【发布时间】：2016-02-14 22:13:35
【问题描述】：
这是我第一次尝试使用 Matplotlib，我需要一些指导。我正在尝试使用 4 个 y 轴生成图，左侧两个，右侧两个，共享 x 轴。这是我在共享 Dropbox 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()
这是简单的熊猫图的样子：
我想将其绘制成类似于下面的示例，TMAX 和 TMIN 在主 y 轴上（在相同的比例上）。
我的尝试：
我在matplotlib listserv 上找到了一个示例。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()
还有输出
我怎样才能把最高温度和最低温度的刻度放在同一个刻度上？另外，我怎样才能摆脱黑色的第二个 y 轴，从 0 缩放到 10？
有没有更简单的方法来实现这一点？
【问题讨论】：
标签： python pandas matplotlib plot