[英]Filter dataframe rows according to max column value
I have this df
dataframe: 我有这个df
数据框:
artist track class1 class2 class3
0 Portishead Roads 0.98 0.02 0.0
1 Yo La Tengo Our Way to Fall 0.14 0.86 0.0
2 Radiohead Fake Plastic Trees 0.03 0.97 0.0
given these user inputs: 给定这些用户输入:
input_value = 0.8
input_class = 'class2'
I use the following code in reorder the dataframe according to class2 max value: 我使用以下代码根据class2最大值对数据帧重新排序:
for col in df.ix[:,'class1':'class3']:
if col == input_class:
reordered_df = df.iloc[(df[input_class] - input_value).argsort()]
like so: 像这样:
1 Yo La Tengo Our Way to Fall 0.14 0.86 0.0
2 Radiohead Fake Plastic Trees 0.03 0.97 0.0
0 Portishead Roads 0.98 0.02 0.0
however, I still need to satisfy one class condition , that is class2 value must be the highest float value in each row . 但是,我仍然需要满足一个类条件,即class2值必须是每一行中的最高float值。 in other words: 换一种说法:
0 Portishead Roads 0.98 0.02 0.0
should be discarted, because max value belongs to another class. 应该讨论,因为最大值属于另一个类。
how do I insert this condition on the snippet above? 如何在上面的代码段中插入此条件?
Find the max
row-wise along the columns, compare to class2
, and discard accordingly. 找到沿列的max
行,与class2
比较,并相应地丢弃。
reordered_df
artist track class1 class2 class3
1 Yo La Tengo Our Way to Fall 0.14 0.86 0.0
2 Radiohead Fake Plastic Trees 0.03 0.97 0.0
0 Portishead Roads 0.98 0.02 0.0
reordered_df[reordered_df.max(1) == reordered_df.class2]
artist track class1 class2 class3
1 Yo La Tengo Our Way to Fall 0.14 0.86 0.0
2 Radiohead Fake Plastic Trees 0.03 0.97 0.0
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