Python for Data Analysis: Step-By-Step with Projects - Cleaning Text

Python for Data Analysis: Step-By-Step with Projects - Cleaning Text

Assessment

Interactive Video

•

Information Technology (IT), Architecture

•

University

•

Practice Problem

•

Hard

Created by

Wayground Content

FREE Resource

This video tutorial covers methods for cleaning and standardizing tax data, focusing on address data in a dataset of New York taxi rides. It demonstrates using pandas for string processing, including converting text to lowercase, removing whitespace, and analyzing text lengths. The tutorial also introduces string indexing, slicing, and regular expressions for more advanced text manipulation.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What library is used to load the dataset in this lesson?

Pandas

SciPy

Matplotlib

NumPy

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it important to standardize text data?

To make text analysis more efficient

To add more columns

To remove all data

To increase file size

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which method is used to convert text to lowercase in pandas?

upper()

capitalize()

lower()

title()

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the 'strip' method do in text processing?

Converts text to uppercase

Removes leading and trailing whitespace

Adds whitespace

Splits text into a list

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of analyzing text length in a dataset?

To convert text to numbers

To count the number of words

To identify outliers and inconsistencies

To find the longest word

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can you filter rows containing a specific keyword in pandas?

Using the 'search' method

Using the 'contains' method

Using the 'find' method

Using the 'locate' method

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the 'replace' method do in text processing?

Removes text

Replaces specified text with new text

Converts text to numbers

Adds new text

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