An overview and examples of using LAMBDA functions in Python.

A LAMBDA function is a small function written on one line of code.

Links to topics on this page

• Components of a lambda function
• Nest a lambda function inside a named function
• Lambda example – create a new column by splitting another column’s values
• Pros and cons of using lambda functions

In Python, a lambda function looks like this:

lambda x : x + x

• It’s referred to as an ‘anonymous function’ because it is not ‘named’, i.e. not assigned to a named object/variable.
• It’s a short term function because it’s only used once where it’s defined, and can’t be called elsewhere in the script or from another script.
• A lambda function can take multiple arguments but only one expression.

The components of a lambda function are:

• the word lambda
• its argument(s), e.g. x, y
• a colon :
• the expression (operation) to perform on the arguments. This is equivalent to the ‘body’ of a function.

lambda x : x + x

… is equivalent to the defined adder() function below:

def adder(x):

return x + x

---

Nest a lambda function inside a named function

Lambda functions are often used within the scope of a larger function. We can nest a lambda function inside a named function.

Below we transform our adder() function to include an input y and the addition expression (+).

def adder(x):

return lambda y: x + y

• The argument of adder() is x
• The argument of the lambda function is y. The function is also taking the argument x from the parent function adder().
• The lambda expression is x + y.
• The output of adder() is the result of the lambda operation.

---

Lambda example – create a new column in Python by splitting another column’s values

In this example below we create a new column taking the first word from a string in another column, e.g. extracting a person’s first name from their full name. In this example:

• df is our dataframe
• Full_name is the name of the existing column
• First_name is the name of the new column.

Full_name	First_name
Peter Pan	Peter
Captain Hook	Captain

df[“First_name”] = df [“Full_name”].apply(lambda x: x.split(” “)[0])

• The nested lambda function is: lambda x: x.split(" ")[0])
• Input x string is split into words separated by a space " " and the first element [0] of the resulting output is selected.
• The apply function is used to iterate through the rows of the table df.

---

Pros and cons of using Lambda functions

What’s good about Lambda functions

• Good for creating simple logical operations.
• The conciseness of the lambda function structure can make code easy to read.
• If you just need to run a function once in your code, e.g. a quick data transformation, or string split, lambda functions are just the job..

What’s not so good about Lambda functions

• You can only carry out one operation or expression in a lambda function, so the function cannot involve complex logic.
• Lambda functions can’t include default values for arguments.
• Lambda functions can’t span more than one line.
• Keep it simple – if it’s not clear what the function is doing use a named function instead.
• Well written code will include docstrings – text used to document what a function, module, class or method does – which can’t be added to lambda functions. See more about docstrings here: https://peps.python.org/pep-0257/