如何在Python中为数据帧中的记录分配唯一值计数

Question

I have a data frame like this:

IP_address
   IP1
   IP1
   IP1
   IP4
   IP4
   IP4
   IP4
   IP4
   IP7
   IP7
   IP7

I would like to take count of unique values in this column and add the count as a variable by itself. At the end, it should look like this:

IP_address  IP_address_Count
   IP1               3
   IP1               3
   IP1               3
   IP4               5
   IP4               5
   IP4               5
   IP4               5
   IP4               5
   IP7               3
   IP7               3
   IP7               3

I am able to take the unique values of the column using the below code:

unique_ip_address_count = (df_c_train.drop_duplicates().IP_address.value_counts()).to_dict()

However, I am not sure how to match these in a loop in python so that i can get the desired results in python. Any sort of help is much appreciated.

I am not able to find a equivalent answer in stackoverflow. If there is anything please direct me there. Thank you.

Answer 1

You can use value_counts() with map

df['count'] = df['IP_address'].map(df['IP_address'].value_counts())


    IP_address  count
0   IP1         3
1   IP1         3
2   IP1         3
3   IP4         5
4   IP4         5
5   IP4         5
6   IP4         5
7   IP4         5
8   IP7         3
9   IP7         3
10  IP7         3

Answer 2

Using pd.factorize
This should be a very fast solution that scales well for large data

f, u = pd.factorize(df.IP_address.values)
df.assign(IP_address_Count=np.bincount(f)[f])

   IP_address  IP_address_Count
0         IP1                 3
1         IP1                 3
2         IP1                 3
3         IP4                 5
4         IP4                 5
5         IP4                 5
6         IP4                 5
7         IP4                 5
8         IP7                 3
9         IP7                 3
10        IP7                 3

Answer 3

NumPy way -

tags, C = np.unique(df.IP_address, return_counts=1, return_inverse=1)[1:]
df['IP_address_Count'] = C[tags]

Sample output -

In [275]: df
Out[275]: 
   IP_address  IP_address_Count
0         IP1                 3
1         IP1                 3
2         IP1                 3
3         IP4                 5
4         IP4                 5
5         IP4                 5
6         IP4                 5
7         IP4                 5
8         IP7                 3
9         IP7                 3
10        IP7                 3

Answer 4

In [75]: df['IP_address_Count'] = df.groupby('IP_address')['IP_address'].transform('size')

In [76]: df
Out[76]:
   IP_address  IP_address_Count
0         IP1                 3
1         IP1                 3
2         IP1                 3
3         IP4                 5
4         IP4                 5
5         IP4                 5
6         IP4                 5
7         IP4                 5
8         IP7                 3
9         IP7                 3
10        IP7                 3

Answer 5

ip_set = df.IP_address.unique()
dict_temp = {}
for ip in ip_set:
    dict_temp[ip] = df[df.IP_address == ip].IP_address.value_counts()[0]
df['counts'] = [dict_temp[ip] for ip in df.IP_address]

This seems to give me the sort of output that you desire

EDIT: Vaishali's use of map is perfect