Filter Pandas DataFrame by ip address range

Question 1

Strings are orderable in python, so you should be able to get away with just that:

In [11]: '61.245.160.0' < '61.245.175.255'
Out[11]: True

Either boolean mask:

In [12]: df[('61.245.160.0' < df.ip) & (df.ip < '61.245.175.255')]

or take a slice (if ip were the index):

In [13]: df.loc['61.245.160.0':'61.245.175.255']

Question 2

I have an approach using ipaddress.

For example, I want to know if host0 = 10.2.23.5 belongs to any of the following networks NETS = ['10.2.48.0/25','10.2.23.0/25','10.2.154.0/24'].

>>> host0 = ip.IPv4Address('10.2.23.5')
>>> NETS = ['10.2.48.0/25','10.2.23.0/25','10.2.154.0/24']
>>> nets  = [ip.IPv4Network(x) for x in NETS]
>>> [x for x in nets if (host2 >= x.network_address and host2 <= x.broadcast_address)]
[IPv4Network('10.2.23.0/25')]

Now, in order to get together this approach with Pandas, one shall do the following: create a function and apply it to each row of the DF.

def fnc(row):
    host = ip.IPv4Address(row)
    vec = [x for x in netsPy if (host >= x.network_address and host <= x.broadcast_address)]

    if len(vec) == 0:
        return '1'
    else:
        return '-1'

You later on apply it to the DF.

df['newCol'] = df['IP'].apply(fnc)

This will create a new column newCol where each row will be either 1 or -1 , depending on whether the IP address belongs to either network of your interest.

Question 3

Assuming you have the following DF:

In [48]: df
Out[48]:
               ip
0    61.245.160.1
1  61.245.160.100
2  61.245.160.200
3  61.245.160.254

let's find all IPs falling between (but not including) 61.245.160.99 and 61.245.160.254:

In [49]: ip_from = '61.245.160.99'

In [50]: ip_to = '61.245.160.254'

if we will compare IPs as strings - it will be compared lexicographically so it won't work properly as @adele has pointed out:

In [51]: df.query("'61.245.160.99' < ip < '61.245.160.254'")
Out[51]:
Empty DataFrame
Columns: [ip]
Index: []

In [52]: df.query('@ip_from < ip < @ip_to')
Out[52]:
Empty DataFrame
Columns: [ip]
Index: []

We can use numerical IP representation:

In [53]: df[df.ip.apply(lambda x: int(IPAddress(x)))
   ....:      .to_frame('ip')
   ....:      .eval('{} < ip < {}'.format(int(IPAddress(ip_from)),
   ....:                                  int(IPAddress(ip_to)))
   ....:       )
   ....: ]
Out[53]:
               ip
1  61.245.160.100
2  61.245.160.200

Explanation:

In [66]: df.ip.apply(lambda x: int(IPAddress(x)))
Out[66]:
0    1039507457
1    1039507556
2    1039507656
3    1039507710
Name: ip, dtype: int64

In [67]: df.ip.apply(lambda x: int(IPAddress(x))).to_frame('ip')
Out[67]:
           ip
0  1039507457
1  1039507556
2  1039507656
3  1039507710

In [68]: (df.ip.apply(lambda x: int(IPAddress(x)))
   ....:    .to_frame('ip')
   ....:    .eval('{} < ip < {}'.format(int(IPAddress(ip_from)),
   ....:                               int(IPAddress(ip_to))))
   ....: )
Out[68]:
0    False
1     True
2     True
3    False
dtype: bool

PS here is a bit faster (vectorized) function which will return numerical IP representation:

def ip_to_int(ip_ser):
    ips = ip_ser.str.split('.', expand=True).astype(np.int16).values
    mults = np.tile(np.array([24, 16, 8, 0]), len(ip_ser)).reshape(ips.shape)
    return np.sum(np.left_shift(ips, mults), axis=1)

Demo:

In [78]: df['int_ip'] = ip_to_int(df.ip)

In [79]: df
Out[79]:
               ip      int_ip
0    61.245.160.1  1039507457
1  61.245.160.100  1039507556
2  61.245.160.200  1039507656
3  61.245.160.254  1039507710

check:

In [80]: (df.ip.apply(lambda x: int(IPAddress(x))) == ip_to_int(df.ip)).all()
Out[80]: True