pandas绘图

以你之姓@ 2022-05-12 08:08 258阅读 0赞

pandas绘图显示 : plt.show()

保存到本地 : plt.savefig(‘image.png’)

%matplotlib inline

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import pandas as pd
    import matplotlib.pyplot as plt

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present = pd.read_table('data.txt', sep=' ')

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present.shape

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(63, 3)

present.columns

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Index(\[u’year’, u’boys’, u’girls’\], dtype=’object’) 可以看到这个数据集共有63条记录，共有三个字段：Year，boys，girls。为了简化计算将year作为索引

present_year = present.set_index('year')

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plot是画图的最主要方法，Series和DataFrame都有plot方法。 我们可以这样看一下男生出生比例的趋势图：

present_year['boys'].plot()
    plt.legend(loc='best')

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161906652) 这是Series上的plot方法，通过DataFrame的plot方法，你可以将男生和女生出生数量的趋势图画在一起。

present_year.plot()       #has index,column

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161915641)

present_year.girls.plot(color='g')
    present_year.boys.plot(color='b')
    plt.legend(loc='best')

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161926953) 可以看到DataFrame提供plot方法与在多个Series调用多次plot方法的效果是一致。

present_year[:10].plot(kind='bar')

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161937157) plot默认生成是曲线图，你可以通过kind参数生成其他的图形，可选的值为：line, bar, barh, kde, density, scatter。

present_year[:10].plot(kind='barh')

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161946579) 如果你需要累积的柱状图，则只需要指定stacked=True。

present_year[:10].plot(kind='bar', stacked=True)

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724161955969) 制作相对的累积柱状图，需要一点小技巧。 首先需要计算每一行的汇总值，可以在DataFrame上直接调用sum方法，参数为1，表示计算行的汇总。默认为0，表示计算列的汇总。

present_year.sum(1)[:5]

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year 1940 2360399 1941 2513427 1942 2808996 1943 2936860 1944 2794800 dtype: int64 有了每一行的汇总值之后，再用每个元素除以对应行的汇总值就可以得出需要的数据。这里可以使用DataFrame的div函数，同样要指定axis的值为0。

present_year.div(present_year.sum(1),axis=0)[:10].plot(kind='barh', stacked=True)

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724162020329) 散点图和相关 plot也可以画出散点图。使用kind=’scatter’, x和y指定x轴和y轴使用的字段。

present_year.plot(x='boys', y='girls', kind='scatter')

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!\[这里写图片描述\](https://img-blog.csdn.net/20160724162029436) 我们再来载入一下鸢尾花数据。

iris = pd.read_csv('iris.csv')
    iris.head(5)

<table> 
 <thead> 
 <tr> 
 <th style="vertical-align:middle;"> 
 <table> 
 <thead> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">&nbsp;</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalLength</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalWidth</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalLength</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalWidth</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">Name</th> 
 </tr> 
 </thead> 
 <tbody> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">0</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">5.1</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">3.5</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.4</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">Iris-setosa</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">1</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">4.9</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">3.0</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.4</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">Iris-setosa</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">2</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">4.7</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">3.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.3</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">Iris-setosa</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">3</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">4.6</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">3.1</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.5</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">Iris-setosa</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">4</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">5.0</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">3.6</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.4</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.2</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">Iris-setosa</td> 
 </tr> 
 </tbody> 
 </table><pre style="margin-left:0px;"><code>iris.corr()</code></pre> 
 <ul style="margin-left:0px;"> 
 <li>1</li> 
 </ul> 
 <table> 
 <thead> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">&nbsp;</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalLength</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalWidth</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalLength</th> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalWidth</th> 
 </tr> 
 </thead> 
 <tbody> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalLength</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.000000</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.109369</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.871754</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.817954</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">SepalWidth</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.109369</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.000000</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.420516</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.356544</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalLength</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.871754</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.420516</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.000000</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.962757</td> 
 </tr> 
 <tr> 
 <th style="background-color:#eff3f5;vertical-align:middle;">PetalWidth</th> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.817954</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">-0.356544</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">0.962757</td> 
 <td style="border-color:#dddddd;vertical-align:middle;">1.000000</td> 
 </tr> 
 </tbody> 
 </table> 
 <ul style="margin-left:0px;"> 
 <li> from pandas.tools.plotting import scatter_matrix scatter_matrix(iris, alpha=0.2, figsize=(6, 6), diagonal='kde') </li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724151006954"> 箱图 DataFrame提供了boxplot方法可以用来画箱图。 <pre style="margin-left:0px;"><code>iris.boxplot()</code></pre> 
 <ul style="margin-left:0px;"> 
 <li>&nbsp;</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724151440860"> <pre style="margin-left:0px;"><code>iris.boxplot(by='Name', figsize=(8, 8))</code></pre> 
 <ul style="margin-left:0px;"> 
 <li>1</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724152021681"> 直方图和概率密度分布 
 <ul style="margin-left:0px;"> 
 <li>iris.ix[:,:-1].hist()</li> 
 <li>iris.plot(kind='kde')</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724152304357"> <img alt="" src="https://img-blog.csdn.net/20160724152618314"> 多变量的可视化 Radviz 
 <ul style="margin-left:0px;"> 
 <li>from pandas.tools.plotting import radviz</li> 
 <li>radviz(iris, 'Name')</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724153212073"> 
 <ul style="margin-left:0px;"> 
 <li>from pandas.tools.plotting import andrews_curves</li> 
 <li>andrews_curves(iris, 'Name')</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724153507200"> Parallel Coordinates 
 <ul style="margin-left:0px;"> 
 <li>from pandas.tools.plotting import parallel_coordinates</li> 
 <li>parallel_coordinates(iris, 'Name')</li> 
 </ul><img alt="" src="https://img-blog.csdn.net/20160724153849904"> </th> 
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