如何删除（删除）数据框中的连续值

Question

我有一个数据框，其中有一列有 0 个值。我希望找到那些 0 值并检查直到最后它们是否为 0，只删除末尾的值而不是中间的值。这就是我到目前为止所得到的。

for DSCD in FirmReturnIndexValues['DSCD'].unique():
    secondary_df=FirmReturnIndexValues[FirmReturnIndexValues['DSCD']==DSCD]
    t=secondary_df[(secondary_df['RIe Pct Return'].values == 0)].index.values.tolist()
    t.sort()
    if len(t)>=1:
        print(np.diff((t)))

例如这部分是 t:

[69438, 69439, 69440, 69441, 69442, 69443, 69444, 69445, 69446, 69447, 69448, 69449, 69450, 69451, 69452, 69453, 69454, 69455, 69456, 69457, 69458, 69459, 69460, 69461, 69462, 69463, 69464, 69465, 69466, 69468, 69548, 69570, 69571, 69572, 69573, 69574, 69575, 69576, 69577, 69578, 69579, 69580, 69581, 69582, 69583, 69584, 69585, 69586, 69587, 69588, 69589, 69590, 69591, 69592, 69593, 69594, 69595, 69596, 69597, 69598, 69599, 69600, 69601, 69602, 69603, 69604, 69605, 69606, 69607, 69608, 69609, 69610, 69611, 69612, 69613, 69614, 69615, 69616, 69617, 69618, 69619, 69620, 69621, 69622, 69623, 69624, 69625, 69626, 69627, 69628, 69629, 69630, 69631, 69632, 69633, 69634, 69635, 69636, 69637, 69638, 69639, 69640, 69641, 69642, 69643, 69644, 69645, 69646, 69647, 69648, 69649, 69650, 69651, 69652, 69653, 69654, 69655, 69656, 69657, 69658]

这是我从代码中获得的索引，当我使用

np.diff()

方法时，我得到这个值，并且我希望删除（删除）的值被粗体：

[ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 80 22 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]

所以我有两个问题。

1. 如何删除粗体的？
2. 第一个 for 循环包含 8000 个 DSCD，无论如何它可以更有效吗？

另一个例子： 列出t：

[74536, 74537, 74538, 74540, 74542, 74543, 74544, 74545, 74547, 74551, 74554, 74555, 74559, 74560, 74561, 74562, 74563, 74566, 74567, 74568, 74569, 74571, 74572, 74573, 74574, 74575, 74578, 74579, 74580, 74582, 74584, 74585, 74586, 74587, 74588, 74589, 74590, 74591, 74592, 74595, 74596, 74597, 74598, 74599, 74601, 74602, 74603, 74604, 74605, 74606, 74607, 74608, 74609, 74610, 74612, 74613, 74614, 74615, 74616, 74617, 74618, 74619, 74620, 74621, 74622, 74623, 74624, 74625, 74626, 74627, 74628, 74629, 74630, 74631, 74632, 74633, 74634, 74635, 74636, 74637, 74638, 74639, 74640, 74641, 74642, 74643, 74644, 74645, 74646, 74647, 74648, 74649, 74650, 74651, 74652, 74653, 74654, 74655, 74656, 74657, 74658, 74659, 74660, 74661, 74662, 74663, 74664, 74665, 74666, 74667, 74668, 74669, 74670, 74671, 74672, 74673, 74674, 74675, 74676, 74677, 74678, 74679, 74680, 74681, 74682, 74683, 74684, 74685, 74686, 74687, 74688, 74689, 74690, 74691, 74692, 74693, 74694, 74695, 74696, 74697, 74698, 74699, 74700, 74701, 74702, 74703, 74704, 74705, 74706, 74707, 74708, 74709, 74710, 74711, 74712, 74713, 74714, 74715, 74716, 74717, 74718, 74719, 74720, 74721, 74722, 74723, 74724, 74725, 74726, 74727, 74728, 74729, 74730, 74731, 74732, 74733, 74734, 74735, 74736, 74737, 74738, 74739, 74740, 74741, 74742, 74743, 74744, 74745, 74746, 74747, 74748, 74749, 74750, 74751, 74752, 74753, 74754, 74755, 74756, 74757, 74758, 74759, 74760, 74761, 74762, 74763, 74764, 74765, 74766, 74767, 74768, 74769, 74770, 74771, 74772, 74773, 74774, 74775, 74776, 74777, 74778, 74779, 74780, 74781, 74782, 74783, 74784, 74785, 74786, 74787, 74788, 74789, 74790, 74791, 74792, 74793, 74794, 74795, 74796, 74797, 74798, 74799, 74800, 74801, 74802, 74803, 74804, 74805, 74806, 74807, 74808, 74809, 74810, 74811, 74812, 74813, 74814, 74815, 74816, 74817, 74818, 74819, 74820, 74821, 74822, 74823, 74824, 74825, 74826, 74827, 74828, 74829, 74830, 74831, 74832, 74833, 74834, 74835, 74836, 74837, 74838, 74839, 74840, 74841, 74842]

np.diff():

的结果

[1 1 2 2 1 1 1 2 4 3 1 4 1 1 1 1 3 1 1 1 2 1 1 1 1 3 1 1 2 2 1 1 1 1 1 1 1 1 3 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]

Answer 1

我会这样做：

s = pd.Series([69438, 69439, 69440, 69441, 69442, 69443, 69444, 69445, 69446, 69447, 69448, 69449, 69450, 69451, 69452, 69453, 69454, 69455, 69456, 69457, 69458, 69459, 69460, 69461, 69462, 69463, 69464, 69465, 69466, 69468, 69548, 69570, 69571, 69572, 69573, 69574, 69575, 69576, 69577, 69578, 69579, 69580, 69581, 69582, 69583, 69584, 69585, 69586, 69587, 69588, 69589, 69590, 69591, 69592, 69593, 69594, 69595, 69596, 69597, 69598, 69599, 69600, 69601, 69602, 69603, 69604, 69605, 69606, 69607, 69608, 69609, 69610, 69611, 69612, 69613, 69614, 69615, 69616, 69617, 69618, 69619, 69620, 69621, 69622, 69623, 69624, 69625, 69626, 69627, 69628, 69629, 69630, 69631, 69632, 69633, 69634, 69635, 69636, 69637, 69638, 69639, 69640, 69641, 69642, 69643, 69644, 69645, 69646, 69647, 69648, 69649, 69650, 69651, 69652, 69653, 69654, 69655, 69656, 69657, 69658])

s[:s.index[s.diff().diff().ne(0)][-1]+2]

returns:
[69438, 69439, 69440, 69441, 69442, 69443, 69444, 69445, 69446, 69447, 69448, 69449, 69450, 69451, 69452, 69453, 69454, 69455, 69456, 69457, 69458, 69459, 69460, 69461, 69462, 69463, 69464, 69465, 69466, 69468, 69548, 69570, 69571, 69572]