根据上一行的值从 dataframe 中删除行

Question

I have a dataframe like the following:

2018-10-16 15:15:00 1810.388020
2018-10-16 15:20:00 1813.299467
2018-10-16 15:25:00 1812.550495
2018-10-16 15:45:00 18140.981919
2018-10-16 15:35:00 1814.473347
2018-10-16 15:40:00 1816.349779
2018-10-16 15:45:00 740.981919
2018-10-16 15:50:00 1819.066781
2018-10-16 15:55:00 1820.333191

I need a dataframe like the following:

2018-10-16 15:15:00 1810.388020
2018-10-16 15:20:00 1813.299467
2018-10-16 15:25:00 1812.550495
2018-10-16 15:35:00 1814.473347
2018-10-16 15:40:00 1816.349779
2018-10-16 15:50:00 1819.066781
2018-10-16 15:55:00 1820.333191

That is:For every row having more value than 1.5 the previous row value or less than 0.5 the previous row value, drop it

Answer 1

Idea is compare previous and also next values, chaining by |for bitwise OR and last by bitwise AND with filtering by boolean indexing :

m11 = df['col'] > df['col'].shift(-1) * 0.5
m12 = df['col'] > df['col'].shift() * 0.5
m21 = df['col'] < df['col'].shift(-1) * 1.5
m22 = df['col'] < df['col'].shift() * 1.5

df = df[(m11 | m12) & (m21 | m22)]
print (df)
                             col
2018-10-16 15:15:00  1810.388020
2018-10-16 15:20:00  1813.299467
2018-10-16 15:25:00  1812.550495
2018-10-16 15:35:00  1814.473347
2018-10-16 15:40:00  1816.349779
2018-10-16 15:50:00  1819.066781
2018-10-16 15:55:00  1820.333191

Detail :

df['m11'] = df['col'] > df['col'].shift(-1) * 0.5
df['m12'] = df['col'] > df['col'].shift() * 0.5
df['m21'] = df['col'] < df['col'].shift(-1) * 1.5
df['m22'] = df['col'] < df['col'].shift() * 1.5

df['m1'] = (df['m11'] | df['m12'])
df['m2'] = (df['m21'] | df['m22'])

df['mask'] = df['m1'] & df['m2']

print (df)
                              col    m11    m12    m21    m22     m1     m2  \
2018-10-16 15:15:00   1810.388020   True  False   True  False   True   True   
2018-10-16 15:20:00   1813.299467   True   True   True   True   True   True   
2018-10-16 15:25:00   1812.550495  False   True   True   True   True   True   
2018-10-16 15:45:00  18140.981919   True   True  False  False   True  False   
2018-10-16 15:35:00   1814.473347   True  False   True   True   True   True   
2018-10-16 15:40:00   1816.349779   True   True  False   True   True   True   
2018-10-16 15:45:00    740.981919  False  False   True   True  False   True   
2018-10-16 15:50:00   1819.066781   True   True   True  False   True   True   
2018-10-16 15:55:00   1820.333191  False   True  False   True   True   True   

                      mask  
2018-10-16 15:15:00   True  
2018-10-16 15:20:00   True  
2018-10-16 15:25:00   True  
2018-10-16 15:45:00  False  
2018-10-16 15:35:00   True  
2018-10-16 15:40:00   True  
2018-10-16 15:45:00  False  
2018-10-16 15:50:00   True  
2018-10-16 15:55:00   True