CODEWITHSUNDEEP
python-3.xpandaspandas-groupbypivot-tabledummy-variable
Krishnang K Dalal
Krishnang K Dalal

Reputation: 2566

Pandas Group By And Get Dummies

I want to make get dummy variables per unique value. Idea is to turn the data frame into a multi-label target. How can I do it?

Data:

           ID                      L2
           A                 Firewall
           A                 Security
           B           Communications
           C                 Business
           C                 Switches

Desired Output:

ID   Firewall  Security  Communications  Business   Switches
 A      1          1             0              0         0
 B      0          0             1              0         0
 C      0          0             0              1         1

I have tried pd.pivot_table but it requires a column to aggregate on. I have also tried answer on this link but it sums the values rather than just turning into binary dummy columns. I would much appreciate your help. Thanks a lot!

Upvotes: 6

Views: 2620

Answers (4)

Soumendra Mishra
Soumendra Mishra

Reputation: 3663

You can try this:

df1 = pd.read_csv("file.csv")
df2 = df1.groupby(['ID'])['L2'].apply(','.join).reset_index()
df3 = df2["L2"].str.get_dummies(",")
df = pd.concat([df2, df3], axis = 1)
print(df)

Output:

  ID                 L2  Business  Communications  Firewall  Security  Switches
0  A  Firewall,Security         0               0         1         1         0
1  B     Communications         0               1         0         0         0
2  C  Business,Switches         1               0         0         0         1

Alternative Option:

df = df.groupby(['ID'])['L2'].apply(','.join).str.get_dummies(",").reset_index()
print(df)

Upvotes: 2

Ben.T
Ben.T

Reputation: 29635

you can use pivot_table if you change the aggfunc=any.

print(df.pivot_table(index='ID', columns='L2', 
                     aggfunc=any, fill_value=False)\
        .astype(int))
L2  Business  Communications  Firewall  Security  Switches
ID                                                        
A          0               0         1         1         0
B          0               1         0         0         0
C          1               0         0         0         1

and maybe reset_index at the end to put the ID as column

Upvotes: 1

BENY
BENY

Reputation: 323316

Let us set_index then get_dummies, since we have multiple duplicate in each ID ,we need to sum with level = 0

s = df.set_index('ID')['L2'].str.get_dummies().max(level=0).reset_index()
Out[175]: 
  ID  Business  Communications  Firewall  Security  Switches
0  A         0               0         1         1         0
1  B         0               1         0         0         0
2  C         1               0         0         0         1

Upvotes: 4

Quang Hoang
Quang Hoang

Reputation: 150785

crosstab, then convert to boolean:

pd.crosstab(df['ID'],df['L2']).astype(bool)

Output:

L2  Business  Communications  Firewall  Security  Switches
ID                                                        
A      False           False      True      True     False
B      False            True     False     False     False
C       True           False     False     False      True

Upvotes: 8

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