CODEWITHSUNDEEP
pythonpython-3.xpandas
Chris90
Chris90

Reputation: 1998

Creating new columns with True or False value if they exist in a column in df

I have a column in my df that looks like below:

Service
DoorDash, Grubhub / Seamless, UberEats, Postmates
DoorDash, UberEats, Caviar, Tock
DoorDash
None
Caviar, Tock
None
Tock
DoorDash, Grubhub / Seamless, UberEats, Postmates
Grubhub / Seamless, UberEats

Is there an efficient manner in which I can create a new column for each service and if that service exists in the Service column, the value in the new column will be a boolean of True or False?

So if I have a list of service names such as:

DoorDash, Grubhub / Seamless, UberEats, Caviar, Postmates, JustEat, Deliveroo, Foodora, Grab, Talabat

I want to create a column for each of the names in the above list and have a value of True or False depending on if that service exists in the Service column?

Expected Output:

Service                                            |  DoorDash | Grubhub / Seamless | UberEats| Caviar | Postmates | JustEat | Deliveroo | Foodora | Grab | Talabat | Tock      
 DoorDash, Grubhub / Seamless, UberEats, Postmates     True          True                True     False    True        False     False      False     False  False     False
 DoorDash, UberEats, Caviar, Tock                      True          False               True     True     False       False     False      False     False  False     True
 DoorDash                                              True          False               False    False    False       False     False      False     False  False     False
 None                                                  False         False               False    False    False       False     False      False     False  False     False
 Caviar, Tock                                          False         False               False    True     False       False     False      False     False  False     True
 None                                                  False         False               False    False    False       False     False      False     False  False     False
 Tock                                                  False         False               False    False    False       False     False      False     False  False     True 
 DoorDash, Grubhub / Seamless, UberEats, Postmates     True          True                True     False    True        False     False      False     False  False     False
 Grubhub / Seamless, UberEats                          False         True                True     False    False       False     False      False     False  False     False

Thank you for looking

Upvotes: 1

Views: 80

Answers (1)

jezrael
jezrael

Reputation: 862661

Use Series.str.get_dummies with convert to boolean, add missing values by list in DataFrame.reindex and last add to original:

L = ['DoorDash', 'Grubhub / Seamless', 'UberEats', 'Caviar', 
     'Postmates', 'JustEat', 'Deliveroo', 'Foodora', 'Grab', 'Talabat']
df1 = (df.join(df['Service'].str.get_dummies(', ')
                            .astype(bool)
                            .reindex(L, axis=1, fill_value=False)))

print (df1)
                                             Service  DoorDash  \
0  DoorDash, Grubhub / Seamless, UberEats, Postmates      True   
1                   DoorDash, UberEats, Caviar, Tock      True   
2                                           DoorDash      True   
3                                               None     False   
4                                       Caviar, Tock     False   
5                                               None     False   
6                                               Tock     False   
7  DoorDash, Grubhub / Seamless, UberEats, Postmates      True   
8                       Grubhub / Seamless, UberEats     False   

   Grubhub / Seamless  UberEats  Caviar  Postmates  JustEat  Deliveroo  \
0                True      True   False       True    False      False   
1               False      True    True      False    False      False   
2               False     False   False      False    False      False   
3               False     False   False      False    False      False   
4               False     False    True      False    False      False   
5               False     False   False      False    False      False   
6               False     False   False      False    False      False   
7                True      True   False       True    False      False   
8                True      True   False      False    False      False   

   Foodora   Grab  Talabat  
0    False  False    False  
1    False  False    False  
2    False  False    False  
3    False  False    False  
4    False  False    False  
5    False  False    False  
6    False  False    False  
7    False  False    False  
8    False  False    False

Upvotes: 1

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