AssertionError Traceback (most re in 1 assert news_df.isna(). sum().sum() (18731, 3) ----> 2 assert news_df.shape == AssertionFrror:

Fundamentals of Information Systems
9th Edition
ISBN:9781337097536
Author:Ralph Stair, George Reynolds
Publisher:Ralph Stair, George Reynolds
Chapter10: Ethical, Legal, And Social Issues Of Information Systems
Section: Chapter Questions
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Python AssertionError?

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
I assert isinstance(news_df, pd.DataFrame)
assert len(news_df) == 18773
assert list(news_df.columns) == ['category', 'subject', 'message']
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
news_df.dropna(axis = 0, how = 'any', thresh = None, subset = None, inplace
news df =
False)
%3D
news_df.dropna()
news_df
category subject message
I assert news_df.isna().sum().sum() == 0
assert news_df.shape
(18731, 3)
==
AssertionError
Traceback (most recent call last)
<ipython-input-8-3f29b6a44f79> in <module>
1 assert news_df.isna().sum().sum()
(18731, 3)
== 0
----> 2 assert news_df.shape
==
AssertionError:
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 I assert isinstance(news_df, pd.DataFrame) assert len(news_df) == 18773 assert list(news_df.columns) == ['category', 'subject', 'message'] 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 news_df.dropna(axis = 0, how = 'any', thresh = None, subset = None, inplace news df = False) %3D news_df.dropna() news_df category subject message I assert news_df.isna().sum().sum() == 0 assert news_df.shape (18731, 3) == AssertionError Traceback (most recent call last) <ipython-input-8-3f29b6a44f79> in <module> 1 assert news_df.isna().sum().sum() (18731, 3) == 0 ----> 2 assert news_df.shape == AssertionError:
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