Python Matplotlib - 带有日期值的 x 轴平滑 plot 线

Question

Im trying to smooth a graph line out but since the x-axis values are dates im having great trouble doing this. Say we have a dataframe as follows

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline

startDate = '2015-05-15'
endDate = '2015-12-5'
index = pd.date_range(startDate, endDate)
data = np.random.normal(0, 1, size=len(index))
cols = ['value']

df = pd.DataFrame(data, index=index, columns=cols)

Then we plot the data

fig, axs = plt.subplots(1,1, figsize=(18,5))
x = df.index
y = df.value
axs.plot(x, y)
fig.show()

we get

Now to smooth this line there are some usefull staekoverflow questions allready like:

• Generating smooth line graph using matplotlib ,
• Plot smooth line with PyPlot
• Creating numpy linspace out of datetime

But I just cant seem to get some code working to do this for my example, any suggestions?

Answer 1

You can use interpolation functionality that is shipped with pandas . Because your dataframe has a value for every index already, you can populate it with an index that is more sparse, and fill every previously non-existent indices with NaN values. Then, after choosing one of many interpolation methods available , interpolate and plot your data:

index_hourly = pd.date_range(startDate, endDate, freq='1H')
df_smooth = df.reindex(index=index_hourly).interpolate('cubic')
df_smooth = df_smooth.rename(columns={'value':'smooth'})

df_smooth.plot(ax=axs, alpha=0.7)
df.plot(ax=axs, alpha=0.7)
fig.show()

Answer 2

There is one workaround, we will create two plots - 1) non smoothed /interploted with date labels 2) smoothed without date labels.

Plot the 1) using argument linestyle=" " and convert the dates to be plotted on x-axis to string type.

Plot the 2) using the argument linestyle="-" and interpolating the x-axis and y-axis using np.linespace and make_interp_spline respectively.

Following is the use of the discussed workaround for your code.

# your initial code
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import make_interp_spline
%matplotlib inline
startDate = "2015-05-15"
endDate = "2015-07-5" #reduced the end date so smoothness is clearly seen
index = pd.date_range(startDate, endDate)
data = np.random.normal(0, 1, size=len(index))
cols = ["value"]

df = pd.DataFrame(data, index=index, columns=cols)
fig, axs = plt.subplots(1, 1, figsize=(40, 12))
x = df.index
y = df.value

# workaround by creating linespace for length of your x axis
x_new = np.linspace(0, len(df.index), 300)
a_BSpline = make_interp_spline(
    [i for i in range(0, len(df.index))],
    df.value,
    k=5,
)
y_new = a_BSpline(x_new)

# plot this new plot with linestyle = "-"
axs.plot(
    x_new[:-5], # removing last 5 entries to remove noise, because interpolation outputs large values at the end.
    y_new[:-5],
    "-",
    label="interpolated"
)

# to get the date on x axis we will keep our previous plot but linestyle will be None so it won't be visible
x = list(x.astype(str))
axs.plot(x, y, linestyle=" ", alpha=0.75, label="initial")
xt = [x[i] for i in range(0,len(x),5)]
plt.xticks(xt,rotation="vertical")
plt.legend()
fig.show()

Resulting Plot

Overalpped plot to see the smoothing.

Answer 3

Depending on what exactly you mean by "smoothing," the easiest way can be the use of savgol_filter or something similar. Unlike with interpolated splines, this method means that the smoothed line does not pass through the measured points, effectively filtering out higher-frequency noise.

from scipy.signal import savgol_filter

...
windowSize = 21
polyOrder = 1
smoothed = savgol_filter(values, windowSize, polyOrder)
axes.plot(datetimes, smoothed, color=chart.color)

The higher the polynomial order value, the closer the smoothed line is to the raw data.

Here is an example.