for idx,ids in enumerate(uniq):
SV = df_CenteredWin[df_CenteredWin['subVoyageIDs'] == ids]
SV['minGroup']= np.isnan(SV.groupby(pd.TimeGrouper('30T')).DateTime.diff().dt.seconds)
SV['groups'] = (SV['minGroup'].shift(1) != SV['minGroup']).astype(int).cumsum()
SV_Noise = SV[SV['zScore_Noise'] == 'noise']
uniqueID= SV_Noise.groups.unique()
print(uniqueID, SV_Noise.subVoyageIDs.unique())
for idx, groupid in enumerate(uniqueID):
groups = SV[SV['groups'] == groupid]
groups_nosie = groups[groups['zScore_Noise'] == 'noise']
data = pd.DataFrame(data = { 'distance' : groups.Distance,
'Speed' : groups.Speed,
'Z-Score' : groups.centeredZScore,
'flagged' : groups.zScore_Noise.values})
display(data.style.apply(lambda x: ['background: Yellow' if x.name == 'noise' else data for i in x]))
有人能告诉我这行有什么问题吗?我该怎么纠正
display(data.style.apply(lambda x: ['background: Yellow' if x.name == 'noise' else data for i in x]))
我有以下数据,我试图突出显示标记列等于'noise'的行
DateTime Speed Score Distance flagged
2011-01-09 12:21:59 1.840407 -0.845713 0.030673 noisefree
2011-01-09 12:23:00 4.883493 2.307917 0.082748 noisefree
2011-01-09 12:24:00 4.413968 1.752545 0.073566 noisefree
2011-01-09 12:24:59 4.950600 2.178342 0.081135 noisefree
2011-01-09 12:26:00 10.014879 4.355568 0.169697 noise
2011-01-09 12:27:00 7.534325 2.535460 0.125572 noisefree
2011-01-09 12:27:59 6.965328 2.122056 0.114154 noisefree
2011-01-09 12:29:00 6.993480 1.963185 0.118501 noisefree
错误是:
AttributeError: 'DataFrame' object has no attribute 'rstrip'
你很接近。我不太清楚为什么会出现这样的错误,但有一个问题是您在列表的
else
块中返回初始数据帧。你知道吗如果你把那条线换成这条,你的运气会好一些。你知道吗
在本例中,您将迭代
df
中的每一行,突出显示等于noise
的单元格。你知道吗来自Conditionally format Python pandas cell的帮助/可能的副本
编辑: 在scott boston和How to use Python Pandas Stylers for coloring an entire row based on a given column?上偷梁换柱
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