This is my data,
prakash 101
Ram 107
akash 103
sakshi 115
vidushi 110
aman 106
lakshay 99
I want to select all rows from akash to vidushi or all rows from Ram to aman. In real scenarios, there will be thousand of rows and multiple columns and I will be getting multiple queries to select a range of rows on the basis of some column value. how can i do that?
Heres the right way to do it..
start = 'akash'
end = 'vidushi'
l = list(df['names']) #ordered list of names
subl = l[l.index(start):l.index(end)+1] #list of names between the start and end
df[df['names'].isin(subl)] #filter dataset for list of names
2 akash 103
3 sakshi 115
4 vidushi 110
Create some variables (which you can adjust), then use .loc and .index[0] (note: df[0] can be replaced with the name of your header, so if your header is called Names, then change all instances of df[0] to df['Names']:
var1 = 'Ram'
var2 = 'aman'
a = df.loc[df[0]==var1].index[0]
b = df.loc[df[0]==var2].index[0]
c = df.iloc[a:b+1,:]
c
output:
0 1
1 Ram 107
2 akash 103
3 sakshi 115
4 vidushi 110
5 aman 106
try set_index then use loc
df = pd.DataFrame({"name":["prakash","Ram","akash","sakshi","vidushi","aman","lakshay"],"val":[101,107,103,115,110,106,99]})
(df.set_index(['name']).loc["akash":"vidushi"]).reset_index()
output:
name val
0 akash 103
1 sakshi 115
2 vidushi 110
You can use the range to select rows
print x[2:4]
#output
akash 103
sakshi 115
vidushi 110
aman 106
If you want to fill the values based on a specific column you can use np.where