Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Accessing using .loc[] [3/3] | Accessing DataFrame Values
Introduction to pandas [track]
course content

Conteúdo do Curso

Introduction to pandas [track]

Introduction to pandas [track]

1. Basics
2. Reading and Exploring Data
3. Accessing DataFrame Values
4. Aggregate Functions

Accessing using .loc[] [3/3]

Two chapters ago it was mentioned that boolean array can be used to access rows with the .loc[] property. It means you can filter rows without using if/else statements!

A condition you want to use for filtering must be passed as the first parameter. For example,

1234567
# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # Get the cars manufactured before 2000 print(df.loc[df.year < 2000])
copy

If you want to filter based on several conditions, you need to put them within separate parentheses, and combine them by either the & (and), or | (or) operator. You use the & operator if you need all the conditions to be hold. If you are interested in at least one of the conditions, use the | operator.

1234567
# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # Get the Diesel cars cheaper than 15k print(df.loc[(df.fuelType == 'Diesel') & (df.price < 15000)])
copy

Assume you have the same dataframe df. You want to get only cars that are cheaper (the price column) than 13000. Choose the correct way to do it.

Selecione a resposta correta

Tudo estava claro?

Seção 3. Capítulo 8
We're sorry to hear that something went wrong. What happened?
some-alt