CODEWITHSUNDEEP
pythonpandas
Logan0015
Logan0015

Reputation: 177

How do you replace duplicate values with multiple unique strings in Pandas?

import pandas as pd
import numpy as np
data = {'Name':['Tom', 'Tom', 'Jack', 'Terry'], 'Age':[20, 21, 19, 18]} 
df = pd.DataFrame(data)

Lets say I have a dataframe that looks like this. I am trying to figure out how to check the Name column for the value 'Tom' and if I find it the first time I replace it with the value 'FirstTom' and the second time it appears I replace it with the value 'SecondTom'. How do you accomplish this? I've used the replace method before but only for replacing all Toms with a single value. I don't want to add a 1 on the end of the value, but completely change the string to something else.

Edit:

If the df looked more like this below, how would we check for Tom in the first column and the second column and then replace the first instance with FirstTom and the second instance with SecondTom

data = {'Name':['Tom', 'Jerry', 'Jack', 'Terry'], 'OtherName':[Tom, John, Bob,Steve]}

Upvotes: 12

Views: 5075

Answers (4)

jezrael
jezrael

Reputation: 862541

EDIT: For count duplicated per rows use:

df = pd.DataFrame(data = {'Name':['Tom', 'Jerry', 'Jack', 'Terry'], 
                          'OtherName':['Tom', 'John', 'Bob','Steve'],
                          'Age':[20, 21, 19, 18]})

print (df)
    Name OtherName  Age
0    Tom       Tom   20
1  Jerry      John   21
2   Jack       Bob   19
3  Terry     Steve   18

import inflect
p = inflect.engine()

#map by function for dynamic counter
f = lambda i: p.number_to_words(p.ordinal(i))
#columns filled by names
cols = ['Name','OtherName']
#reshaped to MultiIndex Series
s = df[cols].stack()
#counter per groups
count = s.groupby([s.index.get_level_values(0),s]).cumcount().add(1)
#mask for filter duplicates
mask = s.reset_index().duplicated(['level_0',0], keep=False).values
#filter only duplicates and map, reshape back and add to original data
df[cols] = count[mask].map(f).unstack().add(df[cols], fill_value='')
print (df)
       Name  OtherName  Age
0  firstTom  secondTom   20
1     Jerry       John   21
2      Jack        Bob   19
3     Terry      Steve   18

Use GroupBy.cumcount with Series.map, but only for duplicated values by Series.duplicated:

data = {'Name':['Tom', 'Tom', 'Jack', 'Terry'], 'Age':[20, 21, 19, 18]} 
df = pd.DataFrame(data)

nth = {
0: "First",
1: "Second",
2: "Third",
3: "Fourth"
}

mask = df.Name.duplicated(keep=False)
df.loc[mask, 'Name'] = df[mask].groupby('Name').cumcount().map(nth) + df.loc[mask, 'Name']
print (df)
        Name  Age
0   FirstTom   20
1  SecondTom   21
2       Jack   19
3      Terry   18

Dynamic dictionary should be like:

import inflect
p = inflect.engine()

mask = df.Name.duplicated(keep=False)
f = lambda i: p.number_to_words(p.ordinal(i))
df.loc[mask, 'Name'] = df[mask].groupby('Name').cumcount().add(1).map(f) + df.loc[mask, 'Name']
print (df)

        Name  Age
0   firstTom   20
1  secondTom   21
2       Jack   19
3      Terry   18

Upvotes: 8

BENY
BENY

Reputation: 323226

We can do cumcount

df.Name=df.Name+df.groupby('Name').cumcount().astype(str)
df
     Name  Age
0    Tom0   20
1    Tom1   21
2   Jack0   19
3  Terry0   18

Update 

suf = lambda n: "%d%s"%(n,{1:"st",2:"nd",3:"rd"}.get(n if n<20 else n%10,"th"))
g=df.groupby('Name')


df.Name=df.Name.radd(g.cumcount().add(1).map(suf).mask(g.Name.transform('count')==1,''))
df
     Name  Age
0  1stTom   20
1  2ndTom   21
2    Jack   19
3   Terry   18

Update 2 for column

suf = lambda n: "%d%s"%(n,{1:"st",2:"nd",3:"rd"}.get(n if n<20 else n%10,"th"))

g=s.groupby([s.index.get_level_values(0),s])
s=s.radd(g.cumcount().add(1).map(suf).mask(g.transform('count')==1,''))
s=s.unstack()
     Name OtherName
0  1stTom    2ndTom
1   Jerry      John
2    Jack       Bob
3   Terry     Steve

Upvotes: 10

piRSquared
piRSquared

Reputation: 294228

transform

nth = ['First', 'Second', 'Third', 'Fourth']

def prefix(d):
    n = len(d)
    if n > 1:
        return d.radd([nth[i] for i in range(n)])
    else:
        return d

df.assign(Name=df.groupby('Name').Name.transform(prefix))

          Name  Age
0     FirstTom   20
1    SecondTom   21
2         Jack   19
3        Terry   18
4   FirstSteve   17
5  SecondSteve   16
6   ThirdSteve   15
​

Upvotes: 6

anky
anky

Reputation: 75080

Just adding in to the existing solutions , you can use inflect to create dynamic dictionary

import inflect
p = inflect.engine()

df['Name'] += df.groupby('Name').cumcount().add(1).map(p.ordinal).radd('_')
print(df)

        Name  Age
0    Tom_1st   20
1    Tom_2nd   21
2   Jack_1st   19
3  Terry_1st   18

Upvotes: 12

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