shift() for lagging data in Pandas

Introduction

We use shift() to move data up or down in a table. This helps us compare current values with past values easily.

When you want to compare today's sales with yesterday's sales.

When you need to calculate the change in stock prices from one day to the next.

When analyzing time series data to see trends over time.

When creating features for machine learning that depend on past values.

When you want to find the difference between current and previous measurements.

Syntax

Pandas

DataFrame.shift(periods=1, freq=None, axis=0, fill_value=None)

periods tells how many steps to move the data. Positive moves data down (lag), negative moves up (lead).

axis=0 means shift rows, axis=1 means shift columns.

Examples

This creates a new column with values shifted down by 1 row (previous row's value).

Pandas

df['lagged'] = df['value'].shift(1)

This shifts values up by 1 row, showing next row's value.

Pandas

df['lead'] = df['value'].shift(-1)

This shifts all rows down by 2 steps, adding NaN at the top.

Pandas

df.shift(2)

Sample Program

This code creates a table of sales by day. It adds a column with the previous day's sales using shift(1). Then it calculates the change in sales from the previous day.

Pandas

import pandas as pd

data = {'day': [1, 2, 3, 4, 5], 'sales': [100, 150, 200, 130, 170]}
df = pd.DataFrame(data)

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

print(df)

Important Notes

Missing values appear where there is no previous data after shifting (usually at the start).

You can use fill_value to replace missing values after shifting.

Summary

shift() moves data up or down to compare current and past values.

It is useful for time-based comparisons and feature creation.

Remember that shifting creates missing values where data is moved out of range.

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