matplotlib基础

matplotlib基础

1.基本绘图

控制颜色

• 颜色之间的对应关系为

  b—-blue
  c—-cyan
  g—-green
  k——black
  m—-magenta
  r—-red
  w—-white
  y——yellow

• 表示颜色的方式:

  a:用全名
  b:16进制如：#FF00FF
  c:RGB或RGBA元组（1,0,1,1）
  d:灰度强度如：‘0.7’

控制线型

• 符号和线型之间的对应关系

  • 实线
    — 短线
    -. 短点相间线
    ： 虚点线

控制标记风格

• 标记风格有多种：

  . Point marker
  , Pixel marker
  o Circle marker
  v Triangle down marker
  ^ Triangle up marker
  < Triangle left marker
  > Triangle right marker
  1 Tripod down marker
  2 Tripod up marker
  3 Tripod left marker
  4 Tripod right marker
  s Square marker
  p Pentagon marker
  * Star marker
  h Hexagon marker
  H Rotated hexagon D Diamond marker
  d Thin diamond marker
  | Vertical line (vlinesymbol) marker
  _ Horizontal line (hline symbol) marker
  + Plus marker
  x Cross (x) marker

示例代码

import pandas as pd
import numpy as np

# 导入matplot库
import matplotlib.pyplot as plt

# 设置输出图像大小
plt.rc('figure', figsize=(10, 5))

# 生成数据
x = np.linspace(0, 2, 10)  				# [0,2]之间均匀产生10个值
plt.plot(x, x, 'o-', label='linear')	# y = x 曲线
plt.plot(x, x ** 2, 'x-', label='quadratic')	# y=x^2曲线
plt.legend(loc='best')
plt.title('Linear vs Quadratic progression')
plt.xlabel('Input')
plt.ylabel('Output');
plt.show()

生成结果

2.直方图

API

matplotlib.pyplot.hist(
		x, 	                        # numpy数组
		bins=10, 					# bin数量
		range=None, 
		normed=False, 				# 归一化
		weights=None, 
		cumulative=False, 			# 累积函数
		bottom=None, 
		histtype='bar', 			# {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, 
		align='mid', 
		orientation='vertical', 
		rwidth=None, 
		log=False, 
		color=None, 
		label=None, 
		stacked=False, 
		hold=None, 
		data=None, 
		**kwargs)

示例代码

samples = np.random.normal(loc=1.0, scale=0.5, size=1000)
print(samples.shape)
print(samples.dtype)
print(samples[:30])

# step画出累积分布
plt.hist(samples, bins=50, cumulative=True, histtype='step')

# 直方图分布
plt.hist(samples, bins=50, histtype='bar')
plt.show()

生成结果

3.双直方图

示例代码

samples_1 = np.random.normal(loc=1, scale=.5, size=10000)
samples_2 = np.random.standard_t(df=10, size=10000)
bins = np.linspace(-3, 3, 50)

# Set an alpha and use the same bins since we are plotting two hists
plt.hist(samples_1, bins=bins, alpha=0.5, label='samples 1')
plt.hist(samples_2, bins=bins, alpha=0.5, label='samples 2')
plt.legend(loc='upper left');
plt.show()

生成结果

4.散点图

API

matplotlib.pyplot.scatter(
x,
y, 
s=20, 				# 点大小
c=None, 			# 颜色
marker='o', 		
cmap=None, 
norm=None, 
vmin=None, 
vmax=None, 
alpha=None, 
linewidths=None, 
verts=None, 
edgecolors=None, 
hold=None, 
data=None, 
**kwargs)

示例代码

plt.scatter(samples_1, samples_2, alpha=0.1);
plt.show()

示例代码

5.参考

[1] http://blog.csdn.net/kkxgx/article/details/6951959
[2] http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb