Breaking News: Grepper is joining You.com. Read the official announcement!

Map values in Pandas column using dictionary

Add Answer

Sumaia Parveen Shupti answered on May 11, 2020 Popularity 10/10 Helpfulness 6/10

answer Map values in Pandas column using dictionary

related maping value to data in pandas dataframe

related map column dataframe python

Map values in Pandas column using dictionary

Comment

Subjects = {"Sheldon" : "Science",
            "Raj" : "Chemistry",
            "Leonard" : "Maths",
            "Howard" : "Astronaut",
            "Amy" : "Science"}
                     
df["Subjects"] = df["first_name"].map(Subjects)

Popularity 10/10 Helpfulness 6/10 Language python

Source: www.projectpro.io

Tags: dictionary pandas python using

Link to this answer
Share Copy Link

Contributed on Aug 04 2022

Sumaia Parveen Shupti

0 Answers Avg Quality 2/10

Closely Related Answers

maping value to data in pandas dataframe

Comment

note: u can assigne values in each of the common values in the dataframe 

df['new_coloum'] = df['coloum'].map({'value_1':1,'value_2':0})

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: dataframe data

Link to this answer
Share Copy Link

Contributed on May 11 2020

JAKKA9

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 9/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe data

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map ma

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

map column dataframe python

Comment

df = pd.DataFrame({'col1':pd.date_range('2015-01-02 15:00:07', periods=3),
                   'col2':pd.date_range('2015-05-02 15:00:07', periods=3),
                   'col3':pd.date_range('2015-04-02 15:00:07', periods=3),
                   'col4':pd.date_range('2015-09-02 15:00:07', periods=3),
                   'col5':[5,3,6],
                   'col6':[7,4,3]})

print (df)
                 col1                col2                col3  \
0 2015-01-02 15:00:07 2015-05-02 15:00:07 2015-04-02 15:00:07   
1 2015-01-03 15:00:07 2015-05-03 15:00:07 2015-04-03 15:00:07   
2 2015-01-04 15:00:07 2015-05-04 15:00:07 2015-04-04 15:00:07   

                 col4  col5  col6  
0 2015-09-02 15:00:07     5     7  
1 2015-09-03 15:00:07     3     4  
2 2015-09-04 15:00:07     6     3  

list_of_cols_to_change = ['col1','col2','col3','col4']
df[list_of_cols_to_change] = df[list_of_cols_to_change].apply(lambda x: x.dt.date)
print (df)
         col1        col2        col3        col4  col5  col6
0  2015-01-02  2015-05-02  2015-04-02  2015-09-02     5     7
1  2015-01-03  2015-05-03  2015-04-03  2015-09-03     3     4
2  2015-01-04  2015-05-04  2015-04-04  2015-09-04     6     3

Popularity 8/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: dataframe map

Link to this answer
Share Copy Link

Contributed on Jun 20 2022

Mike Rocket

0 Answers Avg Quality 2/10

Map values in Pandas column using dictionary

Contents

More Related Answers

Map values in Pandas column using dictionary

Closely Related Answers

maping value to data in pandas dataframe

map column dataframe python

map column dataframe python

map column dataframe python

map column dataframe python

map column dataframe python

map column dataframe python

map column dataframe python

map column dataframe python

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.