Question

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]))

任何人都可以向我解释此行中的错误以及如何纠正它

display(data.style.apply(lambda x: ['background: Yellow' if x.name == 'noise' else data for i in x]))

我有以下数据，我要在其中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'

Answer 1

你很近。我不确定为什么会出现THAT错误，但是一个问题是您要在列表理解的else块内返回初始数据帧。

如果用该行替换那一行，则可能会更好。

df.style.apply(lambda x: ["background: yellow" if v == "noise" else "" for v in x], axis = 1)

在这种情况下，您要遍历df中的每一行，突出显示等于noise的单元格。

来自Conditionally format Python pandas cell

的帮助/可能的副本

编辑：剥夺@ scott-boston和How to use Python Pandas Stylers for coloring an entire row based on a given column?，

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)

有人可以帮助我在Pandas Python的样式点处更正以下代码吗？

1 个答案: