shift() for lagging data in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new column 'lag_1' that contains the previous row's 'value' using shift().

Pandas

df['lag_1'] = df['value'].[1](1)

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

Afillna

Bsort

Cdrop

Dshift

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort() instead of shift()

Using drop() which removes data

Using fillna() which fills missing values

The shift() function moves the data down by the specified number of periods, creating lagged values.

2fill in blank

medium

Complete the code to calculate the difference between 'value' and its lagged version 'lag_1'.

Pandas

df['diff'] = df['value'] - df['value'].[1](1)

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

Ashift

Bdrop

Csort

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values

Using sort() which changes order

Using drop() which removes data

Using shift(1) gets the previous row's value to subtract from the current row's value.

3fill in blank

hard

Fix the error in the code to create a lagged column 'lag_2' with a lag of 2 rows.

Pandas

df['lag_2'] = df['value'].[1](2)

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

Ashift

Bsort_values

Cfillna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values() which sorts data

Using fillna() which fills missing values

Using dropna() which removes missing values

shift(2) correctly shifts the data by 2 rows to create a lag of 2.

4fill in blank

hard

Fill both blanks to create a new column 'pct_change' that calculates the percent change from the previous row.

Pandas

df['pct_change'] = (df['value'] - df['value'].[1]([2])) / df['value'].[1](1) * 100

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

Ashift

B1

C2

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using shift(2) instead of shift(1) for previous row

Using fillna() inside the calculation

Using wrong numbers for lag

shift(1) gets the previous row's value to calculate the percent change.

5fill in blank

hard

Fill all three blanks to create a lagged column 'lag_1' and fill NaNs forward using fillna.

Pandas

df['lag_1'] = df['value'].[1](1).[2](method='[3]')

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

Ashift

Bfillna

Cffill

Dbfill

Attempts:

3 left

💡 Hint

Common Mistakes

Using bfill instead of ffill

Wrong order of shift and fillna

Forgetting method in fillna

shift(1) creates the lag introducing NaN at the start, fillna(method='ffill') propagates the first non-NaN value forward to fill NaNs.