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19AI403-Introduction to Data Science

19AI403-Introduction to Data Science

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Given a Pandas DataFrame df with columns "product", "count", and "status", which of the following statements will select the rows where the "status" column is equal to "no" and the "count" column is equal to 0?

df.iloc[df['status'] == 'no' and df['count'] = 0]

df.loc[(df['status'] == 'no') & (df['count'] == 0), ['product', 'count']]

df.iloc[(df['status'] == ' ') & (df['count'] = 30), [0, 1]]

df.loc[df['status'] == 'no' and df['count'] >=0]

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What does the ffill parameter in the fillna() method stand for?

Forward fill

Fill first

Fill from last

Fast fill

Which of the following statements about data science are true? (Select all that apply)

a) Data science is a process for extracting insights from data

b) Data science involves the use of statistical and computational methods

c) Data science is used exclusively in the field of computer science

d) Data science can be used in a variety of industries and domains

e) Data science does not involve the use of visualization tools

a), b), d)

c), e)

a), d)

What does the inplace parameter in the fillna() method do?

Creates a new DataFrame

Modifies the original DataFrame

Reverts the changes made by the method

Drops the filled values

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In Pandas, how can you fill missing values using linear interpolation?

df.fillna(method='linear')

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df.interpolate()

✅

df.interpolate_linear()

❌

df.fillna_linear()

❌

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How can you fill missing values in a Pandas DataFrame with the mean value?

df.fillna_mean()

df.fillna(method='mean')

df.fillna_average()

df.fillna(value=df.mean())

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Given a Pandas DataFrame df with columns "name", "age", and "gender", which of the following statements will select the rows where the "gender" column is equal to "F" and the "age" column is greater than or equal to 30?

df.iloc[df['gender'] == 'F' and df['age'] >= 30]

❌

df.loc[df['gender'] == 'F' and df['age'] >= 30]

❌

df.iloc[(df['gender'] == 'F') & (df['age'] >= 30), [0, 1]]

❌

df.loc[(df['gender'] == 'F') & (df['age'] >= 30), ['name', 'age']]

✅

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Z score is given by ___________________

Z=(x+σ)/μ

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