Source: AI introductory learning

Author: Xiao Wu Ge

The map in pandas is similar to the built-in map() method in Python. The map() method in pandas connects functions, dictionary indexes, or some special objects that need to accept a single input value with each element of the corresponding single column. And get the result serially.

Here we want to get the new columns of gender column F and M converted to female and male. There are several ways to implement a data set first.

The map() function can be used for a column of a Series object or a DataFrame object. It receives a function or a dictionary object as a parameter, and returns the value after the function or dictionary mapping process.

Usage: Series .map(arg, na_action=None)

parameter:

arg : function, dict, or Series

Mapping correspondence.

na_action : {None,'ignore'}, default None

If'ignore', propagate NaN values, without passing them to the mapping

correspondence.

Return: Pandas Series with same as index as caller

Official: https:// pandas .pydata.org/ pandas -docs/stable/reference/api/ pandas . Series .map.html

First build a data set, the following case application

 data = pd. DataFrame ( {"name":['Jack', 'Alice' , 'Lily' ,

'Mshis' , 'Gdli' , 'Agosh' , 'Filu' , 'Mack' , 'Lucy' , 'Pony' ],

"gender" :['F', 'M' , 'F' , 'F' , 'M' , 'F' , 'M' , 'M' , 'F' , 'F' ],


"age" :[25, 34 , 49 , 42 , 28 , 23 , 45 , 21 , 34 , 29 ]} )
data name gender age 0 Jack F 25 1 Alice M 34 2


Lily F 49 3 Mshis F 42 4 Gdli M 28 5 Agosh F 23 6
Filu M 45 7 Mack M 21 8 Lucy F 34 9 Pony F 29
1 Dictionary mapping

Here we write a dictionary of one-to-one mapping between F, M and female and male, and then use the map() method to get the mapping column:
#Define F-gt;female, M-gt; male mapping dictionary gender2xb = {'F':'female','M':'male'}
#Using the map() method to get the mapping column corresponding to the gender column data.gender.map(gender2xb)
0 female 1 male 2 female 3 female 4 male 5 female 6 male 7 male 8 female 9 female

	
2 lambda function
Here we pass in the lambda function to map() to achieve the required functions: 
		

			

		
#Because we already know that there are only F and M in the gender of the data gender column, write the following lambda function
		

			

		

data.gender.map (lambda x: 'Women' if x == 'F' else ' male')
0 female 1 male 2 female 3 female 4 male 5 female 6 male 7 male 8 female 9 female


#Age's square data.age.map(lambda x: x**2)
0 625 1 1156 2 2401 3 1764 4 784 5 529 6 2025 7 441 8 1156 9 84
3 General functions

The map function, you can also pass in the regular function defined by def, take a look at the following case

# Gender transition def gender_to_xb (x): return 'female' if x == 'F' else ' male'
data.gender.map(gender_to_xb) 0 female 1 male 2 female
3 female 4 male 5 female 6 male 7 male 8 female 9 female
4 Special objects

The content that map() can pass in can sometimes be very special, as in the following example: some objects that receive a single input value and have an output can also be processed by the map() method:

 data.gender. map ( "This kid's gender is {}" .format) 0

 This kid's gender is F 1 This kid's gender is M 2

 This kid's gender is F 3 This kid's gender is F 4

 This kid's gender is M 5 This kid's gender is F 6

 This kid's gender is M 7 This kid's gender is M 8

 This kid's gender is F 9 This kid's gender is F

 s = pd. Series (['cat','dog', np.nan,'rabbit']) s 0 cat 1 dog 2 NaN 3 rabbit

When na_action is the default value

 s.map('I am a {}'.format) 0 I am a cat 1 I am a dog 2 I am a nan 3 I am a rabbit

When na_action is ignore

s.map('I am a {}'.format, na_action='ignore')0 I am a cat1 I am a dog2 NaN3 I am a rabbit