CODEWITHSUNDEEP
pythonpandasdataframe
Sanjeev Aashu
Sanjeev Aashu

Reputation: 17

how to count unique row & its numbers of appearance in pandas

How to count unique row and its numbers of appearance in pandas?

    Lead ID  bank_account_id  NO.of account
0   308148.0          12460.0              1
1   310443.0          12654.0              1
2   310443.0          12655.0              1
3   312745.0          12835.0              1
4   312745.0          12836.0              1
5   312745.0          12837.0              1
6   312745.0          12838.0              1
7   312745.0          12839.0              1
8   313082.0          13233.0              1
9   314036.0          13226.0              1
10  314559.0          13271.0              1
11  314559.0          13273.0              1
12  316728.0          13228.0              1
13  316728.0          13230.0              1
14  316728.0          13232.0              1
15  316728.0          13234.0              1
16  316728.0          13235.0              1
17  316728.0          13272.0              1
18  318465.0          13419.0              1
19  318465.0          13420.0              1
20  318465.0          13421.0              1
21  318465.0          13422.0              1
22  318465.0          13423.0              1
23  318465.0          13424.0              1
24  318465.0          13425.0              1
25  321146.0          13970.0              1
26  321146.0          13971.0              1
27  321218.0          14779.0              1
28  321356.0          15142.0              1
29  321356.0          15144.0              1
30  321356.0          15146.0              1

In this dataset I want to get bank_account_id corresponding to every unique Lead ID & total number of bank_account_id every Lead ID is having.

Upvotes: 0

Views: 91

Answers (3)

Fabiano Morelli
Fabiano Morelli

Reputation: 21

Hy, try user a single df.value_counts() you will receive a good aggregation table.

Lead ID   bank_account_id  NO.of account
321356.0  15146.0          1                1
316728.0  13232.0          1                1
310443.0  12654.0          1                1
          12655.0          1                1

Upvotes: 1

Mr. T
Mr. T

Reputation: 12410

How about using MultiIndex for the count?

import pandas as pd

df = pd.DataFrame({'Lead ID': ['308148.0', '310443.0', '310443.0', '312745.0', '312745.0', '312745.0', '312745.0', '312745.0', '313082.0', '314036.0', '314559.0', '314559.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '321146.0', '321146.0', '321218.0', '321356.0', '321356.0', '321356.0'],
                   'bank_account_id': ['12460.0', '12654.0', '12655.0', '12835.0', '12836.0', '12837.0', '12838.0', '12839.0', '13233.0', '13226.0', '13271.0', '13273.0', '13228.0', '13230.0', '13232.0', '13234.0', '13235.0', '13272.0', '13419.0', '13420.0', '13421.0', '13422.0', '13423.0', '13424.0', '13425.0', '13970.0', '13971.0', '14779.0', '15142.0', '15144.0', '15146.0'],
                   'NO.of account': ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']})

df2 = df.set_index(["Lead ID", "bank_account_id"])
print(df2.groupby(level="Lead ID").size())

Output:

Lead ID
308148.0    1
310443.0    2
312745.0    5
313082.0    1
314036.0    1
314559.0    2
316728.0    6
318465.0    7
321146.0    2
321218.0    1
321356.0    3
dtype: int64

Upvotes: 1

Red
Red

Reputation: 27567

Yo can use df.groupby():

import pandas as pd

df = pd.DataFrame({'Lead ID': ['308148.0', '310443.0', '310443.0', '312745.0', '312745.0', '312745.0', '312745.0', '312745.0', '313082.0', '314036.0', '314559.0', '314559.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '321146.0', '321146.0', '321218.0', '321356.0', '321356.0', '321356.0'],
                   'bank_account_id': ['12460.0', '12654.0', '12655.0', '12835.0', '12836.0', '12837.0', '12838.0', '12839.0', '13233.0', '13226.0', '13271.0', '13273.0', '13228.0', '13230.0', '13232.0', '13234.0', '13235.0', '13272.0', '13419.0', '13420.0', '13421.0', '13422.0', '13423.0', '13424.0', '13425.0', '13970.0', '13971.0', '14779.0', '15142.0', '15144.0', '15146.0'],
                   'NO.of account': ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']})
df2 = df[df.duplicated('Lead ID', keep=False)].groupby('Lead ID')['bank_account_id'].apply(list).reset_index()
print(df2)

Output:

    Lead ID                                    bank_account_id
0  310443.0                                 [12654.0, 12655.0]
1  312745.0      [12835.0, 12836.0, 12837.0, 12838.0, 12839.0]
2  314559.0                                 [13271.0, 13273.0]
3  316728.0  [13228.0, 13230.0, 13232.0, 13234.0, 13235.0, ...
4  318465.0  [13419.0, 13420.0, 13421.0, 13422.0, 13423.0, ...
5  321146.0                                 [13970.0, 13971.0]
6  321356.0                        [15142.0, 15144.0, 15146.0]

You can also use a for loop to iterate through the values of your data frame with zip():

import pandas as pd

df = pd.DataFrame({'Lead ID': ['308148.0', '310443.0', '310443.0', '312745.0', '312745.0', '312745.0', '312745.0', '312745.0', '313082.0', '314036.0', '314559.0', '314559.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '316728.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '318465.0', '321146.0', '321146.0', '321218.0', '321356.0', '321356.0', '321356.0'],
                   'bank_account_id': ['12460.0', '12654.0', '12655.0', '12835.0', '12836.0', '12837.0', '12838.0', '12839.0', '13233.0', '13226.0', '13271.0', '13273.0', '13228.0', '13230.0', '13232.0', '13234.0', '13235.0', '13272.0', '13419.0', '13420.0', '13421.0', '13422.0', '13423.0', '13424.0', '13425.0', '13970.0', '13971.0', '14779.0', '15142.0', '15144.0', '15146.0'],
                   'NO.of account': ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']})

dct = dict()

for l, b in zip(df['Lead ID'], df['bank_account_id']):
    if l in dct:
        dct[l].append(b)
    else:
        dct[l] = [b]
        
print(dct)

Output:

{'308148.0': ['12460.0'], 
 '310443.0': ['12654.0', '12655.0'], 
 '312745.0': ['12835.0', '12836.0', '12837.0', '12838.0', '12839.0'], 
 '313082.0': ['13233.0'], 
 '314036.0': ['13226.0'], 
 '314559.0': ['13271.0', '13273.0'], 
 '316728.0': ['13228.0', '13230.0', '13232.0', '13234.0', '13235.0', '13272.0'], 
 '318465.0': ['13419.0', '13420.0', '13421.0', '13422.0', '13423.0', '13424.0', '13425.0'], 
 '321146.0': ['13970.0', '13971.0'], 
 '321218.0': ['14779.0'], 
 '321356.0': ['15142.0', '15144.0', '15146.0']}

Upvotes: 2

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