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.

Practice

1. What does the shift() function do in pandas?

easy

A. It changes the data type of a column.

B. It moves data up or down to compare current and past values.

C. It removes missing values from the dataset.

D. It sorts the data in ascending order.

Solution

1. Step 1: Understand the purpose of shift()

   The shift() function moves data up or down in a column, creating a lag or lead effect.

2. Step 2: Compare with other options

   Sorting, removing missing values, or changing data types are different pandas functions, not shift().

3. Final Answer:

   It moves data up or down to compare current and past values. -> Option B

4. Quick Check:

   shift() moves data for lagging [OK]

Hint: Remember: shift() moves data to compare past values [OK]

Common Mistakes:

• Confusing shift() with sort()
• Thinking shift() removes missing data
• Assuming shift() changes data types

2. Which of the following is the correct syntax to shift a pandas Series named data by 2 rows downward?

easy

A. data.shift(2)

B. data.shift(-2)

C. data.shift(0)

D. data.shift()

Solution

1. Step 1: Understand shift() parameter

   The argument inside shift() is the number of periods to move. Positive values shift downward (lag), negative shift upward (lead).

2. Step 2: Apply to the question

   To shift downward by 2 rows, use data.shift(2). Negative or zero values do not match the requirement.

3. Final Answer:

   data.shift(2) -> Option A

4. Quick Check:

   Positive number shifts down = data.shift(2) [OK]

Hint: Positive number in shift() moves data down [OK]

Common Mistakes:

• Using negative number to shift downward
• Omitting the argument defaults to shift by 1
• Using zero which means no shift

3. Given the pandas Series:

import pandas as pd
data = pd.Series([10, 20, 30, 40, 50])
shifted = data.shift(1)

What is the value of shifted at index 2?

medium

A. NaN

B. 30.0

C. 20.0

D. 10.0

Solution

1. Step 1: Understand shift(1) effect on Series

   Shifting by 1 moves all values down by one index. The value at index 2 in shifted is the original value at index 1.

2. Step 2: Find original value at index 1

   Original data at index 1 is 20, so shifted at index 2 is 20.0 (float due to NaN presence).

3. Final Answer:

   20.0 -> Option C

4. Quick Check:

   shift(1) moves index 1 value to index 2 [OK]

Hint: Value at index i after shift(1) is original at i-1 [OK]

Common Mistakes:

• Confusing index positions after shift
• Expecting integer type instead of float
• Thinking shifted index 2 is original index 2

4. What is wrong with this code snippet?

import pandas as pd
data = pd.Series([5, 10, 15])
shifted = data.shift('1')

medium

A. shift() cannot be used on Series objects.

B. shift() requires a second argument for axis.

C. The data list must be a DataFrame, not a Series.

D. The argument to shift() should be an integer, not a string.

Solution

1. Step 1: Check shift() argument type

   The shift() function expects an integer or a timedelta, not a string.

2. Step 2: Identify error cause

   Passing a string like '1' causes a TypeError because pandas cannot interpret it as a number of periods.

3. Final Answer:

   The argument to shift() should be an integer, not a string. -> Option D

4. Quick Check:

   shift() needs integer periods, not string [OK]

Hint: Use integer, not string, inside shift() [OK]

Common Mistakes:

• Passing string instead of integer to shift()
• Thinking shift() only works on DataFrames
• Forgetting shift() default axis is 0

5. You have a DataFrame df with a column 'sales'. You want to create a new column 'sales_change' that shows the difference between current sales and sales one day before. Which code correctly achieves this?

hard

A. df['sales_change'] = df['sales'] - df['sales'].shift(1)

B. df['sales_change'] = df['sales'].shift(1) - df['sales']

C. df['sales_change'] = df['sales'].diff(-1)

D. df['sales_change'] = df['sales'].shift(-1) - df['sales']

Solution

1. Step 1: Understand the goal

   We want the change from previous day to current day, so current minus previous.

2. Step 2: Use shift(1) to get previous day sales

   df['sales'].shift(1) gives previous day's sales aligned with current row.

3. Step 3: Calculate difference

   Subtract previous sales from current sales: df['sales'] - df['sales'].shift(1).

4. Final Answer:

   df['sales_change'] = df['sales'] - df['sales'].shift(1) -> Option A

5. Quick Check:

   Current minus previous = sales_change [OK]

Hint: Subtract shifted column from current for change [OK]

Common Mistakes:

• Reversing subtraction order
• Using negative shift which looks ahead
• Using diff(-1) which is future difference