# matplotlib
## matplotlib
* [返回顶层目录](https://github.com/luweikxy/machine-learning-notes/tree/e6cc586690460dc6ac55d91478fbfbbfb7a54aa5/content/coding/SUMMARY.md#%E7%9B%AE%E5%BD%95)
* [返回上层目录](https://github.com/luweikxy/machine-learning-notes/tree/e6cc586690460dc6ac55d91478fbfbbfb7a54aa5/content/coding/python/library/python.md#python)
## matplotlib简介
matplotlib给python提供了丰富的绘图库，尤其是二维图像绘制。
**官网**：
**简单介绍**
Python中最著名的绘图系统，很多其他的绘图例如seaborn（针对pandas绘图而来）也是由其封装而成。创世人John Hunter于2012年离世。这个绘图系统操作起来很复杂，和R的ggplot,lattice绘图相比显得望而却步，这也是为什么我个人不丢弃R的原因，虽然调用
```python
plt.style.use("ggplot")
```
绘制的图形可以大致按照ggplot的颜色显示，但是还是感觉很鸡肋。但是matplotlib的复杂给其带来了很强的定制性。其具有面向对象的方式及Pyplot的经典高层封装。
需要掌握的是：
1. 散点图，折线图，条形图，直方图，饼状图，箱形图的绘制。
2. 绘图的三大系统：pyplot，pylab(不推荐)，面向对象
3. 坐标轴的调整，添加文字注释，区域填充，及特殊图形patches的使用
4. 金融的同学注意的是：可以直接调用Yahoo财经数据绘图（真。。。）
Pyplot快速入门：[Pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html)
下面就是对官网pyplot快速入门教程的学习记录和翻译。
## 图形的基本绘制
这个已经翻译好了
```python
import matplotlib.pyplot as plt
plt.plot([1,2,3,4],[5,6,6,4])
plt.ylabel('some numbers')
plt.show()
```
```python
import matplotlib.pyplot as plt
plt.plot([1,2,3,4],[1,2,9,16],'ro')
plt.axis([0,6,0,20])
plt.show()
```
```python
import numpy as np
import matplotlib.pyplot as plt
#x = np.array(range(5))
x = np.arange(0,5,0.25)
plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^')
plt.ylabel('x^n')
plt.xlabel('x')
plt.show()
```
## 绘图设置
### 控制线条的属性
* 使用关键字参数
```python
import numpy as np
import matplotlib.pyplot as plt
#x = np.array(range(5))
x = np.arange(0,5,0.25)
#plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^')
plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^', linewidth = 3.0)
plt.ylabel('x^n')
plt.xlabel('x')
plt.show()
```
和上一张图相比，通过对线宽属性进行设置，即`linewidth = 3.0`，线条明显变粗了。
* 通过获得线对象，对线对象进行设置
```python
import numpy as np
import matplotlib.pyplot as plt
#x = np.array(range(5))
x = np.arange(0,5,0.25)
#line1, = plt.plot(x, x, 'r--o')
(line1, line2, line3) = plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^', linewidth = 3.0)
print(type(line1))
#
line1.set_antialiased(False)
line2.set_linewidth(5)
plt.ylabel('x^n')
plt.xlabel('x')
plt.show()
```
* dads
```python
import numpy as np
import matplotlib.pyplot as plt
#x = np.array(range(5))
x = np.arange(0,5,0.25)
lines = plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^', linewidth = 3.0)
# use keyword args
plt.setp(lines, color = 'r', linewidth = 6.0)
# 很显然，setp函数一次只能对一个线对象设置属性
# or MATLAB style string value pairs
#plt.setp(lines, 'color', 'r', 'linewidth', 6.0)
plt.ylabel('x^n')
plt.xlabel('x')
plt.show()
```
同时获取多个线对象，分别使用`plt.setp()`函数设置每个线对象的属性。
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,5,0.25)
(line1, line2, line3) = plt.plot(x, x, 'r--o', x, x**2, 'b--s', x, x**3, 'g--^', linewidth = 3.0)
print(type(line1))
#
plt.setp(line1, color = 'r', lw = 10.0)
plt.setp(line2, color = 'g', linewidth = 7.0)
plt.setp(line3, color = 'b', lw = 4.0)
# 这样通过setup函数就能对所有线对象设置属性了
# or MATLAB style string value pairs
#plt.setp(lines, 'color', 'r', 'linewidth', 6.0)
plt.ylabel('x^n')
plt.xlabel('x')
plt.show()
```
#### Line2D线对象的属性
下面是可选的Line2D线对象的属性
| Property | Value Type |
| :--------------------: | :-----------------------------------------------: |
| alpha | float |
| animated | \[True \| False] |
| antialiased or aa | \[True \| False] |
| clip\_box | a matplotlib.transform.Bbox instance |
| clip\_on | \[True \| False] |
| clip\_path | a Path instance and a Transform instance, a Patch |
| color or c | any matplotlib color |
| contains | the hit testing function |
| dash\_capstyle | \[`'butt'` |
| dash\_joinstyle | \[`'miter'` |
| dashes | sequence of on/off ink in points |
| data | (np.array xdata, np.array ydata) |
| figure | a matplotlib.figure.Figure instance |
| label | any string |
| linestyle or ls | \[ `'-'` |
| linewidth or lw | float value in points |
| lod | \[True \| False] |
| marker | \[ `'+'` |
| markeredgecolor or mec | any matplotlib color |
| markeredgewidth or mew | float value in points |
| markerfacecolor or mfc | any matplotlib color |
| markersize or ms | float |
| markevery | \[ None \| integer \| (startind, stride) ] |
| picker | used in interactive line selection |
| pickradius | the line pick selection radius |
| solid\_capstyle | \[`'butt'` |
| solid\_joinstyle | \[`'miter'` |
| transform | a matplotlib.transforms.Transform instance |
| visible | \[True \| False] |
| xdata | np.array |
| ydata | np.array |
| zorder | any number |
为了得到可以设置的线对象的属性列表，调用`plt.setp()`函数，该函数以线对象为参数。
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.array([0,1,2,3,4,5])
print(x)
line1, = plt.plot(x, 'or')
print(type(line1))
#
plt.setp(line1)
```
#### 支持的颜色
如果颜色不显示指出，则默认循环使用不同的颜色，
| character | color |
| :-------: | :-----: |
| ‘b’ | blue |
| ‘g’ | green |
| ‘r’ | red |
| ‘c’ | cyan |
| ‘m’ | magenta |
| ‘y’ | yellow |
| ‘k’ | black |
| ‘w’ | white |
#### 支持的线型
| character | description | |
| :-------: | :--------------------: | ------------ |
| `'-'` | solid line style | |
| `'--'` | dashed line style | |
| `'-.'` | dash-dot line style | |
| `':'` | dotted line style | |
| `'.'` | point marker | |
| `','` | pixel marker | |
| `'o'` | circle marker | |
| `'v'` | triangle\_down marker | |
| `'^'` | triangle\_up marker | |
| `'<'` | triangle\_left marker | |
| `'>'` | triangle\_right marker | |
| `'1'` | tri\_down marker | |
| `'2'` | tri\_up marker | |
| `'3'` | tri\_left marker | |
| `'4'` | tri\_right marker | |
| `'s'` | square marker | |
| `'p'` | pentagon marker | |
| `'*'` | star marker | |
| `'h'` | hexagon1 marker | |
| `'H'` | hexagon2 marker | |
| `'+'` | plus marker | |
| `'x'` | x marker | |
| `'D'` | diamond marker | |
| `'d'` | thin\_diamond marker | |
| \`' | '\` | vline marker |
| `'_'` | hline marker | |
### 绘制多个图像和轴
```python
import matplotlib.pyplot as plt
import numpy as np
def func(t):
return np.exp(-t)*np.cos(2*np.pi*t)
t1 = np.arange(0,5,0.2)
t2 = np.arange(0,5,0.05)
plt.figure(1)
plt.subplot(211)
plt.plot(t1,func(t1),'bo',t2,func(t2),'k')
plt.subplot(212)
plt.plot(t2,np.cos(2*np.pi*t2),'r--')
plt.show()
```
```python
import matplotlib.pyplot as plt
plt.figure(1) # the first figure
plt.subplot(211) # the first subplot in the first figure
plt.plot([1,2,3])
plt.subplot(212) # the second subplot in tthe first figure
plt.plot([4,5,6])
plt.figure(2) # a second figure
plt.plot([4,5,6])
plt.figure(1) # figure 1 current; ubplot(212) still current
plt.subplot(211) # make subplot(211) in figure1 current
plt.title("Easy as 1, 2, 3")# subplot 211 tittle
plt.show()
```
```python
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, bins=10, normed=1, facecolor='g', edgecolor='k', alpha=0.75, histtype='bar')
#hist的参数非常多，但常用的就这六个，只有第一个是必须的，后面六个可选
#x: 需要计算直方图的一维数组
#bins: 直方图的柱数，可选项，默认为10，可不写bins=10,直接在x后面写10
#normed: 是否将得到的直方图向量归一化。默认为0
#facecolor: 直方图颜色
#edgecolor: 直方图边框颜色
#alpha: 透明度
#histtype: 直方图类型，‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’
#返回值：
#n: 直方图向量，是否归一化由参数normed设定
#bins: 返回各个bin的区间范围
#patches: 返回每个bin里面包含的数据，是一个list
print(n)#直方图的y值向量，是否归一化由参数normed决定
#[ 1.28221796e-04 9.14648810e-04 5.06048687e-03 1.65919004e-02
# 2.51229239e-02 2.27209022e-02 1.13262586e-02 3.09441934e-03
# 4.87242824e-04 3.41924789e-05]
print(bins)#返回各个bin的区间范围
#[ 43.48262893 55.18110804 66.87958716 78.57806627 90.27654539
# 101.97502451 113.67350362 125.37198274 137.07046185 148.76894097
# 160.46742008]
print(patches)#返回每一个bin里包含的数据，是一个list
#
print(type(patches))
#
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
```
## 各种图形的绘制
### 柱形图
```python
data = [3685588454, 1399975394, 670070036, 413226974, 298643648, 247590988, 136104912, 115126068, 61071716, 48001466, 38891784, 25692216, 6555850, 5839100, 4661722]
labels = ['0.00-0.01', "", '0.02-0.03', "", '0.04-0.05', "", '0.06-0.07', "", '0.08-0.09', "", '0.10-0.11', "", '0.12-0.13', "", '0.14-0.15']
data = [a/7177459576 for a in data]
print(sum(data))
plt.bar(range(len(data)), data, tick_label=labels, width=0.85)
plt.xticks(rotation=90)
plt.xlabel(u'区间') # 给x轴数据加上名称
plt.ylabel(u'数量占比') # 给y轴数据加上名称
plt.title(u'区间数量占比分布') # 给整个图表加上标题
x = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]
for xx, yy in zip(range(len(data)),data):
plt.text(xx, yy+0.005, str(round(yy*100, 1)) + '%', ha='center')
plt.show()
```
两个bar并列，且加上说明和bar顶部的文字描述
```python
# coding:utf-8
import numpy as np
import matplotlib.pyplot as plt
# 指定默认字体
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['font.family'] = 'sans-serif'
# 用来正常显示负号
plt.rcParams['axes.unicode_minus'] = False
data1 = [30986452, 50727653, 78679555, 126869295, 166980873]
data1 = [round(a/378880486*100,2) for a in data1]
data2 = [4812099, 6957302, 9347045, 11852169, 13049122]
data2 = [round(a/25753696*100,2) for a in data2]
width = 0.3
labels = ['Top50', 'Top100', 'Top200', 'Top500', 'Top1000', ]
plt.bar(np.arange(0, len(data2), 1), data1, tick_label=labels, width=0.3, facecolor = '#9999ff', edgecolor = 'white', label='xx召回')
plt.bar(np.arange(width, len(data2), 1), data2, width=0.3, facecolor = '#ff9999', edgecolor = 'white', label='yy召回')
plt.legend(loc="upper left") # label的位置在左上，没有这句会找不到label去哪了
plt.xlabel(u'Top-K不同K值') # 给x轴数据加上名称
plt.ylabel(u'平均xx率') # 给y轴数据加上名称
plt.title(u'Top-K平均xx率') # 给整个图表加上标题
for xx, yy in zip(range(len(data1)),data1):
plt.text(xx, yy+0.5, str(yy) + '%', ha='center')
for xx, yy in zip(range(len(data2)),data2):
plt.text(xx+width, yy+0.5, str(yy) + '%', ha='center')
plt.show()
```
## 常见问题
### 中文乱码
具体参见如下帖子：
[Mac系统彻底解决matplotlib中文显示乱码的问题](https://blog.csdn.net/Fantasy_Muse/article/details/78585049)
[mac上Matplotlib中文乱码问题](https://blog.csdn.net/minixuezhen/article/details/81516949)
```python
#coding:utf-8
# 指定默认字体
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['font.family'] = 'sans-serif'
# 用来正常显示负号
plt.rcParams['axes.unicode_minus'] = False
# 有中文出现的情况，需要 u'内容'，比如
plt.xlabel(u'横坐标')
```
## 参考资料
* [如何系统地学习Python 中 matplotlib, numpy, scipy, pandas？](https://www.zhihu.com/question/37180159)
matplotlib简介中的简单介绍就是复制的这里。