4. 数据绘图（Matplotlib）

In [1]:

%matplotlib inline

In [2]:

import matplotlib as mpl
import matplotlib.pyplot as plt

In [3]:

import numpy as np

4.1. Get started

画布和图型（Figure and Axis)

In [4]:

x = np.linspace(-5, 2, 100)
y1 = x**3 + 5*x**2 + 10
y2 = 3*x**2 + 10*x
y3 = 6*x + 10
y4 = x**2

In [5]:

len(x) # 100 个从-5 — 2 的数值

Out[5]:

100

最简单的创建办法是这样的

In [6]:

plt.plot(x, y1);
../_images/beginning_04_matplotlib_9_0.png

plt.subplots 是一种简便的创建图表的办法，他会创建一个新的 Figure，并返回一个数组

In [7]:

fig,ax = plt.subplots(2, 3)
../_images/beginning_04_matplotlib_11_0.png 
In [8]:

x = np.linspace(-5, 2, 100)
y1 = x**3 + 5*x**2 + 10
y2 = 3*x**2 + 10*x
y3 = 6*x + 10
y4 = x**2

In [9]:

x1 = -3.33;
y5 = x1**3 + 5*x1**2 + 10;
print y5

28.518463

In [10]:

fig, ax = plt.subplots()
ax.plot(x, y1, color="blue", label="y(x)") # 定义x, y, 颜色，图例上显示的东西
ax.plot(x, y2, color="red", label="y'(x)")
ax.plot(x, y3, color="green", label="y''(x)")
ax.set_xlabel("x") # x标签
ax.set_ylabel("y") # y标签
ax.legend(); # 显示图例
../_images/beginning_04_matplotlib_14_0.png

更复杂的例子

In [11]:

x = np.linspace(-5, 2, 100)
y1 = x**3 + 5*x**2 + 10
y2 = 3*x**2 + 10*x
y3 = 6*x + 10
y4 = x**2

In [12]:

fig, ax = plt.subplots(figsize=(8, 5)) # 定义画布和图形

ax.plot(x, y1, lw=1.5, color="blue", label=r"$y(x)$")
ax.plot(x, y2, lw=1.5, color="red", label=r"$y'(x)$")
ax.plot(x, y3, lw=1.5, color="green", label=r"$y''(x)$")

# 画线，画点，线是由点组成的，可以理解为多个点就组成了线
ax.plot(x, np.zeros_like(x), lw=0.5, color="black") # lw指的是粗细
ax.plot([-3.33, -3.33], [0, (-3.3)**3 + 5*(-3.3)**2 + 10], lw=0.5, ls="--", color="black")# 有时只要知道 x 就行了
ax.plot([0, 0],[0, 10], lw=0.5, ls="--", color="black") # 这个得把相交的点先求值才行
ax.plot([0], [10], lw=0.5, marker='h', color="blue")
ax.plot([-3.33], [(-3.3)**3 + 5*(-3.3)**2 + 10], lw=0.5, marker='o', color="blue")

ax.set_ylim(-15, 40) # 设定y轴上下限
ax.set_yticks([-10, 0, -5, 10, 20, 30])# 故意加一个 -5，有点违和感
ax.set_xticks([-4, -2, 0, 2])

ax.set_xlabel("$x$", fontsize=18) # 设定字体大小
ax.set_ylabel("$y$", fontsize=18)
ax.legend(loc=0, ncol=3, fontsize=14, frameon=False) # loc 等于自己找位置去，ncol 等于列，最后是不要框框
# plt.style.use('ggplot');

Out[12]:

<matplotlib.legend.Legend at 0x106545410>
../_images/beginning_04_matplotlib_17_1.png 
In [13]:

yticks = np.arange(-10, 40, 10)

In [14]:

yticks

Out[14]:

array([-10,   0,  10,  20,  30])

In [15]:

ax.legend??

In [16]:

fig = plt.figure(figsize=(8, 2.5), facecolor="#f1f1f1")

# axes coordinates as fractions of the canvas width and height
left, bottom, width, height = -0.1, -0.1, 0.8, 0.8 # 这一句没看懂，明天保存之后看看
ax = fig.add_axes((left, bottom, width, height), axisbg="#e1e1e1")
x = np.linspace(-2, 2, 1000)
y1 = np.cos(40 * x)
y2 = np.exp(-x**2)

ax.plot(x, y1 * y2)
ax.plot(x, y2, 'g')
ax.plot(x, -y2, 'g')

ax.set_xlabel("x")
ax.set_ylabel("y")

Out[16]:

<matplotlib.text.Text at 0x1061f0e90>
../_images/beginning_04_matplotlib_21_1.png 
In [17]:

fig, ax = plt.subplots(nrows=3, ncols=2)
../_images/beginning_04_matplotlib_22_0.png 
In [18]:

plt.Axes.bar??

例子 4-7：

In [19]:

x = np.linspace(-5, 5, 5)
y = np.ones_like(x)

In [20]:

x

Out[20]:

array([-5. , -2.5,  0. ,  2.5,  5. ])

In [21]:

y

Out[21]:

array([ 1.,  1.,  1.,  1.,  1.])

In [22]:

def axes_settings(fig, ax, title, ymax):
    ax.set_xticks([]) #??
    ax.set_yticks([])
    ax.set_ylim(0, ymax+1)
    ax.set_title(title)

In [23]:

fig, axes = plt.subplots(1, 4, figsize = (16, 3))
../_images/beginning_04_matplotlib_29_0.png

下面两行没还没想清楚

In [ ]:

**??**
x = np.linspace(-5, 5, 5)
y = np.ones_like(x)

def axes_settings(fig, ax, title, ymax):
    ax.set_xticks({}) #??
    ax.set_yticks([])
    ax.set_ylim(0, ymax+1)
    ax.set_title(title)

fig, axes = plt.subplots(1, 4, figsize = (16, 3))

linewidths = [0.5, 1.0, 2.0, 4.0]
for n, linewidth in enumerate(linewidths):
    axes[0].plot(x, y + n, color="blue", linewidth=linewidth)
axes_settings(fig, axes[0], "linewidth", len(linewidths))

In [24]:

# ??
linestyles = ['-', '-.',":"]
for n, linestyle in enumerate(linestyles):
    axes[1].plot(x, y + n, color="")

4.2. Plot types

In [25]:

fignum = 0

def hide_labels(fig, ax): # 隐藏 Labels，如图例，x/y 的属性等
    global fignum    # ？？
    ax.set_xticks([]) # 无 x 轴刻度
    ax.set_yticks([]) # 无 y 轴刻度
    ax.xaxis.set_ticks_position('none') # 无刻度杠
    ax.yaxis.set_ticks_position('none')
    ax.axis('tight')

    fignum += 1

In [26]:

x = np.linspace(-3, 3, 25)
x

Out[26]:

array([-3.  , -2.75, -2.5 , -2.25, -2.  , -1.75, -1.5 , -1.25, -1.  ,
       -0.75, -0.5 , -0.25,  0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,
        1.5 ,  1.75,  2.  ,  2.25,  2.5 ,  2.75,  3.  ])

In [27]:

p = np.arange(-3, 3, 0.25)
p

Out[27]:

array([-3.  , -2.75, -2.5 , -2.25, -2.  , -1.75, -1.5 , -1.25, -1.  ,
       -0.75, -0.5 , -0.25,  0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,
        1.5 ,  1.75,  2.  ,  2.25,  2.5 ,  2.75])

In [28]:

y1 = x**3 + 3*x**2 + 10
y2 = -1.5*x**3 + 10*x**2 - 15
y3 = x**3

In [29]:

fig, ax = plt.subplots(figsize=(4, 3))
ax.plot(x, y1) # plot 是线图
ax.plot(x, y2)
ax.plot(x, y3)
hide_labels(fig, ax)
../_images/beginning_04_matplotlib_38_0.png 
In [30]:

fig, ax = plt.subplots(figsize=(4, 3))
ax.step(x, y1) # step 是阶梯图
ax.step(x, y2)
# ax.step(x, y3) # 加一条会自己生成红色
hide_labels(fig, ax)
../_images/beginning_04_matplotlib_39_0.png 
In [31]:

fig, ax = plt.subplots(figsize=(4, 3))
width = 6/50.0 # width 是宽度，0.12 是 x 间隔的一半
ax.bar(x - width/2, y1, width=width, color="blue") # bar 柱型图，主要分析离散数据
ax.bar(x + width/2, y2, width=width, color="green")
hide_labels(fig, ax)  # 没加这条图会变的很小
../_images/beginning_04_matplotlib_40_0.png 
In [32]:

width

Out[32]:

0.12

In [33]:

6/50 # 没加.0,自然就没有浮点数了

Out[33]:

0

In [34]:

fig, ax = plt.subplots(figsize=(4, 3))
ax.fill_between(x, y1, y2, y3, color="green") # 中间为何是空的，不太明白

hide_labels(fig, ax)
../_images/beginning_04_matplotlib_43_0.png 
In [35]:

fig, ax = plt.subplots(figsize=(4, 3))
ax.hist(y2, bins=30)
ax.hist(y1,bins=30) # 判断的是连续变量，打成了30组

hide_labels(fig, ax)
../_images/beginning_04_matplotlib_44_0.png 
In [36]:

fig, ax = plt.subplots(figsize=(4, 3))

ax.errorbar(x, y2, yerr=y1, fmt="o-") # errorbar 应该是误差棒的意思，点的上下线

hide_labels(fig, ax)
../_images/beginning_04_matplotlib_45_0.png 
In [37]:

fig, ax = plt.subplots(figsize=(4, 3))

ax.stem(x, y2, 'b', markerfmt='bs') # r,是代表颜色，markerfmt 是形状
ax.stem(x, y1, "r", markerfmt='ro') # 这种图主要把x的高度标出来

hide_labels(fig, ax)
../_images/beginning_04_matplotlib_46_0.png 
In [38]:

ax.stem??

In [39]:

fig, ax = plt.subplots(figsize=(4, 3))

x = np.linspace(0, 5, 50)
ax.scatter(x, -1 + x + 0.25 * x**2 + 2 * np.random.rand(len(x)))
ax.scatter(x, np.sqrt(x) + 2 * np.random.rand(len(x)), color='green') # sqrt是什么含义？？

hide_labels(fig, ax)
../_images/beginning_04_matplotlib_48_0.png

4.3. Advanced Features

In [40]:

fig, ax = plt.subplots(figsize=(8, 4))

x = np.linspace(-20, 20, 100)
y = np.sin(x) / x

ax.plot(x, y)

ax.set_ylabel("y label")
ax.set_xlabel("x label")

for label in ax.get_xticklabels() + ax.get_yticklabels(): # get到标度,后循环set
    label.set_rotation(45)  # 然后旋转45度
../_images/beginning_04_matplotlib_50_0.png

Axes

In [41]:

fig, axes = plt.subplots(ncols=2, nrows=3) # 生成一个 2*3 的图形画布, 为何这里不能用 ax
../_images/beginning_04_matplotlib_52_0.png

data1

In [42]:

fig, axes = plt.subplots(1, 2, figsize=(8, 3.5), sharey=True)  # sharey 则是代表是否同用一个 y 轴

data1 = np.random.randn(200, 2) * np.array([3, 1]) #产生两列符合标准正太分布的100组数据，做了一个广播，第一列乘以3，第二列乘以1，把标准差扩大了
#area2 = (np.random.randn(200) + 0.5) * 100

data2 = np.random.randn(200, 2) * np.array([1, 3]) #data1 是在x的方向扩大了变异度，data2是在y的方向
# area2 = (np.random.randn(200) + 0.5) * 100

# 第一列当作x，第二列当作y,marker是形状，size 是大小，alpha 是透明度
axes[0].scatter(data1[:,0], data1[:,1], color="green", marker="s", s=30, alpha=0.5) # alpha 是大小的意思，上限是1
axes[0].scatter(data2[:,0], data2[:,1], color="blue", marker="o", s=30, alpha=0.5) # 加了另外一组数据之后，由于刻度的变化整个图形也有所变化

axes[1].hist([data1[:,1], data2[:,1]], bins=15, color=["green", "blue"], alpha=0.5, orientation='horizontal');# []是代表有两组图
../_images/beginning_04_matplotlib_54_0.png

legends 调节图例

In [43]:

fig, axes = plt.subplots(1, 4, figsize=(16,4))

x = np.linspace(0, 1, 100)

for n in range(4):
    axes[n].plot(x, x, label="y(x) = x" )
    axes[n].plot(x, x + x**2, label="y(x) = x + x**2")
    axes[n].legend(loc=n+1) # 一行把所有的图例都加上了，for 循环的魅力，如果没有+1，则变成 0，0，1，2
    axes[n].set_title("legend(loc=%d)" % (n+1)) # 原来格式化字符还可以这么玩

## ??理解下n=1，2，3，4 时有什么区别
../_images/beginning_04_matplotlib_56_0.png

4.4. Advanced grid layout

inset

In [44]:

fig = plt.figure(figsize=(8, 4))

def f(x):
    return 1/(1 + x**2) + 0.1/(1 + ((3 -x)/0.1)**2)

def plot_and_format_axes(ax, x, f, fontsize): ## ?? 定义里面的那张图，x，尺寸
    ax.plot(x, f(x),linewidth=2)
    ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(5))
    ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(4))
    ax.set_xlabel(r"$x$", fontsize=fontsize)
    ax.set_ylabel(r"$f(x)$", fontsize=fontsize)

# main graph 主要的图
ax = fig.add_axes([0.1, 0.15, 0.8, 0.8], axisbg="#f5f5f5")
x = np.linspace(-4, 14, 10000) #
plot_and_format_axes(ax, x, f, 18)

# inset
x0, x1 = 2.5, 3.5 # 小图x的上下界
ax = fig.add_axes([0.5, 0.5, 0.38, 0.42], axisbg="none") #这条是定义4个角的位置 axisbg 是？？是没有颜色的意思吗？
x = np.linspace(x0, x1, 1000)
plot_and_format_axes(ax, x, f, 14)

# ？？ 理解下那个小块具体是怎么画出来的
../_images/beginning_04_matplotlib_59_0.png 
In [45]:

ncols, nrows = 3, 3

fig, axes = plt.subplots(nrows, ncols)

for m in range(nrows):
    for n in range(ncols):
        axes[m, n].set_xticks([]) # 所有的x轴标度干掉,
        axes[m, n].set_yticks([])
        axes[m, n].text(0.5, 0.5, "axes[%d, %d]" % (m, n),
                       horizontalalignment='center') # ？？那个长的我都不想输的单词是说放图里码？
../_images/beginning_04_matplotlib_60_0.png 
In [46]:

fig, axes = plt.subplots(2, 2, figsize=(6, 6), sharex=True, sharey=True, squeeze=False) # squeeze 可以 y 轴隐藏,yb'ig

x1 = np.random.randn(100) # 生成一维的100个数组
x2 = np.random.randn(100)

axes[0, 0].set_title("Uncorrelated")
axes[0, 0].scatter(x1, x2)

axes[0, 1].set_title("Weakly positively correlated")
axes[0, 1].plot(x1, x1 + x2)

axes[1, 0].set_title("Weakly negatively correlated")
axes[1, 0].scatter(x1, -x1 + x2) # 负相关

axes[1, 1].set_title("Strongly correlated")
axes[1, 1].scatter(x1, x1 + 0.15 * x2) # 减少x2 的分量，强相关了

axes[1, 1].set_xlabel("x")
axes[1, 0].set_ylabel("y")
axes[0, 0].set_ylabel("y")
axes[1, 0].set_xlabel("x")

Out[46]:

<matplotlib.text.Text at 0x1060d0510>
../_images/beginning_04_matplotlib_61_1.png

查看 Matplotlib 的 style

In [47]:

print plt.style.available

[u'seaborn-darkgrid', u'seaborn-notebook', u'classic', u'seaborn-ticks', u'grayscale', u'bmh', u'seaborn-talk', u'dark_background', u'ggplot', u'fivethirtyeight', u'seaborn-colorblind', u'seaborn-deep', u'seaborn-whitegrid', u'seaborn-bright', u'seaborn-poster', u'seaborn-muted', u'seaborn-paper', u'seaborn-white', u'seaborn-pastel', u'seaborn-dark', u'seaborn-dark-palette']