永洪社区
标题: 柱状图太小有办法设定最小高度吗 [打印本页]
作者: Anton 时间: 2023-8-29 14:29
标题: 柱状图太小有办法设定最小高度吗
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柱状图太小有办法设定最小高度吗
作者: yhdata_kse3MyDA 时间: 2023-8-29 14:29
本帖最后由 yhdata_kse3MyDA 于 2023-8-29 16:28 编辑
可以考虑用标准化的方法去缩小极值间的距离,
z = (x - mean) / std
其中,x 是原始数据,mean 是数据的均值,std 是数据的标准差,z 是标准化后的数据。
标准化可以缩小极值(或离群值)间的差距,这是因为标准化过程中使用了数据的均值和标准差,使得数据在转换后具有零均值和单位方差的分布。这种数据转换可以有效地减小极值对数据分布的影响,从而缩小极值间的差距。
标准化的主要思想是将数据转换为一个新的分布,使得这个分布具有以下特性:
- 均值为0:通过减去数据的均值,使得转换后的数据的均值为零。
- 标准差为1:通过除以数据的标准差,使得转换后的数据的标准差为1。
这样做的好处在于,即使数据中存在极大或极小的值,它们在标准化后会受到均值和标准差的约束,从而不会对整体数据分布产生过大的影响。
具体来说,标准化的过程中,对于大的极值,它们离均值较远,除以标准差后会得到较小的值;而对于小的极值,它们离均值较近,除以标准差后会得到较大的值。这种处理方式可以将极值归一化到与其他数据点相近的尺度,从而减小极值对整体数据分布的影响。
总之,标准化在数据分析和建模过程中起到平衡和稳定数据分布的作用,帮助我们更好地理解数据和进行统计分析。
作者: yhdata_lyaa 时间: 2023-8-29 14:33
只有拉高组件的高度,以拉长柱图的高度哦
作者: 喵了个汪 时间: 2023-8-29 14:48
不可以,因为最大值和最小值差距太大了
作者: limited_Moore 时间: 2023-8-29 14:57
把柱状图的最小值设置成一个很小的负数,这样可以把各个柱子之间的差距缩小,看看是否符合需求
作者: yhdata_yzm 时间: 2023-8-29 17:32
这个问题不是图的问题。是你的数据之间的量级差别太大了,导致坐标轴最大值很大,绘图的时候肯定会变小的。针对于这种情况,建议你使用对数刻度-【轴】-【行轴】-选择对数刻度
作者: 一辰 时间: 2023-8-29 17:44
不可以
作者: yhdata_lyaa 时间: 2023-8-29 18:06
![](static/image/smiley/m_emoji/ulemoji_1.png)
作者: yanieye 时间: 2023-8-29 19:33
如果都是正数值,但是数据值之间的差别特别大,可以考虑使用对数刻度。
参考https://www.yonghongtech.com/rea ... t_element_axis.html
作者: BoJie 时间: 2023-8-29 20:53
你这想法有问题,如果可以设置最小高度就已经失去了图表的意义了!
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