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在Python中使用Seaborn和ci =“ sd”函数的线图

[英]Lineplot with Seaborn and ci=“sd” function in Python

I'm trying to produce some snazzy charts in seaborn and need some help. 我正尝试制作一些Seaborn的时髦图表,需要一些帮助。

I have some stock data, consisting of 5 stocks. 我有一些库存数据,包括5只股票。 I'm basically trying to visually display how Stock A has performed in comparison to the others. 我基本上是在试图直观地显示Stock A与其他Stock A相比的表现。 To do this I am looking at cumulative returns, and have also calculated the average cumulative returns for the other 4 stocks. 为此,我查看了累积收益,并且还计算了其他4只股票的平均累积收益。 I have split this data up in to the following 2 df : 我已将此数据拆分为以下2 df

Stock A's data let's call df : 股票A的数据称为df

Date              Stock A               
2019-04-24 07:59  0.433366
2019-04-24 08:59  0.397984
2019-04-24 09:59  0.403971
2019-04-24 10:59  0.399131
2019-04-24 11:59  0.386641
2019-04-24 12:59  0.388572
2019-04-24 13:59  0.396266
2019-04-24 14:59  0.391609
2019-04-24 15:59  0.399412
2019-04-24 16:59  0.401715

And then Stocks B, C, D & E, PLUS the calculated average let's call df2 (I can't print all 5 columns): 然后,股票B,C,D和E,再加上计算出的平均值,我们将其称为df2 (我无法打印所有5列):

Date              Stock B   Stock C    Stock E   Average                                             
2019-04-24 07:59  0.273965  0.000982    0.409717  0.472029
2019-04-24 08:59  0.235606  -0.076309   0.345047  0.407299
2019-04-24 09:59  0.240826  -0.059274   0.346769  0.413197
2019-04-24 10:59  0.234849  -0.056013   0.338185  0.407962
2019-04-24 11:59  0.230158  -0.062947   0.331907  0.397927
2019-04-24 12:59  0.237573  -0.055506   0.334907  0.412206
2019-04-24 13:59  0.239994  -0.047875   0.334213  0.413846
2019-04-24 14:59  0.230461  -0.059781   0.312962  0.395924
2019-04-24 15:59  0.236968  -0.054398   0.320990  0.406967
2019-04-24 16:59  0.239918  -0.049522   0.328713  0.412818

What I am ultimately looking to do is chart all 5 stocks plus the average on one graph, that has a nice grey background and perhaps some grid lines etc (at the minute I can only chart with ugly white backgrounds), but I would like the line for Stock A and for Average to be slightly different and make use of seaborns standard deviation line plot. 我最终要做的是将所有5只股票加上平均值在一张图表上绘制,该图具有很好的灰色背景,也许有些网格线等(目前我只能绘制难看的白色背景),但是我想对于行Stock AAverage会略有不同,并利用seaborns标准差线图。

I found this example code sns.relplot(x="timepoint", y="signal", kind="line", ci="sd", data=fmri) but when I tried to alter it to my needs I got error messages and couldn't get all the data to appear on the same chart. 我发现了这个示例代码sns.relplot(x="timepoint", y="signal", kind="line", ci="sd", data=fmri)但是当我尝试将其更改为自己的需求时,出现了错误消息,并且无法使所有数据都显示在同一图表上。

Here is a near perfect example of what I'm aiming for, but I would like to include Stock B, C, D & E from df2 and change the axis labeling of course. 这是我要达到的目标的近乎完美的示例,但我想将df2 B,C,D和E包括在内,并更改轴标签。

Any help greatly appreciated. 任何帮助,不胜感激。 Cheers 干杯

在此处输入图片说明

This should produce what you asked for: 这应该产生您所要求的:

sns.set() #This sets the style to the seaborn default (gray background with white grid on)
fig,ax = plt.subplots() #create your figure and ax objects
sns.lineplot('Date', 'Stock A', ci="sd", data=df,ax=ax) #plot lines
sns.lineplot('Date', 'Stock B', ci="sd", data=df2,ax=ax)
sns.lineplot('Date', 'Stock C', ci="sd", data=df2,ax=ax)
sns.lineplot('Date', 'Stock E', ci="sd", data=df2,ax=ax)
sns.lineplot('Date', 'Average', ci="sd", data=df2,ax=ax)
plt.xticks(rotation=-45) #makes ticks visible (a long date would be unreadable otherwise)

EDIT 编辑

Answering OP questions from comments: 通过评论回答OP问题:

Transform your dates from strings to datetime objects, then matplotlib will take care of the ticks and tickslabels . 将您的日期从字符串转换为datetime对象,然后matplotlib将处理ticks和tickslabels
As they are right now they are interpreted as strings and they all get plotted. 就像现在一样,它们被解释为字符串,并且都被绘制出来。

df['Date']=pd.to_datetime(df['Date'])
df2['Date']=pd.to_datetime(df2['Date'])

Use the following line to change the ylabel 使用以下行更改ylabel

ax.set_ylabel('Returns')

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