Visualization [CHI]
画图基础¶
画图参考 matplotlib
In [1]:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
#import warnings; warnings.simplefilter('ignore')
# import seaborn as sns; sns.set()
%matplotlib inline
One-Dimensional Data Set¶
- 随机数
In [2]:
np.random.seed(1000)
y = np.random.standard_normal(20)
- 画图
In [3]:
x = range(len(y))
plt.plot(x, y)
# tag: matplotlib_0
# title: Plot given x- and y-values
Out[3]:
- 增加网格，让坐标紧凑
In [4]:
plt.plot(y.cumsum())
plt.grid(True) # adds a grid
plt.axis('tight') # adjusts the axis ranges
# tag: matplotlib_3_a
# title: Plot with grid and tight axes
Out[4]:
修改坐标起始点
In [5]:
plt.plot(y.cumsum())
plt.grid(True)
plt.xlim(-1, 20)
plt.ylim(np.min(y.cumsum()) - 1,
np.max(y.cumsum()) + 1)
# tag: matplotlib_3_b
# title: Plot with custom axes limits
Out[5]:
- 设置图形大小，设置线点性状、颜色、宽度
- 设置图形横坐标，纵坐标标题
In [6]:
plt.figure(figsize=(7, 4))
# the figsize parameter defines the
# size of the figure in (width, height)
plt.plot(y.cumsum(), 'b', lw=1.5)
plt.plot(y.cumsum(), 'ro')
plt.grid(True)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('A Simple Plot')
# tag: matplotlib_4
# title: Plot with typical labels
Out[6]:
Two-Dimensional Data Set¶
In [7]:
np.random.seed(2000)
y = np.random.standard_normal((20, 2)).cumsum(axis=0)
In [8]:
plt.figure(figsize=(7, 4))
#颜色自动匹配
plt.plot(y, lw=1.5)
# plots two lines
plt.plot(y, 'ro')
# plots two dotted lines
plt.grid(True)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('A Simple Plot')
# tag: matplotlib_5
# title: Plot with two data sets
Out[8]:
- 添加题注
In [9]:
plt.figure(figsize=(7, 4))
plt.plot(y[:, 0], lw=1.5, label='1st')
plt.plot(y[:, 1], lw=1.5, label='2nd')
plt.plot(y, 'ro')
plt.grid(True)
plt.legend(loc=0)
#loc=0,best
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('A Simple Plot')
# tag: matplotlib_6
# title: Plot with labeled data sets
Out[9]:
In [10]:
y[:, 0] = y[:, 0] * 100
plt.figure(figsize=(7, 4))
plt.plot(y[:, 0], lw=1.5, label='1st')
plt.plot(y[:, 1], lw=1.5, label='2nd')
plt.plot(y, 'ro')
plt.grid(True)
plt.legend(loc=0)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('A Simple Plot')
# tag: matplotlib_7
# title: Plot with two differently scaled data sets
Out[10]:
In [11]:
fig, ax1 = plt.subplots()
plt.plot(y[:, 0], 'b', lw=1.5, label='1st')
plt.plot(y[:, 0], 'ro')
plt.grid(True)
plt.legend(loc=8)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value 1st')
plt.title('A Simple Plot')
ax2 = ax1.twinx()
#共享横坐标，独立的纵坐标，坐标标在右边
plt.plot(y[:, 1], 'g', lw=1.5, label='2nd')
plt.plot(y[:, 1], 'ro')
plt.legend(loc=0)
plt.ylabel('value 2nd')
# tag: matplotlib_8
# title: Plot with two data sets and two y-axes
Out[11]:
In [12]:
plt.figure(figsize=(7, 5))
plt.subplot(211)
plt.plot(y[:, 0], lw=1.5, label='1st')
plt.plot(y[:, 0], 'ro')
plt.grid(True)
plt.legend(loc=0)
plt.axis('tight')
plt.ylabel('value')
plt.title('A Simple Plot')
plt.subplot(212)
plt.plot(y[:, 1], 'g', lw=1.5, label='2nd')
plt.plot(y[:, 1], 'ro')
plt.grid(True)
plt.legend(loc=0)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
# tag: matplotlib_9
# title: Plot with two sub-plots
Out[12]:
- plt.subplot()分割指定画图区域
In [13]:
plt.figure(figsize=(9, 4))
plt.subplot(221)
plt.plot(y[:, 0], lw=1.5, label='1st')
plt.plot(y[:, 0], 'ro')
plt.grid(True)
plt.legend(loc=0)
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('1st Data Set')
plt.subplot(224)
plt.bar(np.arange(len(y)), y[:, 1], width=0.5,
color='g', label='2nd')
plt.grid(True)
plt.legend(loc=0)
plt.axis('tight')
plt.xlabel('index')
plt.title('2nd Data Set')
# tag: matplotlib_10
# title: Plot combining line/point sub-plot with bar sub-plot
# size: 80
Out[13]:
Other Plot Styles¶
In [14]:
y = np.random.standard_normal((1000, 2))
- 散点图
In [15]:
plt.figure(figsize=(7, 5))
plt.plot(y[:, 0], y[:, 1], 'ro')
plt.grid(True)
plt.xlabel('1st')
plt.ylabel('2nd')
plt.title('Scatter Plot')
# tag: matplotlib_11_a
# title: Scatter plot via +plot+ function
Out[15]:
- 可以直接用plt.scatter函数
In [16]:
plt.figure(figsize=(7, 5))
plt.scatter(y[:, 0], y[:, 1], marker='o')
plt.grid(True)
plt.xlabel('1st')
plt.ylabel('2nd')
plt.title('Scatter Plot')
# tag: matplotlib_11_b
# title: Scatter plot via +scatter+ function
Out[16]:
- plt.scatter可以指定颜色
In [17]:
c = np.random.randint(0, 10, len(y))
In [18]:
plt.figure(figsize=(7, 5))
plt.scatter(y[:, 0], y[:, 1], c=y[:, 1], marker='o')
plt.colorbar()
plt.grid(True)
plt.xlabel('1st')
plt.ylabel('2nd')
plt.title('Scatter Plot')
# tag: matplotlib_11_c
# title: Scatter plot with third dimension
Out[18]:
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