这个文章着重给大伙细致介绍了一个简易案例, 还进行了代码展示, 此文章的案例是借助库达成读取csv文件, 且依照列的从小到大顺序予以排序, 有需要的能够参考一番。该系列自本文起, 会展开简单案例的介绍, 同时进行代码展示, 本文所涉案例是借助库来达成读取csv文件, 且依据列的从小到大予以排序。前言CSV 是什么从一开始, CSV文件就有可能被某些人错当成是Excel的一种文件格式, 在我尚未对CSV文件有所了解之前、也始终都觉得CSV格式的那种文件便是Excel保存之后的另外一种文件格式, 接下来就简要地介绍一下CSV究竟是什么啦。一种被称作逗号分隔值, 也能被叫做字符分隔值的文件格式, 是 CSV Comma-, 它相对简单、通用, 因分隔字符不一定是逗号 , 其文件是以纯文本形式存储表格数据, 相当于一个结构化表的纯文本形式。CSV常常被运用在程序之间进行数据转移的场景当中, 这是由于CSV所具备的存储格式具有通用性以及简单性的特点, 所以它适合去转化为其他的数据格式。举例来说, 把数据库里的数据采取导出操作从而存储成为CSV文件, 之后再把CSV数据文件导入到别的程序里面, 通过这样的方式就便捷地达成了程序与程序之间的数据互换。又比如说, 当运用写爬虫程序去爬取某网站上面的数据之际, 最初的时候, 网站之上的数据通常都是较为杂乱无章的, 把爬取得来的数据存储至CSV文件当中, 能够极为便利地查看数据、修改数据。CSV一般通过Excel来打开予以查看, 还可取用记事本程序来打开, 也能够借助诸如 等编辑器来打开, 察觉到这份内容, CSV是不是显得极为灵活、实用了呢。一、案例描述一份名为.csv的文件具有如下特征, 此文件存在三列, 行数有若干, 其中第一列被称作Wall time, 它所代表的是无用的数据, 第二列名为Step, 在进行绘图操作时, 该列的数据会被用于横轴, 第三列叫做Value, 于绘图时, 这列的数据会被运用至纵轴。从.csv文件里, 读取第三列的元素, 也就是Value, 将其按照从小到大的顺序进行排序, 之后绘制出图像。二、代码讲解首先导入相应的库import pandas as pd import matplotlib.pyplot as plt可用来读取csv文件, 要明确指定列标题, 具体指定读取第二列与第三列, 此第二列当做是横的, 而第三列则当作是纵的。能够用来做排序, by用于指明要进行排序的名称, axis用来指定要排序的轴通常默认情形是0也就是按行来排序, 还能够指定排列顺序是升序还是降序, 默认状态是True意味着升序, 要是False则指定降序排列也就是依照从大到小的顺序来排序。# 对scalars.csv第三列元素进行从大到小排序并绘制图像 txtpd.read_csv(scalars.csv,header0,usecols[1,2]) # 按Value排序默认升序,ascendingFalse指定降序排列即按照从大到小进行排序 txt2txt.sort_values(by[Value],ascendingFalse)最后绘制排序前和排序后的图像。# 创建画布 plt.figure(figsize(10,6),dpi100) # 排序前的图像 plt.plot(txt[Step],txt[Value]) # 显示图形 plt.show() # 排序后的图像 plt.plot(txt[Step],txt2[Value]) # 显示图形 plt.show()三、完整代码完整代码如下import pandas as pd import matplotlib.pyplot as plt # 对scalars.csv第三列元素进行从大到小排序并绘制图像 txtpd.read_csv(scalars.csv,header0,usecols[1,2]) # 按Value排序默认升序,ascendingFalse指定降序排列即按照从大到小进行排序 txt2txt.sort_values(by[Value],ascendingTrue) # 创建画布 plt.figure(figsize(10,6),dpi100) # 排序前的图像 plt.plot(txt[Step],txt[Value]) # 显示图形 plt.show() # 排序后的图像 plt.plot(txt[Step],txt2[Value]) # 显示图形 plt.show()关于用以实现读取csv文件且进行排序的这篇文章, 到此就介绍到这儿了, 更多同读取csv文件相关的内容, 可搜索脚本之家昔日的文章, 或者持续浏览接下来的相关文章, 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