Pick rows above a row that meets a condition in Pandas

Question

I have a dataframe like this. I want to pick the rows above if a certain condition is met. For example here if number > 2, then i want to pick the rows with John, richard and paul. i.e the rows with numbers that led up to three. I also want to pick the 5 rows above myers. i.e myers, Jessica, Ashton, Whiley, Jason.

   name     number
   Adrian     1
   Peter      2
   John       1  
   Richard    2
   Paul       3 
   Ashley     1
   Winchester 0
   Jason      1
   Whiley     2 
   Ashton     3
   Jessica    4
   myers      5

The output could look like this

 name    number
 John       1
 Richard    2
 Paul       3
 Jason      1
 Whiley     2 
 Ashton     3
 Jessica    4
 myers      5

@CodeGeek123 okay, and the number>2 that you are saying, is static or dynamic? i mean as per you define your condition the result should change? — Shubham R, Apr 27 '17 at 11:40
Yes that is correct. For example number can be > 3 or 4 etc. The results should change accordingly — CodeGeek123, Apr 27 '17 at 11:43

score 6 · Accepted Answer · answered Apr 27 '17 at 11:44

6

This awkwardly works:

def get_number(n):
    position = df[df['number']==n].index[0]
    find = range(position - (n-1), position + 1)
    return df.loc[find]

print get_number(3)

answered Apr 27 '17 at 11:44

zipa

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Thanks Zipa. One question though for data sets that done contain the condition I get an index error on line 2 : IndexError: index 0 is out of bounds for axis 0 with size 0 – CodeGeek123 Apr 28 '17 at 15:36

score 0 · Answer 2 · answered Apr 27 '17 at 11:54

0

def Select_no(n):
    position = df[df['number']==n].index[0]
    find = range(position - (n-1), position + 1)
    return df.loc[find]

df1 = Select_no(3)
df2 = df[7:]
df1 = df1.append(df2)

you could also do df1.reset_index(inplace=True), if you want your index to be starting from 0.

answered Apr 27 '17 at 11:54

Shubham R

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5

Did you seriously copy my answer? – zipa Apr 27 '17 at 12:15

Pick rows above a row that meets a condition in Pandas

2 Answers2