Question

这是我第一次尝试使用Matplotlib，我需要一些指导。我试图用4个y轴生成图，左边两个，右边两个共享x轴。这是我在共享保管箱文件夹

上的数据集

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()

这就是简单的熊猫情节：

我想将此绘制类似于下面的示例，在主y轴上使用TMAX和TMIN（在相同比例上）。

我的尝试：

我在matplotlib listserv找到了一个例子。我正在尝试将其改编为我的数据，但有些东西不正常......这就是脚本。

# 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()

输出

如何将比例放在最大和最小温度上？另外，如何摆脱黑色的第二个y轴，从0到10？

有没有更简单的方法来实现这个目标？

Answer 1

如何将最大和最小温度上的刻度放在相同的刻度上？

将它们绘制在相同的轴上。

另外，如何摆脱黑色的第二个y轴，从0到10？

不要创建那些轴。

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。但你创造了五个呢？

一步一步

请记住：不同的y尺度＆lt; - ＆gt;不同的子图共享x轴。

两个具有共同比例的变量（左），两个具有独立比例的变量（右）。

创建主要子图，我们将其称为Content-Security-Policy。绘制您想要的所有内容，例如问题中所述的TMIN和TMAX。
创建共享x轴ax1的双子图。绘制第三个变量，比如说RAIN。
创建另一个双子点twinx(ax=ax1)。绘制第四个变量＆＃39; RAD＆＃39;。
根据您心中的内容调整颜色，标签，脊柱位置。

未经请求的建议：不要试图修复您不理解的代码。

Answer 2

原始图表的变化显示如何在多个轴上绘制变量

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

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

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

fig = plt.figure()

ax = fig.add_subplot(111) # Primary y
ax2 = ax.twinx() # Secondary y

# Plot variables
ax.plot(df.index, df['TMAX'], color='red')
ax.plot(df.index, df['TMIN'], color='green')
ax2.plot(df.index, df['RAIN'], color='orange')
ax2.plot(df.index, df['RAD'],  color='yellow')

# Custom ylimit
ax.set_ylim(0,50)

# Custom x axis date formats
import matplotlib.dates as mdates
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))

Answer 3

我修改了@ bishopo的建议来生成我想要的东西，但是，该图还需要对轴标签的字体大小进行一些调整。

这是我到目前为止所做的工作。

import pandas as pd
%matplotlib inline

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

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

from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt





if 1:
    # Set the figure size, dpi, and background color
    fig = plt.figure(1, (16,9),dpi =300, facecolor = 'W',edgecolor ='k')


    # Update the tick label size to 12
    plt.rcParams.update({'font.size': 12})
    host = host_subplot(111, axes_class=AA.Axes)
    plt.subplots_adjust(right=0.75)

    par1 = host.twinx()
    par2 = host.twinx()
    par3 = host.twinx()

    offset = 60

    new_fixed_axis = par2.get_grid_helper().new_fixed_axis
    new_fixed_axis1 = host.get_grid_helper().new_fixed_axis
    par2.axis["right"] = new_fixed_axis(loc="right",
                                        axes=par2,
                                        offset=(offset, 0))
    par3.axis["left"] = new_fixed_axis1(loc="left",
                                        axes=par3,
                                        offset=(-offset, 0))

    par2.axis["right"].toggle(all=True)
    par3.axis["left"].toggle(all=True)
    par3.axis["right"].set_visible(False)


    # Set limit on both y-axes
    host.set_ylim(-30, 50)
    par3.set_ylim(-30,50)

    host.set_xlabel("Date")
    host.set_ylabel("Minimum Temperature ($^\circ$C)")
    par1.set_ylabel("Solar Radiation (W$m^{-2}$)")
    par2.set_ylabel("Rainfall (mm)")
    par3.set_ylabel('Maximum Temperature ($^\circ$C)')


    p1, = host.plot(df.index,df['TMIN'], 'm,')
    p2, = par1.plot(df.index, df.RAD, color ='#EF9600', linestyle ='--')
    p3, = par2.plot(df.index, df.RAIN, '#09BEEF')
    p4, = par3.plot(df.index, df['TMAX'], '#FF8284')

    par1.set_ylim(0, 36)
    par2.set_ylim(0, 360)

    host.legend()


    host.axis["left"].label.set_color(p1.get_color())
    par1.axis["right"].label.set_color(p2.get_color())
    par2.axis["right"].label.set_color(p3.get_color())
    par3.axis["left"].label.set_color(p4.get_color())



    tkw = dict(size=5, width=1.5)
    host.tick_params(axis='y', colors=p1.get_color(), **tkw)
    par1.tick_params(axis='y', colors=p2.get_color(), **tkw)
    par2.tick_params(axis='y', colors=p3.get_color(), **tkw)
    par3.tick_params(axis='y', colors=p4.get_color(), **tkw)
    host.tick_params(axis='x', **tkw)



    par1.axis["right"].label.set_fontsize(16)
    par2.axis["right"].label.set_fontsize(16)
    par3.axis["left"].label.set_fontsize(16)
    host.axis["bottom"].label.set_fontsize(16)
    host.axis["left"].label.set_fontsize(16)


    plt.figtext(.5,.92,'Weather Data', fontsize=22, ha='center')
    plt.draw()
    plt.show()

    fig.savefig("Test1.png")

输出

多个y轴的绘图和颜色编码

3 个答案:

一步一步