绘制 Pandas 系列数据的平滑曲线
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Plot smooth curves of Pandas Series data
提问by Alaa Ali
My data is:
我的数据是：
>>> ts = pd.TimeSeries(data,indexconv)
>>> tsgroup = ts.resample('t',how='sum')
>>> tsgroup
2014-11-08 10:30:00 3
2014-11-08 10:31:00 4
2014-11-08 10:32:00 7
[snip]
2014-11-08 10:54:00 5
2014-11-08 10:55:00 2
Freq: T, dtype: int64
>>> tsgroup.plot()
>>> plt.show()
indexconvare strings converted using datetime.strptime.
indexconv是使用datetime.strptime.
The plot is very edgy like this (these aren't my actual plots):
情节很像这样（这些不是我的实际情节）：
How can I smooth it out like this:
我怎样才能像这样平滑它：
I know about scipy.interpolatementioned in this article(which is where I got the images from), but how can I apply it for Pandas time series?
我知道这篇文章中scipy.interpolate提到的（这是我从那里获得图像的地方），但是我如何将它应用于 Pandas 时间序列？
I found this great library called Vincentthat deals with Pandas, but it doesn't support Python 2.6.
我发现了一个名为Vincent 的很棒的库，它可以处理 Pandas，但它不支持 Python 2.6。
采纳答案by Alaa Ali
Got it. With help from this question, here's what I did:
Resample my
tsgroupfrom minutes to seconds.\>>> tsres = tsgroup.resample('S') \>>> tsres 2014-11-08 10:30:00 3 2014-11-08 10:30:01 NaN 2014-11-08 10:30:02 NaN 2014-11-08 10:30:03 NaN ... 2014-11-08 10:54:58 NaN 2014-11-08 10:54:59 NaN 2014-11-08 10:55:00 2 Freq: S, Length: 1501Interpolate the data using
.interpolate(method='cubic'). This passes the data toscipy.interpolate.interp1dand uses thecubickind, so you need to have scipy installed (pip install scipy) 1.\>>> tsint = tsres.interpolate(method='cubic') \>>> tsint 2014-11-08 10:30:00 3.000000 2014-11-08 10:30:01 3.043445 2014-11-08 10:30:02 3.085850 2014-11-08 10:30:03 3.127220 ... 2014-11-08 10:54:58 2.461532 2014-11-08 10:54:59 2.235186 2014-11-08 10:55:00 2.000000 Freq: S, Length: 1501
Plot it using
tsint.plot(). Here's a comparison between the originaltsgroupandtsint:
tsgroup从几分钟到几秒钟重新采样我的。\>>> tsres = tsgroup.resample('S') \>>> tsres 2014-11-08 10:30:00 3 2014-11-08 10:30:01 NaN 2014-11-08 10:30:02 NaN 2014-11-08 10:30:03 NaN ... 2014-11-08 10:54:58 NaN 2014-11-08 10:54:59 NaN 2014-11-08 10:55:00 2 Freq: S, Length: 1501使用 插入数据
.interpolate(method='cubic')。这会将数据传递给scipy.interpolate.interp1d并使用cubic种类，因此您需要安装 scipy (pip install scipy) 1。\>>> tsint = tsres.interpolate(method='cubic') \>>> tsint 2014-11-08 10:30:00 3.000000 2014-11-08 10:30:01 3.043445 2014-11-08 10:30:02 3.085850 2014-11-08 10:30:03 3.127220 ... 2014-11-08 10:54:58 2.461532 2014-11-08 10:54:59 2.235186 2014-11-08 10:55:00 2.000000 Freq: S, Length: 1501
使用
tsint.plot(). 这是原始tsgroup和之间的比较tsint：
1If you're getting an error from .interpolate(method='cubic')telling you that Scipy isn't installed even if you do have it installed, open up /usr/lib64/python2.6/site-packages/scipy/interpolate/polyint.pyor wherever your file might be and change the second line from from scipy import factorialto from scipy.misc import factorial.
1如果你从得到一个错误.interpolate(method='cubic')，告诉你，就算你安装了它，打开SciPy的未安装/usr/lib64/python2.6/site-packages/scipy/interpolate/polyint.py或任何你的文件可能是从改变第二行from scipy import factorial到from scipy.misc import factorial。
回答by Marcus
You can smooth out your data with moving averages as well, effectively applying a low-pass filter to your data. Pandas supports this with the rolling()method.
您也可以使用移动平均值平滑数据，有效地对数据应用低通滤波器。Pandas 通过rolling()方法支持这一点。
