### replace one value ###

df["column"].replace("US","UK") # you can also use numerical values

### replace more than one value ###

df["column"].replace(["man","woman","child"],[1,2,3]) # you can also use numerical values

#   man ==> 1

# woman ==> 2

# child ==> 3

In [41]:

df.loc[df['First Season'] > 1990, 'First Season'] = 1

df

​

Out[41]:

                 Team  First Season  Total Games

0      Dallas Cowboys          1960          894

1       Chicago Bears          1920         1357

2   Green Bay Packers          1921         1339

3      Miami Dolphins          1966          792

4    Baltimore Ravens             1          326

5  San Franciso 49ers          1950         1003

df.loc[df['column'] == 'column_value', 'column'] = 'new_column_value'

df['New Column'] = np.where(df['A']==0, df['B'], df['A'])

In [36]:

df['Group'] = df['Group'].map(df1.set_index('Group')['Hotel'])

df

​

Out[36]:

         Date  Group  Family  Bonus

0  2011-06-09  Jamel  Laavin    456

1  2011-07-09  Frank  Grendy    679

2  2011-09-10   Luxy  Fantol    431

3  2011-11-02  Frank  Gondow    569

​

df['column name'] = df['column name'].replace(['old value'], 'new value')