Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Aprende Expanding Functionality of the .iloc[] Attribute | Getting Familiar With Indexing and Selecting Data
Advanced Techniques in pandas

book
Expanding Functionality of the .iloc[] Attribute

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

  • data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
  • data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
  • data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
  • data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
  • data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.
Tarea

Swipe to start coding

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

Solución

import pandas as pd

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/IMDb_Data_final.csv', index_col = 0)

# Extract needed rows and columns
data_extracted = data.iloc[:50, [1,4]]

print(data_extracted.head())

¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 6
import pandas as pd

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/IMDb_Data_final.csv', index_col = 0)

# Extract needed rows and columns
data_extracted = ___

print(data_extracted.head())
toggle bottom row
some-alt