if else in pandas code example

Example 1: pandas lambda if else

df['equal_or_lower_than_4?'] = df['set_of_numbers'].apply(lambda x: 'True' if x <= 4 else 'False')

Example 2: make a condition statement on column pandas

df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]

Example 3: pandas if else

df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met'

Example 4: if condition dataframe python

df.loc[df['age1'] - df['age2'] > 0, 'diff'] = df['age1'] - df['age2']

Example 5: python conditionally create new column in pandas dataframe

# If you only have one condition use numpy.where()
# Example usage with np.where:
df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) # Define df
print(df)
  Type Set
0    A   Z
1    B   Z
2    B   X
3    C   Y

# Add new column based on single condition:
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)
  Type Set  color
0    A   Z  green
1    B   Z  green
2    B   X    red
3    C   Y    red


# If you have multiple conditions use numpy.select()
# Example usage with np.select:
df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) # Define df
print(df)
  Type Set
0    A   Z
1    B   Z
2    B   X
3    C   Y

# Set the conditions for determining values in new column:
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]

# Set the new column values in order of the conditions they should
#	correspond to:
choices = ['yellow', 'blue', 'purple']

# Add new column based on conditions and choices:
df['color'] = np.select(conditions, choices, default='black')

print(df)
# Returns:
  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

Example 6: 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()