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]))
有人能给我解释一下这条线路出了什么问题吗?
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"噪声"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
错误为:
AttributeError: 'DataFrame' object has no attribute 'rstrip'
你很接近。我不确定你为什么会出现这个错误,但有一个问题是,你在列表理解的else
块中返回了初始数据帧。
如果你把那条线换成这个,你可能会有更好的运气。
df.style.apply(lambda x: ["background: yellow" if v == "noise" else "" for v in x], axis = 1)
在这种情况下,您将对df
中的每一行进行迭代,突出显示等于noise
的单元格。
条件格式Python pandas单元格的帮助/可能重复
编辑:剥离@scott-boston和如何使用Python Pandas样式器基于给定列为整行着色?,
def highlight_row(s,keyword,column):
is_keyword = pd.Series(data=False, index=s.index)
is_keyword[column] = s.loc[column] == keyword
return ['background-color: yellow' if is_keyword.any() else '' for v in is_keyword]
df.style.apply(highlight_row, keyword="noise", column=["flagged"], axis=1)