category subject message 16150 politics,National Crime Survey,"Well, I droppe... NaN NaN 256 politics,Re: Europe vs. Muslim Bosnians,"I lik... NaN NaN 8355 recreational,Re: wife wants convertible,": : ... NaN NaN 9609 religion,"Re: After 2000 years, can we say tha... NaN NaN 8288 recreational,Re Aftermarket A/C units,"| I loo... NaN NaN 1b) Remove null observations Given that we are carrying out a classification/prediction task, we're going to drop null values from the dataframe. Drop any rows containing null values in any of the three columns of news_df . Store this back into news_df . I # YOUR CODE HERE I assert news_df.isna().sum().sum() assert news_df.shape == (18731, 3) == 0

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Use Python

category subject message
16150
politics, National Crime Survey,"Well, I droppe...
NaN
NaN
256
politics,Re: Europe vs. Muslim Bosnians,"I lik...
NaN
NaN
8355
recreational,Re: wife wants convertible,": :..
NaN
NaN
9609 religion,"Re: After 2000 years, can we say tha...
NaN
NaN
8288
recreational, Re Aftermarket A/C units,"| I loo...
NaN
NaN
1b) Remove null observations
Given that we are carrying out a classification/prediction task, we're going to drop null values from the dataframe. Drop any
rows containing null values in any of the three columns of news df . Store this back into news df.
I # YOUR CODE HERE
I assert news_df.isna().sum().sum() == ®
assert news_df.shape
== (18731, 3)
Transcribed Image Text:category subject message 16150 politics, National Crime Survey,"Well, I droppe... NaN NaN 256 politics,Re: Europe vs. Muslim Bosnians,"I lik... NaN NaN 8355 recreational,Re: wife wants convertible,": :.. NaN NaN 9609 religion,"Re: After 2000 years, can we say tha... NaN NaN 8288 recreational, Re Aftermarket A/C units,"| I loo... NaN NaN 1b) Remove null observations Given that we are carrying out a classification/prediction task, we're going to drop null values from the dataframe. Drop any rows containing null values in any of the three columns of news df . Store this back into news df. I # YOUR CODE HERE I assert news_df.isna().sum().sum() == ® assert news_df.shape == (18731, 3)
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