python - 对多个 y 轴进行绘图和颜色编码

Question

这是我第一次尝试使用 Matplotlib，我需要一些指导。我正在尝试生成具有 4 个 y 轴的绘图，两个在左侧，两个在右侧，共享 x 轴。这是我在共享保管箱文件夹中的数据集

import pandas as pd
%matplotlib inline

url ='http://dropproxy.com/f/D34'

df= pd.read_csv(url, index_col=0, parse_dates=[0])
df.plot()

这就是简单的 pandas 情节:

我想绘制类似于下面示例的图，其中 TMAX 和 TMIN 在主 y 轴上(相同比例)。

我的尝试:

我在 matplotlib listserv 上找到了一个例子..我正在尝试使其适应我的数据，但有些东西无法正常工作...这是脚本。

# multiple_yaxes_with_spines.py

# This is a template Python program for creating plots (line graphs) with 2, 3,
# or 4 y-axes.  (A template program is one that you can readily modify to meet
# your needs).  Almost all user-modifiable code is in Section 2.  For most
# purposes, it should not be necessary to modify anything else.

# Dr. Phillip M. Feldman,  27 Oct, 2009

# Acknowledgment: This program is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup


# Section 1: Import modules, define functions, and allocate storage.

import matplotlib.pyplot as plt
from numpy import *

def make_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.itervalues():
        sp.set_visible(False)

def make_spine_invisible(ax, direction):
    if direction in ["right", "left"]:
        ax.yaxis.set_ticks_position(direction)
        ax.yaxis.set_label_position(direction)
    elif direction in ["top", "bottom"]:
        ax.xaxis.set_ticks_position(direction)
        ax.xaxis.set_label_position(direction)
    else:
        raise ValueError("Unknown Direction : %s" % (direction,))

    ax.spines[direction].set_visible(True)

# Create list to store dependent variable data:
y= [0, 0, 0, 0, 0]


# Section 2: Define names of variables and the data to be plotted.

# `labels` stores the names of the independent and dependent variables).  The
# first (zeroth) item in the list is the x-axis label; remaining labels are the
# first y-axis label, second y-axis label, and so on.  There must be at least
# two dependent variables and not more than four.

labels= ['Date', 'Maximum Temperature', 'Solar Radiation',
  'Rainfall', 'Minimum Temperature']

# Plug in your data here, or code equations to generate the data if you wish to
# plot mathematical functions.  x stores values of the independent variable;
# y[1], y[2], ... store values of the dependent variable.  (y[0] is not used).
# All of these objects should be NumPy arrays.

# If you are plotting mathematical functions, you will probably want an array of
# uniformly spaced values of x; such an array can be created using the
# `linspace` function.  For example, to define x as an array of 51 values
# uniformly spaced between 0 and 2, use the following command:

#    x= linspace(0., 2., 51)

# Here is an example of 6 experimentally measured y1-values:

#    y[1]= array( [3, 2.5, 7.3e4, 4, 8, 3] )

# Note that the above statement requires both parentheses and square brackets.

# With a bit of work, one could make this program read the data from a text file
# or Excel worksheet.

# Independent variable:
x = df.index
# First dependent variable:
y[1]= df['TMAX']
# Second dependent variable:
y[2]= df['RAD']
y[3]= df['RAIN']
y[4]= df['TMIN']

# Set line colors here; each color can be specified using a single-letter color
# identifier ('b'= blue, 'r'= red, 'g'= green, 'k'= black, 'y'= yellow,
# 'm'= magenta, 'y'= yellow), an RGB tuple, or almost any standard English color
# name written without spaces, e.g., 'darkred'.  The first element of this list
# is not used.
colors= [' ', '#C82121', '#E48E3C', '#4F88BE', '#CF5ADC']

# Set the line width here.  linewidth=2 is recommended.
linewidth= 2


# Section 3: Generate the plot.

N_dependents= len(labels) - 1
if N_dependents > 4: raise Exception, \
   'This code currently handles a maximum of four independent variables.'

# Open a new figure window, setting the size to 10-by-7 inches and the facecolor
# to white:
fig= plt.figure(figsize=(16,9), dpi=120, facecolor=[1,1,1])

host= fig.add_subplot(111)

host.set_xlabel(labels[0])

# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the host axes).  The first element of y_axis is
# not used.
y_axis= (N_dependents+2) * [0]
y_axis[1]= host
for i in range(2,len(labels)+1): y_axis[i]= host.twinx()

if N_dependents >= 3:
   # The following statement positions the third y-axis to the right of the
   # frame, with the space between the frame and the axis controlled by the
   # numerical argument to set_position; this value should be between 1.10 and
   # 1.2.
   y_axis[3].spines["right"].set_position(("axes", 1.15))
   make_patch_spines_invisible(y_axis[3])
   make_spine_invisible(y_axis[3], "right")
   plt.subplots_adjust(left=0.0, right=0.8)

if N_dependents >= 4:
   # The following statement positions the fourth y-axis to the left of the
   # frame, with the space between the frame and the axis controlled by the
   # numerical argument to set_position; this value should be between 1.10 and
   # 1.2.
   y_axis[4].spines["left"].set_position(("axes", -0.15))
   make_patch_spines_invisible(y_axis[4])
   make_spine_invisible(y_axis[4], "left")
   plt.subplots_adjust(left=0.2, right=0.8)

p= (N_dependents+1) * [0]

# Plot the curves:
for i in range(1,N_dependents+1):
   p[i], = y_axis[i].plot(x, y[i], colors[i],
     linewidth=linewidth, label=labels[i])

# Set axis limits.  Use ceil() to force upper y-axis limits to be round numbers.
host.set_xlim(x.min(), x.max())

host.set_xlabel(labels[0], size=16)

for i in range(1,N_dependents+1):
   y_axis[i].set_ylim(0.0, ceil(y[i].max()))
   y_axis[i].set_ylabel(labels[i], size=16)
   y_axis[i].yaxis.label.set_color(colors[i])

   for sp in y_axis[i].spines.itervalues():
     sp.set_color(colors[i])

   for obj in y_axis[i].yaxis.get_ticklines():
      # `obj` is a matplotlib.lines.Line2D instance
      obj.set_color(colors[i])
      obj.set_markeredgewidth(3)

   for obj in y_axis[i].yaxis.get_ticklabels():
      obj.set_color(colors[i])
      obj.set_size(12)
      obj.set_weight(600)

# To enable the legend, uncomment the following two lines:
lines= p[1:]
host.legend(lines, [l.get_label() for l in lines])

plt.draw(); plt.show()

和输出

如何将最高和最低温度的刻度放在同一刻度上？另外，如何去掉第二个黑色 y 轴，从 0 缩放到 10？

有没有更简单的方法来实现这一点？

Answer 1

How can I put the scale on max and min temp on a same scale?

将它们绘制在同一轴上。

Also, how can I get rid of second y-axis with black color, scaled from 0 to 10?

不要创建该轴。

您想要绘制四个变量，其中两个可以放在同一个子图中，因此您只需要三个子图。但您要创建其中五个？

一步一步

请记住:不同的 y 尺度 <-> 共享 x 轴的不同子图。

两个具有共同尺度的变量(左)，两个具有独立尺度的变量(右)。

1. 创建主要子图，我们将其命名为 ax1。在其中绘制您想要的所有内容，在本例中是您问题中所述的 TMIN 和 TMAX。
2. 创建共享 x 轴的孪生子图twinx(ax=ax1)。绘制第三个变量，例如 RAIN。
3. 创建另一个孪生子图twinx(ax=ax1)。绘制第四个变量“RAD”。
4. 根据您的喜好调整颜色、标签、书脊位置......。

主动提供的建议:不要尝试修复您不理解的代码。