生信Python绘图基础

文摘科学 2023-05-16 08:34 江苏

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一、Python绘图介绍

1.1python绘图库介绍

1.1.1Matplotlib库

matplotlib作为Python的基本绘图库，是Python中应用最广泛的绘图工具包之一，matplotlib能和其他很多库结合，如pandas等

1.1.2ggplot2其他库

包括ggplot2和seaborn,还有pyecharts库等都是第三方绘图库，可以优化Python图形，使得Python数据可视化结果更加美观

1.2绘图环境部署

1.2.1安装python包

C:\Users\asp>pip install numpy

C:\Users\asp>python -m pip install --upgrade pip

C:\Users\asp>pip install matplotlib

C:\Users\asp>pip install pandas

二、基本绘图入门

2.1绘制sin图

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdimport osos.chdir(r'C:\34-Python绘图')# 简单图形代码x = np.linspace(0,10,100)y = np.sin(x)plt.plot(x,y)plt.show()

2.2绘制坐标图-标签和图例

x = np.arange(0,1.1,0.01)y = x**2plt.figure(figsize=(9,9),dpi=80) #确定画布大小，dpi:图形分辨率plt.title('lines') #添加标题plt.xlabel('x1')plt.ylabel('y')plt.xlim((0,1)) # 确定x轴的范围plt.ylim((0,1)) # 确定x轴的范围plt.xticks([0,0.2,0.4,0.6,0.8,1]) #确定x轴的刻度plt.yticks([0,0.2,0.4,0.6,0.8,1]) #确定y轴的刻度plt.plot(x,y, label='y =x^2')plt.legend(loc='best') # 图例plt.show()

>>> x = np.arange(0,1.1,0.01)>>> y = x**2>>> plt.figure(figsize=(9,9),dpi=80) #确定画布大小，dpi:图形分辨率

   

>>> plt.title('lines') #添加标题Text(0.5, 1.0, 'lines')

>>> plt.xlabel('x1')Text(0.5, 0, 'x1')

>>> plt.ylabel('y')Text(0, 0.5, 'y')            
>>> plt.xlim((0,1)) # 确定x轴的范围(0, 1)>>> plt.ylim((0,1)) # 确定x轴的范围(0, 1)            
>>> plt.xticks([0,0.2,0.4,0.6,0.8,1]) #确定x轴的刻度>>> plt.yticks([0,0.2,0.4,0.6,0.8,1]) #确定y轴的刻度            
>>> plt.plot(x,y, label='y =x^2')[]            
>>> plt.legend(loc='best') # 图例

>>> plt.show()>>> xarray([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,       0.99, 1.  , 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09])            
>>> yarray([0.0000e+00, 1.0000e-04, 4.0000e-04, 9.0000e-04, 1.6000e-03,       2.5000e-03, 3.6000e-03, 4.9000e-03, 6.4000e-03, 8.1000e-03,       1.0000e-02, 1.2100e-02, 1.4400e-02, 1.6900e-02, 1.9600e-02,       2.2500e-02, 2.5600e-02, 2.8900e-02, 3.2400e-02, 3.6100e-02,       4.0000e-02, 4.4100e-02, 4.8400e-02, 5.2900e-02, 5.7600e-02,       6.2500e-02, 6.7600e-02, 7.2900e-02, 7.8400e-02, 8.4100e-02,       9.0000e-02, 9.6100e-02, 1.0240e-01, 1.0890e-01, 1.1560e-01,       1.2250e-01, 1.2960e-01, 1.3690e-01, 1.4440e-01, 1.5210e-01,       1.6000e-01, 1.6810e-01, 1.7640e-01, 1.8490e-01, 1.9360e-01,       2.0250e-01, 2.1160e-01, 2.2090e-01, 2.3040e-01, 2.4010e-01,       2.5000e-01, 2.6010e-01, 2.7040e-01, 2.8090e-01, 2.9160e-01,       3.0250e-01, 3.1360e-01, 3.2490e-01, 3.3640e-01, 3.4810e-01,       3.6000e-01, 3.7210e-01, 3.8440e-01, 3.9690e-01, 4.0960e-01,       4.2250e-01, 4.3560e-01, 4.4890e-01, 4.6240e-01, 4.7610e-01,       4.9000e-01, 5.0410e-01, 5.1840e-01, 5.3290e-01, 5.4760e-01,       5.6250e-01, 5.7760e-01, 5.9290e-01, 6.0840e-01, 6.2410e-01,       6.4000e-01, 6.5610e-01, 6.7240e-01, 6.8890e-01, 7.0560e-01,       7.2250e-01, 7.3960e-01, 7.5690e-01, 7.7440e-01, 7.9210e-01,       8.1000e-01, 8.2810e-01, 8.4640e-01, 8.6490e-01, 8.8360e-01,       9.0250e-01, 9.2160e-01, 9.4090e-01, 9.6040e-01, 9.8010e-01,       1.0000e+00, 1.0201e+00, 1.0404e+00, 1.0609e+00, 1.0816e+00,       1.1025e+00, 1.1236e+00, 1.1449e+00, 1.1664e+00, 1.1881e+00])>>>

2.3增加元素

x = np.linspace(0,10,100)y = np.sin(x)plt.plot(x,y,ls=':',lw=2,label='x和y的关系')plt.legend()plt.show()

>>> x = np.linspace(0,10,100)>>> y = np.sin(x)>>> plt.plot(x,y,ls=':',lw=2,label='x和y的关系')[]>>> plt.legend()            >>> plt.show()C:\Python\Python36\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 21644 missing from current font.  font.set_text(s, 0.0, flags=flags)

x = np.linspace(0,10,100)y = np.sin(x)plt.plot(x,y,ls='-',lw=2,label='x和y的关系')plt.legend()plt.show()

实点线

2.4线条控制

x = np.linspace(0,10,100)y = np.sin(x)plt.plot(x,y,ls=':',lw=2,label='x和y的关系')plt.legend(loc= 'upper center')plt.show()

# ls- -函数线条风格(='-' 实线, '--' 虚线 ,'-.' 点划线 ,':' 实点线)

# lw 线条宽度

2.5线条调整

# 调整线条样式，宽度，形状和点# marker线条上点的形状# markersize点的大小# c 颜色# markeredgecolor点的边框色# markerfacecolor点的填充色x = np.linspace(0,10,100)y = np.sin(x)#plt.plot(x,y,ls=':',lw=2,marker='D',markersize=2,c= 'r',label='x和y的关系')plt.plot(x,y,ls='--',lw=2,marker='s',markersize=2,c= 'red',markeredgecolor ='blue',markerfacecolor='black',label='x和y的关系')plt.legend(loc= 'center')plt.show()

2.6小技巧

2.6.1支持中文显示

plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False

2.7plot绘图模块

2.7.1基本介绍

matplotlib.plot是最常见的绘图的模块，语法如下：

plt.plot(x,y,ls=,lw=,c=,marker=,markersize=,markeredgecolor=,markerfacecolor, label=)

x: x轴上的数值

y: y轴上的数值

ls: 折线的风格(‘-‘, ’ --‘, ’ -.‘和':‘)

lw: 线条宽度

c: 颜色

marker: 线条上点的形状

markersize: 线条上点的大小

markeredgecolor: 点的边框色

markerfacecolor: 点的填充色

label: 文本标签

2.7.2线条风格参数

线条风格参数'-'       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

2.7.3颜色

cnames = {'aliceblue':            '#F0F8FF','antiquewhite':         '#FAEBD7','aqua':                 '#00FFFF','aquamarine':           '#7FFFD4','azure':                '#F0FFFF','beige':                '#F5F5DC','bisque':               '#FFE4C4','black':                '#000000','blanchedalmond':       '#FFEBCD','blue':                 '#0000FF','blueviolet':           '#8A2BE2','brown':                '#A52A2A','burlywood':            '#DEB887','cadetblue':            '#5F9EA0','chartreuse':           '#7FFF00','chocolate':            '#D2691E',…………………………'violet':               '#EE82EE','wheat':                '#F5DEB3','white':                '#FFFFFF','whitesmoke':           '#F5F5F5','yellow':               '#FFFF00','yellowgreen':          '#9ACD32'}

三、简单绘图例子

3.1安装对应的包

C:\Users\asp>pip install xlrd

3.2读取文件

文件内容

Date	Counts	Times
2017/10/1	399	763
2017/10/2	126	345
2017/10/3	76	249
2017/10/4	59	182
2017/10/5	60	165
2017/10/6	59	332
2017/10/7	80	299
2017/10/8	801	1297

wechat = pd.read_excel('wechat.xlsx')wechat.Date=pd.to_datetime(wechat.Date,format='%Y-%m-%d')查看数据信息wechatwechat.info()>>> wechat = pd.read_excel('wechat.xlsx')wechat.Date=pd.to_datetime(wechat.Date,format='%Y-%m-%d')wechat

>>> wechat         Date  Counts  Times0  2017-10-01     399    7631  2017-10-02     126    3452  2017-10-03      76    2493  2017-10-04      59    1824  2017-10-05      60    165..        ...     ...    ...87 2017-12-27    1199   228288 2017-12-28    1833   283989 2017-12-29    1820   299290 2017-12-30     323   109691 2017-12-31    1894   2710            
[92 rows x 3 columns]

>>> wechat.info()            RangeIndex: 92 entries, 0 to 91Data columns (total 3 columns):Date      92 non-null datetime64[ns]Counts    92 non-null int64Times     92 non-null int64dtypes: datetime64[ns](1), int64(2)memory usage: 2.3 KB

3.3绘制单条折线图

# 绘制单条折线图plt.plot(wechat.Date,                 # x轴数据         wechat.Counts,               # y轴数据         linestyle = '-',               # 折线类型         linewidth = 2,                 # 折线宽度         color = 'steelblue',        # 折线颜色         marker = 'o',                      # 折线图中添加圆点         markersize = 6,               # 点的大小         markeredgecolor='black',        # 点的边框色         markerfacecolor='red')        # 点的填充色# 添加y轴标签plt.ylabel('人数')plt.xticks(rotation=45)# 添加图形标题plt.title('每天微信文章阅读人数趋势')# 显示图形plt.show()

3.4保存图形到文件

######保存图形x = np.arange(0,1.1,0.01)y = x**2plt.figure(figsize=(9,9),dpi=80) #确定画图大小，dpi:图形分辨率plt.title('lines') #添加标题plt.xlabel('x')plt.ylabel('y')plt.xlim((0,1)) # 确定x轴的范围plt.ylim((0,1)) # 确定x轴的范围plt.xticks([0,0.2,0.4,0.6,0.8,1]) #确定x轴的刻度plt.yticks([0,0.2,0.4,0.6,0.8,1]) #确定y轴的刻度            
plt.plot(x,y,label='y =x^2')plt.legend(loc='best')plt.savefig(r'C:\34-Python绘图\\保存1.pdf')plt.show()

Python绘图其实也很强大，以上是基础内容。

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