
Mastering Data Preparation with Pandas
Authored by María de los Angeles Constantino González
Computers
University
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of data cleaning in pandas?
To automate data entry processes in pandas.
To reduce the size of the dataset in pandas.
The purpose of data cleaning in pandas is to improve data quality by correcting inaccuracies and inconsistencies.
To enhance data visualization in pandas.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Name two common techniques for data cleaning in pandas.
Encoding categorical variables and feature scaling
Visualizing data and creating reports
Handling missing values and removing duplicates
Normalizing data and aggregating values
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can you convert a column's data type in a DataFrame?
Use the 'convert()' function on the DataFrame.
Change the data type in the DataFrame settings.
Use the 'astype()' method on the DataFrame column.
Apply the 'transform()' method to the DataFrame column.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How do you merge two DataFrames on a common column?
Join df1 and df2 using df1.append(df2)
Combine df1 and df2 with df1 + df2
Use pd.merge(df1, df2, on='common_column')
Use df1.concat(df2, axis=1)
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can you reset the index of a DataFrame after a groupby operation?
df.agg('function').groupby('column_name').reset()
df.reset_index().groupby('column_name')
df.groupby('column_name').agg('function').reset_index()
df.groupby('column_name').sum()
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What function would you use to concatenate two DataFrames vertically?
Use df1 + df2
Use pd.concat([df1, df2])
Use df1.merge(df2)
Use df1.append(df2)
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can you filter rows in a DataFrame based on a condition?
Use df[df['column'] == value]
Use df.select('condition')
Use df.query('condition')
Use df.filter('condition')
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