Difference between map, applymap and apply methods in Pandas

Pandas library is extensively used for data manipulation and analysis. map(), applymap() and apply() methods are methods of Pandas library.

applymap() method only works on a pandas dataframe where function is applied on every element individually.

apply() method can be applied both to series and dataframes where function can be applied both series and individual elements based on the type of function provided.

map() method only works on a pandas series where type of operation to be applied depends on argument passed as a function, dictionary or a list.

Note that the type of Output totally depends on the type of function used as an argument with the given method.

Pandas apply() method:
This method which can be used on both on a pandas dataframe and series. The function passed as an argument typically works on rows/columns. Code below illustrates how apply() method works on Pandas dataframe.

# Importing pandas library with an alias pd 
import pandas as pd 
   
# Dataframe generation 
gfg_string = 'geeksforgeeks'
gfg_list = 5 * [pd.Series(list(gfg_string))] 
   
gfg_df = pd.DataFrame(data = gfg_list) 
print("Original dataframe:\n" + \ 
      gfg_df.to_string(index = False, 
      header = False), end = '\n\n') 
   
# Using apply method for sorting  
# rows of characters present in  
# the original dataframe 
new_gfg_df = gfg_df.apply(lambda x:x.sort_values(), axis = 1) 
  
print("Transformed dataframe:\n" + \ 
       new_gfg_df.to_string(index = False, 
            header = False), end = '\n\n') 

Output:

Below Code illustrates how apply() method on Pandas series:

# Importing pandas library with an alias pd 
import pandas as pd 
   
# Series generation 
gfg_string = 'geeksforgeeks'
gfg_series = pd.Series(list(gfg_string)) 
print("Original series\n" + \ 
       gfg_series.to_string(index = False, 
            header = False), end = '\n\n') 
   
# Using apply method for converting characters 
# present in the original series 
new_gfg_series = gfg_series.apply(str.upper) 
print("Transformed series:\n" + \ 
       new_gfg_series.to_string(index = False, 
                header = False), end = '\n\n') 

Output:

Pandas applymap() method :
This method can be used on a pandas dataframe. The function passed as an argument typically works on elements of the dataframe applymap() is typically used for elementwise operations. Code below illustrates how applymap method works on pandas dataframe:

# Importing pandas library with an alias pd 
import pandas as pd 
   
# DataFrame generation 
gfg_string = 'geeksforgeeks'
gfg_list = 5 * [pd.Series(list(gfg_string))] 
gfg_df = pd.DataFrame(data = gfg_list) 
  
print("Original dataframe:\n" + \ 
       gfg_df.to_string(index = False, 
        header = False), end = '\n\n') 
   
# Using applymap method for transforming  
# characters into uppercase characters  
# present in the original dataframe 
new_gfg_df = gfg_df.applymap(str.upper) 
print("Transformed dataframe:\n" + \ 
       new_gfg_df.to_string(index = False, 
            header = False), end = '\n\n') 

Output:

Pandas map() method :
This method is used on series function, list and dictionary passed as an argument. This method is generally used to map values from two series having one column same. Code below illustrates how map method works on pandas series:

# Importing pandas library with an alias pd 
import pandas as pd 
   
# Series generation 
gfg_string = 'geeksforgeeks'
gfg_series = pd.Series(list(gfg_string)) 
print("Original series\n" + \ 
       gfg_series.to_string(index = False, 
            header = False), end = '\n\n') 
   
# Using apply method for converting characters 
# present in the original series 
new_gfg_series = gfg_series.map(str.upper) 
print("Transformed series:\n" + \ 
       new_gfg_series.to_string(index = False, 
                header = False), end = '\n\n') 

Output:

My Personal Notes

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