Shift and lag operations in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to shift the 'sales' column down by one row.

Data Analysis Python

df['previous_sales'] = df['sales'].[1](1)

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

Aroll

Blag

Cshift

Dmove

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lag' which is not a pandas function.

Trying to use 'roll' or 'move' which do not exist in pandas.

The shift function moves the data down by the specified number of rows, creating a lag effect.

2fill in blank

medium

Complete the code to shift the 'temperature' column up by two rows.

Data Analysis Python

df['future_temp'] = df['temperature'].[1](-2)

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

Aroll

Blag

Cmove

Dshift

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'roll' or 'move' which are not pandas functions.

Using positive numbers to shift up.

Using shift(-2) moves the data up by two rows, showing future values.

3fill in blank

hard

Fix the error in the code to correctly create a lagged column named 'lagged_price'.

Data Analysis Python

df['lagged_price'] = df.price.[1](1)

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

Ashift

Blag

Cmove

Droll

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lag' which is not a pandas method.

Using dot notation without quotes for column names is okay but method must be correct.

The correct pandas method to create lagged columns is shift. Using df.price.shift(1) works correctly.

4fill in blank

hard

Fill both blanks to create a 3-period lag for the 'sales' column.

Data Analysis Python

df['sales_lag3'] = df['sales'].[1]([2])

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

Ashift

B3

Clag

Ddiff

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lag' which is not a pandas method.

Using negative number which would create a lead instead of lag.

To lag by 3 periods, use shift(3). Positive periods shift index down, equivalent to lagging the values.

5fill in blank

hard

Fill all three blanks to compute the next sales per store group, filling missing with 0.

Data Analysis Python

df['next_sales'] = df.groupby('store')['sales'].[1]([2]).[3](0)

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

Ashift

B-1

Cfillna

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting groupby, causing global shift instead of per-group.

Using positive periods for next values (would be lag).

Forgetting fillna to replace NaNs.

groupby('store')['sales'].shift(-1).fillna(0) creates group-wise leads (next values) and fills trailing NaNs with 0.