Fastest way to merge pandas dataframe on ranges

Question

I have a dataframe A

    ip_address
0   13
1   5
2   20
3   11
.. ........

and another dataframe B

    lowerbound_ip_address   upperbound_ip_address           country
0    0                       10                             Australia
1    11                      20                             China

based on this I need to add a column in A such that

ip_address  country
13          China
5           Australia

I have an idea that I should write define a function and then call map on each row of A. But how would I search through each row of B for this. Is there a better way to do this.

Answer 1

Use pd.IntervalIndex

In [2503]: s = pd.IntervalIndex.from_arrays(dfb.lowerbound_ip_address,
                                            dfb.upperbound_ip_address, 'both')

In [2504]: dfa.assign(country=dfb.set_index(s).loc[dfa.ip_address].country.values)
Out[2504]:
   ip_address    country
0          13      China
1           5  Australia
2          20      China
3          11      China

---

Details

In [2505]: s
Out[2505]:
IntervalIndex([[0, 10], [11, 20]]
              closed='both',
              dtype='interval[int64]')

In [2507]: dfb.set_index(s)
Out[2507]:
          lowerbound_ip_address  upperbound_ip_address    country
[0, 10]                       0                     10  Australia
[11, 20]                     11                     20      China

In [2506]: dfb.set_index(s).loc[dfa.ip_address]
Out[2506]:
          lowerbound_ip_address  upperbound_ip_address    country
[11, 20]                     11                     20      China
[0, 10]                       0                     10  Australia
[11, 20]                     11                     20      China
[11, 20]                     11                     20      China

Setup

In [2508]: dfa
Out[2508]:
   ip_address
0          13
1           5
2          20
3          11

In [2509]: dfb
Out[2509]:
   lowerbound_ip_address  upperbound_ip_address    country
0                      0                     10  Australia
1                     11                     20      China

Answer 2

Try pd.merge_asof

df['lowerbound_ip_address']=df['ip_address']
pd.merge_asof(df1,df,on='lowerbound_ip_address',direction ='forward',allow_exact_matches =False)
Out[811]: 
   lowerbound_ip_address  upperbound_ip_address    country  ip_address
0                      0                     10  Australia           5
1                     11                     20      China          13

Answer 3

IntervalIndex is as of pandas 0.20.0 and the solution by @JohnGalt using it is excellent.

Prior to that version, this solution would work which expands the ip addresses by country for the complete range.

df_ip = pd.concat([pd.DataFrame(
    {'ip_address': range(row['lowerbound_ip_address'], row['upperbound_ip_address'] + 1), 
     'country': row['country']}) 
    for _, row in dfb.iterrows()]).set_index('ip_address')
>>> dfa.set_index('ip_address').join(df_ip)
              country
ip_address           
13              China
5           Australia
20              China
11              China