = PolyCollection(verts, facecolors=colors)
fig, ax = plt.subplots()
ax.add_collection(bars)
ax.autoscale()
loc = mdates.MinuteLocator(byminute=[0,15,30,45])
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
ax.set_yticks([1,2,3])
ax.set_yticklabels(["sleep", "eat", "work"])
plt.show()
Note that such plots can equally be generated with a Broken Bar plot (broken_barh), however, the (unsorted) data used here, make it a bit easier using a PolyCollection.
请注意，此类图同样可以使用断条图 ( broken_barh) 生成，但是，此处使用的（未排序的）数据使使用 PolyCollection 更容易一些。
And now you would need to explain to me how you can sleep and eat at the same time - something I can never quite get at, as hard as I try.
现在你需要向我解释你如何可以同时睡觉和吃饭——这是我永远无法理解的，尽管我尽力了。
回答by GuillaumeS
My solution using Altair (example):
我使用 Altair 的解决方案（示例）：
import altair as alt
import datetime as dt
import pandas as pd
alt.renderers.enable('jupyterlab')
data = pd.DataFrame()
data['from'] = [dt.datetime(2018, 7, 17, 0, 15),
dt.datetime(2018, 7, 17, 0, 30),
dt.datetime(2018, 7, 17, 0, 45),
dt.datetime(2018, 7, 17, 1, 0),
dt.datetime(2018, 7, 17, 1, 15),
dt.datetime(2018, 7, 17, 1, 30)]
data['to'] = [dt.datetime(2018, 7, 17, 0, 30),
dt.datetime(2018, 7, 17, 0, 45),
dt.datetime(2018, 7, 17, 1, 0),
dt.datetime(2018, 7, 17, 1, 15),
dt.datetime(2018, 7, 17, 1, 30),
dt.datetime(2018, 7, 17, 1, 45)]
data['activity'] = ['sleep','eat','work','sleep','eat','work']
#data
alt.Chart(data).mark_bar().encode(
x='from',
x2='to',
y='activity',
color=alt.Color('activity', scale=alt.Scale(scheme='dark2'))
)
Output:
输出：
