Grouping by multiple columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by 'city' and calculate the mean of 'temperature'.

Pandas

grouped = df.groupby([1])['temperature'].mean()

Drag options to blanks, or click blank then click option'

A'city'

B'humidity'

C'date'

D'temperature'

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by 'temperature' instead of 'city' will not group locations.

Forgetting quotes around the column name causes errors.

The groupby function groups data by the specified column. Here, we group by 'city' to find the average temperature per city.

2fill in blank

medium

Complete the code to group the DataFrame by both 'city' and 'date' columns.

Pandas

grouped = df.groupby([1]).sum()

Drag options to blanks, or click blank then click option'

A['humidity']

B['city']

C['city', 'date']

D['temperature']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single string instead of a list for multiple columns.

Grouping by columns not present in the DataFrame.

Grouping by multiple columns requires a list of column names. Here, we group by both 'city' and 'date' to aggregate data for each city on each date.

3fill in blank

hard

Fix the error in the code to group by 'city' and 'date' and calculate the mean temperature.

Pandas

result = df.groupby([1]).mean()['temperature']

Drag options to blanks, or click blank then click option'

A'city', 'date'

B['city', 'date']

C('city', 'date')

D'city date'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing multiple columns as a single string separated by commas.

Using parentheses instead of square brackets for the list.

The groupby method expects a list of column names to group by multiple columns. Using a list like ['city', 'date'] is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each city to the count of dates where temperature is above 20.

Pandas

counts = {city: df[(df['city'] == city) & (df['temperature'] [1] 20)]['date'].[2]() for city in df['city'].unique()}

Drag options to blanks, or click blank then click option'

A>

Bcount

Cmean

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for the temperature condition.

Using 'mean' instead of 'count' to count dates.

The condition checks if temperature is greater than 20. Then count() counts the number of dates matching the condition for each city.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each city to the average humidity on days when temperature is below 15.

Pandas

avg_humidity = {city: df[(df['city'] == city) & (df['temperature'] [1] 15)]['[2]'].[3]() for city in df['city'].unique()}

Drag options to blanks, or click blank then click option'

A<

Bhumidity

Cmean

Dtemperature

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' for the temperature condition.

Selecting 'temperature' column instead of 'humidity'.

Using 'count' instead of 'mean' to calculate average.

The condition checks if temperature is less than 15. Then the mean humidity is calculated for those days per city.