[英]Coloring a part of a time series plot in seaborn
Based on a dataframe containing two columns, one with a date and time and one with a price value, I got the following plots:基于包含两列的 dataframe,一列带有日期和时间,一列带有价格值,我得到以下图:
import seaborn as sns
# Use seaborn style defaults and set the default figure size
sns.set(rc={'figure.figsize':(20, 7)})
df['value'].plot(linewidth=0.5);
cols_plot = ['value']
axes = df[cols_plot].plot(marker='.', alpha=0.5, linestyle='None', figsize=(20, 7), subplots=True)
for ax in axes:
ax.set_ylabel('Price')
I want to use a different color for a part of the graph (namely, a period of 7 days).我想为图表的一部分(即 7 天)使用不同的颜色。 I first tried using a marker, but the attribute
.axvline
doesn't work.我首先尝试使用标记,但属性
.axvline
不起作用。 I know that normally one uses something like plt.plot
and inside it there are parameters specifying the interval and color, but in my case I have an array.我知道通常使用
plt.plot
之类的东西,其中有指定间隔和颜色的参数,但就我而言,我有一个数组。 not a plot.不是 plot。
EDIT: This is a sample of the data array:编辑:这是数据数组的示例:
+-----------------------------------+------------+
| Start Value |
+-----------------------------------+------------+
08.06.2019 08:00 33
08.06.2019 09:00 65
08.07.2019 08:00 45
08.07.2019 09:00 57
08.08.2019 08:00 52
+-----------------------------------+------------+
I only want to color the graph spanning the month July.我只想为跨越 7 月的图表着色。
I am not sure that I have understood your question so I will go with an example:我不确定我是否理解了你的问题,所以我将以 go 为例:
import matplotlib.pyplot as plt
t=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
a=[10, 50, 100, 40, 20, 10, 80, 50, 78, 41]
plt.plot(t[0:5], a[0:5], color='red')
plt.plot(t[6:10], a[6:10], color='blue')
Do you want to do something similar?你想做类似的事情吗?
EDIT:编辑:
Hi sorry for the wait,嗨对不起久等了
So, I assume that you have two variables, one containing the valeus and another one containing the dates.因此,我假设您有两个变量,一个包含 valeus,另一个包含日期。 Personnaly, I went for something like that:
Personnaly,我去了类似的东西:
date = ['08.06.2019', '08.06.2019', '08.07.2019', '08.07.2019', '08.08.2019']
value = [33, 65, 45, 57, 52]
t =[]
a=[]
for i in range(len(date)):
t.append(date[i].split("."))
for i in range(len(t)):
a.append(int(t[i][1]))
plt.xticks((6, 7, 8), ('08.06.2019', '08.07.2019', '08.08.2019'))
for i in range(len(a)):
if a[i] == 7 :
plt.scatter(a[i], value[i], color = "red")
else :
plt.scatter(a[i], value[i], color ="blue")
It allows you to display a scatter plot, if you want a plot with lines you can take your inspiration from this !它允许您显示散点图 plot,如果您想要带有线条的 plot,您可以从中获得灵感! Hope it helps !
希望能帮助到你 !
You can plot a normal time series plot first你可以先 plot 一个正常的时间序列 plot
fig = plt.figure(figsize=(15,4))
ax1=plt.subplot(121)
sns.lineplot(x="Date", y="Value", data=df, ax=ax1) # plot normal time series plot
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%b-%Y")) # change to nicer date format
And then plot just the region of interest to overlay it on top of the normal time series plot.然后 plot 只是将感兴趣的区域覆盖在正常时间序列 plot 之上。
# plot subset on top of the normal time series
sns.lineplot(x="Date", y="Value",
data=df[(df['Date'] > '2018-11-30') & (df['Date'] < '2019-01-01')],
color='green', ax=ax1)
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