diff --git a/README.md b/README.md index d3c1b1e..e50523f 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， - 使用前请将matplotlib升级至V3.4.2以上(2022年1月)，否则可能会出现报错！ - 本教程独立网站已上线：[https://datawhalechina.github.io/fantastic-matplotlib/](https://datawhalechina.github.io/fantastic-matplotlib/) -- 使用时若发现任何问题，或是你对项目内容有好的建议，欢迎留言交流，联系邮箱skywateryang@126.com +- 使用时若发现任何问题，或是你对项目内容有好的建议，欢迎留言交流，联系邮箱skywateryang@126.com，微信`skywateryang` ## 目录 @@ -60,6 +60,8 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， + + ## 致谢 感谢以下Datawhale成员对项目推进作出的贡献(排名不分先后)： diff --git a/docs/_images/index_10_01.png b/docs/_images/index_10_01.png index 1a2c21d..42323e9 100644 Binary files a/docs/_images/index_10_01.png and b/docs/_images/index_10_01.png differ diff --git a/docs/_images/index_10_02.png b/docs/_images/index_10_02.png index 3687698..86d86f3 100644 Binary files a/docs/_images/index_10_02.png and b/docs/_images/index_10_02.png differ diff --git a/docs/_images/index_11_0.png 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一、概述 ### 1. matplotlib的三层api @@ -224,7 +224,7 @@ Patch类的构造函数： hatch=None, fill=True, capstyle=None, joinstyle=None, **kwargs) -本小节重点讲述三种最常见的子类，矩形，多边形和楔型。 +本小节重点讲述三种最常见的子类，矩形，多边形和楔形。 #### a. Rectangle-矩形 @@ -251,10 +251,11 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别 + **log**: bool，默认False,即y坐标轴是否选择指数刻度 + **stacked**: bool，默认为False，是否为堆积状图 -hist绘制直方图 + ```{code-cell} ipython3 +# hist绘制直方图 x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 @@ -268,10 +269,11 @@ plt.xlim(0,100); #设置x轴分布范围 plt.show() ​ -`Rectangle`矩形类绘制直方图 + ```{code-cell} ipython3 +# Rectangle矩形类绘制直方图 df = pd.DataFrame(columns = ['data']) df.loc[:,'data'] = x df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True) @@ -402,10 +404,11 @@ matplotlib.pyplot.pie语法： + **colors**：饼图循环使用的颜色序列。如果取值为None，将使用当前活动循环中的颜色。 + **startangle**：饼状图开始的绘制的角度。 -pie绘制饼状图 + ```{code-cell} ipython3 +# pie绘制饼状图 labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] explode = (0, 0.1, 0, 0) @@ -420,10 +423,11 @@ ax1.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle. ​ -wedge绘制饼图 + ```{code-cell} ipython3 +# wedge绘制饼图 fig = plt.figure(figsize=(5,5)) ax1 = fig.add_subplot(111) theta1 = 0 diff --git a/docs/_sources/第五回：样式色彩秀芳华/index.md.txt b/docs/_sources/第五回：样式色彩秀芳华/index.md.txt index c8f94b0..734778f 100644 --- a/docs/_sources/第五回：样式色彩秀芳华/index.md.txt +++ b/docs/_sources/第五回：样式色彩秀芳华/index.md.txt @@ -7,7 +7,11 @@ kernelspec: name: python3 --- # 第五回：样式色彩秀芳华 - +```{code-cell} ipython3 +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +``` 第五回详细介绍matplotlib中样式和颜色的使用，绘图样式和颜色是丰富可视化图表的重要手段，因此熟练掌握本章可以让可视化图表变得更美观，突出重点和凸显艺术性。 关于绘图样式，常见的有3种方法，分别是修改预定义样式，自定义样式和rcparams。 @@ -23,11 +27,7 @@ kernelspec: matplotlib贴心地提供了许多内置的样式供用户使用，使用方法很简单，只需在python脚本的最开始输入想使用style的名称即可调用，尝试调用不同内置样式，比较区别 -```{code-cell} ipython3 -import matplotlib as mpl -import matplotlib.pyplot as plt -import numpy as np -``` + ```{code-cell} ipython3 @@ -125,7 +125,7 @@ plt.plot([1,2,3,4],[2,3,4,5]); -另外matplotlib也还提供了了一种更便捷的修改样式方式，可以一次性修改多个样式。 +另外matplotlib也还提供了一种更便捷的修改样式方式，可以一次性修改多个样式。 ```{code-cell} ipython3 @@ -142,7 +142,6 @@ plt.plot([1,2,3,4],[2,3,4,5]); `色相`： 没有明显的顺序性、一般不用来表达数据量的高低，而是用来表达数据列的类别。 `明度和饱和度`： 在视觉上很容易区分出优先级的高低、被用作表达顺序或者表达数据量视觉通道。 具体关于色彩理论部分的知识，不属于本教程的重点，请参阅有关拓展材料学习。 -[ECharts数据可视化实验室](https://vis.baidu.com/chartcolor/basis/) [学会这6个可视化配色基本技巧，还原数据本身的意义](https://zhuanlan.zhihu.com/p/88892542) 在matplotlib中，设置颜色有以下几种方式： diff --git a/docs/_sources/第四回：文字图例尽眉目/index.md.txt b/docs/_sources/第四回：文字图例尽眉目/index.md.txt index 679c7ef..6ac1044 100644 --- a/docs/_sources/第四回：文字图例尽眉目/index.md.txt +++ b/docs/_sources/第四回：文字图例尽眉目/index.md.txt @@ -368,7 +368,7 @@ ax.xaxis.set_major_formatter(formatoddticks); 在普通的绘图中，我们可以直接通过上图的set_ticks进行设置刻度的位置，缺点是需要自己指定或者接受matplotlib默认给定的刻度。当需要更改刻度的位置时，matplotlib给了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过axs.xaxis.set_major_locator(locator)绘制即可 -locator=plt.MaxNLocator(nbins=7) +locator=plt.MaxNLocator(nbins=7)#自动选择合适的位置，并且刻度之间最多不超过7（nbins）个间隔 locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置 locator=plt.AutoLocator()#自动分配刻度值的位置 locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始 @@ -385,7 +385,7 @@ for n, ax in enumerate(axs.flat): locator = matplotlib.ticker.AutoLocator() axs[0, 0].xaxis.set_major_locator(locator) -locator = matplotlib.ticker.MaxNLocator(nbins=10) +locator = matplotlib.ticker.MaxNLocator(nbins=3) axs[0, 1].xaxis.set_major_locator(locator) diff --git a/docs/searchindex.js b/docs/searchindex.js index 88b642b..531d7e6 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/docs/第二回：艺术画笔见乾坤/index.html b/docs/第二回：艺术画笔见乾坤/index.html index feb11e8..8ac23d8 100644 --- a/docs/第二回：艺术画笔见乾坤/index.html +++ b/docs/第二回：艺术画笔见乾坤/index.html @@ -307,7 +307,9 @@
-
+
+

第二回：艺术画笔见乾坤

+
import numpy as np
 import pandas as pd
@@ -321,8 +323,6 @@
-
-

第二回：艺术画笔见乾坤

一、概述

@@ -432,7 +432,7 @@
-../_images/index_2_01.png +../_images/index_3_01.png
@@ -446,7 +446,7 @@
-../_images/index_3_01.png +../_images/index_4_01.png
@@ -460,7 +460,7 @@
-../_images/index_4_01.png +../_images/index_5_01.png
@@ -490,10 +490,10 @@
-
[<matplotlib.lines.Line2D object at 0x00000298AE900A30>, <matplotlib.lines.Line2D object at 0x00000298AE900DC0>]
+
[<matplotlib.lines.Line2D object at 0x0000029FF51E0A90>, <matplotlib.lines.Line2D object at 0x0000029FF51E0E20>]
 

 

-../_images/index_6_1.png
+../_images/index_7_1.png
@@ -513,7 +513,7 @@
-../_images/index_7_01.png +../_images/index_8_01.png

​
@@ -545,7 +545,7 @@ pyplot里有个专门绘制误差线的功能，通过 -../_images/index_9_01.png +../_images/index_10_01.png

​
@@ -562,7 +562,7 @@ linewidth=None, linestyle=None, antialiased=None, hatch=None, fill=True, capstyle=None, joinstyle=None, **kwargs)

-

本小节重点讲述三种最常见的子类，矩形，多边形和楔型。

+

本小节重点讲述三种最常见的子类，矩形，多边形和楔形。

a. Rectangle-矩形

Rectangle矩形类在官网中的定义是： 通过锚点xy及其宽度和高度生成。 @@ -586,10 +586,10 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别

  • log: bool，默认False,即y坐标轴是否选择指数刻度

  • stacked: bool，默认为False，是否为堆积状图

    • -

    hist绘制直方图

    -
    x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 
+
    # hist绘制直方图
+x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 
 bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... 
 plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 
 plt.xlabel('scores') 
@@ -599,14 +599,14 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
 
    
 
    
 
    
-../_images/index_11_01.png
+../_images/index_12_0.png

    ​

    -

    Rectangle矩形类绘制直方图

    -
    df = pd.DataFrame(columns = ['data'])
+
    # Rectangle矩形类绘制直方图
+df = pd.DataFrame(columns = ['data'])
 df.loc[:,'data'] = x
 df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True)
 
@@ -632,7 +632,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
 
    
 
    
 
    
-../_images/index_13_01.png
+../_images/index_14_0.png

    ​

    @@ -665,7 +665,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
    -../_images/index_15_01.png +../_images/index_16_0.png

    ​

    @@ -684,7 +684,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
    -../_images/index_17_01.png +../_images/index_18_0.png

    ​

    @@ -714,7 +714,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_19_0.png +../_images/index_20_0.png

    ​

    @@ -741,10 +741,10 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边

  • colors：饼图循环使用的颜色序列。如果取值为None，将使用当前活动循环中的颜色。

  • startangle：饼状图开始的绘制的角度。

    • -

    pie绘制饼状图

    -
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
+
    # pie绘制饼状图
+labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
 sizes = [15, 30, 45, 10] 
 explode = (0, 0.1, 0, 0) 
 fig1, ax1 = plt.subplots() 
@@ -754,15 +754,15 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
 
    
 
    
 
    
-../_images/index_21_0.png
+../_images/index_22_0.png

    ​

    ​

    -

    wedge绘制饼图

    -
    fig = plt.figure(figsize=(5,5))
+
    # wedge绘制饼图
+fig = plt.figure(figsize=(5,5))
 ax1 = fig.add_subplot(111)
 theta1 = 0
 sizes = [15, 30, 45, 10] 
@@ -781,7 +781,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
 
    
 
    
 
    
-../_images/index_23_0.png
+../_images/index_24_0.png

    ​

    @@ -813,7 +813,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_25_0.png +../_images/index_26_0.png

    ​

    @@ -851,7 +851,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_27_0.png +../_images/index_28_0.png

    ​

    @@ -881,7 +881,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 [<AxesSubplot:>, <Axes:>] -../_images/index_29_1.png +../_images/index_30_1.png

    由于Figure维持了current axes，因此你不应该手动的从Figure.axes列表中添加删除元素，而是要通过Figure.add_subplot()、Figure.add_axes()来添加元素，通过Figure.delaxes()来删除元素。但是你可以迭代或者访问Figure.axes中的Axes，然后修改这个Axes的属性。

    @@ -897,7 +897,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_31_0.png +../_images/index_32_0.png

    ​

    @@ -925,7 +925,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_33_0.png +../_images/index_34_0.png

    ​

    @@ -983,7 +983,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 
    array([-0.2,  4.2])
    -../_images/index_35_1.png +../_images/index_36_1.png

    ​

    @@ -1014,7 +1014,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
    -../_images/index_37_0.png +../_images/index_38_0.png

    ​

    @@ -1048,7 +1048,7 @@ x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴
    -../_images/index_39_0.png +../_images/index_40_0.png

    ​

    diff --git a/docs/第五回：样式色彩秀芳华/index.html b/docs/第五回：样式色彩秀芳华/index.html index a0dfd10..ca95198 100644 --- a/docs/第五回：样式色彩秀芳华/index.html +++ b/docs/第五回：样式色彩秀芳华/index.html @@ -262,6 +262,15 @@

    第五回：样式色彩秀芳华

    +
    +
    +
    import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
    +
    +
    +

    第五回详细介绍matplotlib中样式和颜色的使用，绘图样式和颜色是丰富可视化图表的重要手段，因此熟练掌握本章可以让可视化图表变得更美观，突出重点和凸显艺术性。
    关于绘图样式，常见的有3种方法，分别是修改预定义样式，自定义样式和rcparams。
    关于颜色使用，本章介绍了常见的5种表示单色颜色的基本方法，以及colormap多色显示的方法。

    @@ -274,22 +283,13 @@

    matplotlib贴心地提供了许多内置的样式供用户使用，使用方法很简单，只需在python脚本的最开始输入想使用style的名称即可调用，尝试调用不同内置样式，比较区别

    -
    import matplotlib as mpl
-import matplotlib.pyplot as plt
-import numpy as np
-
    -
    -
    -
    -
    -
    plt.style.use('default')
 plt.plot([1,2,3,4],[2,3,4,5]);
    -../_images/index_2_02.png +../_images/index_3_02.png
    @@ -300,7 +300,7 @@
    -../_images/index_3_02.png +../_images/index_4_02.png

    那么matplotlib究竟内置了那些样式供使用呢？总共以下26种丰富的样式可供选择。

    @@ -337,7 +337,7 @@ ytick.labelsize : 16

    -../_images/index_7_02.png +../_images/index_8_02.png

    值得特别注意的是，matplotlib支持混合样式的引用，只需在引用时输入一个样式列表，若是几个样式中涉及到同一个参数，右边的样式表会覆盖左边的值。

    @@ -349,7 +349,7 @@ ytick.labelsize : 16

    -../_images/index_9_02.png +../_images/index_10_02.png
    @@ -365,7 +365,7 @@ ytick.labelsize : 16

    -../_images/index_11_02.png +../_images/index_12_01.png
    @@ -377,10 +377,10 @@ ytick.labelsize : 16

    -../_images/index_12_0.png +../_images/index_13_01.png
    -

    另外matplotlib也还提供了了一种更便捷的修改样式方式，可以一次性修改多个样式。

    +

    另外matplotlib也还提供了一种更便捷的修改样式方式，可以一次性修改多个样式。

    mpl.rc('lines', linewidth=4, linestyle='-.')
@@ -389,7 +389,7 @@ ytick.labelsize : 16
    -../_images/index_14_0.png +../_images/index_15_01.png
    @@ -401,7 +401,6 @@ ytick.labelsize : 16

    色相： 没有明显的顺序性、一般不用来表达数据量的高低，而是用来表达数据列的类别。
    明度和饱和度： 在视觉上很容易区分出优先级的高低、被用作表达顺序或者表达数据量视觉通道。
    具体关于色彩理论部分的知识，不属于本教程的重点，请参阅有关拓展材料学习。
    -ECharts数据可视化实验室
    学会这6个可视化配色基本技巧，还原数据本身的意义

    在matplotlib中，设置颜色有以下几种方式：

    @@ -422,7 +421,7 @@ ytick.labelsize : 16

    -../_images/index_17_02.png +../_images/index_18_01.png
    @@ -437,7 +436,7 @@ ytick.labelsize : 16

    -../_images/index_19_01.png +../_images/index_20_01.png

    RGB颜色和HEX颜色之间是可以一一对应的，以下网址提供了两种色彩表示方法的转换工具。
    @@ -453,7 +452,7 @@ ytick.labelsize : 16

    -../_images/index_21_01.png +../_images/index_22_01.png
    @@ -467,7 +466,7 @@ ytick.labelsize : 16

    -../_images/index_23_01.png +../_images/index_24_01.png
    @@ -481,7 +480,7 @@ ytick.labelsize : 16

    -../_images/index_25_01.png +../_images/index_26_01.png

    @@ -507,7 +506,7 @@ ytick.labelsize : 16

    -../_images/index_27_01.png +../_images/index_28_01.png

    在以下官网页面可以查询上述五种colormap的字符串表示和颜色图的对应关系
    diff --git a/docs/第四回：文字图例尽眉目/index.html b/docs/第四回：文字图例尽眉目/index.html index 5049942..977ff3a 100644 --- a/docs/第四回：文字图例尽眉目/index.html +++ b/docs/第四回：文字图例尽眉目/index.html @@ -391,7 +391,7 @@

    -../_images/index_5_01.png +../_images/index_5_02.png

    ​

    @@ -467,7 +467,7 @@ ylabel方式类似，这里不重复写出。
    -../_images/index_7_03.png +../_images/index_7_01.png

    ​

    @@ -493,7 +493,7 @@ ylabel方式类似，这里不重复写出。
    -../_images/index_9_03.png +../_images/index_9_01.png

    ​

    @@ -523,7 +523,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_11_03.png +../_images/index_11_01.png

    ​

    @@ -589,7 +589,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_17_03.png +../_images/index_17_01.png

    ​

    @@ -608,7 +608,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_19_02.png +../_images/index_19_0.png

    ​

    @@ -668,7 +668,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_23_02.png +../_images/index_23_0.png

    ​

    @@ -689,7 +689,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_25_02.png +../_images/index_25_0.png

    ​

    @@ -698,7 +698,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多

    b) Tick Locators

    在普通的绘图中，我们可以直接通过上图的set_ticks进行设置刻度的位置，缺点是需要自己指定或者接受matplotlib默认给定的刻度。当需要更改刻度的位置时，matplotlib给了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过axs.xaxis.set_major_locator(locator)绘制即可
    -locator=plt.MaxNLocator(nbins=7)
    +locator=plt.MaxNLocator(nbins=7)#自动选择合适的位置，并且刻度之间最多不超过7（nbins）个间隔 locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置
    locator=plt.AutoLocator()#自动分配刻度值的位置
    locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始
    @@ -714,7 +714,7 @@ locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度

    locator = matplotlib.ticker.AutoLocator() axs[0, 0].xaxis.set_major_locator(locator) -locator = matplotlib.ticker.MaxNLocator(nbins=10) +locator = matplotlib.ticker.MaxNLocator(nbins=3) axs[0, 1].xaxis.set_major_locator(locator) @@ -728,7 +728,7 @@ locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度

    -../_images/index_27_02.png +../_images/index_27_0.png

    ​

    @@ -784,7 +784,7 @@ locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度

    -../_images/index_31_01.png +../_images/index_31_0.png

    legend其他常用的几个参数如下：

    @@ -844,7 +844,7 @@ ax.legend(loc='upper center') 等同于ax.legend(loc=9)

    -../_images/index_33_01.png +../_images/index_33_0.png

    ​

    @@ -877,7 +877,7 @@ ax.legend(loc='upper center') 等同于ax.legend(loc=9)

    -../_images/index_37_01.png +../_images/index_37_0.png

    ​