learn-co-students / dsc-0-04-03-lambda-functions-online-ds-ft-100118

Lambda Functions

Introduction

Lambda functions are often a convenient way to write throw away functions on the fly. If you need to write a more complicated function you may still need to use the more formal def method, but lambda functions provide a quick and concise way to write functions.

Objectives

You will be able to:

• Understand what lambda functions are and why they are useful
• Use lambda functions to transform data within lists and DataFrames

An Example

Let's say you want to count the number of words in each yelp review.

import pandas as pd
df = pd.read_csv('Yelp_Reviews.csv')
df.head(2)

<style> .dataframe thead tr:only-child th { text-align: right; }

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

</style>

	Unnamed: 0	business_id	cool	date	funny	review_id	stars	text	useful	user_id
0	1	pomGBqfbxcqPv14c3XH-ZQ	0	2012-11-13	0	dDl8zu1vWPdKGihJrwQbpw	5	I love this place! My fiance And I go here atl...	0	msQe1u7Z_XuqjGoqhB0J5g
1	2	jtQARsP6P-LbkyjbO1qNGg	1	2014-10-23	1	LZp4UX5zK3e-c5ZGSeo3kA	1	Terrible. Dry corn bread. Rib tips were all fa...	3	msQe1u7Z_XuqjGoqhB0J5g

df['text'].map(lambda x: len(x.split())).head()

0    58
1    30
2    30
3    82
4    32
Name: text, dtype: int64

Similar to defining functions in general or naming the iterable in for loops, the variable that you use after calling the lambda keyword does not matter

df['text'].map(lambda review_text: len(review_text.split())).head()

0    58
1    30
2    30
3    82
4    32
Name: text, dtype: int64

Lambda functions with conditionals

Lambda functions can also accept some conditionals if chained in a list comprehension

df['text'].map(lambda x: 'Good' if any([word in x.lower() for word in ['awesome', 'love', 'good', 'great']]) else 'Bad').head()

0    Good
1     Bad
2    Good
3     Bad
4     Bad
Name: text, dtype: object

Note:

The above is terribly poor style and does in no way represent pep-8 or pythonic style. (For example, no line should be over 72 characters according to pep-8; the previous line was 127 characters.) That said, it is an interesting demonstration of chaining a conditional, the any method and a list comprehension all inside a lambda function!
Shew!

Returning to a more manageable example...

Perhaps we want to naively select the year from the date string rather then convert it to a datetime object.

df.date.map(lambda x: x[:4]).head()

0    2012
1    2014
2    2014
3    2011
4    2016
Name: date, dtype: object

Lambda Functions are also useful within the sort method

#Without a key
names = ['Miriam Marks','Sidney Baird','Elaine Barrera','Eddie Reeves','Marley Beard',
         'Jaiden Liu','Bethany Martin','Stephen Rios','Audrey Mayer','Kameron Davidson',
         'Carter Wong','Teagan Bennett']
sorted(names)

['Audrey Mayer',
 'Bethany Martin',
 'Carter Wong',
 'Eddie Reeves',
 'Elaine Barrera',
 'Jaiden Liu',
 'Kameron Davidson',
 'Marley Beard',
 'Miriam Marks',
 'Sidney Baird',
 'Stephen Rios',
 'Teagan Bennett']

#Sorting by last name
names = ['Miriam Marks','Sidney Baird','Elaine Barrera','Eddie Reeves','Marley Beard',
         'Jaiden Liu','Bethany Martin','Stephen Rios','Audrey Mayer','Kameron Davidson',
'Teagan Bennett']
sorted(names, key=lambda x: x.split()[1])

['Sidney Baird',
 'Elaine Barrera',
 'Marley Beard',
 'Teagan Bennett',
 'Kameron Davidson',
 'Jaiden Liu',
 'Miriam Marks',
 'Bethany Martin',
 'Audrey Mayer',
 'Eddie Reeves',
 'Stephen Rios']

Summary

Lambda functions can be a convenient way to write "throw away" functions that you want to declare inline. In the next lesson we'll give you some practice with creating them!