如果 x 轴是 pandas 的日期时间索引，如何绘制多色线 [英] How to plot multi-color line if x-axis is date time index of pandas

问题描述

我正在尝试使用熊猫系列绘制多色线.我知道 matplotlib.collections.LineCollection 会大幅提升效率.但 LineCollection 要求线段必须是浮动的.我想使用熊猫的数据时间索引作为 x 轴.

points = np.array((np.array[df_index.astype('float'), values]).T.reshape(-1,1,2))segment = np.concatenate([points[:-1],points[1:]],axis=1)lc = LineCollection(段)fig = plt.figure()plt.gca().add_collection(lc)plt.show()

但是图片不能让我满意.有什么解决办法吗?

解决方案

要生成多色线，您需要先将日期转换为数字，因为 matplotlib 在内部仅适用于数字值.

对于转换 matplotlib 提供了 matplotlib.dates.date2num.这理解日期时间对象，因此您首先需要使用 series.index.to_pydatetime() 将时间序列转换为日期时间，然后应用 date2num.

s = pd.Series(y, index=dates)inxval = mdates.date2num(s.index.to_pydatetime())

然后您可以像往常一样处理数字点，例如绘制为多边形或 LineCollection[

<小时>

由于人们在抽象这个概念时似乎有问题，这里有一段与上面相同的代码，没有使用熊猫和独立的颜色数组:

将 matplotlib.pyplot 导入为 plt导入 matplotlib.dates 作为 mdates将 numpy 导入为 np;np.random.seed(42)从 matplotlib.collections 导入 LineCollection日期 = np.arange("2017-01-01", "2017-06-20", dtype="datetime64[D]" )y = np.cumsum(np.random.normal(size=len(dates)))c = np.cumsum(np.random.normal(size=len(dates)))图, ax = plt.subplots()#先将日期转换为数字inxval = mdates.date2num(dates)点 = np.array([inxval, y]).T.reshape(-1,1,2)segment = np.concatenate([points[:-1],points[1:]],axis=1)lc = LineCollection(segments, cmap="plasma", linewidth=3)# 将颜色设置为日期值lc.set_array(c)ax.add_collection(lc)ax.xaxis_date()ax.autoscale_view()plt.show()

I am trying to plot a multi-color line using pandas series. I know matplotlib.collections.LineCollection will sharply promote the efficiency. But LineCollection require line segments must be float. I want to use datatime index of pandas as x-axis.

points = np.array((np.array[df_index.astype('float'), values]).T.reshape(-1,1,2))
segments = np.concatenate([points[:-1],points[1:]], axis=1)
lc = LineCollection(segments)
fig = plt.figure()
plt.gca().add_collection(lc)
plt.show()

But the picture can't make me satisfied. Is there any solution?

解决方案

To produce a multi-colored line, you will need to convert the dates to numbers first, as matplotlib internally only works with numeric values.

For the conversion matplotlib provides matplotlib.dates.date2num. This understands datetime objects, so you would first need to convert your time series to datetime using series.index.to_pydatetime() and then apply date2num.

s = pd.Series(y, index=dates)
inxval = mdates.date2num(s.index.to_pydatetime())

You can then work with the numeric points as usual , e.g. plotting as Polygon or LineCollection[1,2].

The complete example:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
from matplotlib.collections import LineCollection

dates = pd.date_range("2017-01-01", "2017-06-20", freq="7D" )
y = np.cumsum(np.random.normal(size=len(dates)))

s = pd.Series(y, index=dates)

fig, ax = plt.subplots()

#convert dates to numbers first
inxval = mdates.date2num(s.index.to_pydatetime())
points = np.array([inxval, s.values]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)

lc = LineCollection(segments, cmap="plasma", linewidth=3)
# set color to date values
lc.set_array(inxval)
# note that you could also set the colors according to y values
# lc.set_array(s.values)
# add collection to axes
ax.add_collection(lc)


ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter("%b")
ax.xaxis.set_major_formatter(monthFmt)
ax.autoscale_view()
plt.show()

---

Since people seem to have problems abstacting this concept, here is a the same piece of code as above without the use of pandas and with an independent color array:

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np; np.random.seed(42)
from matplotlib.collections import LineCollection

dates = np.arange("2017-01-01", "2017-06-20", dtype="datetime64[D]" )
y = np.cumsum(np.random.normal(size=len(dates)))
c = np.cumsum(np.random.normal(size=len(dates)))


fig, ax = plt.subplots()

#convert dates to numbers first
inxval = mdates.date2num(dates)
points = np.array([inxval, y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)

lc = LineCollection(segments, cmap="plasma", linewidth=3)
# set color to date values
lc.set_array(c)
ax.add_collection(lc)

ax.xaxis_date()
ax.autoscale_view()
plt.show()

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