pandas lambda if else
df['equal_or_lower_than_4?'] = df['set_of_numbers'].apply(lambda x: 'True' if x <= 4 else 'False')pandas lambda if else
df['equal_or_lower_than_4?'] = df['set_of_numbers'].apply(lambda x: 'True' if x <= 4 else 'False')make a condition statement on column pandas
df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]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)if condition dataframe python
df.loc[df['age1'] - df['age2'] > 0, 'diff'] = df['age1'] - df['age2']pandas if else
df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met'if else python pandas 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()Copyright © 2021 Codeinu
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