导入

import matplotlib.pyplot as plt
import numpy as np

画图

# 简单的绘图
x = np.linspace(0, 2 * np.pi, 50)
# 设定图像的大小
plt.figure(figsize=(10,5))
# 如果没有第一个参数 x,图形的 x 坐标默认为数组的索引
plt.plot(x, np.sin(x))
plt.show() # 显示图形

调整label字体大小

plt.xlabel("time",fontsize=20)
plt.ylabel("DO",fontsize=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)

# 调整全部字体大小
matplotlib.rcParams.update({'font.size': 22})

调整横坐标数值

x=[1,2,3,4,5]
plt.xticks(np.arange(1, 6, 1))
plt.plot(x, np.sin(x))

调整线条颜色

x = range(len(data))
# 调整图像大小
plt.figure(figsize=(10,5))

# 绘制曲线
plt.plot(x, y1, label="actual")
plt.plot(x, y2, color='r',label="prediction")

# 设置坐标轴范围
plt.xlim((-5,5))
plt.ylim((-2,2))

#设置坐标轴刻度
plt.xticks(np.arange(-5, 5, 0.5))
plt.yticks(np.arange(-2, 2, 0.3))

# 手动修正坐标轴刻度
plt.yticks([-0.25, 0, 0.25, 0.50, 0.75],['-0.25', '0', '0.25', '0.50', '0.75'])

# plt.legend()添加标注
plt.legend(loc="upper left")
# 自动调整标注位置
plt.legend(loc="best")

plt.title("上证50指数历史最高价、收盘价走势折线图")
plt.xlabel(Time Slot Count")
plt.ylabel("value")

plt.show()

散点图

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii

plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

subplots

#First create some toy data:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

#Creates just a figure and only one subplot
#fig 是整体图形,ax是分别的图形
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')

# 调整子图大小
fig, ax = plt.subplots(2,2,figsize=(15,15))
# or
f.set_figwidth(20)
f.set_figheight(15)

#Creates two subplots and unpacks the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)

#Creates four polar axes, and accesses them through the returned array
fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
axes[0, 0].plot(x, y)
axes[1, 1].scatter(x, y)

#Share a X axis with each column of subplots
plt.subplots(2, 2, sharex='col')

#Share a Y axis with each row of subplots
plt.subplots(2, 2, sharey='row')

#Share both X and Y axes with all subplots
plt.subplots(2, 2, sharex='all', sharey='all')

fig and axarr

f, axarr = plt.subplots(2, sharex=True)
f.suptitle('Sharing X axis')
axarr[0].plot(x, y)
axarr[1].scatter(x, y)

images

spines

an axis spine -- the line noting the data area boundaries

ax = fig.add_subplot(2, 2, 4)
ax.set_title('spines at data (1, 2)')
ax.plot(x, y)
ax.spines['left'].set_position(('data', 1))
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position(('data', 2))
ax.spines['top'].set_color('none')
ax.spines['left'].set_smart_bounds(True)
ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')

images

柱状图

import matplotlib.pyplot as plt

data = [5, 20, 15, 25, 10]

plt.bar(range(len(data)), data)
plt.show()

# 设置柱体颜色
plt.bar(range(len(data)), data, fc='g')

# 设置不同的柱体颜色
plt.bar(range(len(data)), data, color='rgb') # or `color=['r', 'g', 'b']`

# 设置label
data = [5, 20, 15, 25, 10]
labels = ['Tom', 'Dick', 'Harry', 'Slim', 'Jim']

plt.bar(range(len(data)), data, tick_label=labels)
plt.show()

# multi bar
import matplotlib.pyplot as multi_bar_plt
import numpy as np

N = 4
# the x locations for the groups
ind = np.arange(N)
# the width of the bars
width = 0.15

fig = multi_bar_plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)

# ANN, ARIMA, RNN, SG-RNN
step1 = [0.65, 0.581, 0.164, 0.134]
step2 = [0.6925, 0.892, 0.27, 0.4077]
step3 = [0.766, 0.685, 0.226, 0.52]
step4 = [0.765, 0.7068, 0.295, 0.251]
step5 = [0.802, 0.733, 0.416, 0.2551]

rects_step1 = ax.bar(ind, step1, width, color='r')
rects_step2 = ax.bar(ind+width, step2, width, color='g')
rects_step3 = ax.bar(ind+width*2, step3, width, color='y')
rects_step4 = ax.bar(ind+width*3, step4, width, color='b')
rects_step5 = ax.bar(ind+width*4, step5, width, color='k')

ax.set_ylabel('RMSE')
ax.set_xticks(ind+width)
ax.set_xticklabels(('ANN', 'ARIMA', 'RNN', 'SG-RNN'))
ax.legend((rects_step1[0], rects_step2[0], rects_step3[0], rects_step4[0], rects_step5[0]), ('Step1', 'Step2', 'Step3', 'Step4', 'Step5'))

multi_bar_plt.show()

combine multiple line labels in legend

handles, labels = plt.gca().get_legend_handles_labels()
i =1
while i<len(labels):
    if labels[i] in labels[:i]:
        del(labels[i])
        del(handles[i])
    else:
        i +=1

plt.legend(handles, labels)

调整legend

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(10)

fig = plt.figure()
ax = plt.subplot(111)

for i in xrange(5):
    line, = ax.plot(x, i * x, label='$y = %ix$'%i)

# Shrink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])

# Put a legend below current axis
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=5)

plt.show()

images

生成eps图

# plt调用gcf函数取得当前绘制的figure并调用savefig函数
# get current figure
foo_fig = plt.gcf() 
foo_fig.savefig('foo.eps', format='eps', dpi=1000)
plt.show()