Openpyxl如何使用指定数据在一个图标中创建多个系列
使用的数据如下：
DateNamevalue1value2value3value4value5
2020/5/1Tom10303.5019935.7012602.2414412.4221852.01
2020/5/2Tom10152.4419442.0712745.0814829.2921303.35
2020/5/3Tom9719.7119430.3212765.5814735.4021343.91
2020/5/4Tom9821.2718534.1613320.2714846.8520589.91
2020/5/5Tom9703.1217878.8511953.1814481.3420583.99
2020/5/6Tom9199.3716576.509643.3311983.8722118.69
2020/5/1Jerry10579.1021587.9814023.4416035.8624312.60
2020/5/2Jerry10805.7721829.5913469.1016446.9223871.89
2020/5/3Jerry10758.5721662.9613865.9316373.3724094.35
2020/5/4Jerry10587.4022131.1214257.1016490.8224232.62
2020/5/5Jerry10633.4722563.5213803.2816316.6324086.41
2020/5/6Jerry10590.8522260.7313482.4216054.5624140.94
2020/5/1John10174.0519448.3010664.6213140.4226110.40
2020/5/2John10828.7522928.2114187.5615936.4624422.81
2020/5/3John11135.1723182.3714321.0817119.6425088.01
2020/5/4John11190.6123158.9814314.7317031.2624333.50
2020/5/5John10557.7121297.5213105.3616207.6122948.15
2020/5/6John10524.2420856.0313499.4017028.9923677.26
生成图表如下：
data:image/png;base64,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
看不到生成图表，一直在转圈，也没明白你问题的意思 疾风怪盗 发表于 2020-8-2 13:19
看不到生成图表，一直在转圈，也没明白你问题的意思
图片贴不上去，我大概说一下；就是生成的图表中X轴是时间(Date)，图表中按Name生成3条线(系列)，系列值是Value1,2,3...，说白了就是要实现数据透视表的效果。 woodfish 发表于 2020-8-2 23:18
图片贴不上去，我大概说一下；就是生成的图表中X轴是时间(Date)，图表中按Name生成3条线(系列)，系列值是 ...
没明白你的意思，X轴是时间，有三条折线？那数据呢？一个时间一个点上有5个数据？这些数据是要求和还是干嘛？ woodfish 发表于 2020-8-2 23:18
图片贴不上去，我大概说一下；就是生成的图表中X轴是时间(Date)，图表中按Name生成3条线(系列)，系列值是 ...
上传图片请用图床
https://imgchr.com/ 疾风怪盗 发表于 2020-8-3 01:09
没明白你的意思，X轴是时间，有三条折线？那数据呢？一个时间一个点上有5个数据？这些数据是要求和还是干 ...
所要生成的图表图片已上传，请查阅。https://imgchr.com/i/adE99U _2_ 发表于 2020-8-3 08:35
上传图片请用图床
https://imgchr.com/
非常感谢！ 楼主解决没，同求解决方案
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