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技术 2022年11月8日
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Pandas单变量画图

[数据可视化之一]Pandas单变量画图 [数据可视化之一]Pandas单变量画图 [数据可视化之一]Pandas单变量画图 [数据可视化之一]Pandas单变量画图
Bar Chat Line Chart Area Chart Histogram
df.plot.bar() df.plot.line() df.plot.area() df.plot.hist()
适合定类数据和小范围取值的定序数据 适合定序数据和定距数据 适合定序数据和定距数据 适合定距数据

pandas库是Python数据分析最核心的一个工具库:“杀手级特征”,使整个生态系统融合在一起。除了数据读取、转换之外,也可以进行数据可视化。易于使用和富有表现力的pandas绘图API是pandas流行的重要组成部分。

在本节中,我们将学习基本的“pandas”绘图工具,从最简单的可视化类型开始:单变量或“单变量”可视化。这包括条形图和折线图等基本工具。通过这些,我们将了解pandas绘制库结构,并花一些时间检查数据类型。

数据分类:

  1. Norminal Data 定类变量:变量的不同取值仅仅代表了不同类的事物。问卷的人口特征中最常使用的问题,而调查被访对象的“性别”,就是 定类变量。对于定类变量,加减乘除等运算是没有实际意义的;
  2. Ordinal Data定序变量:变量的值不仅能够代表事物的分类,还能代表事物按某种特性的排序,这样的变量叫定序变量。问卷的人口特征中最常使用的问题“教育程度“,以及态度量表题目等都是定序变量,定序变量的值之间可以比较大小,或者有强弱顺序,但两个值的差一般没有什么实际意义。
  3. Interval Data 定距变量:变量的值之间可以比较大小,两个值的差有实际意义,这样的变量叫定距变量。有时问卷在调查被访者的“年龄”和“每月平均收入”,都是定距变量。
  4. Ratio Data 定比变量, 有绝对0点,如质量,高度。定比变量与定距变量在市场调查中一般不加以区分,它们的差别在于,定距变量取值为“0”时,不表示“没有”,仅仅是取值为0。定比变量取值为“0”时,则表示“没有”
import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data_first150k.csv", index_col=0)
reviews.head(3)

结果:
[数据可视化之一]Pandas单变量画图

条形图可以说是最简单的数据可视化。他们将类别映射到数字:例如,早餐(一类)消费的鸡蛋数量;或者,世界葡萄酒产区(类别)与其生产的葡萄酒标签数量(数量):

#取数据province特征下前10个最常出现的类别:province省份--->出现次数;
reviews['province'].value_counts().head(10).plot.bar()

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” alt=”” />

这个图表告诉我们什么?它说加州生产的葡萄酒远远超过世界上任何其他省份!我们可能会问,加州葡萄酒总量的百分之几是多少?这个条形图告诉了我们绝对数字,但知道相对比例会更有用:

#取province特征,统计,取前10,计算比例,画图bar plot
(reviews['province'].value_counts().head(10)/len(reviews)).plot.bar()

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” alt=”” />

加州生产葡萄酒占杂志评选到的葡萄酒的几乎三分之一!

条形图非常灵活:高度可以代表任何东西,只要它是一个数字。每个栏都可以代表任何东西,只要它是一个类别。

在这种情况下,类别是标称类别nominal categories:“纯”类别,类别排序没有多大意义。标称分类变量包括国家,邮政编码,奶酪类型等。另一种是序数类别ordinal categories:类别见的排序是有意义,如地震震级,有一定数量公寓的住宅小区,以及当地熟食店的薯条大小。

或者,在我们的案例中,Wine Magazine分配的某个评分的评论数量[ordinal categories]:

#统计各个得分的数目,直接显示:可以发现,第一个bar是87,第二个是88;按照数目多少排序的
reviews['points'].value_counts().plot.bar()

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” alt=”” />

reviews['points'].value_counts().sort_index().plot.bar()

aaarticlea/png;base64,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” alt=”” />

正如你所看到的,每个酿出的酒总分都在80到100之间。而且,如果我们相信葡萄酒杂志是一个品味良好的仲裁者,那么类别92就会比类别91更有意义地“更好”。

折线图Line charts

葡萄酒评论记分卡有20个不同的独特值可供填写,我们的条形图几乎不够。如果杂志评价0-100的话,有100个不同的类别,该怎么办?类别太多了,不适合用条形图处理!

在这种情况下,我们可以使用折线图代替条形图:

#统计各个得分的数目,将index排序-从小到大(显示更合理)
reviews['points'].value_counts().sort_index().plot.line()

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” 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折线图可以传递任意数量的单个取值[100类,1000类],使其成为具有许多唯一值或类别的分布的首选工具[类别性数据,但是有许多许多可能值]

但是,折线图有一个重要的缺点:与条形图不同,它们不适合名义分类数据。虽然条形图区分了点线图的每个“类型”,但它们将它们组合在一起。因此,折线图断言水平轴上的值的顺序,并且对于某些数据,顺序将没有意义。毕竟,从加利福尼亚到华盛顿到托斯卡纳的“下降”并不意味着什么![折线图更适合于ordinal数据,具有一定的连续性,类别间的大小是有关系的,变化趋势也有一定的意义]。

折线图也使得区分单个值变得更加困难[连线]。

通常,如果你的数据可以放入条形图中,只需使用条形图!

面积图Area charts

面积图就是底部有阴影的折线图

reviews['points'].value_counts().sort_index().plot.area()

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” alt=”” />

当仅绘制一个变量时,面积图和折线图之间的差异主要是视觉方面上:一个底部有阴影,一个没有。在这种情况下,它们可以互换使用。

定距数据Interval data

定距变量的例子是太阳的温度。定距变量超出了序数分类变量:它具有有意义的顺序,在某种意义上我们可以量化两个条目之间的差异本身就是定距变量。

例如,如果我说这个样本的水是-20摄氏度,而另一个样本是120摄氏度,那么我可以量化它们之间的差异:140度“值”的热量。

有时差异可能是定性的。至少,能够如此清楚地陈述某些东西感觉比说“测量”要多得多,比如说,你会买这种酒而不是那种,因为这个在一些口味测试中得了92分而且只有一个得到了更确切地说,任何具有无限多个可能值的变量肯定是区间变量。

折线图适用于定距数据。条形图不行 – 除非你的测量能力非常有限,定距数据自然会有很大变化[取值太多]

将一个新工具直方图应用到我们的数据集中的定距变量价格上(我们将价格降低到200美元一瓶一下)。

直方图Histograms

reviews[reviews['price'] < 200]['price'].plot.hist()

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” alt=”” />

直方图看起来很简单,就像一个条形图。它基本上是!实际上,直方图是一种特殊的条形图,它将您的数据拆分为均匀间隔,并显示每个条形区域中有多少行。唯一的分析差异是,每个条形代表不是代表单个值,而是代表一个区间取值范围

然而,直方图有一个主要缺点(之前我们筛选小于200美元的原因)。因为它们将空间分成均匀间隔[在变量price的取值范围内均匀划分成几个范围相同的区间,然后再进行统计画图],所以它们不能很好地处理偏斜的数据

reviews['price'].plot.hist()

aaarticlea/png;base64,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” alt=”” />

这是之前排除大于200美元葡萄酒的真正原因;其中一些葡萄酒真的很贵!图表将“增长”以包含它们[扩大取值范围],从而损害所显示的其余数据。

reviews[reviews['price'] > 1500]

[数据可视化之一]Pandas单变量画图

从上面显示结果可以看出,葡萄酒价格高于1500美元的只有3个,数据严重倾斜。

有许多方法可以处理偏斜的数据问题;但这些超出了本教程的范围。最简单的方法就是:在合理的范围内筛选数据,删除不合理的数据。
这种现象在统计学上称为偏斜,并且是区间变量中相当常见的现象

直方图最适用于没有偏斜的区间变量。它们对于像“points”这样的序数分类变量也很有效:

reviews['points'].plot.hist()

但是图表中出现了数据中不存在的取值,它只是表示一个范围区间。

原文链接:Click me

参考链接:nominal,ordinal,interval,ratio variable怎么区分?

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