[FIXED] How to get next rows after filtered rows in Pandas

Issue

I have a Data Frame called Data. I wrote this filter for filter the rows:

data[data["Grow"] >= 1.5]

It returned some rows like these:

                   PriceYesterday       Open        High        Low 
    ------------------------------------------------------------------
          7            6888.0          6881.66     7232.0       6882.0
          53           7505.0          7555.72     7735.0       7452.0
          55           7932.0          8093.08     8120.0       7974.0  
          64           7794.0          7787.29     8001.0       7719.0
          ...

As you see there are some rows in the indexes 7, 53, 55 ,.... Now I want to get rows in indexes 8, 54, 56, ... too. Is there any straight forward way to do this? Thanks

Solution

You can use Index.intersection for avoid error if matching last row and want select not exist index values:

data = pd.DataFrame({
        'A':list('abcdef'),
         'B':[4,5,4,5,5,4],
         'Grow':[0,8,2,0.4,2,3.3],

})

df1 = data[data["Grow"] >= 1.5]
print (df1)
   A  B  Grow
1  b  5   8.0
2  c  4   2.0
4  e  5   2.0
5  f  4   3.3

df2 = data.loc[data.index.intersection(df1.index + 1)]
print (df2)
   A  B  Grow
2  c  4   2.0
3  d  5   0.4
5  f  4   3.3

Another idea is select by shifted values by Series.shift

df1 = data[data["Grow"] >= 1.5]
df2 = data[data["Grow"].shift() >= 1.5]
print (df2)
   A  B  Grow
2  c  4   2.0
3  d  5   0.4
5  f  4   3.3

df1 = data[data["Grow"] >= 1.5]
df2 = data.loc[df1.index + 1]
print (df2)
    

KeyError: "Passing list-likes to .loc or [] with any missing labels is no longer supported. The following labels were missing: Int64Index([6], dtype='int64'). See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike"

Answered By - jezrael