pandas add new column value based on condition code example

Example 1: compute value based on condition of existing column dataframe

# create a list of our conditions
conditions = [
    (df['likes_count'] <= 2),
    (df['likes_count'] > 2) & (df['likes_count'] <= 9),
    (df['likes_count'] > 9) & (df['likes_count'] <= 15),
    (df['likes_count'] > 15)
    ]

# create a list of the values we want to assign for each condition
values = ['tier_4', 'tier_3', 'tier_2', 'tier_1']

# create a new column and use np.select to assign values to it using our lists as arguments
df['tier'] = np.select(conditions, values)

# display updated DataFrame
df.head()

Example 2: change pandas column value based on condition

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

Example 3: add a value to an existing field in pandas dataframe after checking conditions

# Create a new column called based on the value of another column
# np.where assigns True if gapminder.lifeExp>=50 
gapminder['lifeExp_ind'] = np.where(gapminder.lifeExp >= 50, True, False)
gapminder.head(n=3)