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SQL & PANDAS Quiz

Total questions: 12

Worksheet time: 6mins

Name
Class
Date
1.

SQL: which keyword is used to read /query value from a table in a database?

a)

SELECT

b)

UPDATE

c)

DELETE

d)

JOIN

2.

Which of the following statements is true about the SELECT statement in Google BigQuery?

a)

It specifies the tables to be included in the query

b)

It specifies the columns to be included in the query result

c)

It specifies the conditions for filtering the query result

3.

In Google BigQuery, which keyword is used to specify the table or tables from which you want to retrieve data?

a)

WHERE

b)

GROUP

c)

FROM

d)

Setting

4.

Which of the following statements is true about the WHERE clause in Google BigQuery?

a)

It specifies the columns to be included in the query result

b)

It specifies the tables to be included in the query

c)

It specifies the conditions for filtering the query result

5.

Which of the following methods is used to create an empty DataFrame in Pandas?

a)

pd.empty()

b)

pd.DataFrame()

c)

pd.zeros()

d)

pd.ones()

6.

What is the Pandas method used to merge two DataFrames based on a common column?

a)

df.merge()

b)

df.plus()

c)

df + df

7.

Which method is used to remove columns from a Pandas DataFrame?

a)

df - column name

b)

df.remove()

c)

df.erase()

d)

df.drop()

8.

What is the Pandas method used to group a DataFrame by a specific column or set of columns?

a)

df.group_by()

b)

df.groupby()

c)

df.segment()

d)

df.split()

9.

Which method is used to select a single column from a Pandas DataFrame and return a Series object?

a)

df.select()

b)

df.get()

c)

df[] or df['column_name']

10.

Which method is used to fill the missing values in a Pandas DataFrame with a specified value?

a)

df.fill_missing()

b)

df.fillna()

c)

df.replace_missing()

d)

df.replace_na()

11.

Which method is used to apply a function to each element of a Pandas DataFrame?

a)

df.apply()

b)

df.map()

c)

df.filter()

12.

Which method is used to pivot a Pandas DataFrame based on the values in one or more columns?

a)

df.pivot()

b)

df.stack()

c)

df.unstack()

d)

df.melt()